linear_regression_SOLVED.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression Lab 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import Pytorch Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import torch.optim as optim\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate random data\n",
    "x: input of 100 random floats between -10.0 and 10.0  \n",
    "y: 2*x + 5 + (10% random noise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([100]) torch.Size([100])\n"
     ]
    }
   ],
   "source": [
    "x = 20 * torch.rand(100) - 10\n",
    "y = (2 * x) + 5\n",
    "noise_ratio = 0.2 * torch.rand(100) - 0.1\n",
    "y_range = torch.max(y) - torch.min(y)\n",
    "noise = noise_ratio * y_range\n",
    "y = y + noise\n",
    "print(x.size(), y.size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot randomly generated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x.numpy(), y.numpy(), alpha=0.7, s=15)\n",
    "plt.axhline(0, color=\"black\", linestyle=\"--\", alpha=0.3, linewidth=1)\n",
    "plt.axvline(0, color=\"black\", linestyle=\"--\", alpha=0.3, linewidth=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Initialize parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = torch.rand(1, requires_grad=True)\n",
    "b = torch.rand(1, requires_grad=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gradient iterative descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mseloss: 145.8003692626953\n",
      "params:[0.005971193313598633, 0.8909163475036621]\n",
      "mseloss: 11.112471580505371\n",
      "params:[1.9966375827789307, 2.107496976852417]\n",
      "mseloss: 7.778365612030029\n",
      "params:[1.994032382965088, 2.9187498092651367]\n",
      "mseloss: 6.279983043670654\n",
      "params:[1.992277979850769, 3.4625985622406006]\n",
      "mseloss: 5.606595039367676\n",
      "params:[1.9911017417907715, 3.8271842002868652]\n",
      "mseloss: 5.303967475891113\n",
      "params:[1.9903029203414917, 4.071596145629883]\n",
      "mseloss: 5.167963981628418\n",
      "params:[1.9897783994674683, 4.235444068908691]\n",
      "mseloss: 5.1068434715271\n",
      "params:[1.9894267320632935, 4.345283031463623]\n",
      "mseloss: 5.079375743865967\n",
      "params:[1.9891804456710815, 4.418915748596191]\n",
      "mseloss: 5.067030906677246\n",
      "params:[1.9890235662460327, 4.468278408050537]\n",
      "finished training\n"
     ]
    }
   ],
   "source": [
    "lr = 0.0001\n",
    "for i in range(0, 20000):\n",
    "    y_pred = (w * x) + b\n",
    "    l2 = torch.mean(torch.pow(y - y_pred, 2))\n",
    "    if i%2000 == 0:\n",
    "        print(\"mseloss:\", l2.item())\n",
    "        print(\"params:[{}, {}]\".format(w.item(), b.item()))\n",
    "        \n",
    "    # backward propagate loss\n",
    "    l2.backward()\n",
    "    \n",
    "    # update parameters using gradient descent\n",
    "    w = w - (lr * w.grad)\n",
    "    b = b - (lr * b.grad)\n",
    "    \n",
    "    # detach parameters from computational graph\n",
    "    w = torch.tensor([w.item()], requires_grad=True)\n",
    "    b = torch.tensor([b.item()], requires_grad=True)\n",
    "print(\"finished training\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Red Line (Prediction) vs Blue (Input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_actual = 2(x) + 5\n",
      "y_pred = 1.9889200925827026(x) + 4.501369953155518\n"
     ]
    },
    {
     "data": {
      "image/png": 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/DjnZw29lo2gYc1QX3rvlRCaPPlI2NokBtrTwtdb5Sqmeje4eA5zk/vlF4ENgoh3nEyIeWalwmbWkmOVbduLaV0VhqYsVW3Yyb+zQ35Kt1kzYtZqaGeNpvfsnVp92Pn2efQwONeWkdnyLkG6b2BXOQdtOWuvtAO7/HhzGcwkR86zMsC3avgfXvip+qagmJVlRsnPfb98Eiopg+HBaXXk5bXp1p8WKFWQtnk/bQ3+bO+DvW4RMnIpvjlfpKKXGKqVWKqVW7tixw+lwhPAp3JOurFS41M1mTUqC6hrT2t7ybRlMmmS6a774Ap54Aj77zFTjNOLvW4SsjxPfwlmlU6aU6qy13q6U6gz86O0grfUzwDNgZtqGMR4hQpLtJYHayUpXSc6IPqzYspOSnfvISEvmjE3LuentJ2DHdrjySpgxAw5u+mW6rivnm5/2UlWj6XPIAfxSXt2kTl4mTsW3cLbwFwJXuH++AlgQxnMJEXbRMNM2Iz2FeWOHct2hmtnzJnP383fTplNH+OQTszyCl2QPv7Xcex/YGo1my45fqdWaL79zNei6kS0J45stCV8pNQ9YDmQqpUqVUlcDDwAjlVKbgJHu20LErLKyMqdDgPJyMh64l5tyzuXoknXwz3+S9MUXcPzxzT6truWenprMgC4ZKBQtlCI9JalB141MnIpvdlXpXOzjoeF2vL4Qscj2naveecdMmtqyBS66CB56CLp0sRRD464cpfDadSMVOPHN8UFbIeKVbQOgJSVw7rlmXfrUVFi6FObN85vsPWOo68r59qe9ZPfqwPC+naTrJgFJwhfCotGjRwd0fMgDoJWVcP/90K8fvP8+PPAArF0Lp5xiuXyycVdOz46tmTz6SG47vS/ZvTpQXlVj+vJLf5YyzAQgCV8Ii0pKSgI6PqQB0KVLYdAguPNOOOMMU2M/caJp4WP924OvGOq6bgYemmH68lOTpQwzAUjCF8KiwsLCgI4PagD0u+9M//yIEWbLwUWL4PXXoXv3BodZ/fbgLwYpw0wsslqmEGES0ABoVRU89hjcc4/5ecoU06JPS/N6uNV15/3FIOvXJxZp4QvhtI8/hmOOgfHjYdgws3H4Pff4TPZgX/mklGEmFtnTVgiLbN/TtqzMrFE/Z47pspk1C8aMCWmLQZGYrO5pK106QliUEeSGIU3U1MDTT5sB2X374I474K67oHVre16fMMwBEHFBEr5IeFaTY15eXsClmU189pnZZnD1ahg+HB5/HPr2DTnmq07oxfOffFN/e391DWu3uX7b9WpJsUyoEtKHL0REVojcuRPGjoWhQ+GHH+CVV8zuU0Eke2ga87VzVja4vbToR6m+EU1IwhcJL6ylibW18OyzkJkJzz0Ht9wCGzbAhReG1FffOOZS90bidbeVQmbSiiYk4YuEZ3WCVPdGtfB+rVljFjX7y1/MbNkvvjDr37RpY3vMXdunN7g9vG8nqb4RTUiVjkh4tg9wulxw990wezZ07AgzZ8Lll9tafeOvD18GaROL1SodSfhCeGgu+efn5zNs2DDfT9Ya5s6FCRPgxx/h+uvh3nuhffsIRS8SlZRlChGEusHQtmnJvLXmOxav284ZAzqTM6IPLpfL9xPXr4cbb4SPPjJbC77zDgwZErnAhbBA+vCF8FA3GLptdzmVVbXsq6xpvnLn11/N5KmjjoIvvzT19cuXS7IXUUkSvhAe6gZD91RUA9A2LaW+cifNc6kDreG110xZ5cyZVF5yGTMfXcDFDGTaO0WyzLCISpLwhfBQt7ZMekoLWqYk0bVDen3lzsiRI81BxcVw+ulwwQVw0EHw6ac88MfxfLRTN1vLb3UNeyHCRRK+EB7qVpd875YTGXNUF9DUlzUWr1ljqm8GDoQVK8zaN59/DkOHWqrlj8gELyGaIYO2QnhoXKXz1GVZpkonN5eUa64x1TeXXmpKLQ85pP55VpYZlrXnhdOkhS8STnNdK41b4S++vAzOPhvOPpuali3hww/hpZcaJHuwtsxwSDtgCWEDaeGLhFOX1L0tLFa/B2xtNVd+8DJnv/MCpKbAzJl8dNhhnHXiiV5f08pmJzkj+tR/e5DZr8IJkvBF3GvcTfPldy6fXSv9OrelavG73PjGLA4p28ZXvz+V/q/+G7p25Q/N1eFbENAOWEKEgXTpiLjXuJtm7/5q710rpaXc8fxkpj85Aa3h5alPc+iSt6FrV2ffgBA2kRa+iHn+1sJpPFhaXlnNwK7tfutaObGXGYSdOpWUmhqYPp3Ot93GpS1bNjhPfn6+5fXwZQMSEY2khS9inr9yx8aDpQO7tiNnRB/Tsv/wQ/YPGGRmyw4fDkVF8Le/QaNkb3dMQjhBEr6Ief7KHb1V0Dz72qf8YeotTH54HLV79zLv7sdhwQLo2TMiMQnhBOnSETHPXw18g8HS6mqYPZvrJ91FSnUluaP/zFtnXM6+pFQu9nOePn2sV9VYqcsXItKkhS9inpUaeAA+/RSysuDmm9nebzA3TnyB18aMpayqhaWa+MzMTPtjEiKCpIUvYp7fcscdO2DSJLPF4KGHwmuvceCos+m6dFNANfF5eXm/racTakxCOEASvnCE1R2bQqp2qa2Ff/0L7rgDfvkFbrsNJk+GAw4gAwJOyBUVFYG/USGiiHTpCEc0rmK5ds5Kr1UtQVe7rFoFxx0H110HgwaZ/WUffBAOOCCM70qI6CYtfBER/ma7rtn2M0d1a9egqsVVXsXiddvZV1mDq7yKrh3S/Ve77N5tyiqffBIOPhhefhn+7/9s2U82IyMj5NcQwknSwhcR4W+2a9f26U1mv85aUsz+6lpqajS791ZS/MOvvgdXtYYXX4TMTHjqKbjpJti4ES65xLbNw5vdz1aIGCAJX4SN56qUi9dtp016cn0LvnVqUoMqlqcvz2pS1VK0fQ99O7WhfetUkpJakJKkvA+uFhbCsGFw5ZVw+OGmO2fWLLC5Rb527VpbX0+ISJMuHRE2nqtS7q+upfiHXzmyS9v6uvTGg6aNb9fVsvc6sHX9cxoM2O7ZA1OmwKOPQrt28OyzcNVV0CI87ZitW7cyePDgsLy2EJEgLXwRNp6zTft2akNKkgqoLt1nLbvW8MorZj/ZRx6Bq6823TdXXx22ZC9EPJAWvggLV3kVrvIqSnbuIyM9hYxWKYwa2DmgUkivtewbNsC4cbB0KQwZAm+9BdnZNkcvRHwKe3NIKXW6UmqjUmqzUmpSuM8nosOsJcVoTX0tvYLQZpvu3Wvq6QcNMn30TzwBn30W0WRvddKVENEqrC18pVQSMBsYCZQCnyulFmqtvwrneYXzirbv4aA2LenSLp2qmlqqamqDWx5Ya7OoWU4ObN1qBmZnzDAllxHmcrlIS0uL+HmFsEu4W/jZwGat9RatdSXwCjAmzOcUUcCW/Vu//hrOPBPOPddU3Hz8MTz/vCPJHqCgoMCR8wphl3D34R8KbPO4XQr8zvMApdRYYCxAly5dyM3NrX+sru45Pz+//r4+ffqQmZlJXl5e/VT3jIwMhg0bxtq1a9m6dWv9sSNHjsTlcjX4H3XQoEH06NGjwXk6depEdnY2BQUFlJWV1d8/evRoSkpKKCwsrL8vOzubjIwM8vLy6u/r3r07gwcPJj8/H5d7G7y0tDRGjhzJxo0bKS7+bXZoorynUd17AR34YOkSurRJpk+XLuTn77T0nlrs38/xn3xCu6eeorpFCzZefTXfnHUWB6emkg2OvafS0lKAuPo9xeNnLxHfk1VKa2354EAppf4EnKa1vsZ9+zIgW2t9k7fjs7Ky9MqVK8MWj4gBixaZSVNbtsCFF8LDD0OXLk5HBUBubq7lHa+EiCSl1CqtdZa/48Ldwi8Funnc7gp8H+ZziigQ8KJnJSVwyy3w5pum3HLJErMDVRQZNGiQ0yEIEZJwJ/zPgSOUUr2A74CLgP8L8zlFFPCcdFW36JnXkszKStOKnzbNLIFw//1w662QmhrwOb39kamLxY69ZXv06BHU84SIFmEdtNVaVwPjgPeAImC+1np9OM8pooOlLf6WLYPBg0255emnm/1kJ00KKtmD95U17dxb1rNPV4hYFPaJV1rrRcCicJ9HOMNX102zW/x9/z2MH29my/buDe+8A6NGhRyLrz8ysresEIbMQxch8dWC9rosQlWV6b7JzDR99ffcA+vW2ZLswXspqC3loULECVlaQYTEV6u6ybIIn3wCN9wAX34JZ5xhFjw7/HBbYqj7lvHldy5qtaa8srrB2jt130BC3Vu2U6dOtsQrhFMk4YuQNNt1A/Djj3D77Wat+m7d4I034Jxzgl6j3lsXkucAcXllDQO7tmvwx8auvWWzZc0eEeOkS0eExOeKljU1ZtepzEz4z3/MYGxRkZk1azHZe66nPy13fX2yb9yFZGmA2AYy01bEOmnhi5B4XdGyoIDq664n+YvVrMscwkc593DplacHXA7prbTTW3L3+y3DJp4zLIWIRdLCF/bZuROuvRaOO47ykm08eMU9PDjhcRZXtQuqHNJXcm88COvzW4YQogFp4YvQ1daaRc0mToSff4acHHJ6nkn1AW3cyVoF1c3ireVe12fvOQjr9VuGEKIJSfgiNGvWmOqb5cvh+OPNOvWDBtEzd33I3SzRltxlHR0R6yThi+C4XDB5Mjz+OHTsaFr4l19ev8Wgt2QdqGhruZeUlMjyCiKmScKPEQEvRhYuWpuqmwkToKwMrr8e7r0X2rdvcFi0JWs7FBYWSsIXMU0GbWOEnWvCBO2rr+CUU+DSS01NfUEBzJ7dJNkLIaKTJPwYEala88Zc5VXcP/9zFp52KTWDBlO7di08/TSsWAFZfpffFkJEEUn4McKRNWG05v2pj3PNtWdy9vtzWZp1Kg8/ugDGjq3vq08kMtNWxDrpw49SjfvsrzqhF4Ata8JYsmkTjBvHn95/n5JuR/D3G+5nQ+8BVO2rDe95o1hGRobTIQgREkn4UarxLFOwb02YZpWXm01IZsyAtDTevWYiT/Q/jXZtW4V1FmssyMvLk9JMEdMk4Ucpu/rsA6ruyc2Fv/4Vvv0WLrkEZs5kaLsDKbBptUkhhLMk4Ucpu9aHsbTV4LffQk4OLFwI/fvDBx/ASScBkEHTbxZRUyIqhAhI4o28xQhf68N4W0GyOc1+U9i/H+67zyT5pUvhwQfNzFl3svclKkpEHdC9e3enQxAiJNLCj1K+Ji7NWlLM8q934iqvorDUxYotO5k3dqjPFrbPbwp5eXDjjWZw9vzzzU5U3bpZis2pElGnDR482OkQhAiJtPBjTNH2PbjKq9hTUU1KsqJk575mW9iNvync3LcVXHABnHqqmTX77rvw2muWkz04VCIaBfLz850OQYiQSAs/xvTr3JbCUhcpyYqaGvNNoLkWdv03haoqmDULLp5iNieZNg1uuw3S0gKOwY51cmKRy+VyOgQhQiIJP4a4yqvYX12D1ppfK2o56IBUMlql+G9h5+ebFS3Xr4ezzjL7yfbqFXQc8bhOjhCJQLp0YsisJcWs3ebiqG7taJueDAqG9u7ou4X9ww9mBcsTT4S9e00VTm5uSMk+kaUF8W1IiGgiLfwY4jlYOqBLBlU1td5b2tXVZj/Zv/0NKirMf++4A1q1inzQcWTkyJFOhyBESKSFH0MsDZYuXw7HHmsmUB13HHz5JUyfjkulBFTOKZrauHGj0yEIERJJ+FHCSn19s3u3/vQTXHMN/P73sGMHzJ9vKnD6mGMStXbeTsXFcs1EbJMunShhZUas18HS2lp49lnTZbNnj9mYZPJkaNOmwWFWa+frZtF++Z2LvfuraZ2axMCu7WQ2rRBxQFr4USKoyUyrV8PQoXDttTBgAHzxBcyc2STZg/Xa+bo/PN/tLmfLjr1893OFfCMQIk5Iwo8SAU1m2r3bzJLNyoKSEnjpJfjwQ5P0fWi2O8hD3R+e8qoa0lJbUF5Vk1CzaZszbNgwp0MQIiTSpRMlGk9muuqEXkzLXd9wgbK0ZJgzx0yY2rkTxo0zE6jatfP7+lZr5+uWYkhPSWLX3sqQF28TQkQPpbV2OoZ6WVlZeuXKlU6HERWm5a5vsAbOWeonrpv/MHzyiam+eeIJOPpo288rffi+5ebmynr4IioppVZprf3uOSqfBaoWAAAN60lEQVQtfBvZuWxwXddKm8pyLsx9itOWvcYv6W34YNxUTnxgIhmtW9ocvSGzaIWIX9KHbyM7Sx/7HdKGAR8vZvqdF3DG0vksPPo0Jt43n2eOOJlZyzbbGLUQIlFIC99Gti0bvHEjdz6SQ/IHy9jSPZPbLrqb8qOzSE9NpkNNrQygOqRPn8RYJE7EL2nh+xHIhiMhLxu8dy/ceScMHEjy6lXw+OP03rKeHqNOYU9FtaXXDXSDFGFdZmam0yEIERJJ+H4E0k1jtfSxCa3hrbfMzlP33w8XXwwbN5rSy6SkgF5XZtSGT15entMhCBES6dLxI5BumqAGPLdsgZtugkWL4Mgj4aOPoFG9t7/X9Rws/nbnXnoe2DrhdqOKhIqKCqdDECIkIbXwlVJ/UkqtV0rVKqWyGj12h1Jqs1Jqo1LqtNDCdE7YdneqqICpU02rPj8fHnrIzJQNYnKPZ6t+f3UtxT/8mnC7UQkh/Au1S2cdcB7QYO83pVR/4CLgSOB04AmlVFKI53JE0N00zVm82MyKnTIFzjkHNmyAW2+FlNBKOFOSWtC3UxtSkpS98QoAMjIynA5BiJCE1KWjtS4CUEo1fmgM8IrWej/wjVJqM5ANLA/lfE6wtS5961a4+WZ4803IzIQlS2D48JBf1nOj8j0V1Ywa2Flq6cNAllYQsS5cg7aHAts8bpe670tMlZUwYwb062eWLP7736Gw0JZkD2H6FiKaWLt2rdMhCBESvy18pdQS4BAvD92ltV7g62le7vO6hoNSaiwwFqB79+7+wok9y5aZapsNG0z3zSOPQI8efp8WyKxdmR0bGVu3bmXw4MFOhyFE0PwmfK31iCBetxTo5nG7K/C9j9d/BngGzFo6QZwrOn3/PYwfD6+8Ar17w9tvw5lnWnqqq7yKi59ZTsnOfWSkp/Dzviqg6fr4QggRiHCVZS4E/qOUehjoAhwBFITpXGER9Lo41dXw+ONmE5LKSrjnHpg4EdLTLZ971pJiSnbuIyVZsaeiGkDKK4UQIQu1LPNcpVQpMBR4Ryn1HoDWej0wH/gKeBe4UWtdE2qwkRTUBKb//Q+GDIFbboHjj4d160wlTgDJHkxyz0hPoaYGkpPMH5/DDjpAZtA6TDYxF7EupISvtX5Ta91Va91Sa91Ja32ax2P3aa0P01pnaq0Xhx5qZAW0Ls6PP8JVV8EJJ5jNSd54A9frC5hWtD+oBN2vc1syWqXQJi2ZqmpNj46t0GiZQeswl8vldAhChESWVvDB0oSrmhp48klTYjl3LkyaBEVFcO65zFq6KegEnTOiD0N7d6RHx1ZceGw35o0dypYde+1ZmE0EraAgpnolhWhCllbwofEOVE1KHT//HG64AVauhJNPhtmzTdmlWygrZ3qruvGstZcdqIQQwZCE74PPUsddu8yKls88A4ccAvPmwYUXQqPJZ3YnaL9/gIQQwg9J+FbV1sILL5iKm927ISfHrIXT1vtaNXYnaKm1d96gQYOcDkGIkEjCt2LNGjN56tNPTfXN7NngZwKOJOj408PChDkhopkM2jbH5TIt+SFDoLgYnnvOrGwpsy0TUm5urtMhCBESaeF7o7Xpmx8/HsrK4Lrr4L77oH17pyMTQoigScJvrKjIdN988AEceyzk5kJWlv/nCSFElIv7hG95iYRff4Xp0+Hhh6FNG3jqKbjmGkiKyWX8RRh06tTJ6RCECEnc9+H7XSJBa3j9dVND/+CDcNllZj/Za6+VZC8ayM7OdjoEIUIS9wm/2QlQmzbBGWfA+edDhw7wySdmYPagg5wLWEQtmWkrYl3cJ3yvSySUl5vVLAcMMKWWjzwCq1aZkkshfCgrK3M6BCFCEvcJv/FuUOMrN8GRR5r++vPPN903OTmQHPfDGUKIBBf3Wa5+AtS335rEvnCh6a9ftsysgSOEEAkirhO+q7yKxxev4/A5T3He4jkkJ7dAzZhhNhJPTbXtHEFtlCJizujRo50OQYiQxHWXzhszXuCSsWdz4YJn+OjwLP7xyFtw++22JXsIcqMUEZNKSkqcDkGIkMRnwi8thQsv5KqpY1G6lnGX3cvNf7yLN37ytrd6aEJZBlnElsLCQqdDECIk8dWlU1UFjz5q9pGtqeHp4VfwdPZ5kJYG1bXoMGyRLuvUCyFiRfy08PPz4eijYcIEMxi7fj1bb7wV0tOo0ZCa0oLh/Q62/bSNq4BknXohRLSKjxb+nDlwxRXQowcsWABnnw3A7Z2raJmc1GBA1W6yDHLikJm2ItYpHY5+jiBlZWXplStXBv7En3+Gxx4zq1u2amV/YEIAFRUVpKWlOR2GEE0opVZprf2u8hgfXTrt2sHddzeb7F3lVUzLXc/FzyxnWu56XOVVEQxQxIO8vDynQxAiJPGR8C2Q8kkhRKJLmIQv5ZNCiESXMAnf6yJqQgSge/fuTocgREgSJuFL+aQI1WDZy1jEuPgoy7RAyidFqPLz8xk2bJjTYQgRtIRp4QsRKpfL5XQIQoQkblr4smqlEEI0L25a+FJ2KcJNJl2JWBc3CV/KLkW4jRw50ukQhAhJ3CR8KbsU4bZx40anQxAiJHGT8KXsUoRbcbF0E4rYFjeDtlJ2KYQQzYubFr4QQojmScIXwiKZdCVinSR8IYRIEJLwhbAoPz/f6RCECIkkfCGESBCS8IUQIkFE1Z62SqkdQEmQTz8Q+MnGcOwSrXFB9MYmcQVG4gpMPMbVQ2t9kL+Doirhh0IptdLKJr6RFq1xQfTGJnEFRuIKTCLHJV06QgiRICThCyFEgoinhP+M0wH4EK1xQfTGJnEFRuIKTMLGFTd9+EIIIZoXTy18IYQQzYiphK+U+pNSar1SqlYpldXosTuUUpuVUhuVUqf5eH4vpdRnSqlNSqlXlVKpYYjxVaXUGve/b5VSa3wc961S6kv3cSvtjsPL+aYopb7ziG2Uj+NOd1/DzUqpSRGIa6ZSaoNSqlAp9aZSqp2P4yJyvfy9f6VUS/fveLP7s9QzXLF4nLObUuoDpVSR+/Of4+WYk5RSLo/f7+Rwx+Vx7mZ/N8p41H3NCpVSx0QgpkyPa7FGKbVHKXVzo2Mics2UUs8ppX5USq3zuK+DUirPnYvylFLtfTz3Cvcxm5RSV4QcjNY6Zv4B/YBM4EMgy+P+/sBaoCXQC/gaSPLy/PnARe6fnwKuD3O8DwGTfTz2LXBgBK/dFGCCn2OS3NeuN5Dqvqb9wxzXqUCy++cZwAynrpeV9w/cADzl/vki4NUI/O46A8e4f24DFHuJ6yTg7Uh9ngL53QCjgMWAAo4DPotwfEnAD5ha9YhfM2AYcAywzuO+B4FJ7p8nefvcAx2ALe7/tnf/3D6UWGKqha+1LtJae9t2aAzwitZ6v9b6G2AzkO15gFJKAacA/3Xf9SJwTrhidZ/vAmBeuM4RBtnAZq31Fq11JfAK5tqGjdb6fa11tfvmCqBrOM/nh5X3Pwbz2QHzWRru/l2HjdZ6u9Z6tfvnX4Ai4NBwntNmY4A52lgBtFNKdY7g+YcDX2utg53UGRKtdT6wq9Hdnp8jX7noNCBPa71La70byANODyWWmEr4zTgU2OZxu5Sm/0N0BH72SC7ejrHTH4AyrfUmH49r4H2l1Cql1NgwxuFpnPsr9XM+vkJauY7h9GdMS9CbSFwvK++//hj3Z8mF+WxFhLsL6WjgMy8PD1VKrVVKLVZKRXI3IH+/G6c/Vxfhu+Hl1DXrpLXeDuYPOnCwl2Nsv25Rt+OVUmoJcIiXh+7SWi/w9TQv9zUuP7JyjCUWY7yY5lv3x2utv1dKHQzkKaU2uFsCQWsuLuBJYDrmPU/HdDf9ufFLeHluyGVcVq6XUuouoBqY6+NlbL9e3kL1cl/YPkeBUkodALwO3Ky13tPo4dWYLotf3eMzbwFHRCIu/P9unLxmqcDZwB1eHnbymllh+3WLuoSvtR4RxNNKgW4et7sC3zc65ifMV8lkd8vM2zG2xKiUSgbOA4Y08xrfu//7o1LqTUx3QkgJzOq1U0r9C3jby0NWrqPtcbkHo84Chmt356WX17D9enlh5f3XHVPq/j1n0PTruu2UUimYZD9Xa/1G48c9/wBorRcppZ5QSh2otQ77mjEWfjdh+VxZdAawWmtd1vgBJ68ZUKaU6qy13u7u3vrRyzGlmHGGOl0x45dBi5cunYXARe4Kil6Yv9IFnge4E8kHwPnuu64AfH1jCNUIYIPWutTbg0qp1kqpNnU/YwYu13k71i6N+kzP9XG+z4EjlKlmSsV8FV4Y5rhOByYCZ2ut9/k4JlLXy8r7X4j57ID5LC3z9UfKLu4xgn8DRVrrh30cc0jdWIJSKhvz//bOcMblPpeV381C4HJ3tc5xgKuuOyMCfH7TduqauXl+jnzloveAU5VS7d1dsKe67wteuEeo7fyHSVSlwH6gDHjP47G7MBUWG4EzPO5fBHRx/9wb84dgM/Aa0DJMcb4AXNfovi7AIo841rr/rcd0bYT72r0EfAkUuj9snRvH5b49ClMF8nWE4tqM6adc4/73VOO4Inm9vL1/YBrmDxJAmvuzs9n9WeodgWt0AuarfKHHdRoFXFf3OQPGua/NWszg9+/DHVdzv5tGsSlgtvuafolHhV2YY2uFSeAZHvdF/Jph/uBsB6rc+etqzLjPUmCT+78d3MdmAc96PPfP7s/aZuCqUGORmbZCCJEg4qVLRwghhB+S8IUQIkFIwhdCiAQhCV8IIRKEJHwhhEgQkvCFECJBSMIXQogEIQlfCCESxP8DzW49XAnppFcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x.numpy().reshape(100), y.numpy().reshape(100), alpha=0.7, s=15)\n",
    "plt.axhline(0, color=\"black\", linestyle=\"--\", alpha=0.3, linewidth=1)\n",
    "plt.axvline(0, color=\"black\", linestyle=\"--\", alpha=0.3, linewidth=1)\n",
    "x_pred = np.linspace(-10, 10, 100)\n",
    "print(\"y_actual = {}(x) + {}\".format(2, 5))\n",
    "print(\"y_pred = {}(x) + {}\".format(w.item(), b.item()))\n",
    "y_pred = (w.item() * x_pred) + b.item()\n",
    "plt.plot(x_pred, y_pred, color=\"red\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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