Demo3. Simple Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# For reproducibility\n",
    "np.random.seed(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's design overall scheme."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# inputs\n",
    "x = np.linspace(0, 1, 100, dtype=np.float32)\n",
    "\n",
    "# ground truth\n",
    "slopes = np.random.normal(1, 0.5, 100).astype(np.float32)\n",
    "intercept = 2.\n",
    "\n",
    "# outputs\n",
    "y = x * slopes + intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.81217265,  0.69412178,  0.73591411,  0.4635157 ,  1.43270385,\n",
       "       -0.15076935,  1.87240589,  0.61939657,  1.15951955,  0.87531483,\n",
       "        1.73105395, -0.03007036,  0.83879137,  0.80797285,  1.56688476,\n",
       "        0.45005438,  0.91378587,  0.5610708 ,  1.02110684,  1.29140759,\n",
       "        0.4496904 ,  1.57236183,  1.45079541,  1.25124717,  1.45042801,\n",
       "        0.65813607,  0.93855488,  0.53211528,  0.86605597,  1.26517773,\n",
       "        0.65416962,  0.80162323,  0.65641367,  0.57739717,  0.66437691,\n",
       "        0.99366772,  0.44134483,  1.11720788,  1.8299011 ,  1.37102211,\n",
       "        0.90408224,  0.55618554,  0.62642086,  1.84622729,  1.02540386,\n",
       "        0.68150216,  1.09545779,  2.05012751,  1.06007946,  1.3086015 ,\n",
       "        1.15008521,  0.82387507,  0.42874089,  0.82532865,  0.89555287,\n",
       "        1.2933116 ,  1.41949165,  1.46555102,  1.14279366,  1.44257057,\n",
       "        0.62280101,  1.62643409,  1.25646496,  0.85095358,  1.24425912,\n",
       "        0.96221417,  1.56581473,  1.75990844,  2.09278774,  0.30175182,\n",
       "        0.2779431 ,  0.74776709,  1.08001852,  1.43808448,  1.15781748,\n",
       "       -0.01110061,  0.84689802,  1.41398728,  1.11504734,  1.38100564,\n",
       "        0.88883591,  0.89962095,  1.09328067,  1.20502579,  1.09914982,\n",
       "        1.05950427,  0.66466886,  1.18878186,  1.06091058,  1.56474197,\n",
       "        1.59945893,  1.09257817,  0.81235754,  0.6806348 ,  1.21174717,\n",
       "        1.03867006,  0.82807314,  1.02179837,  0.68999958,  1.34901607], dtype=float32)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slopes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YijMU8TeghEDgJgoKCjUEIR2SggURkTClpSXk558OFAM9gWLy80+ntLTE45ZlpmAwyMKF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\n2KBVywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e702af8898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "plt.plot(x, x * 1 + 2., label=\"ground truth\", c=\"r\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build a graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float32')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float32')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Inputs\n",
    "x_pl = tf.placeholder(tf.float32, [100,], name=\"x_pl\")\n",
    "y_pl = tf.placeholder(tf.float32, [100,], name=\"y_pl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Computation\n",
    "## Variables = Parameters = Weights\n",
    "w = tf.Variable(.1, tf.float32)\n",
    "b = tf.Variable(0., tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## prediction = y_hat = hypothesis\n",
    "preds = x_pl * w + b # (100,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# objective = loss = cost\n",
    "loss = tf.reduce_mean(tf.square(preds - y)) # L2 loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Optimization = Training\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=.1)\n",
    "train_op = optimizer.minimize(loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Launch the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# start a session\n",
    "sess = tf.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# initialize all variables\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "loss_list, w_list, b_list = [], [], []\n",
    "for epoch in range(1, 21):\n",
    "    _, _loss, _w, _b = sess.run([train_op, loss, w, b], {x_pl:x, y_pl:y})\n",
    "#     print(epoch, \"\\t\", _loss, \"\\t\", _w, \"\\t\", _b)\n",
    "    loss_list.append(_loss)\n",
    "    w_list.append(_w)\n",
    "    b_list.append(_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9MHUqPPssXHstvPuu650Vj+zd2zTMmz/+6ivXgq/Tuzf06+fW+fkHBnjdOjVV\nLXAR6ZQU+D6Ij4fHH4cxY+CJJ+D73/e6ok6svLztQN+6tWHfmBgX5gMGwKBBcPLJ7nF2tlv376+Z\nk0Qk6inwfVRYCFdeCbfdBuec407dip/27YPPP4dlyxqW1atdoO/Y0bBffLw7p5KdDUceCWed1RDm\nAwa4QW8acS4i0iYFvh/uvx9eeQVuvRVmzfK6mgiwZw+sWNE02OvCvW4ke69eMHy4O2d+0UVNA713\nb50/ERHpIAW+Hw49FO67z3Xpf+97rotfcBO+LF9+YLCvXdtwPr1fPxfsZ57p1sOHu8sfDjnE09JF\nRDo7Bb6f/ud/4Omn3QC+Tz5xM3ZGjR07Wg72DRsa9snOdmF+/vlNg12TGIiIeEKB76eYGDeAr6DA\nXZf/k594XVGQ7NkDJSWweDG8/z588IEbAQ/uP8LAgS7ML720IdhzcnTJmohImFHgd0BuLtx0E0yf\nDpMnu9PNEc1ad169LtwXL4YlS9yEMykpMHo0XH65+8GHD4chQ9x16yIiEvYU+B30i1/ASy+54H/l\nFa+r8VF5OXz4oQv2uuXrr91rQ4fCscfC1VfDccfBEUe4W3CKiEhE0r/gHZSWBg8/7G6q8+qr7iY7\nYammxp13bxzun33mWvXp6S7cb7jBrUeP1iA6EZFORoEfAOefDxMmwI03wimnhMnp66+/dufb68L9\ngw9ciz4mxt2o5fjj4Uc/cgE/dKguexMR6eQU+AFgDDz6qMvRX/4S7r3XgyL27YN33oHZs2H+fDeh\nDbhrCI891s0UdOyxMGqU65YQEZGoosAPkEGD4I473AC+yy93p7yD7r//hddfh9degzffdCPqBwyA\nM85wd/o59lg47DDNDS8iIgr8QLr1VnjuObjuOtfYDnjO7t/vuuZnz3bLkiUQGwsnnOACvm4yGwW8\niIg0o8APoMREmDEDxo1zd9WbMiUAH7pjB8yb51rxc+e6c/Pdu8Ppp7t79Y4f7+63LiIi0gYFfoCd\ncgoUFcEtt8DZZ/sx2N1aN3q+rhX/3nuuZT9yJFxzjWvFjx7tWvYiIiLtpMAPgt/9zk02d/vt8Mc/\ntuMNe/fC2283hPy6dW6im3HjXJfBGWe4OehFRET8pMAPgt694Ve/cpPxfPe7buzcAdavbwj4t95y\noX/YYa5b4Kyz4KSTdA93EREJGAV+kFx3nbt17jXXwMcf105St3u3m5Zv5kw3dW1cHJx4ohvaf+aZ\nrltAA+7ZiIz+AAAXMklEQVRERCQIFPhBEhsLf/gDHDPa8tKtJVyy5wl44QXYtcsNtCsudrP1ZGR4\nXaqIiEQBBX6w7NzJ0R+9wLrMmfR/6FOqe/cjbupU18efne11dSIiEmUU+IFkLbz7ruuy/8tfoLKS\nnhPO5tJFv6L6hAm8+AuNrBcREW9oAvVA2LbNDc0fPhwKC2HRIvjZz2DDBhJe+zunP3omL/01lnnz\nvC5URESilVr4/qqpcdPZzpwJ//iHG2x33nnw2GMwdmyTm9Fceik89RRcfz385z+6hbyIiISeWvi+\n2rjRXXM3aJAbfPfZZ3D//fDVV24g3sknH3DnOWPc5fTr13t0Yx0REYl6auG3R3W1u0nNzJlunZQE\nF18MV1/tLrJvx6V0OTnw4x+7wL/0UhgyJAR1i4iI1FILvy1ffgk//SlkZcGkSbBpk2uqb9rk+uiP\nO86n6+Z/+lPo08d17VsbxLpFRESaUQu/JZ984gbdzZ4N6emuSX7VVZCX16GPTUmBRx91E+m99JLr\nJBARqWOtpcbWYLFYa7HUPj/I45be13xd9/kdfQ04YHvdY19eq3ve/LWW9m+8raXP8WVbk//etLLd\nx/2T4pI4MevEFl8LJwr8xlauhJ//3KXx0KHw9NNw0UUuqQPkzDPh3HPh2mvdretbnHZXJMzUBUpV\nTRVV+6sOWFfXVLf4WnVNNftr9ru1deuWtnXkeY2tYX9N7do2Wzff3uh5W6/V2JoDlrr/Bi2+Rhuv\ntfK+5qEukWtA+gDW3rzW6zIOyufAN8YUAj8GCoDewDnW2lcDXVhIbdgAv/iFmwu3Tx948km44ora\n+XAD78knYeJEd2e9v/7V3elWxFrLvup97K3ey96qvfXrfdX7qNhfQUV1BZX7Kw94XLm/korqiiaP\nm7zWyv6Nn7cV2nXrYIs1scTFxBEbU7tux/PYmFhiTSwxJobYmNp1o+fNX4uLiWv1tVjTsK3xdoOp\n39Z4MebA7S3t23y/un2MMW0+rnvfwR639L7W1kBAXgMOeNx4n7rH/u7XeN/G+3Rke+NtLb2npfe2\nd7/YmMiYY8WfROsCfAo8CfwtsOWE2LZtcM897rx8Whr89rdu8vsg37SmWzeYPx8mT3bB/9RTcPnl\nQT2kdIC1lsr9leyq3NXi8m3lt+yu3M2eqj0HhPXe6r0thnhr+/kjITaBhNgEEmMT3Tousf5548d1\nr6UnppOQ0rAtITaB+Nh44mLiiI+JJz42vn7d0rb4mNrt7dzWUmg331YXWCISPD4HvrV2LjAXwETq\nX2h5OTz4oJssJybGjaa7+WYX+iGSnAwvvww/+IHrTNi6FaZNC9nhO739Nfspryhnx74d7Ny3k537\ndlK2r6zFsG4tyBu/Xl1T3ebxYkwMyXHJJMcnt7pOTUjl0C6Huuet7JMUl9Tia4lxiS0GeEJsgoJS\nRNolus7h793rWvP33OPuXHfDDXDbbdC9uyflxMXBE09Az55wyy2wZQvcd59umAeuVb27ajc79+1k\nx96G0G4c4Dv27mBnxc4W9ymvKG/1s2NNLGmJaaQmpJKW4NZ1S6/UXk2eN3+9yWuJDa8lxiYqeEUk\nrEVH4FdVuQF406fD5s3wP//jBuf17et1ZRgDv/mNC/2bb3Yt/ZkzIT7e68oCq7qmmu17trN199Y2\nl6/3fl0f3K21qpPikshIyqBbUjcykjLISMqgb9e+HHHoEW57crcDXu+W3I2uiV1JS0hTq1hEolJI\nAn/q1Kmkp6c32VZUVERRUVFwD1xT40bc//zn8MUXUFTkBucdfnhwj+uHH/4QevSAKVNg+3ZXdgAv\nDgg4ay1lFWUHDfDGQd5cclwyPVN70qNLD3p06cHwQ4eTmZLZENK1gV0X4HVLUlxwx1iIiIRacXEx\nxcXFTbaVlZUF9BimtesN2/VmY2poY5S+MSYfKCkpKSE/P9/v4/jMWpgzx52b//RTdy3cr38Nubmh\nq8FP8+e7KfmPOgpeew0OOST0Neyv2c+mXZvYULaB9WXrWV+2ng3lG+rXW3ZtYevurQeM3I41sRza\n5dD6AO/RpQc9Uno0fd5o6ZLQJfQ/nIhIhCgtLaWgoACgwFpb2tHP63xd+osWwe23u3VhISxcCCeG\n/4QIdcaPh7ffhjPOcGXPmwf9+wfu86217Ny3sz7A15etd8Fe3vB447cbm3SnpyakMiB9AP3T+1PQ\nu4Deqb1bDPBuyd2IMZq8UUQkHPlzHX4XYDDUX4Q40BiTC3xjrd0QyOJ88sknrkU/Z46bEW/OHDjt\ntIgcAXf00e77ymmnwfHHu1b/sGHte291TTXrdq6rD/PmrfP1ZevZVbmrfv+4mDj6de1HVnoWA9IH\nUJhVSFZ6Fv279nfr9P6kJ6brnLeISITzp4U/CngbsLXL72q3PwN8L0B1td/nn7tz9C++6O5I8+KL\ncMEFB9yxLtIMHQrvvQcTJriW/muvuan765TtK2PF9hWs/HolK7avqF9WfbOqSVd7jy496sN73GHj\nyErPqg/yrPQsenbpGTGTRoiIiP/8uQ7/34TDTXfKyuAnP3HXtfXu7dZTpgRtdjwv9Opdw3OvreeS\nm1Zw0k9WcOrklezp4oJ9867N9fv179qfnMwcxg0cxw2jb2BI9yEMSB9Av679SI5P9vAnEBGRcBGZ\n6bhwoZuabscOeOABNzF9kGfHC6bdlbtZ+fVKVm6vba1/7UL9868/d7Ov5UFMTRKv/2coowcN5fsF\nY8jJzCEnM4ch3Ydo8JuIiBxUZAV+ZaW7rO7ee93J7Xfegexsr6tqt+qaapZtW8ZHGz9iyZYl9d3w\nG8obhj70Su1FTmYOx/c7nu+O/G59sPfpksV118bw5I/hwgeg6BYPfxAREYk4kRP4K1e629QuWQK/\n/KXrzo8N33PP1lrW7FzDhxs/5KONH/HhVx9SuqmUPVV7MBiGZg5lWOYwLjvqMnIycxjafShDM4eS\nkZTR6mfOnAm9esGPf9wwK1+ED1UQEZEQCf/Atxb++Ef40Y/c9Wnvvw+jRnld1QG27Nriwv2rj9yy\n8aP6yWayM7IZ3Xc0k4ZOYnTf0eT3zic1IdXnYxgDv/qVm5Xvhz90s/I98UTnm5VPREQCL7wDf+tW\nNw3ua6+5u9j99rfQxfvz1eUV5ZR8VVIf8B9u/LC+W/7QlEMZ3Xc0N46+kdF9RzOqzygO7XJoQI9/\n441w6KHupjt1s/KFwX8WEREJY+Eb+LNnw/e+51r4//wnnHWWJ2VUVFewZMuS+m75jzZ+xIrtK7BY\nUhNSKehdwOQRkzm6z9GM7juarPSskFyzPnmyu+fPuefCuHHuO5FH9wASEZEIEH6Bv2ePu3Xc44+7\nKXGffNL1YYfq8FV7+PfafzNv9Tze3fAuSzYvoaqmiviYeHJ75TI2eyy3nnArR/c5mpzMHE+vYT/1\nVDdu8fTT3aSCgZ6VT0REOo/wCvzSUjcwb906dxvba64J+kx51lqWb1/O3FVzmbd6Hv9e+28q9leQ\nlZ7FSQNOYkruFEb3HU1uz1wS4xKDWos/Ro2Cd99tmJVv3jwYPtzrqkREJNyER+Dv3++up//Zz9xd\nY0pLIScnaIfbuW8nb375JvNWz2PuqrlsKN9AYmwiY7PHcs8p9zBh8ARyMnMiZjrZIUNc6NfNyjd7\ndtNZ+URERLwP/HXr3OizhQvhttvg7rshISGgh6ixNZRuKmXeqnnMXT2X9ze8z367n5zMHM4fdj6n\nDT6NMQPGkBIfxvejPYg+fWDBApg4EU4+GW64wV2+16OH15WJiEg48DbwX3gBrrsO0tPdyegxYwL2\n0Vt3b2X+6vnMXTWX+avns23PNtIS0hg3cByPnfEYpw0+jeyM7IAdLxxkZLgu/XvugYcfdmdFbrzR\nDYnIzPS6OhER8ZI3gb9zpwv64mJ3zv7RR11adUDV/ioW/3cxc1fNZe7quZRucrcOzu+dz1X5VzFh\n8ASO63cc8bGd+6L15GSYPh1uvhl+9zt45BF47DG46SaYNg0OOcTrCkVExAuhD/x//9vNg19e7lr4\nRUV+f9SGsg3MWTWHuavm8uaaNymvKCczJZPxg8Zz8zE3M37QeHqmhm6Efzg55BD49a9h6lQ3fcHD\nD8Pvf+++CEydCt26eV2hiIiEkrHWBu/DjckHSkpKSsgfMcLdxvb++13X/bPPQlaWz5+5u3I3f132\nV2YtmcU7a98h1sRybL9jmTB4AhMGTyC/dz4xRvPNNrd1qxsX+dhjbojE1Kku/NPTva5MRERaUlpa\nSkFBAUCBtba0o58Xmhb+mjVw1VWwdKm78c20aT7Ng2+tZeH6hcz6dBZ/WfYXdlXu4uTDTuaZc55h\n4tCJbc4/L06PHi7wp01z37nuvde1+qdNc939Xbt6XaGIiARTaFr4CQnkDxwIzz8P+fntfv+6net4\ndsmzzFoyiy93fMnAbgO5MvdKLs+9vNMNuAu1TZtc6P/xj25a3ltucQP8Un2f4l9ERIIg0C380AT+\nRReR//TTkHLwy952V+7mb8v/xqwls3hrzVt0ie/CRUdcxJUjr6QwqzBiro2PFP/9rxvVP3Om696/\n9VY3nlJz84uIeCvQgR+ak90/+UmbYW+tZdH6RVz16lX0/l1vrvjHFdTYGp455xk237KZpyY9xZgB\nYxT2QdCvnzuvv2oVnH8+3HEHDBwIDz7oZjkWEZHOwdPRbevL1vOrBb/i8N8fTuHThby55k2mHTeN\nL2/6krenvM0VuVf4dRtZ8V1WFvzhD/DFF27ynltvhUGD4H//F/bu9bo6ERHpqJAH/p6qPTz/f89z\n6p9OJfvhbO5ZdA8nZp3I21PeZvVNq7lr7F0c1u2wUJcltbKzXff+55+7m/JMm+aC/9FHYd8+r6sT\nERF/hSTwrbW8u/5drn71anr9theX/f0yqvZX8dSkp9g8bTOzzpnF2OyxupwujAwcCE89BStWuLvy\n/fCHcPjh7iaG5eVeVyciIr4KyaC9/j/uz4YuGxiQPoApuVO4IvcKBh0yKGjHlcBbudLN4Fdc7K6o\nPPFEd7OeCRPc/Y40vEJEJLAicpT+mb89k2nnTeOk7JPUio9wa9e6u/HNnQtvveUG9vXu3RD+48Zp\n+l4RkUCIyMAvKSkh34fr7yUyVFTAokUu/OfMgc8+g5gYOOaYhi8Ao0a5bSIi4pvIvCxPOqXERDjl\nFDeD39KlsH69m8inTx93455jjoGePd39kf70J9iyxeuKRUSil7e3x5VOpX9/N4PyVVdBVRUsXuxa\n/3PnuvskgZto8fTTXev/2GMhTv8HioiEhFr4EhTx8VBY6O7YV1ICmze7+yUNHequ9y8shMxMuOAC\neOIJN+OfiIgEj9pXEhI9e7q7Il9+Oezf774E1J37/8EPoKYGRoyA44+HYcMgJ8ctWVkaAyAiEggK\nfAm52FgYPdotP/85fP01vPGG+wLwwQeuJ6Bukp/kZNcrUPcFICfHfSE4/HD3moiItI8CXzzXvTtc\nfLFbwLX216+H5cvdxD91y1tvwdatbh9j3KyAdV8AGn8hOPRQz34UEZGwpcCXsBMT48I8O9sN8Gvs\n66/dJECNvwi8+io8/LD7ogDuC0TjLwB1Xwqys13vgohINFLgS0Tp3t2d5z/++KbbKyrcHf9WrGjo\nGfj0U/jzn2H3brdPTIwbKNijhxtT0Hjd0jadMhCRzkSBLz4rLi6mqKjI6zKaSEyEI45wS2PWwsaN\n7kvAmjXulMCWLW69aZP7UrB1q+s5aC41tX1fDHr0gG7dIndwYTj+PsV/+n1Ka/wKfGPM9cAtQC9g\nCXCjtfajQBYm4SuS/kExBvr1c0tbqqpg+/aGLwONvxjUrUtKGh5XVjZ9f2ys+4KQlubWjR/7uw7V\n6YdI+n3Kwen3Ka3xOfCNMRcDvwO+D3wITAXmGWOGWGu3B7g+kZCIj3f3BOjd++D7WgtlZU2/DGzb\nBt9+C7t2HbjeuLHl7Qeb1To5ueELQFKS68VISHDrQC47d7ovM3FxrS+xsS1v002TRCKHPy38qcAf\nrbXPAhhjrgHOBL4H3B/A2kTCkjGQkeGWIUP8+wxr3Y2HWvoi0NJ63z43TqH5Ul7e8vbKygO3tWXU\nKP9+jrovAi19IajbHhPjlsaPA7EtJsb9LurWjZdAbYP2P/Zl38ZflFp63N5tLb2+ahXcd1/T31NL\nX8zas62972vv6x15b0cF87O7doUrrwze5weKT4FvjIkHCoDf1G2z1lpjzBvAcQGuTaTTMga6dHFL\nz57BP5617rRFS18Orr0WHnwQqqvdpEjV1a0vbb3e2mvWutdqalpeWnutre2Vle6xtW5p/DhQ2+r+\nu9X1xDR+vbXX2vO4cc9OS4/bu62118vL4f5mTa/WepMCtb09r3fkvR3dP4j3iAPcBGGdLvCBTCAW\naH4blC3A0Bb2TwJYvny575VJ2CorK6O0tMM3bpIwYW0ZUFrfKk9M9Loi6YipU8t46CH9fYZaMP5J\nbJSdSYH4PJ9uj2uM6Q1sBI6z1n7QaPt9wBhr7XHN9r8EeD4QhYqIiESpS621L3T0Q3xt4W8H9gPN\nOyF7Aptb2H8ecCmwFtjna3EiIiJRLAnIxmVph/nUwgcwxiwGPrDW/rD2uQHWA49Yax8IRFEiIiIS\nWP6M0n8QmGWMKaHhsrwUYFYA6xIREZEA8jnwrbUvGWMygem4rvxPgdOstdsCXZyIiIgEhs9d+iIi\nIhJ5InT2bxEREfGFAl9ERCQKBDXwjTHXG2PWGGP2GmMWG2OODubxJDiMMXcZY2qaLcu8rkvazxhT\naIx51Rizsfb3N7GFfaYbY74yxuwxxvzLGDPYi1rl4A72+zTGPN3C3+zrXtUrbTPG3G6M+dAYU26M\n2WKM+bsx5oCJuzv6Nxq0wG90k527gDzcXfXm1Q74k8izFDdIs1ftcqK35YiPuuAG2F4HHDBwxxjz\nE+AG3E2xRgO7cX+vCaEsUtqtzd9nrTk0/ZvVLfTCVyHwe+AYYBwQD8w3xiTX7RCIv9GgDdpr5Xr9\nDbjr9XWTnQhijLkLmGStzfe6Fuk4Y0wNcI619tVG274CHrDWPlT7vCtuyuwp1tqXvKlU2qOV3+fT\nQLq19jzvKhN/1TaMt+JmsF1Uu63Df6NBaeE3usnOm3XbrPtmoZvsRK7Da7sPVxtjnjPG9Pe6IAkM\nY8xhuBZg47/XcuAD9PcaycbWdg+vMMbMMMYc4nVB0m4ZuJ6bbyBwf6PB6tJv6yY7vYJ0TAmexcCV\nwGnANcBhwAJjTBcvi5KA6YX7x0V/r53HHOAK4GTgVuAk4PXanlYJY7W/o4eBRdbaurFSAfkb9Wem\nPYky1trG8zgvNcZ8CKwDLgKe9qYqEWlNsy7ez4wx/wFWA2OBtz0pStprBjAcOCHQHxysFr6vN9mR\nCGLd/VQ/BzSKu3PYDBj099ppWWvX4P5d1t9sGDPGPAqcAYy11m5q9FJA/kaDEvjW2iqgBDilbltt\nN8UpwHvBOKaEjjEmFfcPx6aD7SvhrzYMNtP077UrbsSw/l47AWNMP6A7+psNW7VhPwn4jrV2fePX\nAvU3Gswufd1kp5MwxjwA/BPXjd8X+AVQBRR7WZe0X+14i8G4VgLAQGNMLvCNtXYD7pzhncaYVbjb\nWf8S+C/wigflykG09fusXe4CXsaFxGDgPlyvXEBusyqBZYyZgbtsciKw2xhT15Ivs9bW3Vq+w3+j\nQZ1L3xhzHW7ASN1Ndm601n4ctANKUBhjinHXiXYHtgGLgJ/WfuuUCGCMOQl37rb5H/wz1trv1e5z\nN+4a3wxgIXC9tXZVKOuU9mnr94m7Nv8fwEjc7/IrXND/XDc5C0+1l1a2FMbftdY+22i/u+nA36hu\nniMiIhIFNJe+iIhIFFDgi4iIRAEFvoiISBRQ4IuIiEQBBb6IiEgUUOCLiIhEAQW+iIhIFFDgi4iI\nRAEFvoiISBRQ4IuIiEQBBb6IiEgU+P/arBm/mGFdsgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e702c89278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(loss_list, label=\"loss\")\n",
    "plt.plot(w_list, label=\"w\")\n",
    "plt.plot(b_list, label=\"b\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}