{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Td_EDk49y8wt"
      },
      "source": [
        "# 머신 러닝 교과서 3판"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8vj8Oyxiy8w0"
      },
      "source": [
        "# 14장 - 텐서플로의 구조 자세히 알아보기 (2/3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9SUdqsLFy8w0"
      },
      "source": [
        "**아래 링크를 통해 이 노트북을 주피터 노트북 뷰어(nbviewer.jupyter.org)로 보거나 구글 코랩(colab.research.google.com)에서 실행할 수 있습니다.**\n",
        "\n",
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://nbviewer.org/github/rickiepark/python-machine-learning-book-3rd-edition/blob/master/ch14/ch14_part2.ipynb\"><img src=\"https://jupyter.org/assets/share.png\" width=\"60\" />주피터 노트북 뷰어로 보기</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/python-machine-learning-book-3rd-edition/blob/master/ch14/ch14_part2.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩(Colab)에서 실행하기</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sZOZo9xwy8w0"
      },
      "source": [
        "### 목차"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I76d0TZxy8w1"
      },
      "source": [
        "- 텐서플로 추정기\n",
        "  - 특성 열 사용하기\n",
        "  - 사전에 준비된 추정기로 머신 러닝 수행하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "CsQvsr58y8w1"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "\n",
        "from IPython.display import Image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "jCgZBvoRzZsB",
        "outputId": "fb992a11-479a-49be-bace-bfc7c24a49ab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.19.0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "tf.__version__"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kZAANkUJy8w1"
      },
      "source": [
        "## 텐서플로 추정기\n",
        "\n",
        "##### 사전에 준비된 추정기 사용하는 단계\n",
        "\n",
        " * **단계 1:** 데이터 로딩을 위해 입력 함수 정의하기\n",
        " * **단계 2:** 추정기와 데이터 사이를 연결하기 위해 특성 열 정의하기\n",
        " * **단계 3:** 추정기 객체를 만들거나 케라스 모델을 추정기로 바꾸기\n",
        " * **단계 4:** 추정기 사용하기: train() evaluate() predict()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "csAr00GCy8w2"
      },
      "outputs": [],
      "source": [
        "tf.random.set_seed(1)\n",
        "np.random.seed(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nOFiP8O9y8w2"
      },
      "source": [
        "### 특성 열 사용하기\n",
        "\n",
        "\n",
        " * 정의: https://developers.google.com/machine-learning/glossary/#feature_columns\n",
        " * 문서: https://www.tensorflow.org/api_docs/python/tf/feature_column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "id": "bJbH-xauy8w2",
        "outputId": "c8b22158-b08a-4597-b9c4-d5d931523e55"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://git.io/JL56E\" width=\"700\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "Image(url='https://git.io/JL56E', width=700)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 271
        },
        "id": "YRGME3Lqy8w3",
        "outputId": "09da8c89-969f-44af-c920-3e360fb54ed9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\n",
            "  16384/Unknown \u001b[1m0s\u001b[0m 9us/step"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "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",
              "     ModelYear  Origin  \n",
              "393         82       1  \n",
              "394         82       2  \n",
              "395         82       1  \n",
              "396         82       1  \n",
              "397         82       1  "
            ],
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"MPG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.8044103344816005,\n        \"min\": 27.0,\n        \"max\": 44.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          44.0,\n          31.0,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cylinders\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 4,\n        \"max\": 4,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Displacement\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.813684902483452,\n        \"min\": 97.0,\n        \"max\": 140.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          97.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Horsepower\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.992855319769443,\n        \"min\": 52.0,\n        \"max\": 86.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          52.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 285.62650437240586,\n        \"min\": 2130.0,\n        \"max\": 2790.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2130.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Acceleration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.81123684721507,\n        \"min\": 11.6,\n        \"max\": 24.6,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          24.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ModelYear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 82,\n        \"max\": 82,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          82\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Origin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "dataset_path = tf.keras.utils.get_file(\"auto-mpg.data\",\n",
        "                                       (\"http://archive.ics.uci.edu/ml/machine-learning-databases\"\n",
        "                                        \"/auto-mpg/auto-mpg.data\"))\n",
        "\n",
        "column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower',\n",
        "                'Weight', 'Acceleration', 'ModelYear', 'Origin']\n",
        "\n",
        "df = pd.read_csv(dataset_path, names=column_names,\n",
        "                 na_values = \"?\", comment='\\t',\n",
        "                 sep=\" \", skipinitialspace=True)\n",
        "\n",
        "df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "lRR0q4Eky8w3",
        "outputId": "41e32f7a-6ac2-47a3-fe29-3db331c42c0c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MPG             0\n",
            "Cylinders       0\n",
            "Displacement    0\n",
            "Horsepower      6\n",
            "Weight          0\n",
            "Acceleration    0\n",
            "ModelYear       0\n",
            "Origin          0\n",
            "dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
              "387  27.0          4         140.0        86.0  2790.0          15.6   \n",
              "388  44.0          4          97.0        52.0  2130.0          24.6   \n",
              "389  32.0          4         135.0        84.0  2295.0          11.6   \n",
              "390  28.0          4         120.0        79.0  2625.0          18.6   \n",
              "391  31.0          4         119.0        82.0  2720.0          19.4   \n",
              "\n",
              "     ModelYear  Origin  \n",
              "387         82       1  \n",
              "388         82       2  \n",
              "389         82       1  \n",
              "390         82       1  \n",
              "391         82       1  "
            ],
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              "      <th>MPG</th>\n",
              "      <th>Cylinders</th>\n",
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              "      <td>119.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>2720.0</td>\n",
              "      <td>19.4</td>\n",
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              "  </style>\n",
              "\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c702d926-bcee-4852-a089-a8e3fe2a65e0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
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              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-cfe2f7ef-5933-490c-8cb9-e1a2d6b9c7d6 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"MPG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.8044103344816005,\n        \"min\": 27.0,\n        \"max\": 44.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          44.0,\n          31.0,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cylinders\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 4,\n        \"max\": 4,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Displacement\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.813684902483452,\n        \"min\": 97.0,\n        \"max\": 140.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          97.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Horsepower\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.992855319769443,\n        \"min\": 52.0,\n        \"max\": 86.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          52.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 285.62650437240586,\n        \"min\": 2130.0,\n        \"max\": 2790.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2130.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Acceleration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.81123684721507,\n        \"min\": 11.6,\n        \"max\": 24.6,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          24.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ModelYear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 82,\n        \"max\": 82,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          82\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Origin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "print(df.isna().sum())\n",
        "\n",
        "df = df.dropna()\n",
        "df = df.reset_index(drop=True)\n",
        "df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "VBr_cK0dy8w4",
        "outputId": "dc1c88ff-931e-4f84-bbc2-ab2f8f676c33"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              count         mean         std     min     25%     50%     75%  \\\n",
              "MPG           313.0    23.404153    7.666909     9.0    17.5    23.0    29.0   \n",
              "Cylinders     313.0     5.402556    1.701506     3.0     4.0     4.0     8.0   \n",
              "Displacement  313.0   189.512780  102.675646    68.0   104.0   140.0   260.0   \n",
              "Horsepower    313.0   102.929712   37.919046    46.0    75.0    92.0   120.0   \n",
              "Weight        313.0  2961.198083  848.602146  1613.0  2219.0  2755.0  3574.0   \n",
              "Acceleration  313.0    15.704473    2.725399     8.5    14.0    15.5    17.3   \n",
              "ModelYear     313.0    75.929712    3.675305    70.0    73.0    76.0    79.0   \n",
              "Origin        313.0     1.591054    0.807923     1.0     1.0     1.0     2.0   \n",
              "\n",
              "                 max  \n",
              "MPG             46.6  \n",
              "Cylinders        8.0  \n",
              "Displacement   455.0  \n",
              "Horsepower     230.0  \n",
              "Weight        5140.0  \n",
              "Acceleration    24.8  \n",
              "ModelYear       82.0  \n",
              "Origin           3.0  "
            ],
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              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
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              "      <th>max</th>\n",
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              "  </thead>\n",
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              "      <th>MPG</th>\n",
              "      <td>313.0</td>\n",
              "      <td>23.404153</td>\n",
              "      <td>7.666909</td>\n",
              "      <td>9.0</td>\n",
              "      <td>17.5</td>\n",
              "      <td>23.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>46.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cylinders</th>\n",
              "      <td>313.0</td>\n",
              "      <td>5.402556</td>\n",
              "      <td>1.701506</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Displacement</th>\n",
              "      <td>313.0</td>\n",
              "      <td>189.512780</td>\n",
              "      <td>102.675646</td>\n",
              "      <td>68.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>260.0</td>\n",
              "      <td>455.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Horsepower</th>\n",
              "      <td>313.0</td>\n",
              "      <td>102.929712</td>\n",
              "      <td>37.919046</td>\n",
              "      <td>46.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>230.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weight</th>\n",
              "      <td>313.0</td>\n",
              "      <td>2961.198083</td>\n",
              "      <td>848.602146</td>\n",
              "      <td>1613.0</td>\n",
              "      <td>2219.0</td>\n",
              "      <td>2755.0</td>\n",
              "      <td>3574.0</td>\n",
              "      <td>5140.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Acceleration</th>\n",
              "      <td>313.0</td>\n",
              "      <td>15.704473</td>\n",
              "      <td>2.725399</td>\n",
              "      <td>8.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>15.5</td>\n",
              "      <td>17.3</td>\n",
              "      <td>24.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ModelYear</th>\n",
              "      <td>313.0</td>\n",
              "      <td>75.929712</td>\n",
              "      <td>3.675305</td>\n",
              "      <td>70.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>82.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Origin</th>\n",
              "      <td>313.0</td>\n",
              "      <td>1.591054</td>\n",
              "      <td>0.807923</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
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            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "import sklearn\n",
        "import sklearn.model_selection\n",
        "\n",
        "\n",
        "df_train, df_test = sklearn.model_selection.train_test_split(df, train_size=0.8)\n",
        "train_stats = df_train.describe().transpose()\n",
        "train_stats"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0Xit7Hj4y8w4",
        "outputId": "4de36760-010b-4f0f-bc47-c28efddae4fc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipython-input-487790972.py:8: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '[-0.8243028   0.3511267  -0.8243028  -0.8243028  -0.8243028   1.52655621\n",
            "  0.3511267  -0.8243028  -0.8243028  -0.8243028   0.3511267  -0.8243028\n",
            "  0.3511267   1.52655621  1.52655621  1.52655621  0.3511267   1.52655621\n",
            " -0.8243028   0.3511267   1.52655621 -0.8243028  -0.8243028   0.3511267\n",
            " -0.8243028  -0.8243028  -0.8243028   0.3511267  -0.8243028   1.52655621\n",
            "  0.3511267  -0.8243028   0.3511267  -0.8243028  -0.8243028   1.52655621\n",
            " -0.8243028   1.52655621  1.52655621 -0.8243028  -0.8243028  -0.8243028\n",
            " -0.8243028   0.3511267  -0.8243028   1.52655621 -0.8243028  -0.8243028\n",
            "  1.52655621 -0.8243028  -0.8243028  -0.8243028   1.52655621  1.52655621\n",
            "  0.3511267   0.3511267   1.52655621 -0.8243028  -0.8243028   1.52655621\n",
            "  1.52655621 -0.8243028  -0.8243028   0.3511267   1.52655621 -0.8243028\n",
            "  0.3511267  -0.8243028   1.52655621  1.52655621 -0.8243028  -0.8243028\n",
            " -1.41201755  1.52655621  0.3511267   1.52655621 -0.8243028  -0.8243028\n",
            " -0.8243028   1.52655621  1.52655621  0.3511267   0.3511267   1.52655621\n",
            " -0.8243028   1.52655621 -0.23658805 -0.8243028  -0.8243028   0.3511267\n",
            "  0.3511267  -1.41201755 -0.8243028   1.52655621 -0.8243028   1.52655621\n",
            " -0.8243028   1.52655621  0.3511267  -0.8243028   0.3511267  -1.41201755\n",
            "  0.3511267  -0.8243028  -0.8243028  -0.8243028  -0.8243028  -0.8243028\n",
            " -0.8243028   1.52655621  1.52655621 -0.8243028   0.3511267  -0.8243028\n",
            " -0.8243028   0.3511267   1.52655621 -0.8243028  -0.8243028   0.3511267\n",
            "  0.3511267  -0.8243028  -0.8243028  -0.23658805 -0.8243028   0.3511267\n",
            "  0.3511267   0.3511267  -0.8243028   1.52655621  1.52655621 -0.8243028\n",
            "  0.3511267   1.52655621  1.52655621 -0.8243028   0.3511267   0.3511267\n",
            " -0.8243028  -0.8243028  -0.8243028  -0.8243028   1.52655621 -0.8243028\n",
            " -0.8243028   0.3511267  -0.8243028  -0.8243028   1.52655621 -0.8243028\n",
            "  1.52655621 -0.8243028  -0.8243028   1.52655621  1.52655621 -0.8243028\n",
            "  0.3511267  -0.8243028   1.52655621  0.3511267  -0.8243028   1.52655621\n",
            " -0.8243028  -0.8243028   1.52655621  1.52655621 -0.8243028   1.52655621\n",
            "  0.3511267  -0.8243028  -0.8243028   0.3511267   1.52655621  1.52655621\n",
            " -0.8243028  -0.8243028  -0.8243028  -0.8243028  -0.8243028  -0.8243028\n",
            " -0.8243028   1.52655621 -0.8243028   0.3511267   1.52655621 -0.8243028\n",
            " -0.23658805 -0.8243028  -0.8243028  -0.8243028  -0.8243028  -0.8243028\n",
            "  0.3511267  -0.8243028  -0.8243028   1.52655621  1.52655621 -0.8243028\n",
            "  1.52655621 -0.8243028   1.52655621 -0.8243028  -0.8243028  -0.8243028\n",
            " -0.8243028   0.3511267  -0.8243028   1.52655621 -0.8243028   0.3511267\n",
            "  1.52655621 -0.8243028  -0.8243028  -0.8243028   0.3511267  -0.8243028\n",
            " -0.8243028  -0.8243028  -0.8243028   0.3511267  -0.8243028   1.52655621\n",
            " -0.8243028  -0.8243028  -0.8243028   1.52655621 -0.8243028   1.52655621\n",
            " -0.8243028   0.3511267  -0.8243028   0.3511267  -0.8243028  -0.8243028\n",
            "  0.3511267   1.52655621 -0.8243028   0.3511267   1.52655621  0.3511267\n",
            " -0.8243028  -0.8243028  -0.8243028   1.52655621  1.52655621 -0.8243028\n",
            " -0.8243028  -0.8243028   1.52655621  0.3511267   0.3511267  -0.8243028\n",
            " -0.8243028  -0.8243028   1.52655621  0.3511267   0.3511267   0.3511267\n",
            "  1.52655621 -0.8243028   1.52655621  0.3511267  -0.8243028  -0.8243028\n",
            " -0.8243028  -0.8243028   1.52655621 -0.8243028   1.52655621 -0.8243028\n",
            "  1.52655621 -0.8243028  -0.8243028  -0.8243028  -0.8243028  -0.8243028\n",
            "  1.52655621 -0.8243028  -0.8243028  -0.8243028   0.3511267  -0.8243028\n",
            "  1.52655621 -0.8243028  -0.8243028  -0.8243028  -0.8243028   1.52655621\n",
            " -0.8243028   1.52655621 -0.8243028  -1.41201755 -0.8243028   1.52655621\n",
            " -0.8243028   1.52655621  0.3511267   0.3511267   0.3511267  -0.8243028\n",
            " -0.8243028   0.3511267  -0.8243028   1.52655621 -0.8243028  -0.8243028\n",
            " -0.8243028   0.3511267  -0.8243028   0.3511267   1.52655621 -0.8243028\n",
            "  1.52655621]' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
            "  df_train_norm.loc[:, col_name] = (df_train_norm.loc[:, col_name] - mean)/std\n",
            "/tmp/ipython-input-487790972.py:9: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '[-0.8243028  -0.8243028  -0.8243028  -0.8243028  -0.8243028  -0.8243028\n",
            " -0.8243028   1.52655621 -0.8243028  -0.8243028   0.3511267  -0.8243028\n",
            "  1.52655621 -0.8243028  -0.8243028   1.52655621 -0.8243028  -0.8243028\n",
            "  1.52655621  0.3511267  -0.8243028   0.3511267   1.52655621  1.52655621\n",
            "  1.52655621  1.52655621 -0.8243028   0.3511267   0.3511267  -0.8243028\n",
            "  1.52655621 -0.8243028   1.52655621  0.3511267   0.3511267   1.52655621\n",
            "  0.3511267   0.3511267  -0.8243028  -0.8243028   1.52655621  1.52655621\n",
            "  0.3511267   0.3511267   0.3511267   0.3511267   1.52655621 -0.8243028\n",
            "  0.3511267   1.52655621  1.52655621  0.3511267  -0.8243028  -0.8243028\n",
            " -0.8243028   0.3511267   0.3511267   0.3511267  -0.8243028  -0.8243028\n",
            "  0.3511267  -0.8243028  -0.8243028   1.52655621  0.3511267   0.3511267\n",
            " -0.8243028   1.52655621 -0.8243028   1.52655621 -0.8243028   1.52655621\n",
            "  1.52655621 -0.8243028  -0.8243028  -0.8243028   1.52655621  1.52655621\n",
            "  1.52655621]' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
            "  df_test_norm.loc[:, col_name] = (df_test_norm.loc[:, col_name] - mean)/std\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      MPG  Cylinders  Displacement  Horsepower    Weight  Acceleration  \\\n",
              "203  28.0  -0.824303     -0.901020   -0.736562 -0.950031      0.255202   \n",
              "255  19.4   0.351127      0.413800   -0.340982  0.293190      0.548737   \n",
              "72   13.0   1.526556      1.144256    0.713897  1.339617     -0.625403   \n",
              "235  30.5  -0.824303     -0.891280   -1.053025 -1.072585      0.475353   \n",
              "37   14.0   1.526556      1.563051    1.636916  1.470420     -1.359240   \n",
              "\n",
              "     ModelYear  Origin  \n",
              "203         76       3  \n",
              "255         78       1  \n",
              "72          72       1  \n",
              "235         77       1  \n",
              "37          71       1  "
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_train_norm\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"MPG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.981353268713271,\n        \"min\": 13.0,\n        \"max\": 30.5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          19.4,\n          14.0,\n          13.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cylinders\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.175429502520899,\n        \"min\": -0.8243027980937295,\n        \"max\": 1.5265562069480685,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -0.8243027980937295,\n          0.3511267044271694,\n          1.5265562069480685\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Displacement\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.137623714031951,\n        \"min\": -0.9010196973264667,\n        \"max\": 1.563050504928142,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.41380037107026135,\n          1.563050504928142,\n          1.1442559646239991\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Horsepower\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1121032669054438,\n        \"min\": -1.0530252500433732,\n        \"max\": 1.636915871166799,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -0.3409820120759746,\n          1.636915871166799,\n          0.713896858986838\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2105911244818783,\n        \"min\": -1.0725851775112334,\n        \"max\": 1.4704204122935787,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.29319029912629085,\n          1.4704204122935787,\n          1.3396170663861022\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Acceleration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8263413945969514,\n        \"min\": -1.359240485750421,\n        \"max\": 0.5487369309010532,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.5487369309010532,\n          -1.359240485750421,\n          -0.6254030178075461\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ModelYear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 71,\n        \"max\": 78,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          78,\n          71,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Origin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "numeric_column_names = ['Cylinders', 'Displacement', 'Horsepower', 'Weight', 'Acceleration']\n",
        "\n",
        "df_train_norm, df_test_norm = df_train.copy(), df_test.copy()\n",
        "\n",
        "for col_name in numeric_column_names:\n",
        "    mean = train_stats.loc[col_name, 'mean']\n",
        "    std  = train_stats.loc[col_name, 'std']\n",
        "    df_train_norm.loc[:, col_name] = (df_train_norm.loc[:, col_name] - mean)/std\n",
        "    df_test_norm.loc[:, col_name] = (df_test_norm.loc[:, col_name] - mean)/std\n",
        "\n",
        "df_train_norm.tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gsn1sbM5y8w4"
      },
      "source": [
        "#### 수치형 열"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jyFdfmKFy8w5",
        "outputId": "5ef6376f-cf19-41ac-ac75-516f2449ad68"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:From /tmp/ipython-input-2109796009.py:4: numeric_column (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use Keras preprocessing layers instead, either directly or via the `tf.keras.utils.FeatureSpace` utility. Each of `tf.feature_column.*` has a functional equivalent in `tf.keras.layers` for feature preprocessing when training a Keras model.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[NumericColumn(key='Cylinders', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None),\n",
              " NumericColumn(key='Displacement', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None),\n",
              " NumericColumn(key='Horsepower', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None),\n",
              " NumericColumn(key='Weight', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None),\n",
              " NumericColumn(key='Acceleration', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None)]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "numeric_features = []\n",
        "\n",
        "for col_name in numeric_column_names:\n",
        "    numeric_features.append(tf.feature_column.numeric_column(key=col_name))\n",
        "\n",
        "numeric_features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OtZQ-fNHy8w5",
        "outputId": "3c2e1104-7181-4664-8620-2e9ae13fc7a7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:From /tmp/ipython-input-1410326794.py:5: bucketized_column (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use Keras preprocessing layers instead, either directly or via the `tf.keras.utils.FeatureSpace` utility. Each of `tf.feature_column.*` has a functional equivalent in `tf.keras.layers` for feature preprocessing when training a Keras model.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[BucketizedColumn(source_column=NumericColumn(key='ModelYear', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), boundaries=(73, 76, 79))]\n"
          ]
        }
      ],
      "source": [
        "feature_year = tf.feature_column.numeric_column(key=\"ModelYear\")\n",
        "\n",
        "bucketized_features = []\n",
        "\n",
        "bucketized_features.append(tf.feature_column.bucketized_column(\n",
        "    source_column=feature_year,\n",
        "    boundaries=[73, 76, 79]))\n",
        "\n",
        "print(bucketized_features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V1OIxbvmy8w6",
        "outputId": "2adafd46-52f6-46ac-89eb-29987c3b192d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:From /tmp/ipython-input-4078329294.py:1: categorical_column_with_vocabulary_list (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use Keras preprocessing layers instead, either directly or via the `tf.keras.utils.FeatureSpace` utility. Each of `tf.feature_column.*` has a functional equivalent in `tf.keras.layers` for feature preprocessing when training a Keras model.\n",
            "WARNING:tensorflow:From /tmp/ipython-input-4078329294.py:6: indicator_column (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use Keras preprocessing layers instead, either directly or via the `tf.keras.utils.FeatureSpace` utility. Each of `tf.feature_column.*` has a functional equivalent in `tf.keras.layers` for feature preprocessing when training a Keras model.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='Origin', vocabulary_list=(1, 2, 3), dtype=tf.int64, default_value=-1, num_oov_buckets=0))]\n"
          ]
        }
      ],
      "source": [
        "feature_origin = tf.feature_column.categorical_column_with_vocabulary_list(\n",
        "    key='Origin',\n",
        "    vocabulary_list=[1, 2, 3])\n",
        "\n",
        "categorical_indicator_features = []\n",
        "categorical_indicator_features.append(tf.feature_column.indicator_column(feature_origin))\n",
        "\n",
        "print(categorical_indicator_features)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LM8nvPgJy8w6"
      },
      "source": [
        "### 사전에 준비된 추정기로 머신러닝 수행하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e5TtiGv6y8w7",
        "outputId": "0ab27fe6-499c-4e16-d399-f34c10861e11"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "키: dict_keys(['Cylinders', 'Displacement', 'Horsepower', 'Weight', 'Acceleration', 'ModelYear', 'Origin'])\n",
            "ModelYear: tf.Tensor([82 78 76 72 78 73 70 78], shape=(8,), dtype=int64)\n"
          ]
        }
      ],
      "source": [
        "def train_input_fn(df_train, batch_size=8):\n",
        "    df = df_train.copy()\n",
        "    train_x, train_y = df, df.pop('MPG')\n",
        "    dataset = tf.data.Dataset.from_tensor_slices((dict(train_x), train_y))\n",
        "\n",
        "    # 셔플, 반복, 배치\n",
        "    return dataset.shuffle(1000).repeat().batch(batch_size)\n",
        "\n",
        "## 조사\n",
        "ds = train_input_fn(df_train_norm)\n",
        "batch = next(iter(ds))\n",
        "print('키:', batch[0].keys())\n",
        "print('ModelYear:', batch[0]['ModelYear'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E0EbaOx2y8w7",
        "outputId": "73eba0f4-e189-488f-b42b-a94b5a2ba37c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NumericColumn(key='Cylinders', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='Displacement', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='Horsepower', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='Weight', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='Acceleration', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), BucketizedColumn(source_column=NumericColumn(key='ModelYear', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), boundaries=(73, 76, 79)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='Origin', vocabulary_list=(1, 2, 3), dtype=tf.int64, default_value=-1, num_oov_buckets=0))]\n"
          ]
        }
      ],
      "source": [
        "all_feature_columns = (numeric_features +\n",
        "                       bucketized_features +\n",
        "                       categorical_indicator_features)\n",
        "\n",
        "print(all_feature_columns)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**최신 텐서플로에서 `tf.estimator`가 삭제되어 더이상 사용할 수 없습니다.**"
      ],
      "metadata": {
        "id": "zAw02RlcYStW"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2tSgfsKHy8w7"
      },
      "outputs": [],
      "source": [
        "# regressor = tf.estimator.DNNRegressor(\n",
        "#     feature_columns=all_feature_columns,\n",
        "#     hidden_units=[32, 10],\n",
        "#     model_dir='models/autompg-dnnregressor/')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sESlS5e5y8w7",
        "scrolled": false
      },
      "outputs": [],
      "source": [
        "# EPOCHS = 1000\n",
        "# BATCH_SIZE = 8\n",
        "# total_steps = EPOCHS * int(np.ceil(len(df_train) / BATCH_SIZE))\n",
        "# print('훈련 스텝:', total_steps)\n",
        "\n",
        "# regressor.train(\n",
        "#     input_fn=lambda:train_input_fn(df_train_norm, batch_size=BATCH_SIZE),\n",
        "#     steps=total_steps)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k7-xm6ggy8w8"
      },
      "outputs": [],
      "source": [
        "# reloaded_regressor = tf.estimator.DNNRegressor(\n",
        "#     feature_columns=all_feature_columns,\n",
        "#     hidden_units=[32, 10],\n",
        "#     warm_start_from='models/autompg-dnnregressor/',\n",
        "#     model_dir='models/autompg-dnnregressor/')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BcSjYtdQy8w8"
      },
      "outputs": [],
      "source": [
        "# def eval_input_fn(df_test, batch_size=8):\n",
        "#     df = df_test.copy()\n",
        "#     test_x, test_y = df, df.pop('MPG')\n",
        "#     dataset = tf.data.Dataset.from_tensor_slices((dict(test_x), test_y))\n",
        "\n",
        "#     return dataset.batch(batch_size)\n",
        "\n",
        "# eval_results = reloaded_regressor.evaluate(\n",
        "#     input_fn=lambda:eval_input_fn(df_test_norm, batch_size=8))\n",
        "\n",
        "# for key in eval_results:\n",
        "#     print('{:15s} {}'.format(key, eval_results[key]))\n",
        "\n",
        "# print('평균 손실 {:.4f}'.format(eval_results['average_loss']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B8u7CKJ_y8w8"
      },
      "outputs": [],
      "source": [
        "# pred_res = regressor.predict(input_fn=lambda: eval_input_fn(df_test_norm, batch_size=8))\n",
        "\n",
        "# print(next(iter(pred_res)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1WMTIRkXy8w9"
      },
      "source": [
        "#### Boosted Tree Regressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zcrXe74iy8w9"
      },
      "outputs": [],
      "source": [
        "# boosted_tree = tf.estimator.BoostedTreesRegressor(\n",
        "#     feature_columns=all_feature_columns,\n",
        "#     n_batches_per_layer=20,\n",
        "#     n_trees=200)\n",
        "\n",
        "# boosted_tree.train(\n",
        "#     input_fn=lambda:train_input_fn(df_train_norm, batch_size=BATCH_SIZE))\n",
        "\n",
        "# eval_results = boosted_tree.evaluate(\n",
        "#     input_fn=lambda:eval_input_fn(df_test_norm, batch_size=8))\n",
        "\n",
        "# print(eval_results)\n",
        "\n",
        "# print('평균 손실 {:.4f}'.format(eval_results['average_loss']))"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "ch14_part2.ipynb",
      "provenance": [],
      "gpuType": "A100"
    },
    "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": "3.7.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}