{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_x = tf.placeholder(tf.float32, shape=[None])\n",
    "label_y = tf.placeholder(tf.float32, shape=[None])\n",
    "\n",
    "weight = tf.Variable(tf.random_normal(shape=[1]))\n",
    "\n",
    "\n",
    "hypothesis = tf.multiply(input_x, weight)\n",
    "loss = tf.reduce_mean(tf.square(hypothesis - label_y))\n",
    "optimize = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(loss)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-65-c15f0cb911e2>:2: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n",
      "0 [ 1.57790422]\n",
      "1 [ 1.52396655]\n",
      "2 [ 1.47506297]\n",
      "3 [ 1.43072379]\n",
      "4 [ 1.39052284]\n",
      "5 [ 1.354074]\n",
      "6 [ 1.32102704]\n",
      "7 [ 1.2910645]\n",
      "8 [ 1.26389849]\n",
      "9 [ 1.23926795]\n",
      "10 [ 1.21693623]\n",
      "11 [ 1.19668889]\n",
      "12 [ 1.17833126]\n",
      "13 [ 1.16168702]\n",
      "14 [ 1.14659619]\n",
      "15 [ 1.13291383]\n",
      "16 [ 1.12050855]\n",
      "17 [ 1.10926104]\n",
      "18 [ 1.0990634]\n",
      "19 [ 1.08981752]\n",
      "20 [ 1.08143461]\n"
     ]
    },
    {
     "data": {
      "image/png": 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O6tkFd16iCrW1tHNPqE9WqNrZiBsyK9RfjFOF2hZ65hJIFaq4ShVqcLS4J4wqVHGVKtRg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\nDgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1820ef6cf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    sess.run(tf.initialize_all_variables())\n",
    "    for i in range(0, 21):\n",
    "        sess.run(optimize, feed_dict={input_x: [1, 2, 3], label_y: [1, 2, 3]})\n",
    "        print(i, weight.eval())\n",
    "\n",
    "    fig = plt.figure()\n",
    "    subplot = fig.add_subplot(1, 2, 1)\n",
    "    subplot.plot([1, 2, 3], hypothesis.eval(feed_dict={input_x: [1, 2, 3]}))\n",
    "    subplot.set_title(\"testing\")\n",
    "    subplot = fig.add_subplot(1, 2, 2)\n",
    "    subplot.plot([1, 2, 3], hypothesis.eval(feed_dict={input_x: [1, 2, 3]}))\n",
    "\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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