{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# A linear regression learning algorithm example using TensorFlow library.\n",
    "\n",
    "# Author: Aymeric Damien\n",
    "# Project: https://github.com/aymericdamien/TensorFlow-Examples/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "rng = numpy.random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "learning_rate = 0.01\n",
    "training_epochs = 1000\n",
    "display_step = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Training Data\n",
    "train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,\n",
    "                         7.042,10.791,5.313,7.997,5.654,9.27,3.1])\n",
    "train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,\n",
    "                         2.827,3.465,1.65,2.904,2.42,2.94,1.3])\n",
    "n_samples = train_X.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# tf Graph Input\n",
    "X = tf.placeholder(\"float\")\n",
    "Y = tf.placeholder(\"float\")\n",
    "\n",
    "# Set model weights\n",
    "W = tf.Variable(rng.randn(), name=\"weight\")\n",
    "b = tf.Variable(rng.randn(), name=\"bias\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Construct a linear model\n",
    "pred = tf.add(tf.mul(X, W), b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Mean squared error\n",
    "cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)\n",
    "# Gradient descent\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Initializing the variables\n",
    "init = tf.initialize_all_variables()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0050 cost= 0.207037717 W= 0.451217 b= -0.649001\n",
      "Epoch: 0100 cost= 0.192011863 W= 0.439226 b= -0.562735\n",
      "Epoch: 0150 cost= 0.178721249 W= 0.427948 b= -0.481599\n",
      "Epoch: 0200 cost= 0.166965470 W= 0.41734 b= -0.40529\n",
      "Epoch: 0250 cost= 0.156567261 W= 0.407363 b= -0.333518\n",
      "Epoch: 0300 cost= 0.147369981 W= 0.39798 b= -0.266015\n",
      "Epoch: 0350 cost= 0.139234960 W= 0.389155 b= -0.202527\n",
      "Epoch: 0400 cost= 0.132039562 W= 0.380854 b= -0.142815\n",
      "Epoch: 0450 cost= 0.125675321 W= 0.373048 b= -0.0866538\n",
      "Epoch: 0500 cost= 0.120046206 W= 0.365705 b= -0.0338331\n",
      "Epoch: 0550 cost= 0.115067400 W= 0.3588 b= 0.0158462\n",
      "Epoch: 0600 cost= 0.110663772 W= 0.352305 b= 0.0625707\n",
      "Epoch: 0650 cost= 0.106768914 W= 0.346196 b= 0.106516\n",
      "Epoch: 0700 cost= 0.103324078 W= 0.340451 b= 0.147848\n",
      "Epoch: 0750 cost= 0.100277305 W= 0.335047 b= 0.186722\n",
      "Epoch: 0800 cost= 0.097582638 W= 0.329965 b= 0.223284\n",
      "Epoch: 0850 cost= 0.095199391 W= 0.325184 b= 0.257671\n",
      "Epoch: 0900 cost= 0.093091547 W= 0.320689 b= 0.290013\n",
      "Epoch: 0950 cost= 0.091227390 W= 0.31646 b= 0.320432\n",
      "Epoch: 1000 cost= 0.089578770 W= 0.312484 b= 0.349041\n",
      "Optimization Finished!\n",
      "Training cost= 0.0895788 W= 0.312484 b= 0.349041 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Launch the graph\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=\", \"{:.9f}\".format(c), \\\n",
    "                \"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": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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OVIcOHbR3714tXLhQmzdv1jPPPKOoqChJ0pQpU5Senq7t27dX3Nqwf//++uUv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\nWrlyZbVfV1RUpCuuuEKtWrXStGnTdOjQIU2fPl1ffvmlPB6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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Regression result"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "IPython (Python 2.7)",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}