study_lab_01.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 데이터 시각화 (학습 전)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_data = [1, 2, 3, 4, 5]\n",
    "y_data = [1, 2, 3, 4, 5]\n",
    "\n",
    "W = tf.Variable(2.)\n",
    "b = tf.Variable(.5)\n",
    "\n",
    "hypothesis = W * x_data + b\n",
    "\n",
    "plt.plot(x_data, y_data, 'bo')\n",
    "plt.plot(x_data, hypothesis, 'r-')\n",
    "plt.ylim(0, 8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression 학습"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    0|   1.97500|   0.49300|  14.25000\n",
      "   10|   1.75535|   0.43141|   8.83005\n",
      "   20|   1.58252|   0.38279|   5.47265\n",
      "   30|   1.44654|   0.34437|   3.39290\n",
      "   40|   1.33955|   0.31399|   2.10458\n",
      "   50|   1.25538|   0.28994|   1.30652\n",
      "   60|   1.18918|   0.27086|   0.81214\n",
      "   70|   1.13712|   0.25570|   0.50588\n",
      "   80|   1.09618|   0.24363|   0.31615\n",
      "   90|   1.06400|   0.23399|   0.19860\n",
      "  100|   1.03871|   0.22625|   0.12577\n",
      "  110|   1.01885|   0.22002|   0.08064\n",
      "  120|   1.00325|   0.21498|   0.05266\n",
      "  130|   0.99102|   0.21087|   0.03531\n",
      "  140|   0.98143|   0.20749|   0.02455\n",
      "  150|   0.97392|   0.20469|   0.01786\n",
      "  160|   0.96805|   0.20235|   0.01370\n"
     ]
    }
   ],
   "source": [
    "x_data = [1, 2, 3, 4, 5]\n",
    "y_data = [1, 2, 3, 4, 5]\n",
    "\n",
    "W = tf.Variable(2.)\n",
    "b = tf.Variable(.5)\n",
    "\n",
    "learning_rate = 0.001\n",
    "epoch = 170\n",
    "for step in range(epoch):\n",
    "    with tf.GradientTape() as tape:\n",
    "        hypothesis = W * x_data + b\n",
    "        cost = tf.reduce_mean(tf.square(hypothesis - y_data))\n",
    "    W_grad, b_grad = tape.gradient(cost, [W, b])\n",
    "    W.assign_sub(learning_rate * W_grad)\n",
    "    b.assign_sub(learning_rate * b_grad)\n",
    "    \n",
    "    if step % 10 == 0:\n",
    "        print(\"{:5}|{:10.5f}|{:10.5f}|{:10.5f}\".format(step, W.numpy(), b.numpy(), cost.numpy()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 데이터 시각화 (학습 후)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_data, y_data, 'bo')\n",
    "plt.plot(x_data, hypothesis, 'r-')\n",
    "plt.ylim(0, 8)\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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}