W10_04_logistic_regression_TF20.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression with Tensorflow 2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3.0\n"
     ]
    }
   ],
   "source": [
    "#import tensorflow as tf\n",
    "import tensorflow as tf\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "연속형 데이터를 linear regression에 적용했다면 범주형 데이터는 logitic regression에 적용 가능"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x = [[1,1],[1,3],[2,2],[2,4],[3,1],[3,3],[4,2],[4,4],[5,7],[5,9],[6,6],[6,8],[7,7],[7,9],[8,6],[8,8]]\n",
    "train_y = [[0],[0],[0],[0],[0],[0],[0],[0],[1],[1],[1],[1],[1],[1],[1],[1]]\n",
    "\n",
    "train_x = np.array(train_x)\n",
    "train_y = np.array(train_y)\n",
    "\n",
    "# 데이터의 형태를 float32 형태로 넣기 위함\n",
    "train_x = train_x.astype('float32')\n",
    "train_y = train_y.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 2)\n",
      "(16, 1)\n"
     ]
    }
   ],
   "source": [
    "print(train_x.shape)\n",
    "print(train_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": [
    "# 그래프 위에 나타내보기\n",
    "plt.scatter(train_x[:,0:1], train_x[:,1:2], c=train_y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LogisticWithTF():\n",
    "    def __init__(self):\n",
    "        self.epochs = 1000\n",
    "        self.learning_rate = 0.17\n",
    "        #TensoFlow v2에서는 tf.placeholder API가 삭제되고 지정된 함수에 값을 직접 넘겨주게 된다.\n",
    "        self.w = tf.Variable(tf.random.normal(shape=[2,1], dtype=tf.float32))\n",
    "        self.b = tf.Variable(tf.random.normal(shape=[1], dtype=tf.float32))\n",
    "        \n",
    "        \n",
    "    #train_on_batch( ) : batch단위로 학습할 메인 모델을 정의\n",
    "    #텐서플로는 자동 미분(주어진 입력 변수에 대한 연산의 그래디언트(gradient)를 계산하는 것)을 위한 tf.GradientTape API를 제공\n",
    "    def train_on_batch(self, x, y):\n",
    "        with tf.GradientTape() as tape:\n",
    "            # sigmoid function 은 우리의 가설\n",
    "            logit = tf.matmul(x, self.w) + self.b\n",
    "            hypothesis = tf.sigmoid(logit)\n",
    "            eps = 1e-10\n",
    "            #오차함수\n",
    "            loss = -tf.reduce_mean(y*tf.math.log(hypothesis+eps) + (1-y)*tf.math.log(1-hypothesis+eps))\n",
    "            \n",
    "        #gradient => cost를 minimize시키는 W,b를 찾는 대표적인 알고리즘 중 하나\n",
    "        #tape.gradient를 호출해서 W와 b의 경사도(미분값)을 각각 기록하여 튜플로 반환 \n",
    "        loss_dw, loss_db = tape.gradient(loss, [self.w, self.b])\n",
    "        \n",
    "        #assign_sub(-=연산)을 가져와 W와 b를 각각 업데이트\n",
    "        self.w.assign_sub(self.learning_rate * loss_dw)\n",
    "        self.b.assign_sub(self.learning_rate * loss_db)\n",
    "\n",
    "        return loss\n",
    "\n",
    "    #fitModel( ) : train_x, train_y를 가지고 model을 학습하는 메서드\n",
    "    def fitModel(self, x, y):\n",
    "        #tf.data.Dataset.from_tensor_slices => tf.data.Dataset 를 생성하는 함수로 입력된 텐서로부터 slices를 생성\n",
    "        dataset = tf.data.Dataset.from_tensor_slices((x, y))\n",
    "        dataset = dataset.shuffle(buffer_size=16).batch(8)\n",
    "\n",
    "        loss_mem = []\n",
    "\n",
    "        for e in range(self.epochs):\n",
    "            for step, (x,y) in enumerate(dataset):\n",
    "                loss = self.train_on_batch(x,y)\n",
    "            loss_mem.append(loss)\n",
    "        return loss_mem\n",
    "\n",
    "    #predictModel( ) : train_x를 가지고 학습된 model의 결과에 적용해서 label을 도출하는 메서드\n",
    "    def predictModel(self, x):\n",
    "        logit = tf.matmul(x, self.w) + self.b\n",
    "        hypothesis = tf.sigmoid(logit)\n",
    "        return hypothesis\n",
    "\n",
    "    #evalModel( ) : test_x, test_y를 가지고 학습된 model에 적용해서 res_y를 구하고 이를 test_y와 비교한 정확도 도출\n",
    "    def evalModel(self, x, y):\n",
    "        logit = tf.matmul(x, self.w) + self.b\n",
    "        hypothesis = tf.sigmoid(logit)\n",
    "\n",
    "        res_y = np.round(hypothesis, 0)\n",
    "        accuracy = np.sum(res_y == y) / len(y)\n",
    "        return accuracy, res_y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticWithTF()\n",
    "loss_mem = model.fitModel(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": [
    "epochs_x = list(range(len(loss_mem)))\n",
    "plt.plot(epochs_x, loss_mem)\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_y = model.predictModel(train_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAD4CAYAAADFAawfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAY6ElEQVR4nO3deZRcZZ3/8fe3a+ktnXQITViCBFRAT45AKCGsQlg0IYPA6BmccUZRf9ERWRR/Cs644KyuP3RYJIIiP1yGYVMcQHDAETxslbBFQlgmEJIQ0iFrd7pr/c4fXQhJqrsrya2+T1V/Xuf0oXPv7Xs/55J8+vZTT/Vj7o6IiISrJe4AIiIyMhW1iEjgVNQiIoFTUYuIBE5FLSISuGQ9Trr77rv79OnT63FqEZGmtHDhwrXu3lNtX12Kevr06WSz2XqcWkSkKZnZS8Pt09CHiEjgVNQiIoFTUYuIBE5FLSISOBW1yC7wch+efxQvLos7StPx8qbKvR32NbZxo6ZZH2Z2AfB/AAN+6O6X1TOUSCMo9/0Q+r4PlgIv4skDsclXY4kpcUdraO6O918BfVdX7m0BT83AJl+FtXTHHS8Woz5Rm9kMhkr6COAQYJ6Zva3ewURC5rnfQd/lQA68DxiE4tP4hs/EnKwJ5H4DfT/kjXubg8KT+IbPxZ0sNrUMfbwDeNjdt7h7Efhv4Kz6xhIJm/f/CBjYZmsRCovx0so4IjUN77+W7e9tAfKP4KW1cUSKXS1FvRg4zsymmFkHMBfYd9uDzGy+mWXNLNvb2xt1TpGwlF6rvt1SUN4wplGaTnld9e2WBN84tlkCMWpRu/sS4BvA3cBdwONAqcpxC9w94+6Znp6q74IUaR5tJwKpKjsckm8f6zTNJf0eqr98loLEfmOdJgg1zfpw92vd/XB3Px5YDzxb31giYbPOj0HLZCD9+hagHbr+HrP0CF8po7EJn4KWSWx9b9tg4tcwq8tvvQherbM+9nD3NWb2FobGp2fVN5ZI2KxlN9j9drz/esjdD4mpWOc5WDoTd7SGZ4k9YMqv8S3XQe5BSOyNdX4cSx8Sd7TY1Prt6WYzmwIUgHPdfUP9Iok0BmuZjHVdAF0XxB2l6VhiCtZ1EXTFnSQMNRW1ux9X7yAiIlKd3pkoIhI4FbWISOBU1CIigVNRi4gETkUtIhI4FbWISOBU1CIigVNRi4gETkUtIhI4FbWISOBU1CIigVNRi4gETkUtIhI4FbWISOBU1CIigRuf69qIjENeeoXywK+gvB5rfQ+WnoWZxR2rKXhpJT7wKyj3YW0nQCoT6b2tdSmuzwKfABx4CjjH3QcjSyEidVUe/C/KGy4AykAeH/gZlj6Klu4rMUvEHa+hlQfugI1fZOjeFvCBG6B1Nkz6DmbRDFqMehYz2wc4H8i4+wwgAZwdydVFpO7cc5Q3fg4YBPKVjVvw/IP44J1xRmt4Xu6DjRcDOYZWKgR8AHL3DX1EpNa6TwLtNrQEcAewKrIEIlJXns8ytJL3tju24AO3jXWc5pJ/GKqtjO5b8MHbI7vMqEXt7iuBbwPLgVeAje5+97bHmdl8M8uaWba3tzeygCKyq0YY2rDU2MVoRiMOG0X3EmAtQx+TgfcD+wN7A51m9uFtj3P3Be6ecfdMT09PZAFFZNdY+nCqlrW1Y+0fGPM8TSV91DA72rH2syK7TC1DHycDy9y9190LwC3A0ZElEJG6MkuRmHw1WCdDI5dpoA1rOxNrnR1zusZm1op1Xw7WztC9bR366Dh7hBLfcbU8my8HZplZBzAAnARkI0sgInVn6QyJnj/guXugvAlrPRpLvj3uWE3BWo+Gngdg8B7wPmg9DkvuH+k1Ri1qd3/YzG4CFgFF4DFgQaQpRKTurGUC1n5m3DGakrV0QUd0Qx3bqmm0292/Cny1bilERGRYegu5iEjgVNQiIoFTUYuIBE5FLSISOBW1iEjgVNQiIoFTUYuIBE5FLSISOBW1iEjgVNQiIoFTUYuIBE5FLSISOBW1iEjgVNQiIoFTUYuIBC661RdFxpli8WXWb/oOg7k/kGjpYdLEc+lsPy3uWE2hXHyJ4ubvUS48Ai1TSU34WxJtJ8cdKzajFrWZHQT8+5s2HQB8xd0vq1cokdAVi6tY9eoplL0PKFEqrWDtuvModC2je+Jn4o7X0MrF5eTW/hl4P1CG0kry688nOfFiUp1/E3e8WIw69OHuS939UHc/FDgc2ALcWu9gIiHbuPlyyt4PlP60zX2AjZu/S7m8Jb5gTaDY9/03SvpPBihu/ibuubhixWpHx6hPAl5w95fqEUakUQzm/sDQEqLbSlIoPjfWcZpKOf8IW5f0G7z48tiGCcSOFvXZwM+r7TCz+WaWNbNsb2/vricTCVgyOa36Ds+TSEwd2zBNxlr2qr7Di1hiytiGCUTNRW1maeB04D+q7Xf3Be6ecfdMT09PVPlEgjSp6zOYtW+zNU1b27EkE3vGkqlZJLs+DWx7b1tpaZuNtUyOI1LsduSJeg6wyN1frVcYkUbR1noUu3V/kxabhFkHkKa9bTY9u/0g7mgNL9H6HpITvwzWBZV7m2g7mfSkb8cdLTY7Mj3vQwwz7CEyHnV1foAJHWdQLC6nJTGZxDh92quHVOeHSHb8OV5aibXshrVMijtSrGoqajPrBE4BPlnfOCKNxSxJKnVA3DGaklkaS+4fd4wg1FTU7t4PjM9RfBGRmOkt5CIigVNRi4gETkUtIhI4FbWISOBU1CIigVNRi4gETkUtIhI4FbWISOBU1CIigVNRi4gETkUtIhI4FbWISOBU1CIigVNRi4gETkUtMo4Uy5vIFV/B3eOO0nS8vIFyaXVd7m2tCwd0A9cAMwAHPubuD0aeRkTqoljawAuvXcTGgfvBWki17MYBU77BpPbj4o7W8MqltQxsuIBS7mHAsMQetHd/l2TrkZFdo9Yn6u8Bd7n7wcAhwJLIEohI3T2z5hw2DtyPU8A9R770Cs/2fpKBwvNxR2to7s6W1/6SUu5BIA/k8NLLbFn3N5SLL0V2nVGL2swmAccD11aC5d19Q2QJRKSutuSXMlBYilPYanvZ86zedF08oZpEufAE5dJyoLj1Di+S7///kV2nlifq/YFe4Mdm9piZXVNZQ3ErZjbfzLJmlu3t7Y0soIjsmnxpFUaiyp4Sg4VlY56nmZRLK6leowXKxejubS1FnQRmAle5+2FAP3Dxtge5+wJ3z7h7pqenJ7KAIrJrOlLvpOz57bYbrXS1RTeOOh4lUjPAC9vvsHYSrbMiu04tRb0CWOHuD1f+fBNDxS0iDSCdnErPhA/SYu1v2pok0TKBqV0fji1XM2hJ7key/TRg63trNol0x19Edp1RZ324+2oze9nMDnL3pcBJwNORJRCRupu+29fpSL+D1Zt+TMk30912ItO6P0sqsVvc0Rpee/d3yKcOodB/He79JNveS2vXhVjLxMiuYbXM+TOzQxmanpcG/gc4x93XD3d8JpPxbDYbVUYRkaZnZgvdPVNtX03zqN39caDqCUREpL70zkQRkcCpqEVEAqeiFhEJnIpaRCRwKmoRkcCpqEVEAqeiFhEJnIpaRCRwKmoRkcCpqEVEAqeiFhEJnIpaRCRwKmoRkcCpqEVEAqeiFhEJnIpaRCRwNS0cYGYvApuBElAcbhUCERGJXk1FXXGiu6+tWxIREalKQx8iIoGrtagduNvMFprZ/GoHmNl8M8uaWba3tze6hCIi41ytRX2su88E5gDnmtnx2x7g7gvcPePumZ6enkhDioiMZzUVtbuvrPx3DXArcEQ9Q4mIyBtGLWoz6zSzrtc/B04FFtc7mIiIDKll1sdU4FYze/34n7n7XXVNJSIifzJqUbv7/wCHjEEWERGpQtPzREQCp6IWEQmcilpEJHAqahGRwKmoRUQCp6IWEQmcilpEJHAqahGRwKmoRUQCp6IWEQmcilpEJHAqahGRwKmoRUQCp6IWEQmcilpEJHC1LBwg21i6aBm3LbiXtas2cMQpM5j7kePpnNged6ym8MfFK7jl5kdZv76fo45+O/PmHUZ7RzruWCKxMnev7UCzBJAFVrr7vJGOzWQyns1mI4gXnv+68SH+7fM/Iz9YwN1Jt6Xo7pnI5fd+ia7uzrjjNbRf/+oxrrzit+TzBdyhtTVJzx4Tuerqc+joaI07nkhdmdlCd89U27cjQx8XAEuiidSY8rkCV3zxF+QG8rz+DS4/WGD9mo3cdvW9MadrbAMDea684rfkckMlDZDLFVnz6iZu/+WieMOJxKymojazacBpwDX1jRO2ZU+vZGjpyK0VckUevPOJsQ/URJ5d+grJ5PZ/HfP5Ig888GwMiUTCUesT9WXAF4DycAeY2Xwzy5pZtre3N4pswema1EGxUKq6b9KUCWOcprlM6GqnVKr+16u7u2OM04iEZdSiNrN5wBp3XzjSce6+wN0z7p7p6emJLGBI9j5gD95y0F60JLa+ba0dac745OyYUjWHAw7oYerUibS0bP0jS2tbijPPqjpsJzJu1PJEfQxwupm9CPwCmG1mN9Q1VcC+dsOnecuBe9LWkaajq410a4q/OP+9HHnqu+KO1tDMjH/5xtnsM2032tpSdHSmSaeTfPSc45h5+P5xxxOJVc2zPgDM7ATg8+N51geAu7PsjytZ37uJAw/bT7M9IuTuPPfcq2zaNMDBB+/FhAltcUcSGRMjzfrQPOqdYGYcMGNa3DGakplx4IF7xh1DJCg7VNTu/jvgd3VJIiIiVekt5CIigVNRi4gETkUtIhI4FbWISOBU1CIigVNRi4gETkUtIhI4FbWISOBU1CIigVNRi4gETkUtIhI4FbWISOBU1CIigVNRi4gETkUtIhK4WtZMbDOzR8zsCTP7o5ldGnWIF554kQuP+zKnps7m9O6P8IPPX08+V4j6MuPSs4uWcf4Jl/K+iR/lzL0/xbVfuZFioRh3LBHZAbUsHJADZrt7n5mlgAfM7E53fyiKAK++1Mtn3/NVBjYPAjCweZDbr7qb1cvW8LWbPx/FJcatlc+v5v/O+RcG+3MAbNk0wC+vupu1K9fxxWs/FXM6EanVqE/UPqSv8sdU5aP2hRZHccv37qAwuPUTXn6wwKN3Pc4ry9ZEdZlx6cbL7iA/uPVPJrmBAvffluW11RviCSUiO6ymMWozS5jZ48Aa4B53f7jKMfPNLGtm2d7e3poDPLdoWdUfxVOtKVYsXVXzeWR7LzzxEuVSebvt6dYkq55fHUMiEdkZNRW1u5fc/VBgGnCEmc2ocswCd8+4e6anp6fmAAcefgDJ1PYjMIVcgX0P3rvm88j23nbIfrQktv9fnM8V2fttWkBWpFHs0KwPd98A3Ae8L6oAZ10wl1Tb1kWdbk9zxJzD2HP6HlFdZlz64IVzSbelttqWbk9z3BnvZsqe3fGEEpEdVsusjx4z66583g6cAjwTVYA93rI7/++/L2XGsQfTkmiho6ud0//2VC756flRXWLc2udte/KtOy/h4He/lZaE0TmpgzM/fSoX/eDjcUcTkR1g7iO/Lmhm7wJ+AiQYKvYb3f3rI31NJpPxbDYbWUgRkWZnZgvdPVNt36jT89z9SeCwyFOJiEhN9M5EEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKeie4O88//iKP/uZxNq3rG/0LpGbuznOLlrHwnifp29AfdxyRIIy6cICZ7QtcD0wFHFjg7t+rd7BQrV25jkvm/DOrX1xDS7KFYq7IX37pLP7q786KO1rDW/1iL3/3Z99g7ap1JBItFPJFPvK1D/KBC0+LO5pIrGp5oi4CF7n7O4FZwLlm9s76xgrXV878FsufWclgf44tGwfIDxb4xb/exoO3L4w7WkNzd758xjdZ9cJqBvtz9G8aurfXf/1mHrt3cdzxRGI1alG7+yvuvqjy+WZgCbBPvYOFaOXzq1n+9ArKpfJW2we35Lj1+3fElKo5LFv8MmuWv0a5vPUanrktOX555d0xpRIJww6NUZvZdIbWT3y4yr75ZpY1s2xvb29E8cLSt76PRCpRdd/GtZvHOE1z6VvfT0uy+l/HDWs3jXEakbDUXNRmNgG4GbjQ3bf7l+PuC9w94+6Znp6eKDMGY/937Tc0Sr+NVFuKY85899gHaiIHHr4/pWJpu+3p9jTHvF/3Vsa3morazFIMlfRP3f2W+kYKV7o1xXmXf4zWjjTWYgC0tqeZsmc3Z543J+Z0ja2ts41Pfeuvh+7t0K2ltT3NHtOmcNonZscbTiRm5l7lEfHNB5gZ8BNgnbtfWMtJM5mMZ7PZXU8XqKWPPs+t/3YnvSvWceTcwzht/sl0TuyIO1ZTePqh5/jllb9h/asbmTVvJnPOOZH2CW1xxxKpOzNb6O6ZqvtqKOpjgfuBp4DXX0X7krsP++pZsxe1iEjURirqUedRu/sDgEWeSkREaqJ3JoqIBE5FLSISOBW1iEjgVNQiIoFTUYuIBE5FLSISOBW1iEjgVNQiIoFTUYuIBE5FLSISOBW1iEjgVNQiIoFTUYuIBE5FLSISOBW1iEjgRv191GOhVCrxyB2P8cidjzFp9y5O/cgJ7P3WPeOO1RRKxRIP3p5l4T1PMnnqJN770ROZul9zrmkp0qxqWeHlR8A8YI27z6jlpDuywkupWOKSOf/EkoefY7BvkGQqQSKZ4IvXn8dxfz6rpnNIdflcgS+cfCkvPPHS0L1NJ0kkW/jyjRdx5NyZcccTkTcZaYWXWoY+rgPeF2miN7n3Zw+w5KFnGewbBKBYKJEbyPOtj11BfjBfr8uOC7/50b08/9iyN+5tvkhuS55//fD3KRaKMacTkVqNWtTu/ntgXb0C/PaG3zPYn9tuu5mx+A9L63XZceG3N/ye3Jbtv9mVy2Wezb4QQyIR2RmRvZhoZvPNLGtm2d7e3pq/rrU9XX2HQ7otFVG68Sk9zL31spNuG+a+i0hwIitqd1/g7hl3z/T01P5i1WnzT6ats3W77en2NO+Y9fao4o1L8z55atV7O2FyJ289dPrYBxKRnRL79Lwj5s5k7idOIt2WorWjlY6uNjq7O/jH2y8mkUjEHa+hHf+BWcz+q+O2urcTp0zgH351MWZaWF6kUYw66wPAzKYDv67HrI/XrXphNY/fu5gJkzs58rSZtLZv/yQoO+flpSt56vdLmLh7F0fMnUm6VUNKIqEZadbHqPOozeznwAnA7ma2Aviqu18bbUTY+617au50nex70D7se9A+cccQkZ00alG7+4fGIoiIiFQX+xi1iIiMTEUtIhI4FbWISOBU1CIigatpet4On9SsF3hpJ798d2BthHHqqZGyQmPlbaSs0Fh5GykrNFbeXcm6n7tXfbdgXYp6V5hZdri5hKFppKzQWHkbKSs0Vt5GygqNlbdeWTX0ISISOBW1iEjgQizqBXEH2AGNlBUaK28jZYXGyttIWaGx8tYla3Bj1CIisrUQn6hFRORNVNQiIoELpqjN7EdmtsbMFsedZTRmtq+Z3WdmT5vZH83sgrgzDcfM2szsETN7opL10rgz1cLMEmb2mJn9Ou4sIzGzF83sKTN73Mx27Hf7xsDMus3sJjN7xsyWmNlRcWeqxswOqtzT1z82mdmFcecaiZl9tvJvbLGZ/dzM2iI7dyhj1GZ2PNAHXF/r772Oi5ntBezl7ovMrAtYCJzh7k/HHG07NrRCQKe795lZCngAuMDdH4o52ojM7HNABpjo7vPizjMcM3sRyLh7Q7whw8x+Atzv7teYWRrocPcNMccakZklgJXAke6+s2+kqysz24ehf1vvdPcBM7sRuMPdr4vi/ME8Udd7Ed0oufsr7r6o8vlmYAkQ5C989iF9lT+mKh9hfHcehplNA04Drok7SzMxs0nA8cC1AO6eD72kK04CXgi1pN8kCbSbWRLoAFZFdeJgirpRVVa/OQx4OOYow6oMIzwOrAHucfdgs1ZcBnwBKMecoxYO3G1mC81sftxhRrE/0Av8uDKsdI2ZdcYdqgZnAz+PO8RI3H0l8G1gOfAKsNHd747q/CrqXWBmE4CbgQvdfVPceYbj7iV3PxSYBhxhZsEOLZnZPGCNuy+MO0uNjnX3mcAc4NzKEF6oksBM4Cp3PwzoBy6ON9LIKsMzpwP/EXeWkZjZZOD9DH0z3BvoNLMPR3V+FfVOqoz33gz81N1viTtPLSo/5t4HvC/mKCM5Bji9Mvb7C2C2md0Qb6ThVZ6kcPc1wK3AEfEmGtEKYMWbfqK6iaHiDtkcYJG7vxp3kFGcDCxz9153LwC3AEdHdXIV9U6ovEB3LbDE3b8bd56RmFmPmXVXPm8HTgGeiTXUCNz9Enef5u7TGfqR9153j+zJJEpm1ll5MZnKEMKpQLCzltx9NfCymR1U2XQSENwL4Nv4EIEPe1QsB2aZWUelH05i6LWrSART1JVFdB8EDjKzFWb28bgzjeAY4K8Zetp7ffrQ3LhDDWMv4D4zexJ4lKEx6qCnvDWQqcADZvYE8Ajwn+5+V8yZRnMe8NPK34dDgX+ON87wKt/8TmHo6TRolZ9SbgIWAU8x1K2RvZ08mOl5IiJSXTBP1CIiUp2KWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHA/S8HNoCzP1epAgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(train_x[:,0:1], train_x[:,1:2], c=res_y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_x --> predict(예측값) --> train_y(정답)\n",
      "[1. 1.] --> [0.00067678] --> [0.]\n",
      "[1. 3.] --> [0.00802132] --> [0.]\n",
      "[2. 2.] --> [0.00468194] --> [0.]\n",
      "[2. 4.] --> [0.0531795] --> [0.]\n",
      "[3. 1.] --> [0.00272903] --> [0.]\n",
      "[3. 3.] --> [0.03163987] --> [0.]\n",
      "[4. 2.] --> [0.01865274] --> [0.]\n",
      "[4. 4.] --> [0.18497157] --> [0.]\n",
      "[5. 7.] --> [0.9495522] --> [1.]\n",
      "[5. 9.] --> [0.99557024] --> [1.]\n",
      "[6. 6.] --> [0.91631544] --> [1.]\n",
      "[6. 8.] --> [0.9924093] --> [1.]\n",
      "[7. 7.] --> [0.9870224] --> [1.]\n",
      "[7. 9.] --> [0.99890006] --> [1.]\n",
      "[8. 6.] --> [0.97789764] --> [1.]\n",
      "[8. 8.] --> [0.9981107] --> [1.]\n"
     ]
    }
   ],
   "source": [
    "print('train_x --> predict(예측값) --> train_y(정답)')\n",
    "for i in range(len(res_y)):\n",
    "    print(train_x[i],'-->', res_y.numpy()[i],'-->' ,train_y[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "test_x = [[2,3],[3,2],[6,9],[7,8],[8,7]]\n",
    "test_y = [[0],[0],[1],[1],[1]]\n",
    "\n",
    "test_x = np.array(test_x, dtype=np.float32)\n",
    "test_y = np.array(test_y, dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "accuracy, res = model.evalModel(test_x, test_y)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.]\n",
      " [0.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n"
     ]
    }
   ],
   "source": [
    "print(res)"
   ]
  },
  {
   "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": [
    "plt.scatter(test_x[:,0:1], test_x[:,1:2], c=res)\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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}