{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FhGuhbZ6M5tl"
   },
   "source": [
    "##### Copyright 2018 The TensorFlow Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "form",
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:01.130616Z",
     "iopub.status.busy": "2020-09-23T07:19:01.129952Z",
     "iopub.status.idle": "2020-09-23T07:19:01.131921Z",
     "shell.execute_reply": "2020-09-23T07:19:01.132353Z"
    },
    "id": "AwOEIRJC6Une"
   },
   "outputs": [],
   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "cellView": "form",
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:01.136247Z",
     "iopub.status.busy": "2020-09-23T07:19:01.135517Z",
     "iopub.status.idle": "2020-09-23T07:19:01.137920Z",
     "shell.execute_reply": "2020-09-23T07:19:01.137318Z"
    },
    "id": "KyPEtTqk6VdG"
   },
   "outputs": [],
   "source": [
    "#@title MIT License\n",
    "#\n",
    "# Copyright (c) 2017 François Chollet\n",
    "#\n",
    "# Permission is hereby granted, free of charge, to any person obtaining a\n",
    "# copy of this software and associated documentation files (the \"Software\"),\n",
    "# to deal in the Software without restriction, including without limitation\n",
    "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
    "# and/or sell copies of the Software, and to permit persons to whom the\n",
    "# Software is furnished to do so, subject to the following conditions:\n",
    "#\n",
    "# The above copyright notice and this permission notice shall be included in\n",
    "# all copies or substantial portions of the Software.\n",
    "#\n",
    "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
    "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
    "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
    "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
    "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
    "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
    "# DEALINGS IN THE SOFTWARE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EIdT9iu_Z4Rb"
   },
   "source": [
    "# 자동차 연비 예측하기: 회귀"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bBIlTPscrIT9"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/keras/regression\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />TensorFlow.org에서 보기</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/ko/tutorials/keras/regression.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩(Colab)에서 실행하기</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/ko/tutorials/keras/regression.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />깃허브(GitHub) 소스 보기</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/ko/tutorials/keras/regression.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YYwLLNVaaJU9"
   },
   "source": [
    "Note: 이 문서는 텐서플로 커뮤니티에서 번역했습니다. 커뮤니티 번역 활동의 특성상 정확한 번역과 최신 내용을 반영하기 위해 노력함에도\n",
    "불구하고 [공식 영문 문서](https://www.tensorflow.org/?hl=en)의 내용과 일치하지 않을 수 있습니다.\n",
    "이 번역에 개선할 부분이 있다면\n",
    "[tensorflow/docs-l10n](https://github.com/tensorflow/docs-l10n/) 깃헙 저장소로 풀 리퀘스트를 보내주시기 바랍니다.\n",
    "문서 번역이나 리뷰에 참여하려면\n",
    "[docs-ko@tensorflow.org](https://groups.google.com/a/tensorflow.org/forum/#!forum/docs-ko)로\n",
    "메일을 보내주시기 바랍니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AHp3M9ZmrIxj"
   },
   "source": [
    "*회귀*(regression)는 가격이나 확률 같이 연속된 출력 값을 예측하는 것이 목적입니다. 이와는 달리 *분류*(classification)는 여러개의 클래스 중 하나의 클래스를 선택하는 것이 목적입니다(예를 들어, 사진에 사과 또는 오렌지가 포함되어 있을 때 어떤 과일인지 인식하는 것).\n",
    "\n",
    "이 노트북은 [Auto MPG](https://archive.ics.uci.edu/ml/datasets/auto+mpg) 데이터셋을 사용하여 1970년대 후반과 1980년대 초반의 자동차 연비를 예측하는 모델을 만듭니다. 이 기간에 출시된 자동차 정보를 모델에 제공하겠습니다. 이 정보에는 실린더 수, 배기량, 마력(horsepower), 공차 중량 같은 속성이 포함됩니다.\n",
    "\n",
    "이 예제는 `tf.keras` API를 사용합니다. 자세한 내용은 [케라스 가이드](https://www.tensorflow.org/guide/keras)를 참고하세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:01.142347Z",
     "iopub.status.busy": "2020-09-23T07:19:01.141707Z",
     "iopub.status.idle": "2020-09-23T07:19:02.517341Z",
     "shell.execute_reply": "2020-09-23T07:19:02.516720Z"
    },
    "id": "moB4tpEHxKB3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: You are using pip version 20.2.2; however, version 20.2.3 is available.\r\n",
      "You should consider upgrading via the '/tmpfs/src/tf_docs_env/bin/python -m pip install --upgrade pip' command.\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "# 산점도 행렬을 그리기 위해 seaborn 패키지를 설치합니다\n",
    "!pip install -q seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:02.523871Z",
     "iopub.status.busy": "2020-09-23T07:19:02.522704Z",
     "iopub.status.idle": "2020-09-23T07:19:09.250204Z",
     "shell.execute_reply": "2020-09-23T07:19:09.250631Z"
    },
    "id": "1rRo8oNqZ-Rj"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3.0\n"
     ]
    }
   ],
   "source": [
    "import pathlib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F_72b0LCNbjx"
   },
   "source": [
    "## Auto MPG 데이터셋\n",
    "\n",
    "이 데이터셋은 [UCI 머신 러닝 저장소](https://archive.ics.uci.edu/ml/)에서 다운로드할 수 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gFh9ne3FZ-On"
   },
   "source": [
    "### 데이터 구하기\n",
    "먼저 데이터셋을 다운로드합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:09.256811Z",
     "iopub.status.busy": "2020-09-23T07:19:09.256126Z",
     "iopub.status.idle": "2020-09-23T07:19:10.035368Z",
     "shell.execute_reply": "2020-09-23T07:19:10.035976Z"
    },
    "id": "p9kxxgzvzlyz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 8192/30286 [=======>......................] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "16384/30286 [===============>..............] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "32768/30286 [================================] - 0s 4us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'/home/kbuilder/.keras/datasets/auto-mpg.data'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_path = keras.utils.get_file(\"auto-mpg.data\", \"http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\")\n",
    "dataset_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nslsRLh7Zss4"
   },
   "source": [
    "판다스를 사용하여 데이터를 읽습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:10.045122Z",
     "iopub.status.busy": "2020-09-23T07:19:10.041234Z",
     "iopub.status.idle": "2020-09-23T07:19:10.065388Z",
     "shell.execute_reply": "2020-09-23T07:19:10.064873Z"
    },
    "id": "CiX2FI4gZtTt"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Model Year</th>\n",
       "      <th>Origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2790.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2295.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
       "393  27.0          4         140.0        86.0  2790.0          15.6   \n",
       "394  44.0          4          97.0        52.0  2130.0          24.6   \n",
       "395  32.0          4         135.0        84.0  2295.0          11.6   \n",
       "396  28.0          4         120.0        79.0  2625.0          18.6   \n",
       "397  31.0          4         119.0        82.0  2720.0          19.4   \n",
       "\n",
       "     Model Year  Origin  \n",
       "393          82       1  \n",
       "394          82       2  \n",
       "395          82       1  \n",
       "396          82       1  \n",
       "397          82       1  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = ['MPG','Cylinders','Displacement','Horsepower','Weight',\n",
    "                'Acceleration', 'Model Year', 'Origin']\n",
    "raw_dataset = pd.read_csv(dataset_path, names=column_names,\n",
    "                      na_values = \"?\", comment='\\t',\n",
    "                      sep=\" \", skipinitialspace=True)\n",
    "\n",
    "dataset = raw_dataset.copy()\n",
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3MWuJTKEDM-f"
   },
   "source": [
    "### 데이터 정제하기\n",
    "\n",
    "이 데이터셋은 일부 데이터가 누락되어 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:10.072017Z",
     "iopub.status.busy": "2020-09-23T07:19:10.071149Z",
     "iopub.status.idle": "2020-09-23T07:19:10.074636Z",
     "shell.execute_reply": "2020-09-23T07:19:10.074141Z"
    },
    "id": "JEJHhN65a2VV"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MPG             0\n",
       "Cylinders       0\n",
       "Displacement    0\n",
       "Horsepower      6\n",
       "Weight          0\n",
       "Acceleration    0\n",
       "Model Year      0\n",
       "Origin          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9UPN0KBHa_WI"
   },
   "source": [
    "문제를 간단하게 만들기 위해서 누락된 행을 삭제하겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:10.080682Z",
     "iopub.status.busy": "2020-09-23T07:19:10.079608Z",
     "iopub.status.idle": "2020-09-23T07:19:10.084121Z",
     "shell.execute_reply": "2020-09-23T07:19:10.083571Z"
    },
    "id": "4ZUDosChC1UN"
   },
   "outputs": [],
   "source": [
    "dataset = dataset.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8XKitwaH4v8h"
   },
   "source": [
    "`\"Origin\"` 열은 수치형이 아니고 범주형이므로 원-핫 인코딩(one-hot encoding)으로 변환하겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:10.089193Z",
     "iopub.status.busy": "2020-09-23T07:19:10.088203Z",
     "iopub.status.idle": "2020-09-23T07:19:10.090848Z",
     "shell.execute_reply": "2020-09-23T07:19:10.090243Z"
    },
    "id": "gWNTD2QjBWFJ"
   },
   "outputs": [],
   "source": [
    "origin = dataset.pop('Origin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:10.107142Z",
     "iopub.status.busy": "2020-09-23T07:19:10.106348Z",
     "iopub.status.idle": "2020-09-23T07:19:10.109356Z",
     "shell.execute_reply": "2020-09-23T07:19:10.109901Z"
    },
    "id": "ulXz4J7PAUzk"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Model Year</th>\n",
       "      <th>USA</th>\n",
       "      <th>Europe</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2790.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2295.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
       "393  27.0          4         140.0        86.0  2790.0          15.6   \n",
       "394  44.0          4          97.0        52.0  2130.0          24.6   \n",
       "395  32.0          4         135.0        84.0  2295.0          11.6   \n",
       "396  28.0          4         120.0        79.0  2625.0          18.6   \n",
       "397  31.0          4         119.0        82.0  2720.0          19.4   \n",
       "\n",
       "     Model Year  USA  Europe  Japan  \n",
       "393          82  1.0     0.0    0.0  \n",
       "394          82  0.0     1.0    0.0  \n",
       "395          82  1.0     0.0    0.0  \n",
       "396          82  1.0     0.0    0.0  \n",
       "397          82  1.0     0.0    0.0  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['USA'] = (origin == 1)*1.0\n",
    "dataset['Europe'] = (origin == 2)*1.0\n",
    "dataset['Japan'] = (origin == 3)*1.0\n",
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Cuym4yvk76vU"
   },
   "source": [
    "### 데이터셋을 훈련 세트와 테스트 세트로 분할하기\n",
    "\n",
    "이제 데이터를 훈련 세트와 테스트 세트로 분할합니다.\n",
    "\n",
    "테스트 세트는 모델을 최종적으로 평가할 때 사용합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:10.115911Z",
     "iopub.status.busy": "2020-09-23T07:19:10.114188Z",
     "iopub.status.idle": "2020-09-23T07:19:10.117794Z",
     "shell.execute_reply": "2020-09-23T07:19:10.118201Z"
    },
    "id": "qn-IGhUE7_1H"
   },
   "outputs": [],
   "source": [
    "train_dataset = dataset.sample(frac=0.8,random_state=0)\n",
    "test_dataset = dataset.drop(train_dataset.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J4ubs136WLNp"
   },
   "source": [
    "### 데이터 조사하기\n",
    "\n",
    "훈련 세트에서 몇 개의 열을 선택해 산점도 행렬을 만들어 살펴 보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:10.131291Z",
     "iopub.status.busy": "2020-09-23T07:19:10.124708Z",
     "iopub.status.idle": "2020-09-23T07:19:15.381332Z",
     "shell.execute_reply": "2020-09-23T07:19:15.381892Z"
    },
    "id": "oRKO_x8gWKv-"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fb7b9461e48>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train_dataset[[\"MPG\", \"Cylinders\", \"Displacement\", \"Weight\"]], diag_kind=\"kde\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gavKO_6DWRMP"
   },
   "source": [
    "전반적인 통계도 확인해 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:15.392757Z",
     "iopub.status.busy": "2020-09-23T07:19:15.390019Z",
     "iopub.status.idle": "2020-09-23T07:19:15.428235Z",
     "shell.execute_reply": "2020-09-23T07:19:15.427663Z"
    },
    "id": "yi2FzC3T21jR"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cylinders</th>\n",
       "      <td>314.0</td>\n",
       "      <td>5.477707</td>\n",
       "      <td>1.699788</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Displacement</th>\n",
       "      <td>314.0</td>\n",
       "      <td>195.318471</td>\n",
       "      <td>104.331589</td>\n",
       "      <td>68.0</td>\n",
       "      <td>105.50</td>\n",
       "      <td>151.0</td>\n",
       "      <td>265.75</td>\n",
       "      <td>455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horsepower</th>\n",
       "      <td>314.0</td>\n",
       "      <td>104.869427</td>\n",
       "      <td>38.096214</td>\n",
       "      <td>46.0</td>\n",
       "      <td>76.25</td>\n",
       "      <td>94.5</td>\n",
       "      <td>128.00</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>314.0</td>\n",
       "      <td>2990.251592</td>\n",
       "      <td>843.898596</td>\n",
       "      <td>1649.0</td>\n",
       "      <td>2256.50</td>\n",
       "      <td>2822.5</td>\n",
       "      <td>3608.00</td>\n",
       "      <td>5140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acceleration</th>\n",
       "      <td>314.0</td>\n",
       "      <td>15.559236</td>\n",
       "      <td>2.789230</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.80</td>\n",
       "      <td>15.5</td>\n",
       "      <td>17.20</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model Year</th>\n",
       "      <td>314.0</td>\n",
       "      <td>75.898089</td>\n",
       "      <td>3.675642</td>\n",
       "      <td>70.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>79.00</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.624204</td>\n",
       "      <td>0.485101</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.178344</td>\n",
       "      <td>0.383413</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.197452</td>\n",
       "      <td>0.398712</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count         mean         std     min      25%     50%  \\\n",
       "Cylinders     314.0     5.477707    1.699788     3.0     4.00     4.0   \n",
       "Displacement  314.0   195.318471  104.331589    68.0   105.50   151.0   \n",
       "Horsepower    314.0   104.869427   38.096214    46.0    76.25    94.5   \n",
       "Weight        314.0  2990.251592  843.898596  1649.0  2256.50  2822.5   \n",
       "Acceleration  314.0    15.559236    2.789230     8.0    13.80    15.5   \n",
       "Model Year    314.0    75.898089    3.675642    70.0    73.00    76.0   \n",
       "USA           314.0     0.624204    0.485101     0.0     0.00     1.0   \n",
       "Europe        314.0     0.178344    0.383413     0.0     0.00     0.0   \n",
       "Japan         314.0     0.197452    0.398712     0.0     0.00     0.0   \n",
       "\n",
       "                  75%     max  \n",
       "Cylinders        8.00     8.0  \n",
       "Displacement   265.75   455.0  \n",
       "Horsepower     128.00   225.0  \n",
       "Weight        3608.00  5140.0  \n",
       "Acceleration    17.20    24.8  \n",
       "Model Year      79.00    82.0  \n",
       "USA              1.00     1.0  \n",
       "Europe           0.00     1.0  \n",
       "Japan            0.00     1.0  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_stats = train_dataset.describe()\n",
    "train_stats.pop(\"MPG\")\n",
    "train_stats = train_stats.transpose()\n",
    "train_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Db7Auq1yXUvh"
   },
   "source": [
    "### 특성과 레이블 분리하기\n",
    "\n",
    "특성에서 타깃 값 또는 \"레이블\"을 분리합니다. 이 레이블을 예측하기 위해 모델을 훈련시킬 것입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:15.434053Z",
     "iopub.status.busy": "2020-09-23T07:19:15.433134Z",
     "iopub.status.idle": "2020-09-23T07:19:15.435719Z",
     "shell.execute_reply": "2020-09-23T07:19:15.435122Z"
    },
    "id": "t2sluJdCW7jN"
   },
   "outputs": [],
   "source": [
    "train_labels = train_dataset.pop('MPG')\n",
    "test_labels = test_dataset.pop('MPG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mRklxK5s388r"
   },
   "source": [
    "### 데이터 정규화\n",
    "\n",
    "위 `train_stats` 통계를 다시 살펴보고 각 특성의 범위가 얼마나 다른지 확인해 보죠."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-ywmerQ6dSox"
   },
   "source": [
    "특성의 스케일과 범위가 다르면 정규화(normalization)하는 것이 권장됩니다. 특성을 정규화하지 않아도 모델이 *수렴할 수 있지만*, 훈련시키기 어렵고 입력 단위에 의존적인 모델이 만들어집니다.\n",
    "\n",
    "노트: 의도적으로 훈련 세트만 사용하여 통계치를 생성했습니다. 이 통계는 테스트 세트를 정규화할 때에도 사용됩니다. 이는 테스트 세트를 모델이 훈련에 사용했던 것과 동일한 분포로 투영하기 위해서입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:15.444534Z",
     "iopub.status.busy": "2020-09-23T07:19:15.443609Z",
     "iopub.status.idle": "2020-09-23T07:19:15.446195Z",
     "shell.execute_reply": "2020-09-23T07:19:15.445598Z"
    },
    "id": "JlC5ooJrgjQF"
   },
   "outputs": [],
   "source": [
    "def norm(x):\n",
    "  return (x - train_stats['mean']) / train_stats['std']\n",
    "normed_train_data = norm(train_dataset)\n",
    "normed_test_data = norm(test_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BuiClDk45eS4"
   },
   "source": [
    "정규화된 데이터를 사용하여 모델을 훈련합니다.\n",
    "\n",
    "주의: 여기에서 입력 데이터를 정규화하기 위해 사용한 통계치(평균과 표준편차)는 원-핫 인코딩과 마찬가지로 모델에 주입되는 모든 데이터에 적용되어야 합니다. 여기에는 테스트 세트는 물론 모델이 실전에 투입되어 얻은 라이브 데이터도 포함됩니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SmjdzxKzEu1-"
   },
   "source": [
    "## 모델"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6SWtkIjhrZwa"
   },
   "source": [
    "### 모델 만들기\n",
    "\n",
    "모델을 구성해 보죠. 여기에서는 두 개의 완전 연결(densely connected) 은닉층으로 `Sequential` 모델을 만들겠습니다. 출력 층은 하나의 연속적인 값을 반환합니다. 나중에 두 번째 모델을 만들기 쉽도록 `build_model` 함수로 모델 구성 단계를 감싸겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:15.452381Z",
     "iopub.status.busy": "2020-09-23T07:19:15.451397Z",
     "iopub.status.idle": "2020-09-23T07:19:15.454175Z",
     "shell.execute_reply": "2020-09-23T07:19:15.453432Z"
    },
    "id": "c26juK7ZG8j-"
   },
   "outputs": [],
   "source": [
    "def build_model():\n",
    "  model = keras.Sequential([\n",
    "    layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(1)\n",
    "  ])\n",
    "\n",
    "  optimizer = tf.keras.optimizers.RMSprop(0.001)\n",
    "\n",
    "  model.compile(loss='mse',\n",
    "                optimizer=optimizer,\n",
    "                metrics=['mae', 'mse'])\n",
    "  return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:15.461584Z",
     "iopub.status.busy": "2020-09-23T07:19:15.460632Z",
     "iopub.status.idle": "2020-09-23T07:19:17.208270Z",
     "shell.execute_reply": "2020-09-23T07:19:17.207537Z"
    },
    "id": "cGbPb-PHGbhs"
   },
   "outputs": [],
   "source": [
    "model = build_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Sj49Og4YGULr"
   },
   "source": [
    "### 모델 확인\n",
    "\n",
    "`.summary` 메서드를 사용해 모델에 대한 간단한 정보를 출력합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:17.212881Z",
     "iopub.status.busy": "2020-09-23T07:19:17.212245Z",
     "iopub.status.idle": "2020-09-23T07:19:17.216640Z",
     "shell.execute_reply": "2020-09-23T07:19:17.217055Z"
    },
    "id": "ReAD0n6MsFK-"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 64)                640       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 4,865\n",
      "Trainable params: 4,865\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Vt6W50qGsJAL"
   },
   "source": [
    "모델을 한번 실행해 보죠. 훈련 세트에서 `10` 샘플을 하나의 배치로 만들어 `model.predict` 메서드를 호출해 보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:17.222701Z",
     "iopub.status.busy": "2020-09-23T07:19:17.222021Z",
     "iopub.status.idle": "2020-09-23T07:19:17.624954Z",
     "shell.execute_reply": "2020-09-23T07:19:17.624414Z"
    },
    "id": "-d-gBaVtGTSC"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.6247854 ],\n",
       "       [0.33749408],\n",
       "       [0.20907241],\n",
       "       [0.35342616],\n",
       "       [0.91920185],\n",
       "       [0.19368024],\n",
       "       [1.0528278 ],\n",
       "       [0.8886562 ],\n",
       "       [0.40983236],\n",
       "       [1.1161143 ]], dtype=float32)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_batch = normed_train_data[:10]\n",
    "example_result = model.predict(example_batch)\n",
    "example_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QlM8KrSOsaYo"
   },
   "source": [
    "제대로 작동하는 것 같네요. 결괏값의 크기와 타입이 기대했던 대로입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0-qWCsh6DlyH"
   },
   "source": [
    "### 모델 훈련\n",
    "\n",
    "이 모델을 1,000번의 에포크(epoch) 동안 훈련합니다. 훈련 정확도와 검증 정확도는 `history` 객체에 기록됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:17.632949Z",
     "iopub.status.busy": "2020-09-23T07:19:17.632278Z",
     "iopub.status.idle": "2020-09-23T07:19:52.657587Z",
     "shell.execute_reply": "2020-09-23T07:19:52.656933Z"
    },
    "id": "sD7qHCmNIOY0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
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     "output_type": "stream",
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      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "....\n",
      ".."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "..\n",
      "...."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "....."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
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    {
     "name": "stdout",
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     "text": [
      ".\n",
      "....."
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    {
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     "text": [
      "......"
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    {
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     "text": [
      ".....\n",
      "."
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    {
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     "text": [
      "......."
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    {
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      "......"
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      "......"
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      "......"
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      "......"
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    {
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      "......"
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      "......"
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    {
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      "......."
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    {
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      "......"
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      "......"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".\n",
      "....."
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    {
     "name": "stdout",
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     "text": [
      "......"
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    {
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      "......"
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    {
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      "......"
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      "......"
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      "......"
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      "......"
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     "text": [
      ".....\n",
      "."
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    {
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     "text": [
      "......"
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      "......"
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      "......"
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      "......"
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    {
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     "text": [
      "......"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
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    {
     "name": "stdout",
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      "......"
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    {
     "name": "stdout",
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     "text": [
      "......"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...\n",
      "..."
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    },
    {
     "name": "stdout",
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     "text": [
      "......"
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    },
    {
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     "text": [
      "......"
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    {
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      "......"
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    {
     "name": "stdout",
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     "text": [
      "......"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
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     "text": [
      "......"
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    },
    {
     "name": "stdout",
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     "text": [
      "......"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......."
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "......"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
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    },
    {
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      "......"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...."
     ]
    }
   ],
   "source": [
    "# 에포크가 끝날 때마다 점(.)을 출력해 훈련 진행 과정을 표시합니다\n",
    "class PrintDot(keras.callbacks.Callback):\n",
    "  def on_epoch_end(self, epoch, logs):\n",
    "    if epoch % 100 == 0: print('')\n",
    "    print('.', end='')\n",
    "\n",
    "EPOCHS = 1000\n",
    "\n",
    "history = model.fit(\n",
    "  normed_train_data, train_labels,\n",
    "  epochs=EPOCHS, validation_split = 0.2, verbose=0,\n",
    "  callbacks=[PrintDot()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tQm3pc0FYPQB"
   },
   "source": [
    "`history` 객체에 저장된 통계치를 사용해 모델의 훈련 과정을 시각화해 보죠."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:52.672694Z",
     "iopub.status.busy": "2020-09-23T07:19:52.671996Z",
     "iopub.status.idle": "2020-09-23T07:19:52.675162Z",
     "shell.execute_reply": "2020-09-23T07:19:52.674647Z"
    },
    "id": "4Xj91b-dymEy"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>mae</th>\n",
       "      <th>mse</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_mae</th>\n",
       "      <th>val_mse</th>\n",
       "      <th>epoch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2.654293</td>\n",
       "      <td>1.032276</td>\n",
       "      <td>2.654293</td>\n",
       "      <td>9.649401</td>\n",
       "      <td>2.391645</td>\n",
       "      <td>9.649401</td>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2.570626</td>\n",
       "      <td>0.990599</td>\n",
       "      <td>2.570626</td>\n",
       "      <td>10.332931</td>\n",
       "      <td>2.524845</td>\n",
       "      <td>10.332931</td>\n",
       "      <td>996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2.769074</td>\n",
       "      <td>1.061503</td>\n",
       "      <td>2.769074</td>\n",
       "      <td>10.148792</td>\n",
       "      <td>2.465447</td>\n",
       "      <td>10.148792</td>\n",
       "      <td>997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2.732536</td>\n",
       "      <td>1.063235</td>\n",
       "      <td>2.732536</td>\n",
       "      <td>9.852981</td>\n",
       "      <td>2.452246</td>\n",
       "      <td>9.852981</td>\n",
       "      <td>998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2.634594</td>\n",
       "      <td>1.059140</td>\n",
       "      <td>2.634594</td>\n",
       "      <td>9.573156</td>\n",
       "      <td>2.306720</td>\n",
       "      <td>9.573156</td>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         loss       mae       mse   val_loss   val_mae    val_mse  epoch\n",
       "995  2.654293  1.032276  2.654293   9.649401  2.391645   9.649401    995\n",
       "996  2.570626  0.990599  2.570626  10.332931  2.524845  10.332931    996\n",
       "997  2.769074  1.061503  2.769074  10.148792  2.465447  10.148792    997\n",
       "998  2.732536  1.063235  2.732536   9.852981  2.452246   9.852981    998\n",
       "999  2.634594  1.059140  2.634594   9.573156  2.306720   9.573156    999"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist = pd.DataFrame(history.history)\n",
    "hist['epoch'] = history.epoch\n",
    "hist.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:52.685441Z",
     "iopub.status.busy": "2020-09-23T07:19:52.684324Z",
     "iopub.status.idle": "2020-09-23T07:19:53.259019Z",
     "shell.execute_reply": "2020-09-23T07:19:53.258249Z"
    },
    "id": "B6XriGbVPh2t"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_history(history):\n",
    "  hist = pd.DataFrame(history.history)\n",
    "  hist['epoch'] = history.epoch\n",
    "\n",
    "  plt.figure(figsize=(8,12))\n",
    "\n",
    "  plt.subplot(2,1,1)\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Mean Abs Error [MPG]')\n",
    "  plt.plot(hist['epoch'], hist['mae'],\n",
    "           label='Train Error')\n",
    "  plt.plot(hist['epoch'], hist['val_mae'],\n",
    "           label = 'Val Error')\n",
    "  plt.ylim([0,5])\n",
    "  plt.legend()\n",
    "\n",
    "  plt.subplot(2,1,2)\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Mean Square Error [$MPG^2$]')\n",
    "  plt.plot(hist['epoch'], hist['mse'],\n",
    "           label='Train Error')\n",
    "  plt.plot(hist['epoch'], hist['val_mse'],\n",
    "           label = 'Val Error')\n",
    "  plt.ylim([0,20])\n",
    "  plt.legend()\n",
    "  plt.show()\n",
    "\n",
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AqsuANc11FYv"
   },
   "source": [
    "이 그래프를 보면 수 백번 에포크를 진행한 이후에는 모델이 거의 향상되지 않는 것 같습니다. `model.fit` 메서드를 수정하여 검증 점수가 향상되지 않으면 자동으로 훈련을 멈추도록 만들어 보죠. 에포크마다 훈련 상태를 점검하기 위해 *EarlyStopping 콜백(callback)*을 사용하겠습니다. 지정된 에포크 횟수 동안 성능 향상이 없으면 자동으로 훈련이 멈춥니다.\n",
    "\n",
    "이 콜백에 대해 더 자세한 내용은 [여기](https://www.tensorflow.org/versions/master/api_docs/python/tf/keras/callbacks/EarlyStopping)를 참고하세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:53.269476Z",
     "iopub.status.busy": "2020-09-23T07:19:53.268776Z",
     "iopub.status.idle": "2020-09-23T07:19:56.482303Z",
     "shell.execute_reply": "2020-09-23T07:19:56.482852Z"
    },
    "id": "fdMZuhUgzMZ4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "....."
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = build_model()\n",
    "\n",
    "# patience 매개변수는 성능 향상을 체크할 에포크 횟수입니다\n",
    "early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)\n",
    "\n",
    "history = model.fit(normed_train_data, train_labels, epochs=EPOCHS,\n",
    "                    validation_split = 0.2, verbose=0, callbacks=[early_stop, PrintDot()])\n",
    "\n",
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3St8-DmrX8P4"
   },
   "source": [
    "이 그래프를 보면 검증 세트의 평균 오차가 약 +/- 2 MPG입니다. 좋은 결과인가요? 이에 대한 평가는 여러분에게 맡기겠습니다.\n",
    "\n",
    "모델을 훈련할 때 사용하지 않았던 **테스트 세트**에서 모델의 성능을 확인해 보죠. 이를 통해 모델이 실전에 투입되었을 때 모델의 성능을 짐작할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:56.488194Z",
     "iopub.status.busy": "2020-09-23T07:19:56.487493Z",
     "iopub.status.idle": "2020-09-23T07:19:56.546669Z",
     "shell.execute_reply": "2020-09-23T07:19:56.546113Z"
    },
    "id": "jl_yNr5n1kms"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 - 0s - loss: 6.1271 - mae: 1.9506 - mse: 6.1271\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "테스트 세트의 평균 절대 오차:  1.95 MPG\n"
     ]
    }
   ],
   "source": [
    "loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=2)\n",
    "\n",
    "print(\"테스트 세트의 평균 절대 오차: {:5.2f} MPG\".format(mae))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ft603OzXuEZC"
   },
   "source": [
    "## 예측\n",
    "\n",
    "마지막으로 테스트 세트에 있는 샘플을 사용해 MPG 값을 예측해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:56.553051Z",
     "iopub.status.busy": "2020-09-23T07:19:56.552384Z",
     "iopub.status.idle": "2020-09-23T07:19:56.745686Z",
     "shell.execute_reply": "2020-09-23T07:19:56.745006Z"
    },
    "id": "Xe7RXH3N3CWU"
   },
   "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": [
    "test_predictions = model.predict(normed_test_data).flatten()\n",
    "\n",
    "plt.scatter(test_labels, test_predictions)\n",
    "plt.xlabel('True Values [MPG]')\n",
    "plt.ylabel('Predictions [MPG]')\n",
    "plt.axis('equal')\n",
    "plt.axis('square')\n",
    "plt.xlim([0,plt.xlim()[1]])\n",
    "plt.ylim([0,plt.ylim()[1]])\n",
    "_ = plt.plot([-100, 100], [-100, 100])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mU1jBsRLaCeY"
   },
   "source": [
    "모델이 꽤 잘 예측한 것 같습니다. 오차의 분포를 살펴 보죠."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T07:19:56.768268Z",
     "iopub.status.busy": "2020-09-23T07:19:56.767428Z",
     "iopub.status.idle": "2020-09-23T07:19:56.914952Z",
     "shell.execute_reply": "2020-09-23T07:19:56.914312Z"
    },
    "id": "f-OHX4DiXd8x"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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f7W4C8mcwCLwk78zghhufBx6+6UBdT3ZTr/aYJBuSXJPkC90siqcAr+56socl+Zskr+v2f1KSK3LPDUceOnTMf0hyZZJvJjlsa55ckl8l+ack1wAHT7P8mu4/meuSnN49ZrJ7fu8HrmPzuW+25HwGl/UDPA+Ydr6Z7o3zy8BjgBcCX9kU7t3266rqnK15jlrcDHj1outNPgu4tlt1IHBaVe0D/Cnw86o6iMEMnq9IsjdwPLAvg5ttvBg4ZJrjTgD/BvxRVe0PPL+qNgJnAW+vqidV1WVTHvZ+4K+6G45cC7x5aNuSqnoycPqU9cMePWWIZtMbwQOAdVW1f1VdPrwM3MHgcvanMJip9BVJDuge91jgXVX1+Kq6acuv4m99ATg8gxuSnAR8cLqduuGwo7vn+HhgwwjHVsMcotF822VoTpTLGEyEdQhwZVV9t1v/TOCJm8bXgYcwCL3DgfO6oY0fJLl4muM/lcEQw3cBquq/Zyomg5s17FZVl3ar/h348NAum3rD64HJLRxmS0M0dzOYxXG65acBF1bV7V0da4DDGExedlNVXTFT3dO0czmDcN+lqjYODclD9wbE4OYvH6uqTyd5xvAOSS5k8Bp/s6qetxVtaxEz4DXf7pgahl0Y3T68isHc2hdN2e/Z/Zd3L7/uvt/N1v89/O+Ucfapy1ty++y73Mv5wIXA30yzbbo3oOsZvGECUFXHd0Nci+KWlJofDtFoHC4CXpnBPOYk2SeDO+R8CTixG6N/JN00zlNcwWC4Yu/usQ/r1v8SeNDUnavq58BPh4ZVTgYunbpfDy5j8GHnrt1zO75bty3H+3vgvBH3/w/g0CTHDa2b8xlNWpzswWsczmYwHLKhO5PkNuC5DHqoRzGYa/tm4CtTH1hVtyVZAazpTge8FXgG8AngggxuZ3jqlIe9BDirG6Oey1Svjx4adoLBNML/PNMDqmpDknOAK7tVZ1fV17oPhLdaN4X1yL3vqrojg7snvS3JmcCPGbwJ/u1c2tfi5HTB0gKSZCOwbMppkn21dQSD0yiP7bstjYdDNNLCchvwhU2nhPYlyYnAu4Cf9tmOxssevCQ1yh68JDXKgJekRhnwktQoA16SGvX/911hysDo17UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "error = test_predictions - test_labels\n",
    "plt.hist(error, bins = 25)\n",
    "plt.xlabel(\"Prediction Error [MPG]\")\n",
    "_ = plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3PkzkjFkaCed"
   },
   "source": [
    "가우시안 분포가 아니지만 아마도 훈련 샘플의 수가 매우 작기 때문일 것입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vgGQuV-yqYZH"
   },
   "source": [
    "## 결론\n",
    "\n",
    "이 노트북은 회귀 문제를 위한 기법을 소개합니다.\n",
    "\n",
    "* 평균 제곱 오차(MSE)는 회귀 문제에서 자주 사용하는 손실 함수입니다(분류 문제에서 사용하는 손실 함수와 다릅니다).\n",
    "* 비슷하게 회귀에서 사용되는 평가 지표도 분류와 다릅니다. 많이 사용하는 회귀 지표는 평균 절댓값 오차(MAE)입니다.\n",
    "* 수치 입력 데이터의 특성이 여러 가지 범위를 가질 때 동일한 범위가 되도록 각 특성의 스케일을 독립적으로 조정해야 합니다.\n",
    "* 훈련 데이터가 많지 않다면 과대적합을 피하기 위해 은닉층의 개수가 적은 소규모 네트워크를 선택하는 방법이 좋습니다.\n",
    "* 조기 종료(Early stopping)은 과대적합을 방지하기 위한 좋은 방법입니다."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "regression.ipynb",
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": 0
}