03. Simple Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_regression\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtEAAAHVCAYAAAA3sKeMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3X9opdl5H/DniVY212mpHDxJPNdudwlbpTZDVrUwKfOP7aaVY2hXHuNmQ0lNCWz+sKENQXSGFOJAwwxVU0NLEthQk5S2cbZkIm+6pmrscTGYprG22mR2bYssTmLPHRNPGittWdXVzp7+MdJamrmS7nt/ve+99/OBQbrnvtIc3St2v++Z5zwnSykBAAD07jvqngAAAEwaIRoAACoSogEAoCIhGgAAKhKiAQCgIiEaAAAqEqIBAKAiIRoAACoSogEAoKKH6p5AL970pjeVhx9+uO5pAAAw5Z577rk/LaWcO+u6iQjRDz/8cGxtbdU9DQAAplxm/nEv1ynnAACAioRoAACoSIgGAICKhGgAAKhIiAYAgIqEaAAAqEiIBgCAioRoAACoSIgGAICKhGgAAKhIiAYAgIqEaAAAqEiIBgCAioRoAACoSIgGAICKHqp7AgAAEBGxsd2J9c2duL27F+cXWrG2shirS+26p9WVEA0AQO02tjtx5frN2Nu/GxERnd29uHL9ZkREI4O0cg4AAGq3vrnzWoA+tLd/N9Y3d2qa0emEaAAAand7d6/SeN2EaAAAand+oVVpvG5CNAAAtVtbWYzW/Nyxsdb8XKytLNY0o9PZWAgAQO0ONw/qzgEAABWsLrUbG5rvp5wDAAAqEqIBAKAiIRoAACoSogEAoCIhGgAAKhKiAQCgIiEaAAAqEqIBAKAiIRoAACoSogEAoCIhGgAAKhKiAQCgIiEaAAAqEqIBAKAiIRoAACoaSojOzI9n5jcy84UjYx/NzE5mPn/w531HnruSmS9l5k5mrgxjDgAAMC7DWon+lYh4b5fxj5VSHjv486mIiMx8W0Q8ERFvP/iaX8zMuSHNAwAARm4oIbqU8rmI+LMeL388Ij5RSvlWKeUPI+KliHjnMOYBAADjMOqa6I9k5u8flHu88WCsHRFfO3LNrYMxAACYCKMM0b8UEd8XEY9FxNcj4ucPxrPLteX+gcx8MjO3MnPrzp07o5slAABUNLIQXUr5k1LK3VLKqxHxy/Htko1bEfHWI5e+JSJud/n6p0opy6WU5XPnzo1qmgAAUNnIQnRmvvnIw/dHxGHnjmci4onMfH1mPhIRj0bE745qHgAAMGwPDeObZOavRcS7IuJNmXkrIn4mIt6VmY/FvVKNP4qIn4iIKKW8mJlPR8QXI+KViPhwKeXuMOYBAADjkKU8UI7cOMvLy2Vra6vuaQAAMOUy87lSyvJZ1zmxEAAAKhKiAQCgIiEaAAAqEqIBAKAiIRoAACoSogEAoCIhGgAAKhKiAQCgIiEaAAAqEqIBAKAiIRoAACoSogEAoCIhGgAAKhKiAQCgIiEaAAAqEqIBAKAiIRoAACoSogEAoCIhGgAAKhKiAQCgIiEaAAAqEqIBAKAiIRoAACoSogEAoCIhGgAAKhKiAQCgIiEaAAAqEqIBAKAiIRoAACoSogEAoCIhGgAAKnqo7gkAAAzbxnYn1jd34vbuXpxfaMXaymKsLrXrnhZTRIgGAKbKxnYnrly/GXv7dyMiorO7F1eu34yIEKQZGuUcAMBUWd/ceS1AH9rbvxvrmzs1zYhpJEQDAFPl9u5epXHohxANAEyV8wutSuPQDyEaAJgqayuL0ZqfOzbWmp+LtZXFmmbENLKxEACYKoebB3XnYJSEaABg6qwutYVmRko5BwAAVCREAwBARUI0AABUJEQDAEBFQjQAAFQkRAMAQEVCNAAAVCREAwBARUI0AABU5MRCAKBxNrY7ju2m0YRoAKBRNrY7ceX6zdjbvxsREZ3dvbhy/WZEhCBNYyjnAAAaZX1z57UAfWhv/26sb+7UNCN4kBANADTK7d29SuNQByEaAGiU8wutSuNQByEaAGiUtZXFaM3PHRtrzc/F2spiTTOCB9lYCAA0yuHmQd05aDIhGgAYmmG1pltdagvNNJoQDQAMhdZ0zBI10QDAUGhNxywRogGAodCajlkiRAMAQ6E1HbNEiAYAhkJrOmaJjYUAwFBoTccsEaIBgKHRmo5ZoZwDAAAqEqIBAKAiIRoAACoSogEAoCIhGgAAKhpKiM7Mj2fmNzLzhSNj35WZv52Zf3Dw8Y0H45mZ/yozX8rM38/Mvz6MOQAAwLgMayX6VyLivfeNXY6Iz5RSHo2Izxw8joj44Yh49ODPkxHxS0OaAwAwxTa2O3Hx2o145PKzcfHajdjY7tQ9JWbYUEJ0KeVzEfFn9w0/HhG/evD5r0bE6pHxf1vu+Z2IWMjMNw9jHgDAdNrY7sSV6zejs7sXJSI6u3tx5fpNQZrajLIm+ntKKV+PiDj4+N0H4+2I+NqR624djB2TmU9m5lZmbt25c2eE0wQAmm59cyf29u8eG9vbvxvrmzs1zYhZV8fGwuwyVh4YKOWpUspyKWX53LlzY5gWANBUt3f3Ko3DqI0yRP/JYZnGwcdvHIzfioi3HrnuLRFxe4TzAAAm3PmFVqVxGLVRhuhnIuJDB59/KCI+eWT8Hxx06fjBiPjzw7IPAIBu1lYWozU/d2ysNT8XayuLNc2IWffQML5JZv5aRLwrIt6Umbci4mci4lpEPJ2ZPx4RX42IDx5c/qmIeF9EvBQRL0fEPxzGHACA6bW6dG/71PrmTtze3YvzC61YW1l8bRzGLUt5oBy5cZaXl8vW1lbd0wAAYMpl5nOllOWzrnNiIQAAVCREAwBARUI0AABUJEQDAEBFQjQAAFQkRAMAQEVCNAAAVCREAwBARUI0AABUJEQDAEBFQjQAAFQkRAMAQEVCNAAAVCREAwBARUI0AABU9FDdEwCAabCx3Yn1zZ24vbsX5xdasbayGKtL7crXAJNBiAaAAW1sd+LK9Zuxt383IiI6u3tx5frNiIjXQnIv1wCTQzkHAAxofXPntXB8aG//bqxv7lS6BpgcQjQADOj27t6Z471cA0wOIRoABnR+oXXmeC/XAJNDiAaAAa2tLEZrfu7YWGt+LtZWFitdA0wOGwsBYECHGwNP67zRyzXA5MhSSt1zONPy8nLZ2tqqexoAAEy5zHyulLJ81nXKOQAAoCIhGgAAKlITDQADchIhzB4r0QAwgMOTCDu7e1Hi3kmEP/nrz8c/3bhZ99SAERKiAWAA3U4iLBHx73/nq7Gx3alnUsDICdEAMICTThwsEY70hikmRAPAAE47cdCR3jC9hGgAGMDaymLkCc850humlxANAANYXWrH3//Bv/xAkHakN0w3IRoABvTPVi/Ex37ksWgvtCIjor3QiquXLmhzB1NMn2gAGILVpbbQDDPESjQAAFQkRAMAQEVCNAAAVCREAwBARTYWAtCzje1OrG/uxO3dvTi/0Iq1lcWZ2Ew3qz83cDIhGoCebGx34sr1m7G3fzciIjq7e3Hl+s2IiKkOlLP6cwOnU84BQE/WN3deC5KH9vbvxvrmTk0zGo9Z+7k3tjtx8dqNeOTys3Hx2o3Y2O7UPSVoJCvRAPTk9u5epfFpMUs/t1V36J2VaAB6cn6hVWl8Wgz6c0/Syu6srbrDIIRoAHqytrIYrfm5Y2Ot+blYW1msaUbjMcjPfbiy29ndixLfXtltapCepVV3GJQQDUBPVpfacfXShWgvtCIjor3QiquXLkz9P/MP8nNP2srurP5rA/RDTTQAPVtdag8cmiexXVy/P/ekreyurSweq4mOmI1/bYB+CNEAjM2sbVw7v9CKTpfA3NSV3cP3YNJucqAOQjQAY3NaeUOdQW1Uq+OTuLI7jH9tgFkgRAMwcochtduqbES95Q2jXB23sgvTS4gGYKTuD6nd1FneMOrVcSu7MJ105wBgpLqF1KPqLm+YtM1/QDMI0QCM1GlhtAlt8rR1A/ohRAMwUieF0fZCKz5/+T21lzrM6iEywGCEaABGqukhdVYPkQEGY2MhACM1CR0qbP4DqhKiARg5IRWYNso5AACgIiEaAAAqEqIBAKAiIRoAACoSogEAoCIhGgAAKtLiDgD6sLHdaXTva2C0hGgAqGhjuxNXrt+Mvf27ERHR2d2LK9dvRkQI0jAjlHMAQEXrmzuvBehDe/t3Y31zp6YZAeMmRANARbd39yqNA9NHiAaAis4vtCqNA9Nn5CE6M/8oM29m5vOZuXUw9l2Z+duZ+QcHH9846nkAwLCsrSxGa37u2Fhrfi7WVhZrmhEwbuNaiX53KeWxUsrywePLEfGZUsqjEfGZg8cAMBFWl9px9dKFaC+0IiOivdCKq5cu2FQIM6Su7hyPR8S7Dj7/1Yj4rxHxT2qaCwBUtrrUFpphho1jJbpExH/JzOcy88mDse8ppXw9IuLg43ff/0WZ+WRmbmXm1p07d8YwTQAA6M04VqIvllJuZ+Z3R8RvZ+aXe/miUspTEfFURMTy8nIZ5QQBxsHhHJPN+wccNfIQXUq5ffDxG5n5mxHxzoj4k8x8cynl65n55oj4xqjnAVAnh3NMNu8fcL+RlnNk5ndm5l88/Dwi/nZEvBARz0TEhw4u+1BEfHKU8wCom8M5Jpv3D7jfqFeivycifjMzD/+u/1BK+c+Z+YWIeDozfzwivhoRHxzxPABqNe2Hc0x7qcO0v39AdSMN0aWUr0TED3QZ/58R8TdH+XcDNMn5hVZ0ugSuaTicYxZKHab5/QP648RCgDGY5sM5ZqHUYZrfP6A/dfWJBpgphyuy01jyMAulDtP8/gH9EaIBxmRaD+eYlVKHaX3/gP4o5wBgIEodgFlkJRqAgSh1AGaREA3AwJQ6ALNGiAZg6k17H2tg/IRoAKbaLPSxBsbPxkIAptos9LEGxk+IBmCqzUIfa2D8lHMATCA1vr07rY+11xHol5VogAlzWOPb2d2LEt+u8d3Y7tQ9tUY6qY/1u7//nNcR6JsQDTBh1PiebWO7Exev3YhHLj8b65s78YF3tKO90IqMiPZCK65euhCf/fIdryPQN+UcABNGje/punXj+I3nOnH10oVjpRo/+evPd/16ryPQCyvRABPm/EKr0vis6XWl3usIDEKIBmiIoyUIF6/dOLE296Qa37WVxXFMs/F6Xan3OgKDUM4BMAJVuz5UORDk8LGuEt2d1o3jKK8jMIgspdQ9hzMtLy+Xra2tuqcB0JP7A3HEvRXO+2tyj7p47UbX4NdeaMXnL79nZHOdRv28/gCHMvO5UsryWdcp5wAYsn66Z9gsODyrS+24eunCA904BGhgmJRzAAxZP4G41xIEerO61BaagZGyEg3Qg143/UX01/XBJjeAySJEA5yh6gmB/QRiJQgAk0U5B8AZTqtx7hZy++36oAQBYHII0QBnOK3G+aRWdgIxwHQTogHOcNKmv4U3zPfc23nSVO1zDTBr1EQDnOGkGudSonIru0lQtQYcYBYJ0QBnOGnT35/v7Xe9ftJ7O/fT5xpg1ijnAOhBtxrn9c2dqezt7OAXgLNZiQY4wVm9oae1t3M/fa4BZo0QDdBFL3XBJ5V5RETPB7M00bTeHAAMk3IOgC567Q19f5nHYfie5I4d/fa5BpglQjRAF/3WBVc9mKWp9LkGOJ1yDoAu+q0LtikPYDYI0QBd9FsXbFMewGxQzgHQRZW64KOn+7XmH1ybsCkPYPoI0QAn6KUu+P6NhC/vv3rs+YyID7xDfTHAtFHOATCAbhsJjyoR8dkv3xnfhAAYCyEaYAC9bBi0qRBg+gjRAAPoZcOgTYUA00eIBhhAty4eR9lUCDCdbCwEJt7R7hjjPl3v/i4ef6k1H5kRuy/vO+kPYIoJ0cBEq/OY7fvD+8d+5LFaA3OdNxMAs0aIBiZaXcdsVw3v9wfcd3//ufjsl+8MLfDWeTMBMIvURAMTra5jtk8L7/c7DLid3b0ocS/g/rvf+eqxx1eu34yN7c5Y5gPA4IRoYKLVdcx2lfB+Vi/piMEC78Z2Jzo13UwAzCohGpho3bpjjKMjRpXw3muQ7SfwHq5yn0R7PYDREKKBiba61I6rly5Ee6EVGRHthVZcvXRh5HXAVcJ7r0G2n8B72iq39noAo2NjITDxVpfaY988d39ru9M2B66tLB7b9NdNv4H3tNXrcdxMAMwqIRqgT72G926Be1jdOc4vtLrWQ7cXWgI0wAgJ0QBjUGW1vEq/526r3Mo4AEZPiAZokKr9nquUlQAwPEI0wIEmnPjXz+ExddSEA8w6IRqYCWcF5Kac+FfX4TEAVKPFHTD1up0YeP8JgU058a+uw2MAqEaIBqZeLwG5KSvAdR0eA0A1yjmAnjShXrhfvQTkk1rFjXsFuIkbBSf5vQcYFSEaOFNT6oX7tfCG+fjmy/sPjB8NyE1qFdekjYKT/t4DjIpyDuBMTakX7sfGdif+z/995YHx+bk8FpDrOj686Sb5vQcYJSvRwJmaUi/cj/XNndh/tTww/p2ve+iBgNykFeCmOOk97uzuxcZ2x+sFzCwr0cCZJrljxEkhcHdvPy5eu3GsQwcPOu09vr/DCcAsEaKBM01yx4jTQmC3Vncc1+29P6SsA5hlQjRwpkmuFz4tBEbcC4I/9fTvCdInOHzvTzIJJT0Ao6AmGujJpNYLH20Z162FXUTE3VJmruNElbZ1q0vtE1+/SSjpARgFK9HA1FtdasfnL78n2qcEvlkqTejlBMf7TXJJD8AoCNHAzDirtGNWShP6aVs3ySU9AKOgnAOYGYeB76ee/r24Wx5sezcrpQn9tiyc1JIegFGwEg3MlNWldvz83/uBmS5NmOSWhQBNUVuIzsz3ZuZOZr6UmZfrmgcwHhvbnbh47UY8cvnZ2vszz3ppgvpmgMHVUs6RmXMR8QsR8bci4lZEfCEznymlfLGO+QCjdbiR7bAO93AjW0R93TBmuTThaMeSXrpzAPCgumqi3xkRL5VSvhIRkZmfiIjHI0KIhil02kY2wa0es3wTATAMdZVztCPia0ce3zoYe01mPpmZW5m5defOnbFODhiufjeyAUBT1bUSnV3Gjm2VL6U8FRFPRUQsLy8/uI0ezlDlMAlG6/xCy0EdAEyVulaib0XEW488fktE3K5pLkyhfg6TYHRsZANg2tQVor8QEY9m5iOZ+bqIeCIinqlpLkyhfg6TYHRmvRsGANOnlnKOUsormfmRiNiMiLmI+Hgp5cU65sJ0UoPbPOPcyKaUB4BRq+3EwlLKpyLiU3X9/Uw3Nbizq4nt9ACYPk4sZCqpwZ1dSnkAGIfaVqJhlBwmMbuU8gAwDkI0U8thEqPV1LpjpTwAjINyDqCyJrcQVMoDwDhYiQYqq+MY715Xvk8r5Wnq6jkAk0eIBk7VLXiOu+64aseNbqU8unYAMEzKOYATnVS2sfCG+a7Xj6rueBgdN3TtAGCYhGjgRCcFz1JirHXHw1j51rUDgGESooETdetyERGxu7c/1mO8T1rhrrLyPYzvAQCH1EQDJ5rLjLuldB0fZwvBtZXFY/XMEREZEe/+/nMDfQ9dOwDol5Vo4ETdAvRp4/fb2O7ExWs34pHLz8bFazf6boG3utSOD7yjHXlkrETEbzzX6fl7ri61x7p6DsB0sxINnKh9wsEl7R5KIIbdDeOzX74T90f3qm31HMADwLBYiYaGGtYq7iAGObjkZ3/rxaF2w7AxEIAmsRINDdSUnsanHVxymo3tTnzz5f2uz/UbeifxOG+HuwBMLyEaGqiOEwFPcloJxEkh8bTV5n5D76RtDGzKjRAAoyFEQwNNQunCaSHxtHn2G3r7XRU/zShXipt0IwTA8AnR0ECTULpwWkg8af4LrfmBAuQwNwaOeqV4Em6EAOifjYXQQINs6IsYz6bE00LiSfP/6N99+9Dn0a9RHwPucBeA6SZEQwMN0tP4cIW1s7sXJb69wjrsIH1aSJyEnsyjXike9EYIgGZTzgEN1W/pQtVa3H7rgs/a6Nf0nsyjLpkZRQ03AM0hRMOUqbLCOkhd8KSHxHF0+2j6jQQA/ROiYQBN7ANcZYV10A4SkxwSJ/0mAIB6CdHQp6b2Aa6ywjrrHSQm+SYAgHrZWAh9GnV3h35V2dSngwQA9MdKNPSpyau4va6wTtopgADQFFaioU/TsIo7Ca3oAKCJrERDn6ZlFVddMABUJ0RDn6ahu0MTu4sAwCQQomEA3VZxJyWYNrW7CABMAiEa+nBSUJ6kYDpoj+hhqeOmY1JudABoLiEaKjotKDclmPaiCd1F6rjpmKQbHQCaS4iGik4Lyk0Ipr2qcrLhMB1dBf6OzLhbyrHnR33TMUk3OgA0lxZ3UNFpQXmS2t6trSxGa37u2Niou4scrgJ3dveiRDwQoA+N8qZjkm50AGguIRoqOi0o1xFMq9jY7sTFazfikcvPxvrmTnzgHe2x9ojutgrczShvOibpRgeA5lLOARWd1h+6yW3vutUC/8ZznbEertLLau+obzqmpb83APUSoqGis4JyUw8v+egzL9ZeC3xSHfZcZrxaylhuOpp8owPA5BCioQ91BeV+W7NtbHdid2+/63PjrAU+aRV43EeNN/VGB4DJIUTDhBikNdv65s6Jz42zFtgqMADTQoiGCTFIa7bTVpvHXQtsFRiAaaA7B0yIQVqznbTa/MY3zAu0ANAHIRomxCCt2U5qvfczf+ftQ5kbAMwaIRqG7Ggv5ovXbsTGdmco33eQHtSrS+24eunCWHtCA8A0UxMNQzTI5r+zDLopTy0yAAyPEA1DNMjmv14IwgDQDMo5YIgG2fwHAEwOIRqGaJDNfwDA5BCiYYgG2fwHAEwONdEwRE0/ka/fY8MBgOOEaBiypm7+G2XnEACYNco5YEac1jkEAKhGiIYZoXMIAAyPcg7o06TVF59faEWnS2DWOQQAqrMSDX04rC/u7O5FiW/XFw/riO9R0DkEAIbHSjT0ocrJhE1ZsW565xAAmCRCNPSh1/ripnXEaGrnEACYNMo5oA+9nkw4jo4YG9uduHjtRjxy+dm4eO1Go0tKAGBaCNHUZpLDX6/1xaPuiNGtNvsnf/35eHgCX1MAmCTKOahF08ocquq1vnjUHTG6rXSXg4+T9poCwCQRoqlFlY15TdVLffHayuKxm4WI4XbEOGtFe9JeUwCYFEI0tZj2gz+OduRYeMN8vP6h74g/39sfekeMk1a6j5qW1xQAmkRNNLXodWPeJLq/TvmbL+/Ht155NT72I4/F5y+/Z6irwt1qs+83Da8pADSNEE0tpvngj3F05Di0utSOq5cuRPsgKOd9z0/LawoATaOcg1pM88Ef4y5VOVqb3ZSDXQBg2gnR1GZaD/4YdUeO00zrawoATaOcA4ZsmktVAIB7rETDkE1zqQoAcI8QDSOgrAIAptvIyjky86OZ2cnM5w/+vO/Ic1cy86XM3MnMlVHNAQAARmHUK9EfK6X8i6MDmfm2iHgiIt4eEecj4tOZ+VdLKXe7fQMAAGiaOjYWPh4RnyilfKuU8ocR8VJEvLOGeQAAQF9GHaI/kpm/n5kfz8w3Hoy1I+JrR665dTB2TGY+mZlbmbl1586dEU8TAAB6N1CIzsxPZ+YLXf48HhG/FBHfFxGPRcTXI+LnD7+sy7cqDwyU8lQpZbmUsnzu3LlBpgkAAEM1UE10KeWHerkuM385Iv7TwcNbEfHWI0+/JSJuDzIPAAAYp1F253jzkYfvj4gXDj5/JiKeyMzXZ+YjEfFoRPzuqOYBAADDNsruHP88Mx+Le6UafxQRPxERUUp5MTOfjogvRsQrEfFhnTkAAJgkIwvRpZQfO+W5n4uInxvV3w0AAKNUR4s7AACYaEI0AABUJEQDAEBFQjQAAFQ0yu4cMLCN7U6sb+7E7d29OL/QirWVxVhdeuCASwCAsRKiaayN7U5cuX4z9vbvdUDs7O7Fles3IyKGFqSFdACgH8o5aKz1zZ3XAvShvf27sb65M5TvfxjSO7t7UeLbIX1juzOU7w8ATC8hmsa6vbtXabyqUYd0AGB6CdE01vmFVqXxqkYd0gGA6SVE01hrK4vRmp87Ntaan4u1lcVK32djuxMXr92IRy4/Gxev3XitXGPUIR0AmF42FjbYrG96O/xZB3kNum1OXPuPvxc/+1svxjdf3o+MiHLk+n5COgAwe4TohqramWJaA/fqUnugn6Nb3fP+qyW++fJ+RNwL0IdBuj1FrxsAMFpCdEOdtunt/pA3jlZwk6qX+ubDAP35y+8Z/YQAgKmgJrqhqmx602XiZL3WN9tMCABUIUQ3VJVNb3V0mThps17TdNuc2I3NhABAFUJ0Q1XpTDHuLhOTdEjJ6lI7rl66EO2FVmRELLTmY34uj11jMyEAUJWa6Iaq0plibWXxWE10xGiDYZV67Sa4f3PitG7CBADGR4husF47UwyjFVwVk35IyaAdPwAAhOgpMc5geH6hFZ0ugVldMQAwK9REU9mwThI8NCmbFAEADlmJprJhlo8Ms8e1WmcAYFyEaPoyrPKRszYp9hqMHTgDAIyTcg5qddomxSqt9Bw4AwCMkxBNrU7rcV0lGE96xxAAYLII0dTqtE2KVYLxuA+cAQBmmxBNre4/UbC90Iqrly7E6lK7UjAedscQAIDT2FhI7U7apFjlJMZxHzgDAMw2IZrGqhqMnUQIAIyLEE2jCcYAQBOpiQYAgIqEaAAAqEiIBgCAioRoAACoSIgGAICKdOcY0MZ2R29iAIAZI0QPYGO7c+wwkM7uXly5fjMiQpAGAJhiyjkGsL65c+w0vYiIvf27sb65U9OMAAAYByF6ALd39yqNAwAwHYToAZxfaFUaBwBgOgjRA1hbWYzW/Nyxsdb8XKytLNY0IwAAxsHGwgEcbh7UnQMAYLYI0QNaXWrXGpq12AMAGD8heoJpsecmAgCohxB9iqYHtNNa7DVpnqPiJgIAqIuNhSc4DGid3b0o8e2AtrHdqXtqr5n1Fnv6dAMAdRGiTzAJAW3WW+zN+k0EAFAfIfoEkxDQZr3F3qzfRAAA9RGiTzAJAW11qR1XL12I9kIrMiLaC624eunCzNQDz/pNBABQHxsLT7C2sngNrxaUAAAE9klEQVRs01pEMwNa3S326qRPNwBQFyH6BALaZJjlmwgAoD5C9CkENAAAulETDQAAFQnRAABQkRANAAAVCdEAAFCREA0AABXpznGKje2OFncAADxAiD7Bxnbn2GErnd29uHL9ZkSEIA0AMOOE6BOsb+4cO60wImJv/26sb+6MPERbAQcAaDYh+gS3d/cqjQ+LFXAAgOazsfAE5xdalcaH5bQVcAAAmkGIPsHaymK05ueOjbXm52JtZXGkf29dK+AAAPROiD7B6lI7rl66EO2FVmREtBdacfXShZGXVNS1Ag4AQO/URJ9idak99jrktZXFYzXREeNZAQcAoHdCdMMchnbdOQAAmkuIbqA6VsABAOidmmgAAKhooBCdmR/MzBcz89XMXL7vuSuZ+VJm7mTmypHx9x6MvZSZlwf5+wEAoA6DrkS/EBGXIuJzRwcz820R8UREvD0i3hsRv5iZc5k5FxG/EBE/HBFvi4gfPbgWAAAmxkA10aWUL0VEZOb9Tz0eEZ8opXwrIv4wM1+KiHcePPdSKeUrB1/3iYNrvzjIPAAAYJxGVRPdjoivHXl862DspPEHZOaTmbmVmVt37twZ0TQBAKC6M1eiM/PTEfG9XZ766VLKJ0/6si5jJbqH9tLtG5RSnoqIpyIilpeXu14DAAB1ODNEl1J+qI/veysi3nrk8Vsi4vbB5yeNAwDARBhVOcczEfFEZr4+Mx+JiEcj4ncj4gsR8WhmPpKZr4t7mw+fGdEcAABgJAbaWJiZ74+Ifx0R5yLi2cx8vpSyUkp5MTOfjnsbBl+JiA+XUu4efM1HImIzIuYi4uOllBcH+gkAAGDMspTmlxsvLy+Xra2tuqcBAMCUy8znSinLZ13nxEIAAKhIiAYAgIqEaAAAqEiIBgCAioRoAACoaCK6c2TmnYj447rnwVi8KSL+tO5JMFH8zlCV3xmq8jszW/5KKeXcWRdNRIhmdmTmVi9tZeCQ3xmq8jtDVX5n6EY5BwAAVCREAwBARUI0TfNU3RNg4vidoSq/M1Tld4YHqIkGAICKrEQDAEBFQjQAAFQkRNM4mfnBzHwxM1/NTC2FOFFmvjczdzLzpcy8XPd8aLbM/HhmfiMzX6h7LkyGzHxrZn42M7908P+lf1T3nGgOIZomeiEiLkXE5+qeCM2VmXMR8QsR8cMR8baI+NHMfFu9s6LhfiUi3lv3JJgor0TET5VS/lpE/GBEfNh/ZzgkRNM4pZQvlVJ26p4HjffOiHiplPKVUsr/i4hPRMTjNc+JBiulfC4i/qzueTA5SilfL6X8j4PP/3dEfCki2vXOiqYQooFJ1Y6Irx15fCv8zw0Ykcx8OCKWIuK/1zsTmuKhuifAbMrMT0fE93Z56qdLKZ8c93yYSNllTM9OYOgy8y9ExG9ExD8upfyvuudDMwjR1KKU8kN1z4GJdysi3nrk8Vsi4nZNcwGmVGbOx70A/e9LKdfrng/NoZwDmFRfiIhHM/ORzHxdRDwREc/UPCdgimRmRsS/iYgvlVL+Zd3zoVmEaBonM9+fmbci4m9ExLOZuVn3nGieUsorEfGRiNiMe5t9ni6lvFjvrGiyzPy1iPhvEbGYmbcy88frnhONdzEifiwi3pOZzx/8eV/dk6IZHPsNAAAVWYkGAICKhGgAAKhIiAYAgIqEaAAAqEiIBgCAioRoAACoSIgGAICK/j/QW/RCh5/5twAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efbaabe8438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_regression(n_features=1, noise=15)\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "plt.scatter(X, y)\n",
    "X_train = np.hstack((np.ones((X.shape[0], 1)), X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setup Simple Regression\n",
    "n_features = 1\n",
    "n_samples = X_train.shape[0]\n",
    "X_inp = tf.placeholder(dtype=tf.float32, shape=(n_samples, 2))\n",
    "weights = tf.Variable(tf.random_normal((1, 2)))\n",
    "y_pred = tf.matmul(weights, tf.transpose(X_inp))\n",
    "error = tf.losses.mean_squared_error(y.reshape(-1, 1), tf.transpose(y_pred))\n",
    "init = tf.global_variables_initializer()  \n",
    "\n",
    "# Reduce error by gradient descent\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)\n",
    "train = optimizer.minimize(error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss at iteration 0 : 2532.04\n",
      "Loss at iteration 1 : 2465.49\n",
      "Loss at iteration 2 : 2400.87\n",
      "Loss at iteration 3 : 2338.13\n",
      "Loss at iteration 4 : 2277.22\n",
      "Loss at iteration 5 : 2218.08\n",
      "Loss at iteration 6 : 2160.66\n",
      "Loss at iteration 7 : 2104.91\n",
      "Loss at iteration 8 : 2050.77\n",
      "Loss at iteration 9 : 1998.21\n",
      "Loss at iteration 10 : 1947.18\n",
      "Loss at iteration 11 : 1897.63\n",
      "Loss at iteration 12 : 1849.52\n",
      "Loss at iteration 13 : 1802.81\n",
      "Loss at iteration 14 : 1757.45\n",
      "Loss at iteration 15 : 1713.41\n",
      "Loss at iteration 16 : 1670.64\n",
      "Loss at iteration 17 : 1629.12\n",
      "Loss at iteration 18 : 1588.80\n",
      "Loss at iteration 19 : 1549.65\n",
      "Loss at iteration 20 : 1511.63\n",
      "Loss at iteration 21 : 1474.72\n",
      "Loss at iteration 22 : 1438.88\n",
      "Loss at iteration 23 : 1404.07\n",
      "Loss at iteration 24 : 1370.28\n",
      "Loss at iteration 25 : 1337.46\n",
      "Loss at iteration 26 : 1305.59\n",
      "Loss at iteration 27 : 1274.65\n",
      "Loss at iteration 28 : 1244.60\n",
      "Loss at iteration 29 : 1215.42\n",
      "Loss at iteration 30 : 1187.09\n",
      "Loss at iteration 31 : 1159.57\n",
      "Loss at iteration 32 : 1132.86\n",
      "Loss at iteration 33 : 1106.91\n",
      "Loss at iteration 34 : 1081.72\n",
      "Loss at iteration 35 : 1057.25\n",
      "Loss at iteration 36 : 1033.49\n",
      "Loss at iteration 37 : 1010.42\n",
      "Loss at iteration 38 : 988.02\n",
      "Loss at iteration 39 : 966.26\n",
      "Loss at iteration 40 : 945.13\n",
      "Loss at iteration 41 : 924.61\n",
      "Loss at iteration 42 : 904.69\n",
      "Loss at iteration 43 : 885.34\n",
      "Loss at iteration 44 : 866.54\n",
      "Loss at iteration 45 : 848.30\n",
      "Loss at iteration 46 : 830.57\n",
      "Loss at iteration 47 : 813.36\n",
      "Loss at iteration 48 : 796.65\n",
      "Loss at iteration 49 : 780.42\n",
      "Loss at iteration 50 : 764.66\n",
      "Loss at iteration 51 : 749.35\n",
      "Loss at iteration 52 : 734.48\n",
      "Loss at iteration 53 : 720.04\n",
      "Loss at iteration 54 : 706.02\n",
      "Loss at iteration 55 : 692.41\n",
      "Loss at iteration 56 : 679.18\n",
      "Loss at iteration 57 : 666.34\n",
      "Loss at iteration 58 : 653.86\n",
      "Loss at iteration 59 : 641.75\n",
      "Loss at iteration 60 : 629.99\n",
      "Loss at iteration 61 : 618.56\n",
      "Loss at iteration 62 : 607.47\n",
      "Loss at iteration 63 : 596.69\n",
      "Loss at iteration 64 : 586.22\n",
      "Loss at iteration 65 : 576.06\n",
      "Loss at iteration 66 : 566.19\n",
      "Loss at iteration 67 : 556.60\n",
      "Loss at iteration 68 : 547.29\n",
      "Loss at iteration 69 : 538.24\n",
      "Loss at iteration 70 : 529.46\n",
      "Loss at iteration 71 : 520.93\n",
      "Loss at iteration 72 : 512.64\n",
      "Loss at iteration 73 : 504.60\n",
      "Loss at iteration 74 : 496.78\n",
      "Loss at iteration 75 : 489.19\n",
      "Loss at iteration 76 : 481.82\n",
      "Loss at iteration 77 : 474.66\n",
      "Loss at iteration 78 : 467.71\n",
      "Loss at iteration 79 : 460.95\n",
      "Loss at iteration 80 : 454.39\n",
      "Loss at iteration 81 : 448.02\n",
      "Loss at iteration 82 : 441.83\n",
      "Loss at iteration 83 : 435.82\n",
      "Loss at iteration 84 : 429.99\n",
      "Loss at iteration 85 : 424.32\n",
      "Loss at iteration 86 : 418.81\n",
      "Loss at iteration 87 : 413.46\n",
      "Loss at iteration 88 : 408.27\n",
      "Loss at iteration 89 : 403.22\n",
      "Loss at iteration 90 : 398.32\n",
      "Loss at iteration 91 : 393.56\n",
      "Loss at iteration 92 : 388.94\n",
      "Loss at iteration 93 : 384.45\n",
      "Loss at iteration 94 : 380.09\n",
      "Loss at iteration 95 : 375.85\n",
      "Loss at iteration 96 : 371.74\n",
      "Loss at iteration 97 : 367.74\n",
      "Loss at iteration 98 : 363.86\n",
      "Loss at iteration 99 : 360.09\n",
      "Loss at iteration 100 : 356.43\n",
      "Loss at iteration 101 : 352.87\n",
      "Loss at iteration 102 : 349.42\n",
      "Loss at iteration 103 : 346.06\n",
      "Loss at iteration 104 : 342.80\n",
      "Loss at iteration 105 : 339.64\n",
      "Loss at iteration 106 : 336.56\n",
      "Loss at iteration 107 : 333.57\n",
      "Loss at iteration 108 : 330.67\n",
      "Loss at iteration 109 : 327.85\n",
      "Loss at iteration 110 : 325.12\n",
      "Loss at iteration 111 : 322.46\n",
      "Loss at iteration 112 : 319.88\n",
      "Loss at iteration 113 : 317.37\n",
      "Loss at iteration 114 : 314.93\n",
      "Loss at iteration 115 : 312.57\n",
      "Loss at iteration 116 : 310.27\n",
      "Loss at iteration 117 : 308.04\n",
      "Loss at iteration 118 : 305.87\n",
      "Loss at iteration 119 : 303.76\n",
      "Loss at iteration 120 : 301.71\n",
      "Loss at iteration 121 : 299.73\n",
      "Loss at iteration 122 : 297.80\n",
      "Loss at iteration 123 : 295.92\n",
      "Loss at iteration 124 : 294.10\n",
      "Loss at iteration 125 : 292.33\n",
      "Loss at iteration 126 : 290.61\n",
      "Loss at iteration 127 : 288.94\n",
      "Loss at iteration 128 : 287.32\n",
      "Loss at iteration 129 : 285.75\n",
      "Loss at iteration 130 : 284.22\n",
      "Loss at iteration 131 : 282.73\n",
      "Loss at iteration 132 : 281.29\n",
      "Loss at iteration 133 : 279.89\n",
      "Loss at iteration 134 : 278.52\n",
      "Loss at iteration 135 : 277.20\n",
      "Loss at iteration 136 : 275.92\n",
      "Loss at iteration 137 : 274.67\n",
      "Loss at iteration 138 : 273.46\n",
      "Loss at iteration 139 : 272.28\n",
      "Loss at iteration 140 : 271.13\n",
      "Loss at iteration 141 : 270.02\n",
      "Loss at iteration 142 : 268.94\n",
      "Loss at iteration 143 : 267.89\n",
      "Loss at iteration 144 : 266.88\n",
      "Loss at iteration 145 : 265.89\n",
      "Loss at iteration 146 : 264.93\n",
      "Loss at iteration 147 : 263.99\n",
      "Loss at iteration 148 : 263.09\n",
      "Loss at iteration 149 : 262.20\n",
      "Loss at iteration 150 : 261.35\n",
      "Loss at iteration 151 : 260.52\n",
      "Loss at iteration 152 : 259.71\n",
      "Loss at iteration 153 : 258.93\n",
      "Loss at iteration 154 : 258.16\n",
      "Loss at iteration 155 : 257.42\n",
      "Loss at iteration 156 : 256.71\n",
      "Loss at iteration 157 : 256.01\n",
      "Loss at iteration 158 : 255.33\n",
      "Loss at iteration 159 : 254.67\n",
      "Loss at iteration 160 : 254.03\n",
      "Loss at iteration 161 : 253.41\n",
      "Loss at iteration 162 : 252.80\n",
      "Loss at iteration 163 : 252.22\n",
      "Loss at iteration 164 : 251.65\n",
      "Loss at iteration 165 : 251.09\n",
      "Loss at iteration 166 : 250.56\n",
      "Loss at iteration 167 : 250.03\n",
      "Loss at iteration 168 : 249.53\n",
      "Loss at iteration 169 : 249.03\n",
      "Loss at iteration 170 : 248.55\n",
      "Loss at iteration 171 : 248.09\n",
      "Loss at iteration 172 : 247.64\n",
      "Loss at iteration 173 : 247.20\n",
      "Loss at iteration 174 : 246.77\n",
      "Loss at iteration 175 : 246.36\n",
      "Loss at iteration 176 : 245.96\n",
      "Loss at iteration 177 : 245.56\n",
      "Loss at iteration 178 : 245.19\n",
      "Loss at iteration 179 : 244.82\n",
      "Loss at iteration 180 : 244.46\n",
      "Loss at iteration 181 : 244.11\n",
      "Loss at iteration 182 : 243.77\n",
      "Loss at iteration 183 : 243.44\n",
      "Loss at iteration 184 : 243.12\n",
      "Loss at iteration 185 : 242.81\n",
      "Loss at iteration 186 : 242.51\n",
      "Loss at iteration 187 : 242.22\n",
      "Loss at iteration 188 : 241.94\n",
      "Loss at iteration 189 : 241.66\n",
      "Loss at iteration 190 : 241.39\n",
      "Loss at iteration 191 : 241.13\n",
      "Loss at iteration 192 : 240.88\n",
      "Loss at iteration 193 : 240.63\n",
      "Loss at iteration 194 : 240.40\n",
      "Loss at iteration 195 : 240.16\n",
      "Loss at iteration 196 : 239.94\n",
      "Loss at iteration 197 : 239.72\n",
      "Loss at iteration 198 : 239.51\n",
      "Loss at iteration 199 : 239.30\n",
      "Loss at iteration 200 : 239.10\n",
      "Loss at iteration 201 : 238.91\n",
      "Loss at iteration 202 : 238.72\n",
      "Loss at iteration 203 : 238.53\n",
      "Loss at iteration 204 : 238.35\n",
      "Loss at iteration 205 : 238.18\n",
      "Loss at iteration 206 : 238.01\n",
      "Loss at iteration 207 : 237.85\n",
      "Loss at iteration 208 : 237.69\n",
      "Loss at iteration 209 : 237.53\n",
      "Loss at iteration 210 : 237.38\n",
      "Loss at iteration 211 : 237.24\n",
      "Loss at iteration 212 : 237.10\n",
      "Loss at iteration 213 : 236.96\n",
      "Loss at iteration 214 : 236.83\n",
      "Loss at iteration 215 : 236.70\n",
      "Loss at iteration 216 : 236.57\n",
      "Loss at iteration 217 : 236.45\n",
      "Loss at iteration 218 : 236.33\n",
      "Loss at iteration 219 : 236.21\n",
      "Loss at iteration 220 : 236.10\n",
      "Loss at iteration 221 : 235.99\n",
      "Loss at iteration 222 : 235.88\n",
      "Loss at iteration 223 : 235.78\n",
      "Loss at iteration 224 : 235.68\n",
      "Loss at iteration 225 : 235.58\n",
      "Loss at iteration 226 : 235.49\n",
      "Loss at iteration 227 : 235.40\n",
      "Loss at iteration 228 : 235.31\n",
      "Loss at iteration 229 : 235.22\n",
      "Loss at iteration 230 : 235.14\n",
      "Loss at iteration 231 : 235.06\n",
      "Loss at iteration 232 : 234.98\n",
      "Loss at iteration 233 : 234.90\n",
      "Loss at iteration 234 : 234.83\n",
      "Loss at iteration 235 : 234.75\n",
      "Loss at iteration 236 : 234.68\n",
      "Loss at iteration 237 : 234.61\n",
      "Loss at iteration 238 : 234.55\n",
      "Loss at iteration 239 : 234.48\n",
      "Loss at iteration 240 : 234.42\n",
      "Loss at iteration 241 : 234.36\n",
      "Loss at iteration 242 : 234.30\n",
      "Loss at iteration 243 : 234.24\n",
      "Loss at iteration 244 : 234.18\n",
      "Loss at iteration 245 : 234.13\n",
      "Loss at iteration 246 : 234.08\n",
      "Loss at iteration 247 : 234.03\n",
      "Loss at iteration 248 : 233.98\n",
      "Loss at iteration 249 : 233.93\n",
      "Loss at iteration 250 : 233.88\n",
      "Loss at iteration 251 : 233.83\n",
      "Loss at iteration 252 : 233.79\n",
      "Loss at iteration 253 : 233.75\n",
      "Loss at iteration 254 : 233.70\n",
      "Loss at iteration 255 : 233.66\n",
      "Loss at iteration 256 : 233.62\n",
      "Loss at iteration 257 : 233.59\n",
      "Loss at iteration 258 : 233.55\n",
      "Loss at iteration 259 : 233.51\n",
      "Loss at iteration 260 : 233.48\n",
      "Loss at iteration 261 : 233.44\n",
      "Loss at iteration 262 : 233.41\n",
      "Loss at iteration 263 : 233.38\n",
      "Loss at iteration 264 : 233.35\n",
      "Loss at iteration 265 : 233.32\n",
      "Loss at iteration 266 : 233.29\n",
      "Loss at iteration 267 : 233.26\n",
      "Loss at iteration 268 : 233.23\n",
      "Loss at iteration 269 : 233.20\n",
      "Loss at iteration 270 : 233.18\n",
      "Loss at iteration 271 : 233.15\n",
      "Loss at iteration 272 : 233.12\n",
      "Loss at iteration 273 : 233.10\n",
      "Loss at iteration 274 : 233.08\n",
      "Loss at iteration 275 : 233.05\n",
      "Loss at iteration 276 : 233.03\n",
      "Loss at iteration 277 : 233.01\n",
      "Loss at iteration 278 : 232.99\n",
      "Loss at iteration 279 : 232.97\n",
      "Loss at iteration 280 : 232.95\n",
      "Loss at iteration 281 : 232.93\n",
      "Loss at iteration 282 : 232.91\n",
      "Loss at iteration 283 : 232.89\n",
      "Loss at iteration 284 : 232.88\n",
      "Loss at iteration 285 : 232.86\n",
      "Loss at iteration 286 : 232.84\n",
      "Loss at iteration 287 : 232.83\n",
      "Loss at iteration 288 : 232.81\n",
      "Loss at iteration 289 : 232.80\n",
      "Loss at iteration 290 : 232.78\n",
      "Loss at iteration 291 : 232.77\n",
      "Loss at iteration 292 : 232.75\n",
      "Loss at iteration 293 : 232.74\n",
      "Loss at iteration 294 : 232.73\n",
      "Loss at iteration 295 : 232.71\n",
      "Loss at iteration 296 : 232.70\n",
      "Loss at iteration 297 : 232.69\n",
      "Loss at iteration 298 : 232.68\n",
      "Loss at iteration 299 : 232.66\n",
      "Loss at iteration 300 : 232.65\n",
      "Loss at iteration 301 : 232.64\n",
      "Loss at iteration 302 : 232.63\n",
      "Loss at iteration 303 : 232.62\n",
      "Loss at iteration 304 : 232.61\n",
      "Loss at iteration 305 : 232.60\n",
      "Loss at iteration 306 : 232.59\n",
      "Loss at iteration 307 : 232.58\n",
      "Loss at iteration 308 : 232.58\n",
      "Loss at iteration 309 : 232.57\n",
      "Loss at iteration 310 : 232.56\n",
      "Loss at iteration 311 : 232.55\n",
      "Loss at iteration 312 : 232.54\n",
      "Loss at iteration 313 : 232.54\n",
      "Loss at iteration 314 : 232.53\n",
      "Loss at iteration 315 : 232.52\n",
      "Loss at iteration 316 : 232.51\n",
      "Loss at iteration 317 : 232.51\n",
      "Loss at iteration 318 : 232.50\n",
      "Loss at iteration 319 : 232.49\n",
      "Loss at iteration 320 : 232.49\n",
      "Loss at iteration 321 : 232.48\n",
      "Loss at iteration 322 : 232.48\n",
      "Loss at iteration 323 : 232.47\n",
      "Loss at iteration 324 : 232.46\n",
      "Loss at iteration 325 : 232.46\n",
      "Loss at iteration 326 : 232.45\n",
      "Loss at iteration 327 : 232.45\n",
      "Loss at iteration 328 : 232.44\n",
      "Loss at iteration 329 : 232.44\n",
      "Loss at iteration 330 : 232.43\n",
      "Loss at iteration 331 : 232.43\n",
      "Loss at iteration 332 : 232.43\n",
      "Loss at iteration 333 : 232.42\n",
      "Loss at iteration 334 : 232.42\n",
      "Loss at iteration 335 : 232.41\n",
      "Loss at iteration 336 : 232.41\n",
      "Loss at iteration 337 : 232.41\n",
      "Loss at iteration 338 : 232.40\n",
      "Loss at iteration 339 : 232.40\n",
      "Loss at iteration 340 : 232.39\n",
      "Loss at iteration 341 : 232.39\n",
      "Loss at iteration 342 : 232.39\n",
      "Loss at iteration 343 : 232.39\n",
      "Loss at iteration 344 : 232.38\n",
      "Loss at iteration 345 : 232.38\n",
      "Loss at iteration 346 : 232.38\n",
      "Loss at iteration 347 : 232.37\n",
      "Loss at iteration 348 : 232.37\n",
      "Loss at iteration 349 : 232.37\n",
      "Loss at iteration 350 : 232.37\n",
      "Loss at iteration 351 : 232.36\n",
      "Loss at iteration 352 : 232.36\n",
      "Loss at iteration 353 : 232.36\n",
      "Loss at iteration 354 : 232.36\n",
      "Loss at iteration 355 : 232.35\n",
      "Loss at iteration 356 : 232.35\n",
      "Loss at iteration 357 : 232.35\n",
      "Loss at iteration 358 : 232.35\n",
      "Loss at iteration 359 : 232.34\n",
      "Loss at iteration 360 : 232.34\n",
      "Loss at iteration 361 : 232.34\n",
      "Loss at iteration 362 : 232.34\n",
      "Loss at iteration 363 : 232.34\n",
      "Loss at iteration 364 : 232.34\n",
      "Loss at iteration 365 : 232.33\n",
      "Loss at iteration 366 : 232.33\n",
      "Loss at iteration 367 : 232.33\n",
      "Loss at iteration 368 : 232.33\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss at iteration 369 : 232.33\n",
      "Loss at iteration 370 : 232.33\n",
      "Loss at iteration 371 : 232.32\n",
      "Loss at iteration 372 : 232.32\n",
      "Loss at iteration 373 : 232.32\n",
      "Loss at iteration 374 : 232.32\n",
      "Loss at iteration 375 : 232.32\n",
      "Loss at iteration 376 : 232.32\n",
      "Loss at iteration 377 : 232.32\n",
      "Loss at iteration 378 : 232.32\n",
      "Loss at iteration 379 : 232.31\n",
      "Loss at iteration 380 : 232.31\n",
      "Loss at iteration 381 : 232.31\n",
      "Loss at iteration 382 : 232.31\n",
      "Loss at iteration 383 : 232.31\n",
      "Loss at iteration 384 : 232.31\n",
      "Loss at iteration 385 : 232.31\n",
      "Loss at iteration 386 : 232.31\n",
      "Loss at iteration 387 : 232.31\n",
      "Loss at iteration 388 : 232.31\n",
      "Loss at iteration 389 : 232.31\n",
      "Loss at iteration 390 : 232.30\n",
      "Loss at iteration 391 : 232.30\n",
      "Loss at iteration 392 : 232.30\n",
      "Loss at iteration 393 : 232.30\n",
      "Loss at iteration 394 : 232.30\n",
      "Loss at iteration 395 : 232.30\n",
      "Loss at iteration 396 : 232.30\n",
      "Loss at iteration 397 : 232.30\n",
      "Loss at iteration 398 : 232.30\n",
      "Loss at iteration 399 : 232.30\n",
      "Loss at iteration 400 : 232.30\n",
      "Loss at iteration 401 : 232.30\n",
      "Loss at iteration 402 : 232.30\n",
      "Loss at iteration 403 : 232.30\n",
      "Loss at iteration 404 : 232.30\n",
      "Loss at iteration 405 : 232.29\n",
      "Loss at iteration 406 : 232.29\n",
      "Loss at iteration 407 : 232.29\n",
      "Loss at iteration 408 : 232.29\n",
      "Loss at iteration 409 : 232.29\n",
      "Loss at iteration 410 : 232.29\n",
      "Loss at iteration 411 : 232.29\n",
      "Loss at iteration 412 : 232.29\n",
      "Loss at iteration 413 : 232.29\n",
      "Loss at iteration 414 : 232.29\n",
      "Loss at iteration 415 : 232.29\n",
      "Loss at iteration 416 : 232.29\n",
      "Loss at iteration 417 : 232.29\n",
      "Loss at iteration 418 : 232.29\n",
      "Loss at iteration 419 : 232.29\n",
      "Loss at iteration 420 : 232.29\n",
      "Loss at iteration 421 : 232.29\n",
      "Loss at iteration 422 : 232.29\n",
      "Loss at iteration 423 : 232.29\n",
      "Loss at iteration 424 : 232.29\n",
      "Loss at iteration 425 : 232.29\n",
      "Loss at iteration 426 : 232.29\n",
      "Loss at iteration 427 : 232.29\n",
      "Loss at iteration 428 : 232.29\n",
      "Loss at iteration 429 : 232.29\n",
      "Loss at iteration 430 : 232.29\n",
      "Loss at iteration 431 : 232.28\n",
      "Loss at iteration 432 : 232.28\n",
      "Loss at iteration 433 : 232.28\n",
      "Loss at iteration 434 : 232.28\n",
      "Loss at iteration 435 : 232.28\n",
      "Loss at iteration 436 : 232.28\n",
      "Loss at iteration 437 : 232.28\n",
      "Loss at iteration 438 : 232.28\n",
      "Loss at iteration 439 : 232.28\n",
      "Loss at iteration 440 : 232.28\n",
      "Loss at iteration 441 : 232.28\n",
      "Loss at iteration 442 : 232.28\n",
      "Loss at iteration 443 : 232.28\n",
      "Loss at iteration 444 : 232.28\n",
      "Loss at iteration 445 : 232.28\n",
      "Loss at iteration 446 : 232.28\n",
      "Loss at iteration 447 : 232.28\n",
      "Loss at iteration 448 : 232.28\n",
      "Loss at iteration 449 : 232.28\n",
      "Loss at iteration 450 : 232.28\n",
      "Loss at iteration 451 : 232.28\n",
      "Loss at iteration 452 : 232.28\n",
      "Loss at iteration 453 : 232.28\n",
      "Loss at iteration 454 : 232.28\n",
      "Loss at iteration 455 : 232.28\n",
      "Loss at iteration 456 : 232.28\n",
      "Loss at iteration 457 : 232.28\n",
      "Loss at iteration 458 : 232.28\n",
      "Loss at iteration 459 : 232.28\n",
      "Loss at iteration 460 : 232.28\n",
      "Loss at iteration 461 : 232.28\n",
      "Loss at iteration 462 : 232.28\n",
      "Loss at iteration 463 : 232.28\n",
      "Loss at iteration 464 : 232.28\n",
      "Loss at iteration 465 : 232.28\n",
      "Loss at iteration 466 : 232.28\n",
      "Loss at iteration 467 : 232.28\n",
      "Loss at iteration 468 : 232.28\n",
      "Loss at iteration 469 : 232.28\n",
      "Loss at iteration 470 : 232.28\n",
      "Loss at iteration 471 : 232.28\n",
      "Loss at iteration 472 : 232.28\n",
      "Loss at iteration 473 : 232.28\n",
      "Loss at iteration 474 : 232.28\n",
      "Loss at iteration 475 : 232.28\n",
      "Loss at iteration 476 : 232.28\n",
      "Loss at iteration 477 : 232.28\n",
      "Loss at iteration 478 : 232.28\n",
      "Loss at iteration 479 : 232.28\n",
      "Loss at iteration 480 : 232.28\n",
      "Loss at iteration 481 : 232.28\n",
      "Loss at iteration 482 : 232.28\n",
      "Loss at iteration 483 : 232.28\n",
      "Loss at iteration 484 : 232.28\n",
      "Loss at iteration 485 : 232.28\n",
      "Loss at iteration 486 : 232.28\n",
      "Loss at iteration 487 : 232.28\n",
      "Loss at iteration 488 : 232.28\n",
      "Loss at iteration 489 : 232.28\n",
      "Loss at iteration 490 : 232.28\n",
      "Loss at iteration 491 : 232.28\n",
      "Loss at iteration 492 : 232.28\n",
      "Loss at iteration 493 : 232.28\n",
      "Loss at iteration 494 : 232.28\n",
      "Loss at iteration 495 : 232.28\n",
      "Loss at iteration 496 : 232.28\n",
      "Loss at iteration 497 : 232.28\n",
      "Loss at iteration 498 : 232.28\n",
      "Loss at iteration 499 : 232.28\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    n_iterations = 500\n",
    "    for i in range(n_iterations):\n",
    "        sess.run(train, feed_dict={X_inp: X_train})\n",
    "        print(\"Loss at iteration {0} : {1:.2f}\".format(i, sess.run(error, feed_dict={X_inp: X_train})))\n",
    "    y_pred_plot = sess.run(y_pred, feed_dict={X_inp: X_train})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Simple Linear Regression using TensorFlow')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efba507c128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 8))\n",
    "plt.scatter(X, y, label=\"Prediction\")\n",
    "plt.plot(X, y_pred_plot.T, color=\"red\")\n",
    "plt.legend()\n",
    "plt.title(\"Simple Linear Regression using TensorFlow\")"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}