material/deep_learning/boston_house_price_regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "boston_house_price_regression.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vdevF3bdzylf"
      },
      "source": [
        "# 문제\n",
        "```\n",
        "1. 데이터들을 normalization\n",
        "    각 feature별로\n",
        "    출력값 \n",
        "2. DNN. 2->1......  13->1\n",
        "3. metric. 'mape'\n",
        "```\n",
        "\n",
        "```\n",
        "요구사항\n",
        "  - train_loss, val_loss 그래프 그리기\n",
        "  - test데이터에 대한 mape 출력\n",
        "\n",
        "  - 오버피팅 완화해서 성능 개선해 보기\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CBGKFjlkO45b"
      },
      "source": [
        "# importing\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k3u2Jcwey2OG"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense, Flatten, Input\n",
        "\n",
        "import time"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kGVrxNwVO7L0"
      },
      "source": [
        "# data loading"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HKyhzxEKyjZH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "01a24378-9358-4efc-fedf-f4b3230b4985"
      },
      "source": [
        "(raw_train_x, raw_train_y), (raw_test_x, raw_test_y) = tf.keras.datasets.boston_housing.load_data()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/boston_housing.npz\n",
            "57344/57026 [==============================] - 0s 0us/step\n",
            "65536/57026 [==================================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KIebGkT6y5Zr",
        "outputId": "45b111c8-2b39-4282-9595-51d71532337b"
      },
      "source": [
        "print(raw_train_x.shape)\n",
        "print(raw_train_y.shape)\n",
        "print(raw_test_x.shape)\n",
        "print(raw_test_y.shape)\n",
        "      "
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(404, 13)\n",
            "(404,)\n",
            "(102, 13)\n",
            "(102,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dUsnwujqzcDx",
        "outputId": "2f61013a-8189-4d4c-b51d-5d2aadf5b731"
      },
      "source": [
        "print(raw_train_x[0])"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[  1.23247   0.        8.14      0.        0.538     6.142    91.7\n",
            "   3.9769    4.      307.       21.      396.9      18.72   ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TUSpa5OjzFOm",
        "outputId": "927179f3-f33c-4d13-ddec-33d7f3ad07a1"
      },
      "source": [
        "print(raw_train_y[:10])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[15.2 42.3 50.  21.1 17.7 18.5 11.3 15.6 15.6 14.4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w9nlNVp6T6BN"
      },
      "source": [
        "# 데이터 살펴보기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qRjjyTIVR4Xl",
        "outputId": "be048662-aff6-4946-9eb2-1fcc1410a2b2"
      },
      "source": [
        "for i in range(13):\n",
        "  plt.hist(raw_train_x[:,i], bins=100)\n",
        "  plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAANLElEQVR4nO3df6zdd13H8efLlQUZ6jZ209R2s1MWDCHRkRvEjJCFqRlg3EzIwmKwkpn6ByiIiVT+Gf+YDIP8MDFLKpuWBJFlTLeIUZc6gv5hw+1Y2I+Ka+bG2nTtJTB+6B849/aP++24Kfe2ved7bk/Pu89Hspzvz3Pen37TV9/nc875LlWFJKmXH5l1AZKk6TPcJakhw12SGjLcJakhw12SGtoy6wIArrjiitq5c+esy5CkuXLw4MFvVNXCWvvOi3DfuXMnS0tLsy5DkuZKkmfW2+e0jCQ1ZLhLUkOGuyQ1ZLhLUkOGuyQ1ZLhLUkOGuyQ1ZLhLUkOGuyQ1dF78QlXSeDv3fOGl5afvePsMK9H5wM5dkhoy3CWpIcNdkhoy3CWpoTOGe5K7k5xI8tiqbZcneTDJk8PjZcP2JPmzJIeTfDXJ6zezeEnS2s6mc/8r4MZTtu0B9lfVNcD+YR3grcA1w3+7gTunU6YkaSPOGO5V9SXgm6dsvgnYNyzvA25etf3TteLfgUuTbJtWsZKkszPpnPvWqjo2LD8HbB2WtwPPrjruyLDthyTZnWQpydLy8vKEZUiS1jL6A9WqKqAmOG9vVS1W1eLCwpr/C0BJ0oQmDffjJ6dbhscTw/ajwJWrjtsxbJMknUOThvsDwK5heRdw/6rtvzl8a+aNwLdXTd9Iks6RM95bJslngeuBK5IcAW4H7gDuSXIb8Axwy3D4PwBvAw4D/wO8exNqliSdwRnDvapuXWfXDWscW8B7xhYlSRrHX6hKUkOGuyQ1ZLhLUkOGuyQ1ZLhLUkOGuyQ1ZLhLUkOGuyQ1ZLhLUkOGuyQ1ZLhLUkOGuyQ1ZLhLUkOGuyQ1ZLhLUkOGuyQ1ZLhLUkOGuyQ1ZLhLUkOGuyQ1ZLhLUkOGuyQ1ZLhLUkOGuyQ1ZLhLUkOGuyQ1ZLhLUkOGuyQ1ZLhLUkOGuyQ1NCrck/x+kseTPJbks0lenuTqJAeSHE7yuSQXT6tYSdLZmTjck2wHfg9YrKrXARcB7wQ+Any8ql4NfAu4bRqFSpLO3thpmS3AjybZArwCOAa8Bbh32L8PuHnka0iSNmjicK+qo8BHga+zEurfBg4Cz1fVC8NhR4DtY4uUJG3MmGmZy4CbgKuBnwQuAW7cwPm7kywlWVpeXp60DEnSGsZMy/wS8F9VtVxV/wvcB1wHXDpM0wDsAI6udXJV7a2qxapaXFhYGFGGJOlUY8L968Abk7wiSYAbgCeAh4B3DMfsAu4fV6IkaaPGzLkfYOWD04eBR4fn2gt8EPhAksPAq4C7plCnJGkDtpz5kPVV1e3A7adsfgp4w5jnlSSN4y9UJakhw12SGjLcJakhw12SGjLcJakhw12SGjLcJakhw12SGjLcJakhw12SGjLcJakhw12SGjLcJakhw12SGjLcJakhw12SGjLcJakhw12SGjLcJakhw12SGjLcJakhw12SGjLcJakhw12SGjLcJakhw12SGjLcJakhw12SGjLcJakhw12SGjLcJamhUeGe5NIk9yb5jySHkvxiksuTPJjkyeHxsmkVK0k6O2M7908C/1hVPwv8HHAI2APsr6prgP3DuiTpHJo43JP8BPBm4C6Aqvp+VT0P3ATsGw7bB9w8tkhJ0saM6dyvBpaBv0zylSSfSnIJsLWqjg3HPAdsXevkJLuTLCVZWl5eHlGGJOlUY8J9C/B64M6quhb4b06ZgqmqAmqtk6tqb1UtVtXiwsLCiDIkSacaE+5HgCNVdWBYv5eVsD+eZBvA8HhiXImSpI2aONyr6jng2SSvGTbdADwBPADsGrbtAu4fVaEkacO2jDz/d4HPJLkYeAp4Nyv/YNyT5DbgGeCWka8hSdqgUeFeVY8Ai2vsumHM80qSxvEXqpLUkOEuSQ0Z7pLUkOEuSQ0Z7pLUkOEuSQ0Z7pLUkOEuSQ0Z7pLUkOEuSQ0Z7pLUkOEuSQ0Z7pLUkOEuSQ0Z7pLUkOEuSQ0Z7pLUkOEuSQ0Z7pLUkOEuSQ0Z7pLUkOEuSQ0Z7pLUkOEuSQ0Z7pLUkOEuSQ0Z7pLUkOEuSQ0Z7pLUkOEuSQ0Z7pLU0OhwT3JRkq8k+fth/eokB5IcTvK5JBePL1OStBHT6NzfBxxatf4R4ONV9WrgW8BtU3gNSdIGjAr3JDuAtwOfGtYDvAW4dzhkH3DzmNeQJG3c2M79E8AfAi8O668Cnq+qF4b1I8D2tU5MsjvJUpKl5eXlkWVIklabONyT/CpwoqoOTnJ+Ve2tqsWqWlxYWJi0DEnSGraMOPc64NeSvA14OfDjwCeBS5NsGbr3HcDR8WVKkjZi4s69qv6oqnZU1U7gncC/VNVvAA8B7xgO2wXcP7pKSdKGbMb33D8IfCDJYVbm4O/ahNeQJJ3GmGmZl1TVF4EvDstPAW+YxvNKkibjL1QlqSHDXZIaMtwlqSHDXZIaMtwlqSHDXZIaMtwlqSHDXZIaMtwlqSHDXZIaMtwlqSHDXZIaMtwlqSHDXZIaMtwlqSHDXZIaMtwlqSHDXZIaMtwlqSHDXZIaMtwlqSHDXZIaMtwlqSHDXZIaMtwlqSHDXZIa2jLrAiSdX3bu+cJLy0/f8fYZVqIx7NwlqSHDXZIaMtwlqSHDXZIamjjck1yZ5KEkTyR5PMn7hu2XJ3kwyZPD42XTK1eSdDbGdO4vAH9QVa8F3gi8J8lrgT3A/qq6Btg/rEuSzqGJw72qjlXVw8Pyd4FDwHbgJmDfcNg+4OaxRUqSNmYq33NPshO4FjgAbK2qY8Ou54Ct65yzG9gNcNVVV02jDE2R33WW5tvoD1STvBL4PPD+qvrO6n1VVUCtdV5V7a2qxapaXFhYGFuGJGmVUeGe5GWsBPtnquq+YfPxJNuG/duAE+NKlCRt1JhvywS4CzhUVR9btesBYNewvAu4f/LyJEmTGDPnfh3wLuDRJI8M2z4E3AHck+Q24BnglnElSpI2auJwr6p/A7LO7hsmfV5J0njeFXLO+a0WSWvx9gOS1JDhLkkNGe6S1JBz7iNsxny3c+iSpsHOXZIasnOXdM75DnXz2blLUkN27k3ZGUkXNjt3SWrIzn0T2DVLmjU7d0lqyM59lVl13Ktf90LkOx1p+uzcJakhO3ed0ZjO2q5cmg07d0lqyHCXpIYMd0lqyDn3TbbZd46UpLXYuUtSQxdE5+43Nn5gln8W83Id5qVO6XTs3CWpoQuicx/LTm5j/ExAXczz3307d0lq6ILv3NfrMje6fbNt9ut27rbnufuSJmXnLkkNXfCd+7m00Q6yczc9LXbl0trs3CWpobadu12vziXfQeh8Y+cuSQ217dzXY0cvadbOxTu9Tenck9yY5GtJDifZsxmvIUla39Q79yQXAX8O/DJwBPhykgeq6olpvxbYiZ8N/4ykC89mdO5vAA5X1VNV9X3gb4CbNuF1JEnrSFVN9wmTdwA3VtVvD+vvAn6hqt57ynG7gd3D6muAr53F018BfGOK5Z5vHN986zy+zmOD+R3fT1XVwlo7ZvaBalXtBfZu5JwkS1W1uEklzZzjm2+dx9d5bNBzfJsxLXMUuHLV+o5hmyTpHNmMcP8ycE2Sq5NcDLwTeGATXkeStI6pT8tU1QtJ3gv8E3ARcHdVPT6lp9/QNM4ccnzzrfP4Oo8NGo5v6h+oSpJmz9sPSFJDhrskNTQ34d79lgZJnk7yaJJHkizNup6xktyd5ESSx1ZtuzzJg0meHB4vm2WNk1pnbB9OcnS4fo8kedssaxwjyZVJHkryRJLHk7xv2D731+80Y2tz/U6aizn34ZYG/8mqWxoAt27WLQ1mIcnTwGJVzeMPKX5IkjcD3wM+XVWvG7b9CfDNqrpj+Af6sqr64CzrnMQ6Y/sw8L2q+ugsa5uGJNuAbVX1cJIfAw4CNwO/xZxfv9OM7RaaXL+T5qVz95YGc6aqvgR885TNNwH7huV9rPylmjvrjK2NqjpWVQ8Py98FDgHbaXD9TjO2duYl3LcDz65aP0K/C1LAPyc5ONyaoaOtVXVsWH4O2DrLYjbBe5N8dZi2mbspi7Uk2QlcCxyg2fU7ZWzQ7PrNS7hfCN5UVa8H3gq8Z3jr31atzAee/3OCZ+9O4GeAnweOAX8623LGS/JK4PPA+6vqO6v3zfv1W2Ns7a7fvIR7+1saVNXR4fEE8LesTEV1c3yY8zw593lixvVMTVUdr6r/q6oXgb9gzq9fkpexEn6fqar7hs0trt9aY+t2/WB+wr31LQ2SXDJ8uEOSS4BfAR47/Vlz6QFg17C8C7h/hrVM1cnQG/w6c3z9kgS4CzhUVR9btWvur996Y+t0/U6ai2/LAAxfTfoEP7ilwR/PuKSpSfLTrHTrsHJLiL+e9/El+SxwPSu3Uj0O3A78HXAPcBXwDHBLVc3dB5PrjO16Vt7SF/A08Dur5qfnSpI3Af8KPAq8OGz+ECtz03N9/U4ztltpcv1OmptwlySdvXmZlpEkbYDhLkkNGe6S1JDhLkkNGe6S1JDhLkkNGe6S1ND/AyvJmOVd37hPAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "upCEPkBCzMnV",
        "outputId": "bf2e3881-5768-430d-8a09-b880df09dee0"
      },
      "source": [
        "plt.hist(raw_train_y, bins=100)\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "15gRPMZ5L18p"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UO4jOl7iL3uw"
      },
      "source": [
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CpdPpqIAL5p9"
      },
      "source": [
        "import time"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1_TzR5_NMF40"
      },
      "source": [
        ""
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bDYNByo0T3NT"
      },
      "source": [
        ""
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XRDMxXXoOuFJ"
      },
      "source": [
        "## normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PeW0ZfJyMFzR"
      },
      "source": [
        "train_x = raw_train_x\n",
        "train_y = raw_train_y\n",
        "test_x = raw_test_x\n",
        "test_y = raw_test_y"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ggSkckE0NZhM",
        "outputId": "661ce657-3c7c-4ea8-e7af-1c5b9d1754fa"
      },
      "source": [
        "for i in range(13):\n",
        "  target = raw_train_x[:,i]\n",
        "  min = np.min(target)\n",
        "  max = np.max(target)\n",
        "  print(min, max)\n",
        "  train_x[:,i] = (target-min)/(max-min)\n",
        "  print(np.min(train_x[:,i]), np.max(train_x[:,i]))  \n",
        "\n",
        "for i in range(13):\n",
        "  target = raw_test_x[:,i]\n",
        "  min = np.min(target)\n",
        "  max = np.max(target)\n",
        "  print(min, max)\n",
        "  test_x[:,i] = (target-min)/(max-min)\n",
        "  print(np.min(test_x[:,i]), np.max(test_x[:,i]))  "
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.00632 88.9762\n",
            "0.0 1.0\n",
            "0.0 100.0\n",
            "0.0 1.0\n",
            "0.46 27.74\n",
            "0.0 1.0\n",
            "0.0 1.0\n",
            "0.0 1.0\n",
            "0.385 0.871\n",
            "0.0 1.0\n",
            "3.561 8.725\n",
            "0.0 1.0\n",
            "2.9 100.0\n",
            "0.0 1.0\n",
            "1.1296 10.7103\n",
            "0.0 1.0\n",
            "1.0 24.0\n",
            "0.0 1.0\n",
            "188.0 711.0\n",
            "0.0 1.0\n",
            "12.6 22.0\n",
            "0.0 1.0\n",
            "0.32 396.9\n",
            "0.0 1.0\n",
            "1.73 37.97\n",
            "0.0 1.0\n",
            "0.01311 25.0461\n",
            "0.0 1.0\n",
            "0.0 90.0\n",
            "0.0 1.0\n",
            "1.22 27.74\n",
            "0.0 1.0\n",
            "0.0 1.0\n",
            "0.0 1.0\n",
            "0.392 0.871\n",
            "0.0 1.0\n",
            "4.88 8.78\n",
            "0.0 1.0\n",
            "6.0 100.0\n",
            "0.0 1.0\n",
            "1.4655 12.1265\n",
            "0.0 1.0\n",
            "1.0 24.0\n",
            "0.0 1.0\n",
            "187.0 711.0\n",
            "0.0 1.0\n",
            "13.0 21.2\n",
            "0.0 1.0\n",
            "24.65 396.9\n",
            "0.0 1.0\n",
            "1.92 31.99\n",
            "0.0 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K-6gOajjOLwp",
        "outputId": "82b8b019-362e-4989-dcde-2eae9f38a783"
      },
      "source": [
        "target = raw_train_y\n",
        "min = np.min(target)\n",
        "max = np.max(target)\n",
        "print(min, max)\n",
        "train_y = (target-min)/(max-min)\n",
        "print(np.min(train_y), np.max(train_y))  \n",
        "\n",
        "target = raw_test_y\n",
        "min = np.min(target)\n",
        "max = np.max(target)\n",
        "print(min, max)\n",
        "test_y = (target-min)/(max-min)\n",
        "print(np.min(test_y), np.max(test_y))  \n",
        "\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5.0 50.0\n",
            "0.0 1.0\n",
            "5.6 50.0\n",
            "0.0 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uu8TtVzrPDAT"
      },
      "source": [
        "# 모델 정의"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OiI7h3rsPFPw",
        "outputId": "2064f91a-a3b8-4878-9a40-0cb973a1714a"
      },
      "source": [
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='relu', input_shape=(train_x.shape[1],)))\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "model.compile(optimizer=\"Adam\", loss=\"mse\", metrics=[\"mape\"])\n",
        "model.summary()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 10)                140       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 261\n",
            "Trainable params: 261\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C1w9LDDGPFzY"
      },
      "source": [
        "# 학습 실행"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LZIv0iAfzwwm",
        "outputId": "c23b8345-3069-41d3-fc41-350dbdfb51fd"
      },
      "source": [
        "start = time.time()\n",
        "history = model.fit(train_x, train_y, epochs=5000, verbose=0, batch_size=64, validation_split=0.1)\n",
        "print(\"elapsed :\", (time.time() - start))\n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "elapsed : 122.68436551094055\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "byoSG7yOMxKD",
        "outputId": "cb3934a5-0277-4606-d68a-0e53b74ff21c"
      },
      "source": [
        "loss, mape = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\", loss)\n",
        "print(\"mape=\", mape)\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0177 - mape: 782008.0000\n",
            "loss= 0.017714228481054306\n",
            "mape= 782008.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "3veOUR3OQe6c",
        "outputId": "9e5d0356-2529-48ed-d446-f86db0303e53"
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "87hCxh99M39k",
        "outputId": "94d55b4c-742f-4585-a7f2-e0613fc0f158"
      },
      "source": [
        "plt.plot(history.history['loss'][200:])\n",
        "plt.plot(history.history['val_loss'][200:])\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uQNMBVjdQsuI",
        "outputId": "d7c26e38-4e00-4f8a-bafe-98b430eec18b"
      },
      "source": [
        "start = time.time()\n",
        "history = model.fit(train_x, train_y, epochs=5000, verbose=0, batch_size=64, validation_split=0.1)\n",
        "print(\"elapsed :\", (time.time() - start))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "elapsed : 124.6626706123352\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "i5tCtd7RQuSS",
        "outputId": "adf1bbcf-7e8c-4c2d-b81f-c2f164c622cc"
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IQUB1L2FQuPb",
        "outputId": "20e0abc4-9169-4a01-cc2c-49a8e37deb15"
      },
      "source": [
        "loss, mape = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\", loss)\n",
        "print(\"mape=\", mape)\n"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0169 - mape: 960445.6250\n",
            "loss= 0.016852175816893578\n",
            "mape= 960445.625\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YT0AhY__QuMf",
        "outputId": "7b381484-8185-40fb-c51f-365eb3606fc3"
      },
      "source": [
        "y_ = model.predict(test_x)\n",
        "\n",
        "for i in range(50):\n",
        "  print(test_y[i], y_[i])\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.03603603603603605 [0.06141482]\n",
            "0.2972972972972973 [0.26685077]\n",
            "0.30180180180180183 [0.31093037]\n",
            "0.48198198198198194 [0.34740567]\n",
            "0.37387387387387394 [0.34594396]\n",
            "0.42567567567567566 [0.28333545]\n",
            "0.5765765765765766 [0.41965926]\n",
            "0.3896396396396396 [0.33365178]\n",
            "0.3355855855855856 [0.2631094]\n",
            "0.39639639639639646 [0.27135992]\n",
            "0.29279279279279286 [0.25209033]\n",
            "0.20045045045045046 [0.18925868]\n",
            "0.2747747747747748 [0.21601759]\n",
            "1.0 [0.8075156]\n",
            "0.3423423423423424 [0.18255459]\n",
            "0.42117117117117125 [0.2973817]\n",
            "0.41891891891891897 [0.36502382]\n",
            "0.31981981981981983 [0.32458264]\n",
            "0.3040540540540541 [0.22657715]\n",
            "0.3851351351351352 [0.31346977]\n",
            "0.14414414414414414 [0.08299999]\n",
            "0.1036036036036036 [0.17733543]\n",
            "0.32432432432432434 [0.2846079]\n",
            "0.29054054054054057 [0.15058579]\n",
            "0.34459459459459457 [0.29778647]\n",
            "0.3918918918918919 [0.42590576]\n",
            "0.4932432432432432 [0.47269118]\n",
            "0.5518018018018018 [0.36969382]\n",
            "0.08783783783783784 [0.14750563]\n",
            "0.36936936936936937 [0.31928894]\n",
            "0.35135135135135137 [0.3009262]\n",
            "0.19144144144144146 [0.1768106]\n",
            "0.6193693693693694 [0.46554202]\n",
            "0.40090090090090086 [0.34794763]\n",
            "0.3265765765765766 [0.07338063]\n",
            "0.04054054054054056 [-0.08707584]\n",
            "0.22072072072072074 [0.18909489]\n",
            "0.40990990990991 [0.28674257]\n",
            "0.3265765765765766 [0.16750894]\n",
            "0.42567567567567566 [0.39323896]\n",
            "0.6171171171171171 [0.38557726]\n",
            "0.5135135135135135 [0.40677768]\n",
            "0.19144144144144146 [0.14697634]\n",
            "0.9256756756756758 [0.76694393]\n",
            "0.6058558558558559 [0.40271538]\n",
            "0.5405405405405406 [0.34132874]\n",
            "0.5135135135135135 [0.46204346]\n",
            "0.31981981981981983 [0.21019717]\n",
            "0.32882882882882886 [0.3519981]\n",
            "0.4369369369369369 [0.324001]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gnBo-wTdMkvU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "7869d81a-32e9-4095-b59c-1b53d235e336"
      },
      "source": [
        "              \n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "# plt.scatter(test_x,test_y)\n",
        "# plt.scatter(test_x,y_, color=\"r\")\n",
        "plt.scatter(test_x.T[0],test_y)\n",
        "plt.scatter(test_x.T[0],y_, color=\"r\")\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "-McStdWdWtfQ",
        "outputId": "09b744e4-9ff5-4d7b-e22e-95c72f2dd85d"
      },
      "source": [
        "plt.scatter(test_y, y_)\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAagUlEQVR4nO3dbYxcZ3nG8f/tzUI2ArKAjUrWTmxUx2BIYWGUUlkqJKHYCW1sJbwkaQpIKVGgQQXSlRyBKIRKmFpAWzVtMWoaQEDetVoUV5aKHUWKcJp1N8E4ipFxSOIJbRbI5gNZwtq5+2FmNuPxeZuZM2fmPHP9pEg7M8czz8na13nmPs+LuTsiIlJ+K/rdABERyYcCXUQkEAp0EZFAKNBFRAKhQBcRCcRp/frglStX+tq1a/v18SIipXTgwIFfuvuqqNf6Fuhr165ldna2Xx8vIlJKZvZE3GsquYiIBEKBLiISCAW6iEggFOgiIoFQoIuIBKJvo1xERIbN9FyVnXsO8/TCImeNjzG1eQPbJidye38FuohIAabnqtx4z0EWl04AUF1Y5MZ7DgLkFuoquYiIFGDnnsPLYd6wuHSCnXsO5/YZCnQRkQI8vbDY1vOdUKCLiBTgrPGxtp7vhAJdRKQAU5s3MDY6ctJzY6MjTG3ekNtn6KaoiEgBGjc++zrKxcxuAf4UeMbd3xLxugH/CFwCPA981N3/J7cWiogEYtvkRK4B3ipLyeVWYEvC6xcD6+v/XQv8a/fNEhGRdqUGurvfD/w64ZCtwLe9Zj8wbmavz6uBIiKSTR43RSeAp5oeH6s/JyIiBSp0lIuZXWtms2Y2Oz8/X+RHi4gEL49RLlVgTdPj1fXnTuHuu4BdAJVKxXP4bCmRXq9jITLs8uihzwAftpp3As+5+y9yeF8JSGMdi+rCIs5L61hMz0Ve+0WkA6mBbmbfB34EbDCzY2Z2jZldZ2bX1Q/ZDRwFjgDfBD7Rs9ZKaRWxjoXIsEstubj7lSmvO/BXubVIglTEOhYiw05T/6UQRaxjITLsFOhSiCLWsRAZdlrLRQpRxDoWIsNOgS6F6fU6FiLDTiUXEZFAKNBFRAKhQBcRCYQCXUQkEAp0EZFAKNBFRAKhQBcRCYQCXUQkEAp0EZFAKNBFRAKhQBcRCYQCXUQkEAp0EZFAKNBFRAKhQBcRCYQCXUQkEAp0EZFAaMciEZGCTM9Ve7oNowJdRKQA03NVbrznIItLJwCoLixy4z0HAXILdZVcREQKsHPP4eUwb1hcOsHOPYdz+wwFuohIAZ5eWGzr+U4o0EVECnDW+Fhbz3ciU6Cb2RYzO2xmR8xse8TrZ5vZPjObM7Mfm9klubVQRCQAF7xxVVvPdyI10M1sBLgZuBjYCFxpZhtbDvsccIe7TwJXAP+SWwtFRAKw77H5tp7vRJYe+vnAEXc/6u6/A24DtrYc48Cr6j+fCTydWwtFRAIwKDX0CeCppsfH6s81+wJwtZkdA3YDn4x6IzO71sxmzWx2fj6/q5KIyKAbmBp6BlcCt7r7auAS4Dtmdsp7u/sud6+4e2XVqvzqRiIig25q8wbGRkdOem5sdISpzRty+4wsE4uqwJqmx6vrzzW7BtgC4O4/MrPTgZXAM3k0UkSk7BqTh/o9U/QhYL2ZraMW5FcAV7Uc8yRwEXCrmb0JOB1QTUVESqeX0/O3TU7kGuCtUgPd3Y+b2fXAHmAEuMXdD5nZTcCsu88ANwDfNLNPU7tB+lF39561WkSkB4qYnt9L1q/crVQqPjs725fPFhGJsmnHXqoRo04mxsd4YPuFfWjRqczsgLtXol7TTFERkboihhb2kgJdRKSuiKGFvaRAFxGpK2JoYS9pPXQRkboihhb2kgJdRAZer3f6adbroYW9pEAXkYFW9qGERVINXUQGWhE7/YRCgS4iA63sQwmLpEAXkYEWN2RwhRnTc63LSg03BbqIDLSooYQAJ9y58Z6DCvUmCnQRGWjbJif48mXnMWJ2ymuqpZ9MgS4iA2/b5AQvxqw7pVr6SxToIlIKZZ+WXwQFuoiUQtmn5RdBE4tEpBTKPi2/CAp0ESmNMk/LL4JKLiIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEIlOgm9kWMztsZkfMbHvMMR80s0fN7JCZfS/fZoqISJrUtVzMbAS4GfgT4BjwkJnNuPujTcesB24ENrn7s2b2ul41WEREomVZnOt84Ii7HwUws9uArcCjTcd8DLjZ3Z8FcPdn8m6oiHRveq6q1QoDlqXkMgE81fT4WP25ZucC55rZA2a238y2RL2RmV1rZrNmNjs/P99Zi0WkI9NzVW685yDVhUUcqC4sak/OwOR1U/Q0YD3wbuBK4JtmNt56kLvvcveKu1dWrVqV00eLSBY79xxmcenESc9pT86wZAn0KrCm6fHq+nPNjgEz7r7k7o8DP6UW8CIyIOL23tSenOHIUkN/CFhvZuuoBfkVwFUtx0xT65n/h5mtpFaCOZpnQ0Wkfc018xVmnIjYaFl7coYjNdDd/biZXQ/sAUaAW9z9kJndBMy6+0z9tfea2aPACWDK3X/Vy4aLSLzpuSpf/MEhnn1+afm5qDDXnpxhMY/4JRehUqn47OxsXz5bJGSNm5+t9fKGETNedNcol5IyswPuXol6TXuKigQm6uZnsxfdeXzH+wpskRRFU/9FAjI9V6WacpNTNfNwKdBFAtEotSRRzTxsCnSRQKSVWsbHRvnyZeepZh4w1dBFApE0nvwfPvQ2BfkQUA9dJBBxtfGJ8TGF+ZBQoIsEYmrzBsZGR056TjXz4aKSi0ggGr1wraY4vBToIgHZNjmhAB9iCnTJTGtpiww2Bbpk0jqdvLGWNqBQFxkQCnTJJGktbQX64NK3quGiQJdMQllLe5gCTt+qho8CXTI5a3wsco2QMq0LEhdws0/8mn2PzQcX8vpWNXwU6JLJ1OYNpyzJOghjnNvpcccF3Hf3P0ljEel+9mKjzgU6H4YYyrcqyU6BLpkM4hjndksKcUHWuiNAcy+2qBJN1LlM3fUIOCy96MvPtXOxCeFblbRHgS6ZDdoY53ZLCnEBF+XphcVCa9BR57J04tTNZ9opmQzqtyrpHU39l9Jqt6QQNTXeYt77rPGxxAtG3topg2Q9dtvkBF++7Dwmxscwamu6aLXFsKmHLqXVbkkhqmx0wRtXcfeBamQv9tO3Pxz5Pr2oQbfz7aGdksmgfauS3lKgS2l1UlKICrjKOa+JrJPv3HO4sBp01LmMjthJNXRQyUSSKdCltPK6URvXiy2yBh13LlHPqcctccz91BsvRahUKj47O9uXz5bBNIiTfgaxTTLczOyAu1eiXlMPXQbCoM5qzLMGrYuD9JoCXQoVF2q9ntXY7zAd1AuWhEWBLoVJCrVezmrsJkzzuhBoGr4UIdM4dDPbYmaHzeyImW1POO5yM3Mzi6zvyHBLCrW4kSN5jCiJ+9wv/uBQ4p9rXAiqC4s4L10Ipueqbbch6wVreq7Kph17Wbf9Xjbt2NvRZ8nwSg10MxsBbgYuBjYCV5rZxojjXgn8NfBg3o2UMCSFWi/3w4z73GefX0oMzDwnFmW5YOV5AZHhlKWHfj5wxN2PuvvvgNuArRHHfQn4CvDbHNsnAUkKtXZmNbbbi03q5SeFc5Zedda2ZLlgFTkzVcKUJdAngKeaHh+rP7fMzN4OrHH3e3NsmwQmLdS2TU7wwPYLeXzH+3hg+4WxYd5uLzapl59Uo0/rVbfTliwXrLi2VBcWVX6RTLpey8XMVgBfA27IcOy1ZjZrZrPz8/PdfrSUTB5ri3TSi902OcH42Gjka0m997QLULttSbtgJbVF5RfJIssolyqwpunx6vpzDa8E3gLcZ2YAvwfMmNml7n7SzCF33wXsgtrEoi7aLSXV7bjuTkfDfOHSN3e0TADEz9TMe2RO1MzUZhoVI2myBPpDwHozW0ctyK8Armq86O7PASsbj83sPuBvWsNcJA+drvHd6TIBSReguLY4sGnH3raHODa3MW6hLm1OIUlSA93dj5vZ9cAeYAS4xd0PmdlNwKy7z/S6kTI8ksZ9T89V+c0Lx0/5M1lHw+S98mBSj7rTiUONNm7asVebU0jbMk0scvfdwO6W5z4fc+y7u2+WDKOkCUBAZHi++oxR/vbP3px7GSLLhKK0HnU3JRJtTiGd0ExR6Vje0+m/+INDiTcZo3rCZ7zstJ6EedaZpY0e9brt956ylR10XiIZxC3/ZPAp0KUjeU+nh9pEnyhJGz90GphJF6NOpun3Yv9ObU4h7VKgS0c6XZsk7kJw+mhnI2g7Ccy0i1E7o1caF4bqwiLGyRtOq0QiRVOgS0faWZukuSf8mxeOR14I4obqJTGSJw3FSbsYZe1tt14YvN4mpzbGXiUSKZo2iZaOdLo2ycJidFmlE05nS8+mXYyyrisTdWFohHncTFeRXlIPXdqWdfhgVODlyYDPTR9k32Pzbd04TOuBZ70h2cslf0U6oUCXtrSWGRqihg/mFWyjK+ykjZIbHPju/ieX69ZZb8xmGRKY5YZkL26EinRDgS4naa55j58xijs8t7i03EuN63VHDR+MC7xWoytg6cXo1xq16E/d/nDk660xn+XGbF5DAjVWXAaNAl2Wtfa+m4cRNnq/cSWUqN542tokDVFhPrrC2PmBt2aaDp+lLa3yGBKoseIyaBTosiyt5r24dIIRM074qeWP5iVlG+Ebd2wWrzj95B7/1OYNTN31CEsn0t/vzJiVFXtBY8VlkCjQZVmWnu0Jd8ZGRyLLDNNzVabufGS53t1pmAMsRE0yyvh2tUU/RYaPhi3Ksiw38xprmEetaf6FmUORNy+7aUtjR6BP3f5w5veOvBiIDAEFuiyLGn/drNET3zY5wdTmDZw1PsbTC4vs3HOY6blqbmPMm3v8jXHs7dAoExlWKrnIstabfFGjXLZNTqSuitiN5hmWm3bsbXscez9GmeS9SJlIp8y7qHN2o1Kp+Oys9sAoo7i1us2gm79OBnz9Q29bDsd236of0+2jxuWPjY60vbWeSFZmdsDdK1GvqYcukZJ6nXE3T7vtG4yfMZppmGMUAx7YfmF3DehAp4uUifSCauhyirTd7POoUUetleIeveZ5s7gBLP2qm2v6vwwSBbqcZHquyg13PJK40UTazVOID16IHynzXMJN1cZxf/7OszMtnFWULIuUiRRFJRdZ1uiZx40fb/Q6s8zedGB8bPSUkS/NI2VaSxJx79dYvbChcs5rBuYmpKb/yyBRoMuytJmizb3OtM2MGyHczgiQrOE4SLMzNf1fBokCXZYl1X1HV1hkrzMthNsJ37KG4yBdYGS4KdBlWeLqiDFF8bxDuMhw1PhxCY0CXZYlrY64dMK54Y5HgFPXGi9jD7WbTa5FBpUCPTDd9Dobx8WtPX7CPZjQ0/hxCZGGLQYkbfx41PGbduxl3fZ72bRjL9NzVbZNTjCRMOSuefhimWn8uIRIgR6QuF7np25/eDmwG5LCP22ceXVh8ZQLQdlo/LiEKFOgm9kWMztsZkfMbHvE658xs0fN7Mdm9kMzOyf/pkqapN5la289reTw5cvOYyRmYXGrv1+WbwGDKuqipfHjUnapgW5mI8DNwMXARuBKM9vYctgcUHH3PwDuAv4+74ZKurTeZXO5JK3ksG1ygq9+8K2nhJ4RvY9n1LeAQda4aEWt6y5SVlluip4PHHH3owBmdhuwFXi0cYC772s6fj9wdZ6NHGZxNzmjns+yh2cjsLPsWB81JDFpbfKyjRQp4+gckSSpy+ea2fuBLe7+l/XHfwH8obtfH3P8PwP/6+5/F/HatcC1AGefffY7nnjiiS6bH7a4pVkvf8cEdx+oRi7ZCslT8ptncHay7GvczNCoz9G4bpH8JS2fm+tNUTO7GqgAO6Ned/dd7l5x98qqVavy/OhCRI0K6dV7JS2S9f0Hn0qsfz+w/UL+4UNvS6wRd1pyyLIwF5S3ti5SZllKLlVgTdPj1fXnTmJm7wE+C7zL3V/Ip3mDI8+JKGnvlbZIVtriWc1tShqT3knJIcvCXA0a1y1SrCyB/hCw3szWUQvyK4Crmg8ws0ngG9RKM8/k3soBkOdElLT3Slska8QsMtRbb4r2qkbceN/PTR/ku/ufTNxZSOO6RYqTGujuftzMrgf2ACPALe5+yMxuAmbdfYZaieUVwJ1WG+r2pLtf2sN2Fy7PiShp75X0nkk19HaG3HW7jsn0XJW7D1RTt4nTuG6R4mSa+u/uu4HdLc99vunn9+TcroGTZVRIXu8V9/qI2XKdu5s1wfMoH6V9iwCN6xYpmtZyidHag73gjau67hU3pC05O7V5A1N3PsLSiy/1f0dXGDs/8NblwO2mnJJU8mm8nnahSPoWYaDVC0X6QIEeIaoHe/eBKpe/Y4J9j813vdxqpiVnWydpJu3p1qa4MG701LP03OO+RbTuLiQixVGgR4jrwe57bD63sErqYe/cc5ilEydXp5OWr21XUkkn641fbb0mMni0OFeEolbiixuLHvc5jeVrux3bHbeOSZbhkA2aOi8yeNRDj5DnDdA4STcmk6bY5zG2O67kEze2PO68NXVeZLAo0CMUUU5IujGZtibL0wuLXQ87jAtjlVFEykuBHqGIzYqTyjqNz7nhjkciyyBnjo32ZPu0sm7SLCI1CvQYvS4npJV1Gp8d1WM2o2fbp6mMIlJeuimaIM/FuFpl2WAh7sbjwvNLke+pafad6eXvWaRI6qHH6PWu8FnLG1E95nZvXvZTt7X+Xuv171mkSAr0GEXsCt9peaMsY8DLEJZF/J5FiqKSS4xB3hW+LGPA05YYGASD/HsWaVeQPfQ8vubnMRa9l+WGMty8LENYFjHnQKQowfXQG1/zu92Vvttd4fNqxyBLu5kYF4qDFJbd/p5FBklwgZ7X1/xuyxplKDd0I8sFqwxhWZbylUgWwZVcklYSbFc3ZY0ylBu6keVmYlkmKpWhfCWSRXCBHlcTNWq9ym7+4bZTEw+9Npv1gqWwFClOcCWXqc0bIpcOd+iq3NFuTbwM5YZulKE+LjJsggv0bZMTsftcdlJ2aWi3Jh56bTb0C5ZIGQVXcoHaRg1Ri1qNWOfb/nRSEw+53FCW+rjIMAky0OM2aoh7PovQa+KdCPmCJVJGwZVcoFbeaOf5LFRiEJFBF2Sg9yJ8Q6+Ji0j5BVlySarvdjMdXyUGERlkQQY6RIdvGVb/ExHpVJAllzihT8cXkeGWKdDNbIuZHTazI2a2PeL1l5vZ7fXXHzSztXk3NA+hT8cXkeGWGuhmNgLcDFwMbASuNLONLYddAzzr7r8PfB34St4NzYNmN4pIyLL00M8Hjrj7UXf/HXAbsLXlmK3At+o/3wVcZNbFLJ4e0dBDEQlZlkCfAJ5qenys/lzkMe5+HHgOeG3rG5nZtWY2a2az8/PznbW4Cxp6KCIhK3SUi7vvAnYBVCqVzqdtdkFDD0UkVFl66FVgTdPj1fXnIo8xs9OAM4Ff5dFAERHJJkugPwSsN7N1ZvYy4ApgpuWYGeAj9Z/fD+x172LhFBERaVtqycXdj5vZ9cAeYAS4xd0PmdlNwKy7zwD/DnzHzI4Av6YW+iIiUqBMNXR33w3sbnnu800//xb4QL5NExGRdgzVTFERkZAp0EVEAqFAFxEJhAJdRCQQCnQRkUAo0EVEAhHsBhcN3exQJCJSJkEHunYoEpFhUqpAb7e3nbRDkQJdREJTmkDvpLetHYpEZJiU5qZoJ/uBaociERkmpQn0Tnrb2qFIRIZJaQK9k962digSkWFSmhr61OYNJ9XQIVtvWzsUiciwKE2gN0JZY8pFRKKVJtBBvW0RkSSlqaGLiEgyBbqISCAU6CIigVCgi4gEQoEuIhIIc/f+fLDZPPBEh398JfDLHJtTBjrn4aBzHg7dnPM57r4q6oW+BXo3zGzW3Sv9bkeRdM7DQec8HHp1ziq5iIgEQoEuIhKIsgb6rn43oA90zsNB5zwcenLOpayhi4jIqcraQxcRkRYKdBGRQAx0oJvZFjM7bGZHzGx7xOsvN7Pb668/aGZri29lvjKc82fM7FEz+7GZ/dDMzulHO/OUds5Nx11uZm5mpR/iluWczeyD9d/1ITP7XtFtzFuGv9tnm9k+M5ur//2+pB/tzIuZ3WJmz5jZT2JeNzP7p/r/jx+b2du7/lB3H8j/gBHgZ8AbgJcBjwAbW475BPBv9Z+vAG7vd7sLOOcLgDPqP398GM65ftwrgfuB/UCl3+0u4Pe8HpgDXl1//Lp+t7uAc94FfLz+80bg5/1ud5fn/MfA24GfxLx+CfCfgAHvBB7s9jMHuYd+PnDE3Y+6+++A24CtLcdsBb5V//ku4CIzswLbmLfUc3b3fe7+fP3hfmB1wW3MW5bfM8CXgK8Avy2ycT2S5Zw/Btzs7s8CuPszBbcxb1nO2YFX1X8+E3i6wPblzt3vB36dcMhW4Ntesx8YN7PXd/OZgxzoE8BTTY+P1Z+LPMbdjwPPAa8tpHW9keWcm11D7QpfZqnnXP8qusbd7y2yYT2U5fd8LnCumT1gZvvNbEthreuNLOf8BeBqMzsG7AY+WUzT+qbdf++pSrVjkbzEzK4GKsC7+t2WXjKzFcDXgI/2uSlFO41a2eXd1L6F3W9m57n7Ql9b1VtXAre6+1fN7I+A75jZW9z9xX43rCwGuYdeBdY0PV5dfy7yGDM7jdrXtF8V0rreyHLOmNl7gM8Cl7r7CwW1rVfSzvmVwFuA+8zs59RqjTMlvzGa5fd8DJhx9yV3fxz4KbWAL6ss53wNcAeAu/8IOJ3aIlahyvTvvR2DHOgPAevNbJ2ZvYzaTc+ZlmNmgI/Uf34/sNfrdxtKKvWczWwS+Aa1MC97XRVSztndn3P3le6+1t3XUrtvcKm7z/anubnI8nd7mlrvHDNbSa0Ec7TIRuYsyzk/CVwEYGZvohbo84W2slgzwIfro13eCTzn7r/o6h37fSc45S7xJdR6Jj8DPlt/7iZq/6Ch9gu/EzgC/Dfwhn63uYBz/i/g/4CH6//N9LvNvT7nlmPvo+SjXDL+no1aqelR4CBwRb/bXMA5bwQeoDYC5mHgvf1uc5fn+33gF8AStW9c1wDXAdc1/Y5vrv//OJjH32tN/RcRCcQgl1xERKQNCnQRkUAo0EVEAqFAFxEJhAJdRCQQCnQRkUAo0EVEAvH/L6gnrPTRQuYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "veWazimTWEnO"
      },
      "source": [
        "참고 : https://www.marktechpost.com/2019/06/17/regression-with-keras-deep-learning-with-keras-part-3/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Hd2Bav8WETG"
      },
      "source": [
        ""
      ],
      "execution_count": 24,
      "outputs": []
    }
  ]
}