Lab02/Lab02-1_ChanjaeYu.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import tensorflow as tf\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis and Cost\n",
    "\n",
    "\n",
    "▶Hypothesis\n",
    "$$ H(x) = Wx + b $$\n",
    "\n",
    "▶Cost\n",
    "$$ cost(W)=\\frac { 1 }{ m } \\sum _{i=1}^{m}{ { (W{ x }^{ i }-y^{ i } })^{ 2 } }  $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 간단한 데이터 생성\n",
    "\n",
    "x_data = [1, 2, 3, 4, 5]\n",
    "y_data = [1, 2, 3, 4, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 8.0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# 그래프로 점들의 분포 확인\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(x_data, y_data, 'o')\n",
    "plt.ylim(0, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=float32, numpy=2.5>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v =[1., 2., 3., 4.]\n",
    "tf.reduce_mean(v) # reduce_mean() :차원을 줄여 평균을 구하는 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=int32, numpy=9>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.square(3) # 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = [1, 2, 3, 4, 5]\n",
    "y_data = [1, 2, 3, 4, 5]\n",
    "\n",
    "# W,b에 랜덤한 수 배정\n",
    "W = tf.Variable(2.0)\n",
    "b = tf.Variable(0.5)\n",
    "\n",
    "hypothesis = W * x_data + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.0, 0.5)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W.numpy(), b.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.5,  4.5,  6.5,  8.5, 10.5], dtype=float32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hypothesis.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "# Hypothesis 예측 함수와 실제 값을 그래프에 나타내기\n",
    "plt.plot(x_data, hypothesis.numpy(), 'r-')\n",
    "plt.plot(x_data, y_data, 'o')\n",
    "plt.ylim(0, 8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cost는 예측값-실제값의 제곱의 평균을 의미\n",
    "cost = tf.reduce_mean(tf.square(hypothesis - y_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25.0, 7.0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with tf.GradientTape() as tape:\n",
    "    hypothesis = W * x_data + b\n",
    "    cost = tf.reduce_mean(tf.square(hypothesis - y_data))\n",
    "\n",
    "W_grad, b_grad = tape.gradient(cost, [W, b])\n",
    "W_grad.numpy(), b_grad.numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter(W,b) Update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.75, 0.43)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# learning_rate: 학습률, W_grad, b_grad값을 얼마나 반영할지를 나타내는 수치\n",
    "learning_rate = 0.01\n",
    "\n",
    "W.assign_sub(learning_rate * W_grad)\n",
    "b.assign_sub(learning_rate * b_grad)\n",
    "\n",
    "W.numpy(), b.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 8.0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# 그래프에 나타내기\n",
    "plt.plot(x_data, hypothesis.numpy(), 'r-')\n",
    "plt.plot(x_data, y_data, 'o')\n",
    "plt.ylim(0, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Repetition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    0|    2.4520|    0.3760| 45.660004\n",
      "   10|    1.1036|    0.0034|  0.206336\n",
      "   20|    1.0128|   -0.0209|  0.001026\n",
      "   30|    1.0065|   -0.0218|  0.000093\n",
      "   40|    1.0059|   -0.0212|  0.000083\n",
      "   50|    1.0057|   -0.0205|  0.000077\n",
      "   60|    1.0055|   -0.0198|  0.000072\n",
      "   70|    1.0053|   -0.0192|  0.000067\n",
      "   80|    1.0051|   -0.0185|  0.000063\n",
      "   90|    1.0050|   -0.0179|  0.000059\n"
     ]
    }
   ],
   "source": [
    "W = tf.Variable(2.9)\n",
    "b = tf.Variable(0.5)\n",
    "\n",
    "for i in range(100):\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",
    "    # 10회 마다의 결과값 출력\n",
    "    if i % 10 == 0:\n",
    "      print(\"{:5}|{:10.4f}|{:10.4f}|{:10.6f}\".format(i, W.numpy(), b.numpy(), cost))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "W는 1에 b는 0에 Cost는 0에 수렴함을 확인할 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 8.0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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, 'o')\n",
    "plt.plot(x_data, hypothesis.numpy(), 'r-')\n",
    "plt.ylim(0, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(1.9922441, shape=(), dtype=float32)\n",
      "tf.Tensor(0.98742765, shape=(), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "print(W * 2 + b)\n",
    "print(W * 1 + b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 전체 코드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    0|    2.4520|    0.3760| 45.660004\n",
      "   10|    1.1036|    0.0034|  0.206336\n",
      "   20|    1.0128|   -0.0209|  0.001026\n",
      "   30|    1.0065|   -0.0218|  0.000093\n",
      "   40|    1.0059|   -0.0212|  0.000083\n",
      "   50|    1.0057|   -0.0205|  0.000077\n",
      "   60|    1.0055|   -0.0198|  0.000072\n",
      "   70|    1.0053|   -0.0192|  0.000067\n",
      "   80|    1.0051|   -0.0185|  0.000063\n",
      "   90|    1.0050|   -0.0179|  0.000059\n",
      "\n",
      "tf.Tensor(5.0066934, shape=(), dtype=float32)\n",
      "tf.Tensor(2.4946523, shape=(), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "# Data\n",
    "x_data = [1, 2, 3, 4, 5]\n",
    "y_data = [1, 2, 3, 4, 5]\n",
    "\n",
    "# W, b initialize\n",
    "W = tf.Variable(2.9)\n",
    "b = tf.Variable(0.5)\n",
    "\n",
    "# W, b update\n",
    "for i in range(100):\n",
    "    # Gradient descent\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",
    "    if i % 10 == 0:\n",
    "      print(\"{:5}|{:10.4f}|{:10.4f}|{:10.6f}\".format(i, W.numpy(), b.numpy(), cost))\n",
    "\n",
    "print()\n",
    "\n",
    "# predict\n",
    "print(W * 5 + b)\n",
    "print(W * 2.5 + b)"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}