Chapter4.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Chapter4.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BCq2UCUvEkhh",
        "colab_type": "text"
      },
      "source": [
        "<시작하세요! 텐서플로 2.0 프로그래밍> 예제코드입니다.\n",
        "\n",
        "- 예제 코드를 실행하기 위해서는 [파일] > [드라이브에 사본 저장]으로 본인의 계정에 사본을 만든 다음 실행하면 됩니다.\n",
        "- 예제 코드는 [깃허브 저장소](https://github.com/wikibook/tf2)에서도 동일하게 제공됩니다. 예제에 대한 질문이나 책에 대한 질문은 깃허브 저장소의 [Issues](https://github.com/wikibook/tf2/issues)에 올려주세요.\n",
        "- 각 장의 예제 파일은 처음부터 끝까지 실행하는 상황을 가정하고 작성되었습니다. 혹시 세션이 다운되는 등의 이유로 실행이 되지 않는 경우가 있다면, 필요한 라이브러리를 import 하신 후에 실행하시면 됩니다. (tensorflow, numpy, pandas 등)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v6lUugr1JRBt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 텐서플로 2 버전 선택\n",
        "try:\n",
        "    # %tensorflow_version only exists in Colab.\n",
        "    %tensorflow_version 2.x\n",
        "except Exception:\n",
        "    pass"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SzBgd4W8N3Js",
        "colab_type": "text"
      },
      "source": [
        "#  4.1 선형 회귀(Linear Regression)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QChWDkRCN_tY",
        "colab_type": "code",
        "outputId": "a82ca076-42c4-4bb8-cf32-0302b794d6b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "# 4.1 지역별 인구증가율과 고령인구비율 시각화\n",
        "import matplotlib.pyplot as plt\n",
        "population_inc = [0.3, -0.78, 1.26, 0.03, 1.11, 15.17, 0.24, -0.24, -0.47, -0.77, -0.37, -0.85, -0.41, -0.27, 0.02, -0.76, 2.66]\n",
        "population_old = [12.27, 14.44, 11.87, 18.75, 17.52, 9.29, 16.37, 19.78, 19.51, 12.65, 14.74, 10.72, 21.94, 12.83, 15.51, 17.14, 14.42]\n",
        "plt.plot(population_inc,population_old,'bo')\n",
        "plt.xlabel('Population Growth Rate (%)')\n",
        "plt.ylabel('Elderly Population Rate (%)')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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efRT4UUQ8ASDpMtIJ4Qrg/oi4MVv/O6TJWL6Y81jTgX/PLkVsCvy+2crZiXfr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\nvBoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RM75JVEDNtKq",
        "colab_type": "code",
        "outputId": "0815d78c-ff90-4b59-b4b3-2600c43ea909",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "# 4.2 지역별 인구증가율과 고령인구비율 시각화: 극단치 제거\n",
        "import matplotlib.pyplot as plt\n",
        "population_inc = [0.3, -0.78, 1.26, 0.03, 1.11, 15.17, 0.24, -0.24, -0.47, -0.77, -0.37, -0.85, -0.41, -0.27, 0.02, -0.76, 2.66]\n",
        "population_inc = population_inc[:5] + population_inc[6:]\n",
        "population_old = [12.27, 14.44, 11.87, 18.75, 17.52, 9.29, 16.37, 19.78, 19.51, 12.65, 14.74, 10.72, 21.94, 12.83, 15.51, 17.14, 14.42]\n",
        "population_old = population_old[:5] + population_old[6:]\n",
        "plt.plot(population_inc,population_old,'bo')\n",
        "plt.xlabel('Population Growth Rate (%)')\n",
        "plt.ylabel('Elderly Population Rate (%)')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dlw+leC8B/leqCb0wIm5X47HyL4+Ih9L73gr0s/bQyjUjysZe2pAsedS8XdIc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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zjA3y39YnuVf",
        "colab_type": "code",
        "outputId": "93eb5fa0-5be4-4171-8200-aa07d7242b7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "# 4.3 최소제곱법으로 회귀선 구하기\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "X = [0.3, -0.78, 1.26, 0.03, 1.11, 0.24, -0.24, -0.47, -0.77, -0.37, -0.85, -0.41, -0.27, 0.02, -0.76, 2.66]\n",
        "Y = [12.27, 14.44, 11.87, 18.75, 17.52, 16.37, 19.78, 19.51, 12.65, 14.74, 10.72, 21.94, 12.83, 15.51, 17.14, 14.42]\n",
        "\n",
        "# X, Y의 평균을 구합니다.\n",
        "x_bar = sum(X) / len(X)\n",
        "y_bar = sum(Y) / len(Y)\n",
        "\n",
        "# 최소제곱법으로 a, b를 구합니다.\n",
        "a = sum([(y - y_bar) * (x - x_bar) for y, x in list(zip(Y, X))])\n",
        "a /= sum([(x - x_bar) ** 2 for x in X])\n",
        "b = y_bar - a * x_bar\n",
        "print('a:', a, 'b:', b)\n",
        "\n",
        "# 그래프를 그리기 위해 회귀선의 x, y 데이터를 구합니다.\n",
        "line_x = np.arange(min(X), max(X), 0.01)\n",
        "line_y = a * line_x + b\n",
        "\n",
        "# 붉은색 실선으로 회귀선을 그립니다.\n",
        "plt.plot(line_x,line_y,'r-')\n",
        "\n",
        "plt.plot(X,Y,'bo')\n",
        "plt.xlabel('Population Growth Rate (%)')\n",
        "plt.ylabel('Elderly Population Rate (%)')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "a: -0.355834147915461 b: 15.669317743971302\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FZ2ZdIc8w1C+sWt0ArIyIuwqNagQPQ12skXMIQOop+/jQEI88kqqj8rR/VL//7W/1P3Tq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\nyswjZgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mnpySfdvnu2O",
        "colab_type": "code",
        "outputId": "b6a23cee-78d0-425c-e8ac-a79a51fdbc14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        }
      },
      "source": [
        "# 4.4 텐서플로를 이용해서 회귀선 구하기\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "X = [0.3, -0.78, 1.26, 0.03, 1.11, 0.24, -0.24, -0.47, -0.77, -0.37, -0.85, -0.41, -0.27, 0.02, -0.76, 2.66]\n",
        "Y = [12.27, 14.44, 11.87, 18.75, 17.52, 16.37, 19.78, 19.51, 12.65, 14.74, 10.72, 21.94, 12.83, 15.51, 17.14, 14.42]\n",
        "\n",
        "# a와 b를 랜덤한 값으로 초기화합니다.\n",
        "a = tf.Variable(random.random())\n",
        "b = tf.Variable(random.random())\n",
        "\n",
        "# 잔차의 제곱의 평균을 반환하는 함수입니다.\n",
        "def compute_loss():\n",
        "    y_pred = a * X + b\n",
        "    loss = tf.reduce_mean((Y - y_pred) ** 2)\n",
        "    return loss\n",
        "\n",
        "optimizer = tf.keras.optimizers.Adam(lr=0.07)\n",
        "for i in range(1000):\n",
        "    # 잔차의 제곱의 평균을 최소화(minimize)합니다.\n",
        "    optimizer.minimize(compute_loss, var_list=[a,b])\n",
        "    \n",
        "    if i % 100 == 99:\n",
        "        print(i, 'a:', a.numpy(), 'b:', b.numpy(), 'loss:', compute_loss().numpy())\n",
        "\n",
        "line_x = np.arange(min(X), max(X), 0.01)\n",
        "line_y = a * line_x + b\n",
        "\n",
        "# 그래프를 그립니다.\n",
        "plt.plot(line_x,line_y,'r-')\n",
        "plt.plot(X,Y,'bo')\n",
        "plt.xlabel('Population Growth Rate (%)')\n",
        "plt.ylabel('Elderly Population Rate (%)')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "99 a: 0.089875124 b: 7.3387866 loss: 79.01758\n",
            "199 a: -0.14417 b: 11.697883 loss: 25.516514\n",
            "299 a: -0.27338856 b: 14.122254 loss: 12.168657\n",
            "399 a: -0.32996833 b: 15.183949 loss: 10.015839\n",
            "499 a: -0.349331 b: 15.547286 loss: 9.79566\n",
            "599 a: -0.3545309 b: 15.644867 loss: 9.781401\n",
            "699 a: -0.3556281 b: 15.665455 loss: 9.780819\n",
            "799 a: -0.35580865 b: 15.668843 loss: 9.780804\n",
            "899 a: -0.3558318 b: 15.669272 loss: 9.780804\n",
            "999 a: -0.35583338 b: 15.669303 loss: 9.780804\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FZ2ZdIc8w1C+sWt0ArIyIuwqNagQPQ12skXMIQOop+/jQEI88kqqj8rR/VL//7W/1P3Tq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\nyswjZgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gkjyJAqnN8Zs",
        "colab_type": "text"
      },
      "source": [
        "# 4.2 다항 회귀(Polynomial Regression)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "20I8mAhBOAid",
        "colab_type": "code",
        "outputId": "27f112c3-d732-4aef-ba34-175bf0afef9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        }
      },
      "source": [
        "# 4.5 텐서플로우를 이용해서 2차 함수 회귀선 구하기\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "X = [0.3, -0.78, 1.26, 0.03, 1.11, 0.24, -0.24, -0.47, -0.77, -0.37, -0.85, -0.41, -0.27, 0.02, -0.76, 2.66]\n",
        "Y = [12.27, 14.44, 11.87, 18.75, 17.52, 16.37, 19.78, 19.51, 12.65, 14.74, 10.72, 21.94, 12.83, 15.51, 17.14, 14.42]\n",
        "\n",
        "# a, b, c를 랜덤한 값으로 초기화합니다.\n",
        "a = tf.Variable(random.random())\n",
        "b = tf.Variable(random.random())\n",
        "c = tf.Variable(random.random())\n",
        "\n",
        "# 잔차의 제곱의 평균을 반환하는 함수입니다.\n",
        "def compute_loss():\n",
        "    y_pred = a * X*X + b * X + c\n",
        "    loss = tf.reduce_mean((Y - y_pred) ** 2)\n",
        "    return loss\n",
        "\n",
        "optimizer = tf.keras.optimizers.Adam(lr=0.07)\n",
        "for i in range(1000):\n",
        "    # 잔차의 제곱의 평균을 최소화(minimize)합니다.\n",
        "    optimizer.minimize(compute_loss, var_list=[a,b,c])\n",
        "    \n",
        "    if i % 100 == 99:\n",
        "        print(i, 'a:', a.numpy(), 'b:', b.numpy(), 'c:', c.numpy(), 'loss:', compute_loss().numpy())\n",
        "\n",
        "line_x = np.arange(min(X), max(X), 0.01)\n",
        "line_y = a * line_x * line_x + b * line_x + c\n",
        "\n",
        "# 그래프를 그립니다.\n",
        "plt.plot(line_x,line_y,'r-')\n",
        "plt.plot(X,Y,'bo')\n",
        "plt.xlabel('Population Growth Rate (%)')\n",
        "plt.ylabel('Elderly Population Rate (%)')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "99 a: 4.1581616 b: -5.953284 c: 6.047485 loss: 73.82266\n",
            "199 a: 2.9474568 b: -4.87065 c: 9.995847 loss: 33.869064\n",
            "299 a: 1.3955072 b: -2.5227528 c: 12.723314 loss: 16.94876\n",
            "399 a: 0.39398676 b: -0.98995125 c: 14.446153 loss: 11.2515135\n",
            "499 a: -0.15033463 b: -0.15734565 c: 15.384374 loss: 9.791604\n",
            "599 a: -0.4066643 b: 0.23469424 c: 15.8263855 loss: 9.5048895\n",
            "699 a: -0.51161045 b: 0.39519957 c: 16.007366 loss: 9.461606\n",
            "799 a: -0.5490208 b: 0.45241547 c: 16.071882 loss: 9.456587\n",
            "899 a: -0.5606211 b: 0.47015625 c: 16.091887 loss: 9.456142\n",
            "999 a: -0.56373876 b: 0.47492516 c: 16.097261 loss: 9.456114\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eEA2cg-mCBOA",
        "colab_type": "code",
        "outputId": "8c7ebc4a-500a-4f0f-f1c2-b8a1064e3205",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        }
      },
      "source": [
        "# 4.6 텐서플로우를 이용해서 3차 함수 회귀선 구하기\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "X = [0.3, -0.78, 1.26, 0.03, 1.11, 0.24, -0.24, -0.47, -0.77, -0.37, -0.85, -0.41, -0.27, 0.02, -0.76, 2.66]\n",
        "Y = [12.27, 14.44, 11.87, 18.75, 17.52, 16.37, 19.78, 19.51, 12.65, 14.74, 10.72, 21.94, 12.83, 15.51, 17.14, 14.42]\n",
        "\n",
        "# a, b, c, d를 랜덤한 값으로 초기화합니다.\n",
        "a = tf.Variable(random.random())\n",
        "b = tf.Variable(random.random())\n",
        "c = tf.Variable(random.random())\n",
        "d = tf.Variable(random.random())\n",
        "\n",
        "# 잔차의 제곱의 평균을 반환하는 함수입니다.\n",
        "def compute_loss():\n",
        "    y_pred = a * X*X*X + b * X*X + c * X + d\n",
        "    loss = tf.reduce_mean((Y - y_pred) ** 2)\n",
        "    return loss\n",
        "\n",
        "optimizer = tf.keras.optimizers.Adam(lr=0.07)\n",
        "for i in range(1000):\n",
        "    # 잔차의 제곱의 평균을 최소화(minimize)합니다.\n",
        "    optimizer.minimize(compute_loss, var_list=[a,b,c,d])\n",
        "    \n",
        "    if i % 100 == 99:\n",
        "        print(i, 'a:', a.numpy(), 'b:', b.numpy(), 'c:', c.numpy(), 'd:', d.numpy(), 'loss:', compute_loss().numpy())\n",
        "\n",
        "line_x = np.arange(min(X), max(X), 0.01)\n",
        "line_y = a * line_x * line_x * line_x + b * line_x * line_x + c * line_x + d\n",
        "\n",
        "# 그래프를 그립니다.\n",
        "plt.plot(line_x,line_y,'r-')\n",
        "plt.plot(X,Y,'bo')\n",
        "plt.xlabel('Population Growth Rate (%)')\n",
        "plt.ylabel('Elderly Population Rate (%)')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "99 a: -1.2564825 b: 7.2653337 c: -4.759797 d: 0.7626014 loss: 151.67218\n",
            "199 a: -2.8898041 b: 11.835857 c: -5.3980103 d: 0.7626014 loss: 132.65906\n",
            "299 a: -4.171622 b: 14.875216 c: -4.556038 d: 0.7626014 loss: 125.74563\n",
            "399 a: -4.948379 b: 16.676939 c: -3.947625 d: 0.7626014 loss: 123.677315\n",
            "499 a: -5.3436556 b: 17.59378 c: -3.6379592 d: 0.7626014 loss: 123.21343\n",
            "599 a: -5.5156665 b: 17.992847 c: -3.5034103 d: 0.7626014 loss: 123.13554\n",
            "699 a: -5.579892 b: 18.141865 c: -3.4531763 d: 0.7626014 loss: 123.12575\n",
            "799 a: -5.600496 b: 18.189663 c: -3.437067 d: 0.7626014 loss: 123.124825\n",
            "899 a: -5.6061625 b: 18.2028 c: -3.4326386 d: 0.7626014 loss: 123.12475\n",
            "999 a: -5.6074877 b: 18.205883 c: -3.4315991 d: 0.7626014 loss: 123.12474\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zVSJ94YWoI3FhGj3afgDcfDPUqhV1NM6VSyqJ4CxgG+B54Dlg6+A+l2jvvaFh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\nLs95InDOuTznicA55/Lc/wPcbeHYcJ1fPAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vifwuMeTOAu0",
        "colab_type": "text"
      },
      "source": [
        "# 4.3 딥러닝 네트워크를 이용한 회귀"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "00-h5uNNOM7V",
        "colab_type": "code",
        "outputId": "f901d014-8163-4fa3-ebb6-8e26dc0c5b12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        }
      },
      "source": [
        "# 4.7 딥러닝 네트워크를 이용한 회귀\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "\n",
        "X = [0.3, -0.78, 1.26, 0.03, 1.11, 0.24, -0.24, -0.47, -0.77, -0.37, -0.85, -0.41, -0.27, 0.02, -0.76, 2.66]\n",
        "Y = [12.27, 14.44, 11.87, 18.75, 17.52, 16.37, 19.78, 19.51, 12.65, 14.74, 10.72, 21.94, 12.83, 15.51, 17.14, 14.42]\n",
        "\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(units=6, activation='tanh', input_shape=(1,)),\n",
        "    tf.keras.layers.Dense(units=1)\n",
        "])\n",
        "\n",
        "model.compile(optimizer=tf.keras.optimizers.SGD(lr=0.1), loss='mse')\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 6)                 12        \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 7         \n",
            "=================================================================\n",
            "Total params: 19\n",
            "Trainable params: 19\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QylCvZXCJ16b",
        "colab_type": "code",
        "outputId": "9cea4b57-272b-40d3-cd09-d2c4ffca5614",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        }
      },
      "source": [
        "# 4.8 딥러닝 네트워크의 학습\n",
        "model.fit(X, Y, epochs=10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "16/16 [==============================] - 0s 22ms/sample - loss: 255.2328\n",
            "Epoch 2/10\n",
            "16/16 [==============================] - 0s 305us/sample - loss: 121.0506\n",
            "Epoch 3/10\n",
            "16/16 [==============================] - 0s 172us/sample - loss: 9.9392\n",
            "Epoch 4/10\n",
            "16/16 [==============================] - 0s 190us/sample - loss: 9.7631\n",
            "Epoch 5/10\n",
            "16/16 [==============================] - 0s 204us/sample - loss: 9.6806\n",
            "Epoch 6/10\n",
            "16/16 [==============================] - 0s 191us/sample - loss: 9.6263\n",
            "Epoch 7/10\n",
            "16/16 [==============================] - 0s 207us/sample - loss: 9.5849\n",
            "Epoch 8/10\n",
            "16/16 [==============================] - 0s 201us/sample - loss: 9.5494\n",
            "Epoch 9/10\n",
            "16/16 [==============================] - 0s 198us/sample - loss: 9.5156\n",
            "Epoch 10/10\n",
            "16/16 [==============================] - 0s 200us/sample - loss: 9.4809\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fa01057d668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dKNRgRT-VmNo",
        "colab_type": "code",
        "outputId": "ade404ad-a424-46bb-dff1-c8a912c0ac21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        }
      },
      "source": [
        "# 4.9 딥러닝 네트워크의 Y값 예측\n",
        "model.predict(X)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[15.799347 ],\n",
              "       [15.698468 ],\n",
              "       [15.217406 ],\n",
              "       [15.862815 ],\n",
              "       [15.331142 ],\n",
              "       [15.818269 ],\n",
              "       [15.868111 ],\n",
              "       [15.825095 ],\n",
              "       [15.70375  ],\n",
              "       [15.849195 ],\n",
              "       [15.659354 ],\n",
              "       [15.8405485],\n",
              "       [15.864991 ],\n",
              "       [15.864071 ],\n",
              "       [15.708953 ],\n",
              "       [14.338877 ]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3hOY981DK93b",
        "colab_type": "code",
        "outputId": "ae1087c2-0b96-4800-b8c7-66e5a0f73d16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "# 4.10 딥러닝 네트워크의 회귀선 확인\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "line_x = np.arange(min(X), max(X), 0.01)\n",
        "line_y = model.predict(line_x)\n",
        "\n",
        "plt.plot(line_x,line_y,'r-')\n",
        "plt.plot(X,Y,'bo')\n",
        "plt.xlabel('Population Growth Rate (%)')\n",
        "plt.ylabel('Elderly Population Rate (%)')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cWFjjuGmShiTdKKmZySLPuXr+mIjYBjwN7N2U6GrEkan17/rm7HbAf0javzmh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\nG8DdGEOJpRcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hXUej2dfYEi3",
        "colab_type": "code",
        "outputId": "879ab97f-8e9d-4592-89eb-e520732e7a1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "# 그림 4.2 출력 코드\n",
        "import math\n",
        "def sigmoid(x):\n",
        "    return 1 / (1 + math.exp(-x))\n",
        "\n",
        "x = np.arange(-5, 5, 0.01)\n",
        "sigmoid_x = [sigmoid(z) for z in x]\n",
        "tanh_x = [math.tanh(z) for z in x]\n",
        "\n",
        "plt.axhline(0, color='gray')\n",
        "plt.axvline(0, color='gray')\n",
        "plt.plot(x, sigmoid_x, 'b-', label='sigmoid')\n",
        "plt.plot(x, tanh_x, 'r--', label='tanh')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gU9euHaahbtAg/Nrv0SMkgGbNcvcNG4ZGYikB6tbVrZiUasm4Y+gELHP35QBm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\n054aRQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "raf-pMEBKfmn",
        "colab_type": "text"
      },
      "source": [
        "# 4.4 보스턴 주택 가격 데이터세트(Boston Housing Dataset)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nYyev2iFKf-M",
        "colab_type": "code",
        "outputId": "208cbd88-ed60-424e-d71c-7399faefcd5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "source": [
        "# 4.11 데이터 불러오기\n",
        "from tensorflow.keras.datasets import boston_housing\n",
        "(train_X, train_Y), (test_X, test_Y) = boston_housing.load_data()\n",
        "\n",
        "print(len(train_X), len(test_X))\n",
        "print(train_X[0])\n",
        "print(train_Y[0])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "404 102\n",
            "[  1.23247   0.        8.14      0.        0.538     6.142    91.7\n",
            "   3.9769    4.      307.       21.      396.9      18.72   ]\n",
            "15.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SavRNrV6oVxE",
        "colab_type": "code",
        "outputId": "c7dce322-8f79-4fa6-f850-c1fda0cd4454",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "source": [
        "# 4.12 데이터 전처리(정규화)\n",
        "x_mean = train_X.mean(axis=0)\n",
        "x_std = train_X.std(axis=0)\n",
        "train_X -= x_mean\n",
        "train_X /= x_std\n",
        "test_X -= x_mean\n",
        "test_X /= x_std\n",
        "\n",
        "y_mean = train_Y.mean(axis=0)\n",
        "y_std = train_Y.std(axis=0)\n",
        "train_Y -= y_mean\n",
        "train_Y /= y_std\n",
        "test_Y -= y_mean\n",
        "test_Y /= y_std\n",
        "\n",
        "print(train_X[0])\n",
        "print(train_Y[0])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-0.27224633 -0.48361547 -0.43576161 -0.25683275 -0.1652266  -0.1764426\n",
            "  0.81306188  0.1166983  -0.62624905 -0.59517003  1.14850044  0.44807713\n",
            "  0.8252202 ]\n",
            "-0.7821526033779157\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cwSYzQG0Ych9",
        "colab_type": "code",
        "outputId": "b801bd07-d7e7-44e7-9872-65ac97593f90",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        }
      },
      "source": [
        "# 4.13 Boston Housing Dataset 회귀 모델 생성\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(units=52, activation='relu', input_shape=(13,)),\n",
        "    tf.keras.layers.Dense(units=39, activation='relu'),\n",
        "    tf.keras.layers.Dense(units=26, activation='relu'),\n",
        "    tf.keras.layers.Dense(units=1)\n",
        "])\n",
        "\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.07), loss='mse')\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 52)                728       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 39)                2067      \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 26)                1040      \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 27        \n",
            "=================================================================\n",
            "Total params: 3,862\n",
            "Trainable params: 3,862\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TnQV-j20JTgI",
        "colab_type": "code",
        "outputId": "6a72133c-1dd9-4d37-acc6-048ec712ee37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "# 그림 4.5 출력 코드\n",
        "import math\n",
        "def sigmoid(x):\n",
        "    return 1 / (1 + math.exp(-x))\n",
        "\n",
        "x = np.arange(-5, 5, 0.01)\n",
        "sigmoid_x = [sigmoid(z) for z in x]\n",
        "tanh_x = [math.tanh(z) for z in x]\n",
        "relu = [0 if z < 0 else z for z in x]\n",
        "\n",
        "plt.axhline(0, color='gray')\n",
        "plt.axvline(0, color='gray')\n",
        "plt.plot(x, sigmoid_x, 'b-', label='sigmoid')\n",
        "plt.plot(x, tanh_x, 'r--', label='tanh')\n",
        "plt.plot(x, relu, 'g.', label='relu')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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L23LLLf7ecLfeGuueiARGI3JJfkePwvPP++df+pJCXJKOglyS28GDcNVVMGEC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\nkuAU5CIiCe7/A/GAr4zQ7UTwAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wnXGDmwWYud1",
        "colab_type": "code",
        "outputId": "d63b10f2-5981-442b-b130-80a636468811",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 917
        }
      },
      "source": [
        "# 4.14 회귀 모델 학습\n",
        "history = model.fit(train_X, train_Y, epochs=25, batch_size=32, validation_split=0.25)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/25\n",
            "10/10 [==============================] - 0s 16ms/step - loss: 1.4029 - val_loss: 0.6335\n",
            "Epoch 2/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.6887 - val_loss: 0.5134\n",
            "Epoch 3/25\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 0.4122 - val_loss: 0.2192\n",
            "Epoch 4/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.2096 - val_loss: 0.2237\n",
            "Epoch 5/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.1837 - val_loss: 0.2318\n",
            "Epoch 6/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.1648 - val_loss: 0.2011\n",
            "Epoch 7/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.1516 - val_loss: 0.1148\n",
            "Epoch 8/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.1106 - val_loss: 0.1374\n",
            "Epoch 9/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.1003 - val_loss: 0.1347\n",
            "Epoch 10/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.0958 - val_loss: 0.1201\n",
            "Epoch 11/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.1020 - val_loss: 0.2600\n",
            "Epoch 12/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.1208 - val_loss: 0.1434\n",
            "Epoch 13/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.1083 - val_loss: 0.1638\n",
            "Epoch 14/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.1115 - val_loss: 0.1048\n",
            "Epoch 15/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.0916 - val_loss: 0.1302\n",
            "Epoch 16/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.0761 - val_loss: 0.1211\n",
            "Epoch 17/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.0850 - val_loss: 0.1557\n",
            "Epoch 18/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.0666 - val_loss: 0.1431\n",
            "Epoch 19/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.0626 - val_loss: 0.1259\n",
            "Epoch 20/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.0525 - val_loss: 0.1186\n",
            "Epoch 21/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.0522 - val_loss: 0.1038\n",
            "Epoch 22/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.0552 - val_loss: 0.1244\n",
            "Epoch 23/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.0610 - val_loss: 0.1145\n",
            "Epoch 24/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.0511 - val_loss: 0.1372\n",
            "Epoch 25/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.0520 - val_loss: 0.1100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oQFhYTeHmkeb",
        "colab_type": "code",
        "outputId": "5b9902dc-a289-48c9-c781-88d0ea4470c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "# 4.15 회귀 모델 학습 결과 시각화\n",
        "import matplotlib.pyplot as plt\n",
        "plt.plot(history.history['loss'], 'b-', label='loss')\n",
        "plt.plot(history.history['val_loss'], 'r--', label='val_loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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h9+tXvv6f/3SfO7fiE9ouvTRsv//+YZuGbuZM92HDwgcO7ocd5v7oo+Xrhw4N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\nJE39f6kp/Nk4VsAOAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FXIL_Isaao_t",
        "colab_type": "code",
        "outputId": "a89a1d63-d651-43e8-f225-79c43fcd880a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "# 4.16 회귀 모델 평가\n",
        "model.evaluate(test_X, test_Y)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4/4 [==============================] - 0s 2ms/step - loss: 0.5020\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5020208954811096"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JchlHj_HH0cb",
        "colab_type": "code",
        "outputId": "1b266ad5-78e4-4133-ca9d-5d7267a3b83d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        }
      },
      "source": [
        "# 4.17 실제 주택 가격과 예측 주택 가격 시각화\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "pred_Y = model.predict(test_X)\n",
        "\n",
        "plt.figure(figsize=(5,5))\n",
        "plt.plot(test_Y, pred_Y, 'b.')\n",
        "plt.axis([min(test_Y), max(test_Y), min(test_Y), max(test_Y)])\n",
        "\n",
        "# y=x에 해당하는 대각선\n",
        "plt.plot([min(test_Y), max(test_Y)], [min(test_Y), max(test_Y)], ls=\"--\", c=\".3\")\n",
        "plt.xlabel('test_Y')\n",
        "plt.ylabel('pred_Y')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PNuXWWx+lcePGJwRmudq04KobmNGgjcQ6hWYMq02rzb8s8gjPPz+NlJQS7rzz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\nmB4IAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m0FujtGj_bg2",
        "colab_type": "code",
        "outputId": "8259f352-1dfe-4a02-cee4-3d431d17b080",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 665
        }
      },
      "source": [
        "# 4.18 모델 재정의 및 학습\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(units=52, activation='relu', input_shape=(13,)),\n",
        "    tf.keras.layers.Dense(units=39, activation='relu'),\n",
        "    tf.keras.layers.Dense(units=26, activation='relu'),\n",
        "    tf.keras.layers.Dense(units=1)\n",
        "])\n",
        "\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.07), loss='mse')\n",
        "\n",
        "history = model.fit(train_X, train_Y, epochs=25, batch_size=32, validation_split=0.25, callbacks=[tf.keras.callbacks.EarlyStopping(patience=3, monitor='val_loss')])"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/25\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 1.5699 - val_loss: 0.8037\n",
            "Epoch 2/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.5144 - val_loss: 0.3361\n",
            "Epoch 3/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.2673 - val_loss: 0.3426\n",
            "Epoch 4/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.2098 - val_loss: 0.2156\n",
            "Epoch 5/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.1822 - val_loss: 0.2042\n",
            "Epoch 6/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.2239 - val_loss: 0.2355\n",
            "Epoch 7/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.2350 - val_loss: 0.1574\n",
            "Epoch 8/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.1599 - val_loss: 0.1330\n",
            "Epoch 9/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.1313 - val_loss: 0.1334\n",
            "Epoch 10/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.1266 - val_loss: 0.1221\n",
            "Epoch 11/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.0987 - val_loss: 0.1198\n",
            "Epoch 12/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.0983 - val_loss: 0.1166\n",
            "Epoch 13/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.1038 - val_loss: 0.1045\n",
            "Epoch 14/25\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 0.1166 - val_loss: 0.1320\n",
            "Epoch 15/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.1125 - val_loss: 0.1008\n",
            "Epoch 16/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.0881 - val_loss: 0.1741\n",
            "Epoch 17/25\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 0.0995 - val_loss: 0.1052\n",
            "Epoch 18/25\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 0.0782 - val_loss: 0.1227\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JRuoLaJM_zIK",
        "colab_type": "code",
        "outputId": "d390585d-308d-45c2-ad8e-636bdc7b512a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "# 4.19 회귀 모델 학습 결과 시각화\n",
        "import matplotlib.pyplot as plt\n",
        "plt.plot(history.history['loss'], 'b-', label='loss')\n",
        "plt.plot(history.history['val_loss'], 'r--', label='val_loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Gq0pEpEmKJOR7AhXjMTd0W0V3ABeYWS7wKnB1VQ9kZpPMLMfMcgrqeyaPr76C0aPhhRci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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UBTn6vOq_3ar",
        "colab_type": "code",
        "outputId": "78f0d2a8-e126-417e-abe0-0eef5af658e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "# 4.20 회귀 모델 평가\n",
        "model.evaluate(test_X, test_Y)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4/4 [==============================] - 0s 2ms/step - loss: 0.3032\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.30322206020355225"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5dBsLf-GDphx",
        "colab_type": "code",
        "outputId": "4186a0f2-70be-4b18-e7f2-11b4ae781634",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        }
      },
      "source": [
        "# 4.21 실제 주택 가격과 예측 주택 가격 시각화\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "pred_Y = model.predict(test_X)\n",
        "\n",
        "plt.figure(figsize=(5,5))\n",
        "plt.plot(test_Y, pred_Y, 'b.')\n",
        "plt.axis([min(test_Y), max(test_Y), min(test_Y), max(test_Y)])\n",
        "\n",
        "plt.plot([min(test_Y), max(test_Y)], [min(test_Y), max(test_Y)], ls=\"--\", c=\".3\")\n",
        "plt.xlabel('test_Y')\n",
        "plt.ylabel('pred_Y')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bHatSVFTEn/70Jz7++GNOO+00UlIq/xGobglPRkZgm2PXroGWZkqKxhIleWhM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\nHmmQzZYLAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}