ML_linear_regression_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use shift-enter to execute a code block and move to the next one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 1.1 Import tensorflow and other libraries.\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "import pylab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11641b9b0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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izeqLZE1NLZyT0a5v4YMwPDAIbZQktcaAos26ndMwCMMDg9BGSVJrHPJos0HI\naZAkqd0MKNrMfAFJ0igyoGizRsmWg1AvQpKkqsyhaLNG+QKDUC9CkqSq7KHoEnMrJEnDzICiS8yt\nkCQNM4c8usRaDJKkYWZA0SXWYpAkDTOHPCRJUssMKCRJUstGKqBoZy2I+mt95SvWmZAkja6RyqFo\nZy2I+mv97M/Cnj3tubYkSYNmpHoo2lkLov7cxx5r37UlSRo0IxVQtLMWRP25P/ET7bu2JEmDZqQC\nirnrbBxzDDzxRPWch/o1O770pYXX8JAkaRSMVA7F3FoQxx/fWj7FQnUlzJmQJI2qkeqhmMu1NSRJ\nap+BDigeeqj6VE3X1pAkqX0qBRQRcU5E7IyIxyLilog4ZpFjT42I2yLi4Yh4JCJuj4gzFjjuooj4\ndkQ8GhE3RMSLlmrH7/1eMWyxY0fxc/365t9DfQ6EOQ+SJFVXOociIk4HLgXeCWwBJoDrI+LIzHxw\ngVMeAi4G7gIeB04BroyImcy8oXbN3wd+C/g1YGft+Osj4ujMfLxRWx6se7UywxaurSFJUvtU6aGY\nAK7IzE2ZeRdwFvAocOZCB2fm5sy8NjO/lpk7M/NyYBtw/JzD3g28LzOvy8yvAG8Dngv88mINWbFi\n/rbDFpIk9UapgCIi9gXGgJtm92VmAjcCxzZ5jROBI4HP17ZfCBxWd83vA7cudc0//VOHLSRJ6gdl\nhzxWAPsAM3X7Z4CjGp0UEQcD9wLLgCeBszPzc7WnDwOywTUPW6wxhxzSu2GLmZmi/PZ99xU9I1NT\nxTCKJEmjqF11KIIiKGhkD7AWOBA4EdgQETsyc3ML12RiYoLly5fP2zc+Ps74+HhTjW5FO9cFkSSp\nEyYnJ5mcnJy3b/fu3R15rbIBxYPAU8ChdftXsncPw4/VhkV21Da3RcRLgPOAzcB3KIKHQ+uusRK4\nfbHGbNiwgXXr1pVpf9tYx0KS1O8W+pK9detWxsbG2v5apXIoMvMJYJqilwGAiIja9s0lX3dZ7Zo7\nKYKKudc8GHhVyWs21M5ly2dZx0KSpKdVGfK4DLg6IqZ5etro/sBVABGxCdiVmefXts8FvgzcTRFE\nvBE4g2J2yKwPAX8QEf8KfAN4H7ALuLZC+/Zyyilw223Fv3fsgJNPhi1bWrvm1FQxzDE3h0KSpFFV\nOqDIzGsiYgVwEcUwxR3ASZn5QO2Q1RSJl7MOAD5S2/8YRT2Kt2bmp+Zc84MRsT9wBfBs4AvAGxar\nQVHGtm1mkCSmAAAIjElEQVSLb1dhHQtJkp5WKSkzMzcCGxs8d0Ld9gXABU1c873Ae6u0R5Ik9dZA\nr+XRrLVrF9+WJEmtGYnly6+7znwHSZI6aSQCCvMdJEnqrJEY8pAkSZ1lQFFSJ2paSJI06AwoSpot\nub1jR/Fz/fpet0iSpN4zoCipasltezYkScPMgKKkqiW37dmQJA2zkZjl0U5VS267mJgkaZgZUJRU\ndQrqqlVF78TcbUmShoUBRZe4mJgkaZgZUHSJxbUkScPMpExJktQyAwpJktQyAwpJktQyAwpJktQy\nAwpJktQyAwpJktQyAwpJktQyAwpJktQyAwpJktQyAwpJktQyAwpJktQyAwpJktQyAwpJktQyAwpJ\nktQyAwpJktQyAwpJktQyAwpJktQyAwpJktQyAwpJktQyAwpJktQyAwo1bXJystdNGDne8+7znnef\n93w4VAooIuKciNgZEY9FxC0Rccwix/5GRGyOiO/WHjfUHx8RB0TEhyPiWxHxaER8NSJ+s0rb1Dn+\nT9993vPu8553n/d8OJQOKCLidOBS4ELg5cCdwPURsaLBKa8BPgn8AvBq4FvAP0TEqjnHbABeD7wF\neDHwIeDDEXFy2fZJkqTuq9JDMQFckZmbMvMu4CzgUeDMhQ7OzP+cmX+emdsy81+A36i97olzDjsW\nuDozv5CZ92TmxygClVdWaJ8kSeqyUgFFROwLjAE3ze7LzARupAgKmnEAsC/w3Tn7bgbeFBHPrb3O\na4GfAq4v0z5JktQbzyx5/ApgH2Cmbv8McFST1/gAcC9FEDLrt4GPArsi4kngKeAdmfnFBtfYD2D7\n9u1NvqTaYffu3WzdurXXzRgp3vPu8553n/e8u+Z8du7X1gtnZtMPYBXwI+BVdfs/CNzcxPnnAg8C\nP123/3eB7cAvAf8BOBv4PnBCg+u8BUgfPnz48OHDR+XHW8rEAEs9yvZQPEjRe3Bo3f6V7N1rMU9E\n/C7wHuDEzPzqnP37Ae8H3pyZf1/b/ZWIeDlFoPG5BS53PfBW4BvAD0q+B0mSRtl+wAtoc1pBqYAi\nM5+IiGmKhMrPAERE1LYvb3ReRPwecD7w+sy8ve7pfWuPrNv/FA1yPDLzIYqZI5Ikqbyb233Bsj0U\nAJcBV9cCiy0Usz72B64CiIhNwK7MPL+2/R7gImAcuCciZns3HsnMf8vMPRHxeeCSiPgB8E2KKaZv\nA36n6huTJEndUzqgyMxrajUnLqIY+rgDOCkzH6gdshp4cs4p/5WiB+JTdZf6w9o1AE4H/hj4BHAI\nRVBxXmZ+tGz7JElS90UtyVGSJKky1/KQJEktM6CQJEkt69uAoswCZLXj/1NEbK8df2dEvKFbbR0W\n7V70TUsr+3s+57xfjYgfRcRUp9s4bCr8bVkeER+JiG/XzrkrIv5jt9o7DCrc89+p3edHI+KeiLgs\nIpZ1q72DLiJ+LiI+ExH31v5OvKmJc34hIqYj4gcR8S8R8WtlX7cvA4qyC5BFxLEU00g/BvwM8Gng\n0xHxku60ePB1aNE3LaLCPZ8973DgEmBzxxs5ZCr8bdmXoqrvvwfWU1QEfgdFtV81ocI9fwtFkv6F\nFItFnkmRuP/+rjR4OBxAMWHiHPYuybCXiHgB8FmKZTXWAn8G/O+IeF2pV21nlax2PYBbgD+bsx3A\nLuA9DY7/K+Azdfu+BGzs9XsZlEfZe77A+c8AdgNn9Pq9DMqjyj2v3ecvAG8HrgSmev0+BulR4W/L\nWcDXgX163fZBfVS45/8LuKFu358Cm3v9XgbxQVHd+k1LHPMBYFvdvkng78q8Vt/1UFRcgOxY5q8N\nAkUFsGYXLBtpHVz0TQ20cM8vBO7PzCs728LhU/Gen0Lty0lEfCci/l9EnBcRffe3sx9VvOc3A2Oz\nwyIRsYZiWYa/7WxrR9qracNnaJXCVp1WZQGywxocf1h7mza0OrXomxorfc8j4jiKnom1nW3a0Kry\ne74GOIGiRs4bKFZB3li7zsWdaeZQKX3PM3OyNhzyT7VKzPsAf56ZH+hoS0dbo8/QgyNiWWb+sJmL\n9GNA0UjQxFhQC8drb03dw4g4F/gV4DWZ+XjHWzXcFrznEXEg8HGKVXgf7nqrhttiv+fPoPjD+s7a\nN+vbI+J5FOsMGVBU1/CeR8QvUCzVcBZFNeYXAZdHxH2Z6T3vnqj9bPpztB8DiioLkH2n5PGar+2L\nvmlJZe/5EcDhwHW1b21QS6qOiMeBozJzZ4faOiyq/J7fBzxeCyZmbQcOi4hnZuaTDc5Toco9vwjY\nNGdY76u1gPoKDOI6pdFn6PfLfEnsu3HAzHwCmF2ADJi3AFmjxUy+NPf4mtfV9msJFe/57KJv/4Oi\n9Hr9om9aRIV7vh14KcUsprW1x2coVuNdSzHLRouo+Hv+RYpvyHMdBdxnMLG0ivd8f4pEwrl+VDs1\nFjherVvoM/T1lP0M7XUGaoOM018BHqNYIOzFFJHpQ8Bzas9vAv5ozvHHAo8D/43if/b3Uixr/pJe\nv5dBeVS45++p3eNTKSLb2ccBvX4vg/Ioe88XON9ZHh2+5xRrE+2mmEb3U8AbKb7Nndvr9zIojwr3\n/ELgexRTRV9A8eXw68Ane/1eBuVBkSS/luILyI8oFtpcCzy/9vwfA1fPOf4FwCMUuXBHAWfXPlN/\nsczr9uOQB1lyAbLM/FJEjFPMU34/xS/fmzPzn7vb8sFV9p7T3KJvWkSFe64WVfjbsisiXg9soKif\ncG/t3x/sasMHWIXf8/dRfAi+D3ge8ABFb9wfdK3Rg+8VwD9S5D8kRR0QgKsp6nocBjx/9uDM/EZE\nvJFiNfF3UUzr/fXMLJVk7+JgkiSpZX2XQyFJkgaPAYUkSWqZAYUkSWqZAYUkSWqZAYUkSWqZAYUk\nSWqZAYUkSWqZAYUkSWqZAYUkSWqZAYUkSWqZAYUkSWrZ/wdxVpaujC7b8gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1162c9f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1.2 Create input data using NumPy. y = x * 0.1 + 0.3 + noise\n",
    "x_data = np.random.rand(100).astype(np.float32)\n",
    "noise = np.random.normal(scale=0.01, size=len(x_data))\n",
    "y_data = x_data * 0.1 + 0.3 + noise\n",
    "\n",
    "# Uncomment the following line to plot our input data.\n",
    "pylab.plot(x_data, y_data, '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<tensorflow.python.ops.variables.Variable object at 0x113617748>\n",
      "<tensorflow.python.ops.variables.Variable object at 0x1162e35c0>\n",
      "Tensor(\"add_2:0\", shape=(100,), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "# 1.3 Buld inference graph.\n",
    "# Create Variables W and b that compute y_data = W * x_data + b\n",
    "W = tf.Variable(tf.random_uniform([1], 0.0, 1.0))\n",
    "b = tf.Variable(tf.zeros([1]))\n",
    "y = W * x_data + b\n",
    "\n",
    "# Uncomment the following lines to see W and b are.\n",
    "print(W)\n",
    "print(b)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: Tensor(\"Mean_5:0\", shape=(), dtype=float32)\n",
      "optimizer: <tensorflow.python.training.gradient_descent.GradientDescentOptimizer object at 0x1165cef98>\n",
      "train: name: \"GradientDescent_5\"\n",
      "op: \"NoOp\"\n",
      "input: \"^GradientDescent_5/update_Variable_4/ApplyGradientDescent\"\n",
      "input: \"^GradientDescent_5/update_Variable_5/ApplyGradientDescent\"\n",
      "\n",
      "name: \"init_5\"\n",
      "op: \"NoOp\"\n",
      "input: \"^Variable/Assign\"\n",
      "input: \"^Variable_1/Assign\"\n",
      "input: \"^Variable_2/Assign\"\n",
      "input: \"^Variable_3/Assign\"\n",
      "input: \"^Variable_4/Assign\"\n",
      "input: \"^Variable_5/Assign\"\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 1.4 Build training graph.\n",
    "loss = tf.reduce_mean(tf.square(y - y_data))  # Create an operation that calculates loss.\n",
    "optimizer = tf.train.GradientDescentOptimizer(0.05)  # Create an optimizer.\n",
    "train = optimizer.minimize(loss)  # Create an operation that minimizes loss.\n",
    "init = tf.initialize_all_variables()  # Create an operation initializes all the variables.\n",
    "\n",
    "# Uncomment the following 3 lines to see what 'loss', 'optimizer' and 'train' are.\n",
    "print(\"loss:\", loss)\n",
    "print(\"optimizer:\", optimizer)\n",
    "print(\"train:\", train)\n",
    "print(init)"
   ]
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   "execution_count": 34,
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    }
   ],
   "source": [
    "# 1.5 Uncomment the following line to see what we have built.\n",
    "print(tf.get_default_graph().as_graph_def())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([ 0.30844021], dtype=float32), array([ 0.], dtype=float32)]\n"
     ]
    }
   ],
   "source": [
    "# 1.6 Create a session and launch the graph.\n",
    "sess = tf.Session()\n",
    "sess.run(init)\n",
    "y_initial_values = sess.run(y)  # Save initial values for plotting later.\n",
    "\n",
    "# Uncomment the following line to see the initial W and b values.\n",
    "print(sess.run([W, b]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 [array([ 0.31586057], dtype=float32), array([ 0.0210037], dtype=float32)]\n",
      "20 [array([ 0.3415049], dtype=float32), array([ 0.17375012], dtype=float32)]\n",
      "40 [array([ 0.31335852], dtype=float32), array([ 0.19962193], dtype=float32)]\n",
      "60 [array([ 0.28509718], dtype=float32), array([ 0.21393532], dtype=float32)]\n",
      "80 [array([ 0.26028591], dtype=float32), array([ 0.22573291], dtype=float32)]\n",
      "100 [array([ 0.23873843], dtype=float32), array([ 0.23592068], dtype=float32)]\n",
      "120 [array([ 0.22004309], dtype=float32), array([ 0.24475554], dtype=float32)]\n",
      "140 [array([ 0.20382375], dtype=float32), array([ 0.25241998], dtype=float32)]\n",
      "160 [array([ 0.18975256], dtype=float32), array([ 0.25906926], dtype=float32)]\n",
      "180 [array([ 0.17754501], dtype=float32), array([ 0.26483792], dtype=float32)]\n",
      "200 [array([ 0.16695429], dtype=float32), array([ 0.26984254], dtype=float32)]\n",
      "[array([ 0.16695429], dtype=float32), array([ 0.26984254], dtype=float32)]\n"
     ]
    }
   ],
   "source": [
    "# 1.7 Perform training.\n",
    "for step in range(201):\n",
    "    sess.run(train)\n",
    "    # Uncomment the following two lines to watch training happen real time.\n",
    "    if step % 20 == 0:\n",
    "       print(step, sess.run([W, b]))\n",
    "\n",
    "print(sess.run([W, b]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1.0)"
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     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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XX38dgFOnTvHWW28xefJkt/0+/fRTvvvd79KuXTvatGlDUlIS3//+94HwvNbZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1169606d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1.8 Uncomment the following lines to compare.\n",
    "pylab.plot(x_data, y_data, '.', label=\"target_values\")\n",
    "pylab.plot(x_data, y_initial_values, \".\", label=\"initial_values\")\n",
    "pylab.plot(x_data, sess.run(y), \".\", label=\"trained_values\")\n",
    "pylab.legend()\n",
    "pylab.ylim(0, 1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Back to [0_tf_hello_world.ipynb](0_tf_hello_world.ipynb).</p>\n",
    "<p>Next to [2_mnist.ipynb](2_mnist.ipynb).</p>"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}