Lab02/Lab02-YounChanChoi.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab02_simple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = [1, 2, 3, 4, 5]\n",
    "y_data = [1, 2, 3, 4, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 8.0)"
      ]
     },
     "execution_count": 4,
     "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": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(x_data, y_data, 'x')\n",
    "plt.ylim(0, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=float32, numpy=2.5>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v =[1., 2., 3., 4.]\n",
    "tf.reduce_mean(v) # 2.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=int32, numpy=9>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.square(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = [1, 2, 3, 4, 5]\n",
    "y_data = [1, 2, 3, 4, 5]\n",
    "\n",
    "W = tf.Variable(2.0)\n",
    "b = tf.Variable(0.5)\n",
    "\n",
    "hypothesis = W * x_data + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.0, 0.5)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W.numpy(), b.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.5,  4.5,  6.5,  8.5, 10.5], dtype=float32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hypothesis.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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, hypothesis.numpy(), 'r-')\n",
    "plt.plot(x_data, y_data, 'x')\n",
    "plt.ylim(0, 8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25.0, 7.0)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost = tf.reduce_mean(tf.square(hypothesis - y_data))\n",
    "\n",
    "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 Update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.75, 0.43)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 8.0)"
      ]
     },
     "execution_count": 16,
     "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, hypothesis.numpy(), 'r-')\n",
    "plt.plot(x_data, y_data, 'o')\n",
    "plt.ylim(0, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Repeat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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"
     ]
    },
    {
     "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": [
    "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",
    "    if i % 10 == 0:\n",
    "      print(\"{:5}|{:10.4f}|{:10.4f}|{:10.6f}\".format(i, W.numpy(), b.numpy(), cost))\n",
    "\n",
    "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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(5.0066934, shape=(), dtype=float32)\n",
      "tf.Tensor(2.4946523, shape=(), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "print(W * 5 + b)\n",
    "print(W * 2.5 + b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}