ipynb/lab-03-1-minimizing_cost_show_graph.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab-03-1 Minimizing Cost show graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "tf.set_random_seed(777)  # for reproducibility"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## X and Y data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = [1, 2, 3]\n",
    "Y = [1, 2, 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "W = tf.placeholder(tf.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Our Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Our hypothesis for linear model X * W\n",
    "hypothesis = X * W\n",
    "\n",
    "# cost/loss function\n",
    "cost = tf.reduce_mean(tf.square(hypothesis - Y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Launch the graph in a session.\n",
    "sess = tf.Session()\n",
    "# Initializes global variables in the graph.\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Variables for plotting cost function\n",
    "W_history = []\n",
    "cost_history = []\n",
    "\n",
    "for i in range(-30, 50):\n",
    "    curr_W = i * 0.1\n",
    "    curr_cost = sess.run(cost, feed_dict={W: curr_W})\n",
    "    W_history.append(curr_W)\n",
    "    cost_history.append(curr_cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t9Fa5fkwPunTy5emvdJSulCtZtDGPw0dPcMfEfoi43uHP9tJCb4XO/j7cMr43q/cVkaYr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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f00c62e3390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the cost function\n",
    "plt.plot(W_history, cost_history)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}