Assignment_Lab3_Part1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.python.framework import ops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "learning_rate=[0.05,0.005,0.0005]\n",
    "training_epochs=[100,500,1000]\n",
    "display_step = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X=np.asarray([1,2,4,3,5])\n",
    "train_Y=np.asarray([1,3,3,2,5])\n",
    "n_samples = train_X.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = tf.placeholder(\"float\")\n",
    "Y = tf.placeholder(\"float\")\n",
    "W = tf.Variable(0.0, name=\"weight\")\n",
    "b = tf.Variable(0.0, name=\"bias\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def processData(learning_rate,training_epochs):\n",
    "    print(\"LEARNING RATE=\",learning_rate,\"\\t Epoch=\",training_epochs,\"\\n\\n\")\n",
    "    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n",
    "\n",
    "    init = tf.global_variables_initializer()\n",
    "    with tf.Session() as sess:\n",
    "        sess.run(init)\n",
    "\n",
    "    # Fit all training data\n",
    "        for epoch in range(training_epochs):\n",
    "            for (x, y) in zip(train_X, train_Y):\n",
    "                sess.run(optimizer, feed_dict={X: x, Y: y})\n",
    "                \n",
    "        #Display logs per epoch step\n",
    "            if (epoch+1) % display_step == 0:\n",
    "                c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})\n",
    "                print(\"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"%s\"%(c), \"W=\", sess.run(W), \"b=\", sess.run(b))\n",
    "\n",
    "        print (\"Optimization Finished!\")\n",
    "        training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})\n",
    "        print (\"Training cost=\", training_cost, \"W=\", sess.run(W), \"b=\", sess.run(b), '\\n')\n",
    "\n",
    "    #Graphic display\n",
    "        plt.plot(train_X, train_Y, 'ro', label='Original data')\n",
    "        plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')\n",
    "        plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LEARNING RATE= 0.05 \t Epoch= 100 \n",
      "\n",
      "\n",
      "Epoch: 0050 cost= [ 8.96753025] W= 1.48215 b= 0.668724\n",
      "Epoch: 0100 cost= [ 8.9677248] W= 1.48215 b= 0.668766\n",
      "Optimization Finished!\n",
      "Training cost= [ 8.9677248] W= 1.48215 b= 0.668766 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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uWxo/Pb+8nnH1ci0LGeMPbAS6A69Yax+pafn4+HibnJxcPwm9kLWW5TuPMGtx\nCulH8hkU2ZrHJ/YmPqpNwwSIinK9mM7UtStkZDRMBmm89PxyhDFmo7U23p1l3Xpz0lpbDsQZY1oD\nC4wxfa21289YaSKQCBAZGVnHyL5j074TzFyYwpcZx7mkbSiv3TqY8X06NOxMkX376jYuUhd6fnm9\nOs0qsdbmGmNWAhOA7Wd8bx4wD1x73PWW0Evsycln9pJUFm0/RNsWQfz+ur78+NKLCfR3YKZIZOTZ\n94ga8S9MaUB6fnm9WlvHGNOuck8bY0xzYCyQ4ulg3iLnVDFPfrydsc+vYU1aDg9e1ZPV00Zy69Cu\nzpQ2wIwZEHLGxRRCQlzjIhdKzy+v584edyfgL5XHuf2A9621n3o2lvNOF5fx+to9vL5mD8VlFUwZ\nEsl9o3vQLizI6Wj/fYNo+nTXn6+Rka4Xld44kvqg55fXc+vNybry5TcnS8sreO+r/cxdvouj+cVM\n7NeRaeNjiG4b6nQ0EWnE6v3NyabAWsuSbw7x7OJU9hw9zWXRbXj99sEMjLzI6WgiItWouIGvMo7z\nzMKdbNqXS4/2LXjj9njG9G6vc4qIiFdq0sWdfuQUsxansmzHYTq0DGLWjf24cVAEAU696Sgi4oYm\nWdyHTxbxwvI0/vHVfkKbBTBtfC9+mhBN82a6XJiIeL8mVdynikr58+o9vPHZHsorLHcMj+K+0T1o\nE9rM6WgiIm5rEsVdUlbB3zZk8uKKdI6fLuHaAZ15aFwvIsNDar+ziIiXadTFba3l068P8tzSVDKP\nFTC8WziPXh1D/4jWTkcTETlvjba41+8+xsxFO9malUdMxzDm33UpI3q200wREfF5ja64Uw6dZNai\nFFam5tC5VTDP3TyA6wd2wd9PhS0ijUOjKe4DuYXMWZbGR5uyCAsK4LGrY7hjeBTBgZopIiKNi88X\nd15hKX9atZu3P9+LtfCzy6P5xajutA7RTBERaZx8triLy8p5d30mL69MJ6+wlOvjujB1XE8iLtJM\nERFp3HyuuCsqLJ9sPcBzS1PJOlHIlT3b8eiEGGI7t3Q6mohIg/Cp4l67K4eZi1L45sBJ+nRuycwb\n+nN5j7ZOxxIRaVA+UdzfHMhj5qIU1u46SsRFzZk7OY5r+nfGTzNFRKQJ8urizjpRwB+XpvHxlmxa\nNQ/kiR/05rZhXQkK0EwREWm6vLK4cwtKeHlFOu+sz8QYuGdEN+4Z0Y1WzQOdjiYi4jivKu6i0nLm\nr8vg1ZWnHtGcAAAG0klEQVTp5BeXceOgCKaO60mnVs2djiYi4jW8prjzCku5+oU1HMgrYnRMex6Z\nEEOvjmFOxxIR8Tq1Frcx5mLgHaADYIF51tq59R2kVfNAbhocwbBubRnWLby+H15EpNFw51IvZcCv\nrLWxwFDgF8aYWE+EmTqul0pbPCMpCaKiwM/P9TkpyelEIuet1j1ua+1B4GDl16eMMTuBLsAOD2cT\nqR9JSZCYCAUFrtuZma7bAFOmOJdL5DzV6eKKxpgoYCCwwRNhRDxi+vT/lva3Cgpc4yI+yO3iNsa0\nAD4CfmmtPXmW7ycaY5KNMck5OTn1mVHkwuzbV7dxES/nVnEbYwJxlXaStfafZ1vGWjvPWhtvrY1v\n165dfWYUuTCRkXUbF/FytRa3cV0y5k1gp7V2jucjidSzGTMg5IyzRoaEuMZFfJA7e9wJwG3AaGPM\nlsqPiR7OJVJ/pkyBefOga1cwxvV53jy9MSk+y1hr6/1B4+PjbXJycr0/rohIY2WM2WitjXdn2TrN\nKhEREeepuEVEfIyKW0TEx6i4RUR8jIpbRMTHqLhFRHyMiltExMeouEVEfIyKW0TEx6i4RUR8jIpb\nRMTHqLhFRHyMiltExMeouEVEfIyKW0TEx6i4RUR8jIpbRMTHqLhFRHyMiltExMe4c5X3t4wxR4wx\n2xsikIiI1MydPe75wAQP54CkJIiKAj8/1+ekJI+vUkTEFwXUtoC1do0xJsqjKZKSIDERCgpctzMz\nXbcBpkzx6KpFRHyNdxzjnj79v6X9rYIC17iIiFRTb8VtjEk0xiQbY5JzcnLqdud9++o2LiLShNVb\ncVtr51lr46218e3atavbnSMj6zYuItKEecehkhkzICSk+lhIiGtcRESqcWc64N+B9UAvY0yWMeZ/\n6j3FlCkwbx507QrGuD7Pm6c3JkVEzsJYa+v9QePj421ycnK9P66ISGNljNlorY13Z1nvOFQiIiJu\nU3GLiPgYFbeIiI9RcYuI+BgVt4iIj/HIrBJjTA6QeZ53bwscrcc49UW56ka56ka56qYx5upqrXXr\nvxc9UtwXwhiT7O6UmIakXHWjXHWjXHXT1HPpUImIiI9RcYuI+BhvLO55Tgc4B+WqG+WqG+Wqmyad\ny+uOcYuISM28cY9bRERq4Ehx13YBYuPyojEm3RjztTFmkJfkGmmMyTPGbKn8eKqBcl1sjFlpjNlh\njPnGGPPAWZZp8G3mZq4G32bGmGBjzJfGmK2VuX5zlmWc2F7u5HLkOVa5bn9jzGZjzKdn+Z4jr0k3\ncjn1mswwxmyrXOf3zqjn8e1lrW3wD+BKYBCw/RzfnwgsAgwwFNjgJblGAp86sL06AYMqvw4D0oBY\np7eZm7kafJtVboMWlV8HAhuAoV6wvdzJ5chzrHLdU4G/nW39Tr0m3cjl1GsyA2hbw/c9ur0c2eO2\n1q4BjtewyA+Bd6zLF0BrY0wnL8jlCGvtQWvtpsqvTwE7gS5nLNbg28zNXA2uchvkV94MrPw4880c\nJ7aXO7kcYYyJAH4AvHGORRx5TbqRy1t5dHt56zHuLsD+79zOwgsKodLwyj99Fhlj+jT0yo0xUcBA\nXHtr3+XoNqshFziwzSr/vN4CHAGWWWu9Ynu5kQuceY69ADwMVJzj+049v2rLBc5sLwssN8ZsNMYk\nnuX7Ht1e3lrc3moTEGmt7Q+8BHzckCs3xrQAPgJ+aa092ZDrrkktuRzZZtbacmttHBABXGaM6dsQ\n662NG7kafHsZYyYBR6y1Gz29rrpwM5dTr8nLK3+OVwO/MMZc2UDrBby3uLOBi79zO6JyzFHW2pPf\n/qlrrV0IBBpj2jbEuo0xgbjKMcla+8+zLOLINqstl5PbrHKducBKYMIZ33L0OXauXA5trwTgWmNM\nBvAeMNoY89czlnFie9Way6nnl7U2u/LzEWABcNkZi3h0e3lrcX8C3F75zuxQIM9ae9DpUMaYjsYY\nU/n1Zbi237EGWK8B3gR2WmvnnGOxBt9m7uRyYpsZY9oZY1pXft0cGAuknLGYE9ur1lxObC9r7WPW\n2ghrbRQwGVhhrb31jMUafHu5k8uh51eoMSbs26+BccCZM9E8ur0C6uuB6sK4LkA8EmhrjMkCnsb1\nRg3W2teAhbjelU0HCoC7vCTXTcDPjTFlQCEw2Va+hexhCcBtwLbK46MAjwOR38nmxDZzJ5cT26wT\n8BdjjD+uF/L71tpPjTH3fCeXE9vLnVxOPce+xwu2lzu5nNheHYAFlb8vAoC/WWsXN+T20n9Oioj4\nGG89VCIiIueg4hYR8TEqbhERH6PiFhHxMSpuEREfo+IWEfExKm4RER+j4hYR8TH/HxKFOHAaqRFy\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f07d84c6550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LEARNING RATE= 0.005 \t Epoch= 500 \n",
      "\n",
      "\n",
      "Epoch: 0050 cost= [ 0.99609315] W= 0.702903 b= 0.635031\n",
      "Epoch: 0100 cost= [ 0.94557416] W= 0.647106 b= 0.867344\n",
      "Epoch: 0150 cost= [ 0.92935812] W= 0.616453 b= 0.994965\n",
      "Epoch: 0200 cost= [ 0.92393166] W= 0.599615 b= 1.06507\n",
      "Epoch: 0250 cost= [ 0.92200148] W= 0.590364 b= 1.10359\n",
      "Epoch: 0300 cost= [ 0.92125809] W= 0.585282 b= 1.12475\n",
      "Epoch: 0350 cost= [ 0.92094541] W= 0.582491 b= 1.13637\n",
      "Epoch: 0400 cost= [ 0.92080253] W= 0.580957 b= 1.14276\n",
      "Epoch: 0450 cost= [ 0.92073286] W= 0.580114 b= 1.14626\n",
      "Epoch: 0500 cost= [ 0.92069709] W= 0.579651 b= 1.14819\n",
      "Optimization Finished!\n",
      "Training cost= [ 0.92069709] W= 0.579651 b= 1.14819 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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5fKyazO4d+eakwdw4NpP+PTtHu0QRiSMK9yjbc+gYi1aUUhwIsmlvOakpSUwf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\nLiKSgBTuIiIJ6P8DHIdDXuPSzWkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f07d8589828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LEARNING RATE= 0.0005 \t Epoch= 1000 \n",
      "\n",
      "\n",
      "Epoch: 0050 cost= [ 1.14084816] W= 0.743481 b= 0.25066\n",
      "Epoch: 0100 cost= [ 1.1041019] W= 0.760128 b= 0.311944\n",
      "Epoch: 0150 cost= [ 1.08233202] W= 0.748531 b= 0.362743\n",
      "Epoch: 0200 cost= [ 1.0629487] W= 0.736508 b= 0.410359\n",
      "Epoch: 0250 cost= [ 1.04570985] W= 0.725129 b= 0.455243\n",
      "Epoch: 0300 cost= [ 1.0303793] W= 0.714399 b= 0.49756\n",
      "Epoch: 0350 cost= [ 1.01674604] W= 0.704282 b= 0.537457\n",
      "Epoch: 0400 cost= [ 1.00462139] W= 0.694743 b= 0.575075\n",
      "Epoch: 0450 cost= [ 0.99383807] W= 0.68575 b= 0.610542\n",
      "Epoch: 0500 cost= [ 0.98424768] W= 0.677271 b= 0.643981\n",
      "Epoch: 0550 cost= [ 0.97571808] W= 0.669276 b= 0.675509\n",
      "Epoch: 0600 cost= [ 0.96813172] W= 0.661739 b= 0.705234\n",
      "Epoch: 0650 cost= [ 0.9613837] W= 0.654632 b= 0.73326\n",
      "Epoch: 0700 cost= [ 0.95538116] W= 0.647932 b= 0.759685\n",
      "Epoch: 0750 cost= [ 0.95004195] W= 0.641615 b= 0.784598\n",
      "Epoch: 0800 cost= [ 0.94529235] W= 0.635659 b= 0.808087\n",
      "Epoch: 0850 cost= [ 0.94106704] W= 0.630043 b= 0.830233\n",
      "Epoch: 0900 cost= [ 0.93730795] W= 0.624748 b= 0.851114\n",
      "Epoch: 0950 cost= [ 0.9339636] W= 0.619756 b= 0.870801\n",
      "Epoch: 1000 cost= [ 0.93098807] W= 0.615049 b= 0.889363\n",
      "Optimization Finished!\n",
      "Training cost= [ 0.93098807] W= 0.615049 b= 0.889363 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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gJKX/+vLhTBnRl87H6HCKSHJQN4pB3ZTerVNHvpU7gGl5A5TSRSQpqbk34HBK\nL1y6icUffM7BqmpyBvTg11cMZ8pwpXQRSW7qUHX8Y+9Bnl0RPi99feleunXqyLQxA5g2JsTgfscl\nujwRkZiouRNO6cs2fMn8ZcU1KX1kSCldRFJXu+5ah1N64bJiPivdS7eMjkwfM4BpeSHO6KuULiKp\nq901d3dnaSSlv7T6cw4eqmZUqAf3XjGcKcP7cewxaYkuUUSk2dpNc/9y70Geq5vS8wYwfWyI0/so\npYtIsAS6uR9O6YVLi/nLB+GUPnrg8fzmylO4cFhfpXQRCaxANvcv9x7k2eXhM14+2xFO6VePDTEt\nb4BSuoi0C4Fp7u7OO5+F19KPSOnnKKWLSPsTy2X2HgWmANvdfWiU5ycCC4ENkaHn3P2ulizyaL7c\ne5Bnlm/miWWb+WzHXo6LpPTpeSFO69OtrcoQEUkqsST3ucBDwLyjzHnD3ae0SEUxcHf+97OdzF+2\nmf+JpPTcgcdz0zmncIFSuohITJfZe93Mslq/lNi8+ekOfrrwAzbUSulXjw1xam+ldBGRw1pqzf1M\nM3sf2ALc5u4fRptkZvlAPkAoFGrSjo7vks6JXY7h5knhlJ6RrpQuIlKXha9t3cikcHJf1MCa+3FA\ntbuXm9kFwAPunt3YNnNzc72oqCj+ikVE2jEzW+7uuY3N69DcHbn7bncvj9xfDKSbWc/mbldERJqu\n2c3dzPqYmUXu50W2ubO52xURkaaL5VTI+cBEoKeZlQB3AukA7j4buAK4wcyqgH3ANI9lrUdERFpN\nLGfLTG/k+YcInyopIiJJotnLMiIiknzU3EVEAkjNXUQkgNTcRUQCSM1dRCSA1NxFRAJIzV1EJIDU\n3EVEAkjNXUQkgNTcRUQCSM1dRCSA1NxFRAJIzV1EJIDU3EVEAkjNXUQkgBpt7mb2qJltN7MPGnje\nzOxBM1tnZu+b2aiWL1NEROIRS3KfC5x3lOfPB7Ijt3zgd80vqwEFBZCVBR06hD8WFLTarkREUlmj\nzd3dXwe+PMqUqcA8D3sH6GFmfVuqwBoFBZCfD5s2gXv4Y36+GryISBQtsebeH9hc63FJZKxlzZwJ\nFRVHjlVUhMdFROQIbfqCqpnlm1mRmRWVlpbG98nFxfGNi4i0Yy3R3LcAA2o9zoyM1ePuc9w9191z\ne/XqFd9eQqH4xkVE2rGWaO4vAN+OnDUzDihz920tsN0jzZoFnTsfOda5c3hcRESO0LGxCWY2H5gI\n9DSzEuCzoCVkAAAD80lEQVROIB3A3WcDi4ELgHVABXBdq1Q6Y0b448yZ4aWYUCjc2A+Pi4hIDXP3\nhOw4NzfXi4qKErJvEZFUZWbL3T23sXn6D1URkQBScxcRCSA1dxGRAFJzFxEJIDV3EZEAStjZMmZW\nCmxq4qf3BHa0YDktJVnrguStTXXFR3XFJ4h1DXT3Rv8LNGHNvTnMrCiWU4HaWrLWBclbm+qKj+qK\nT3uuS8syIiIBpOYuIhJAqdrc5yS6gAYka12QvLWprviorvi027pScs1dRESOLlWTu4iIHEVSN/dk\nvTh3DHVNNLMyM1sVuf2sDWoaYGavmdkaM/vQzG6JMqfNj1eMdSXieGWY2TIzey9S1y+izEnE8Yql\nrjY/XrX2nWZmK81sUZTnEvLzGENdiTxeG81sdWS/9d4psVWPmbsn7Q04GxgFfNDA8xcALwEGjAOW\nJkldE4FFbXys+gKjIve7AZ8AgxN9vGKsKxHHy4CukfvpwFJgXBIcr1jqavPjVWvf/w4URtt/on4e\nY6grkcdrI9DzKM+32jFL6uTuyXJx7vjranPuvs3dV0Tu7wHWUv9atm1+vGKsq81FjkF55GF65Fb3\nBahEHK9Y6koIM8sELgR+38CUhPw8xlBXMmu1Y5bUzT0GbXNx7qY5M/Jn1ktmNqQtd2xmWcBIwqmv\ntoQer6PUBQk4XpE/5VcB24G/uXtSHK8Y6oLEfH/9FvgBUN3A84n6/mqsLkjcz6MDL5vZcjPLj/J8\nqx2zVG/uyWoFEHL34cB/Ac+31Y7NrCvwLHCru+9uq/02ppG6EnK83P2Qu+cQvu5vnpkNbYv9NiaG\nutr8eJnZFGC7uy9v7X3FI8a6EvbzCPxT5Gt5PnCTmZ3dVjtO9eYe88W525K77z78p7W7LwbSzaxn\na+/XzNIJN9ACd38uypSEHK/G6krU8aq1/13Aa8B5dZ5K6PdXQ3Ul6HhNAC42s43AE8AkM3u8zpxE\nHK9G60rk95e7b4l83A4sAPLqTGm1Y5bqzb1tLs4dJzPrY2YWuZ9H+DjvbOV9GvAHYK2739fAtDY/\nXrHUlaDj1cvMekTuHwucC3xUZ1oijlejdSXieLn7j909092zgGnAq+7+L3WmtfnxiqWuRByvyL66\nmFm3w/eBbwJ1z7BrtWPW6AWyE8mS5eLc8dd1BXCDmVUB+4BpHnlpvBVNAK4BVkfWawF+AoRq1ZWI\n4xVLXYk4Xn2Bx8wsjfAP+1PuvsjMrq9VVyKOVyx1JeJ4RZUExyuWuhJ1vHoDCyK/VzoChe7+l7Y6\nZvoPVRGRAEr1ZRkREYlCzV1EJIDU3EVEAkjNXUQkgNTcRUQCSM1dRCSA1NxFRAJIzV1EJID+Pz7p\nWeKnO6+4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f07d7c6f358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#model_output = tf.add(tf.matmul(x_data, A[W]), b)\n",
    "\n",
    "pred = tf.add(tf.multiply(X, W), b)\n",
    "ridge_param = tf.constant(1.)\n",
    "ridge_loss = tf.reduce_mean(tf.square(W))\n",
    "cost = tf.expand_dims(tf.add(tf.reduce_mean(tf.square(pred - Y)), tf.multiply(ridge_param,ridge_loss)), 0)\n",
    "    \n",
    "#cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)\n",
    "for (x,y) in zip(learning_rate,training_epochs):\n",
    "    processData(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}