SeaquantialModel NN.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mahdid-lilia/SIC_Samsung-Innovation-Campus-AI-Course/blob/main/SeaquantialModel%20NN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1_mha1JS0AAA"
      },
      "source": [
        "## Coding Exercise #0602"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XS5xAWdV0AAL"
      },
      "source": [
        "### 1. Linear regression:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "5X-jPFcH0AAN"
      },
      "outputs": [],
      "source": [
        "# Install the seaborn package to draw the scatterplot matrix. \n",
        "!pip install -q seaborn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "ulqmoi7E0AAQ",
        "outputId": "0e127a92-0dce-4628-b0c4-1a3f230d2a1c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.8.2\n"
          ]
        }
      ],
      "source": [
        "import pathlib\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E-zKLKTK0AAS"
      },
      "source": [
        "#### 1.1. Data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "FFH2wLkW0AAT"
      },
      "outputs": [],
      "source": [
        "# Estimate the automobile fuel efficiency. \n",
        "# Using the Auto MPG data set, create the estimation model of automobile fuel efficiency in the late 1970s and the early 1980s. \n",
        "# Use tf.keras API. For more details, refer to the Keras guide. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "id": "WIV3Dzt60AAU",
        "outputId": "e2667250-0a27-4b06-bcd3-9f7530173588"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/root/.keras/datasets/auto-mpg.data'"
            ]
          },
          "execution_count": 81,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset_path = keras.utils.get_file(\"auto-mpg.data\", \"http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\")\n",
        "dataset_path"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "U2pm_EGvGbdr",
        "outputId": "3bf5dc8c-5b84-4a75-d191-5fcd71252b38"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/root/.keras/datasets/auto-mpg.data'"
            ]
          },
          "execution_count": 82,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset_path"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ielL863W0AAV",
        "outputId": "2e7016f7-5c38-4c20-ce32-b9835245f98f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b58fdeab-ce79-451f-9545-2f159d17e320\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MPG</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>Displacement</th>\n",
              "      <th>Horsepower</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Acceleration</th>\n",
              "      <th>Model Year</th>\n",
              "      <th>Origin</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>27.0</td>\n",
              "      <td>4</td>\n",
              "      <td>140.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>2790.0</td>\n",
              "      <td>15.6</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>44.0</td>\n",
              "      <td>4</td>\n",
              "      <td>97.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2130.0</td>\n",
              "      <td>24.6</td>\n",
              "      <td>82</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>32.0</td>\n",
              "      <td>4</td>\n",
              "      <td>135.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>2295.0</td>\n",
              "      <td>11.6</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>28.0</td>\n",
              "      <td>4</td>\n",
              "      <td>120.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>2625.0</td>\n",
              "      <td>18.6</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>31.0</td>\n",
              "      <td>4</td>\n",
              "      <td>119.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>2720.0</td>\n",
              "      <td>19.4</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b58fdeab-ce79-451f-9545-2f159d17e320')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b58fdeab-ce79-451f-9545-2f159d17e320 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b58fdeab-ce79-451f-9545-2f159d17e320');\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",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
              "393  27.0          4         140.0        86.0  2790.0          15.6   \n",
              "394  44.0          4          97.0        52.0  2130.0          24.6   \n",
              "395  32.0          4         135.0        84.0  2295.0          11.6   \n",
              "396  28.0          4         120.0        79.0  2625.0          18.6   \n",
              "397  31.0          4         119.0        82.0  2720.0          19.4   \n",
              "\n",
              "     Model Year  Origin  \n",
              "393          82       1  \n",
              "394          82       2  \n",
              "395          82       1  \n",
              "396          82       1  \n",
              "397          82       1  "
            ]
          },
          "execution_count": 83,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "column_names = ['MPG','Cylinders','Displacement','Horsepower','Weight',\n",
        "                'Acceleration', 'Model Year', 'Origin']\n",
        "raw_dataset = pd.read_csv(dataset_path, names=column_names,\n",
        "                      na_values = \"?\", comment='\\t',\n",
        "                      sep=\" \", skipinitialspace=True)\n",
        "\n",
        "dataset = raw_dataset.copy()\n",
        "dataset.tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TW1d6j9X0AAX"
      },
      "source": [
        "#### 1.2. Data Cleansing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "jcU1xXIE0AAZ"
      },
      "outputs": [],
      "source": [
        "# Please note that some data are missing in this dataset. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "BKdPYC7Q0AAa",
        "outputId": "8a581a52-497e-48c5-ea54-891b64b41461"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "MPG             0\n",
              "Cylinders       0\n",
              "Displacement    0\n",
              "Horsepower      6\n",
              "Weight          0\n",
              "Acceleration    0\n",
              "Model Year      0\n",
              "Origin          0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 85,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "6Cn_gR2U0AAa"
      },
      "outputs": [],
      "source": [
        "# Missing line deleted"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "1gUa6bWK0AAb"
      },
      "outputs": [],
      "source": [
        "dataset = dataset.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "ZXrHdOUo0AAb"
      },
      "outputs": [],
      "source": [
        "# Since the \"Origin\" raw belongs to categorical data not numerical data, convert to one-hot encoding. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "JNHaCfw90AAc"
      },
      "outputs": [],
      "source": [
        "origin = dataset.pop('Origin')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7XFv_iHv0AAc",
        "outputId": "b33cbc67-51b2-47b9-82fb-2c4421dba8d6"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d27095c5-f044-4378-9f60-d000207aebb4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MPG</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>Displacement</th>\n",
              "      <th>Horsepower</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Acceleration</th>\n",
              "      <th>Model Year</th>\n",
              "      <th>USA</th>\n",
              "      <th>Europe</th>\n",
              "      <th>Japan</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>27.0</td>\n",
              "      <td>4</td>\n",
              "      <td>140.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>2790.0</td>\n",
              "      <td>15.6</td>\n",
              "      <td>82</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>44.0</td>\n",
              "      <td>4</td>\n",
              "      <td>97.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2130.0</td>\n",
              "      <td>24.6</td>\n",
              "      <td>82</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>32.0</td>\n",
              "      <td>4</td>\n",
              "      <td>135.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>2295.0</td>\n",
              "      <td>11.6</td>\n",
              "      <td>82</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>28.0</td>\n",
              "      <td>4</td>\n",
              "      <td>120.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>2625.0</td>\n",
              "      <td>18.6</td>\n",
              "      <td>82</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>31.0</td>\n",
              "      <td>4</td>\n",
              "      <td>119.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>2720.0</td>\n",
              "      <td>19.4</td>\n",
              "      <td>82</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d27095c5-f044-4378-9f60-d000207aebb4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d27095c5-f044-4378-9f60-d000207aebb4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d27095c5-f044-4378-9f60-d000207aebb4');\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",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
              "393  27.0          4         140.0        86.0  2790.0          15.6   \n",
              "394  44.0          4          97.0        52.0  2130.0          24.6   \n",
              "395  32.0          4         135.0        84.0  2295.0          11.6   \n",
              "396  28.0          4         120.0        79.0  2625.0          18.6   \n",
              "397  31.0          4         119.0        82.0  2720.0          19.4   \n",
              "\n",
              "     Model Year  USA  Europe  Japan  \n",
              "393          82  1.0     0.0    0.0  \n",
              "394          82  0.0     1.0    0.0  \n",
              "395          82  1.0     0.0    0.0  \n",
              "396          82  1.0     0.0    0.0  \n",
              "397          82  1.0     0.0    0.0  "
            ]
          },
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset['USA'] = (origin == 1)*1.0\n",
        "dataset['Europe'] = (origin == 2)*1.0\n",
        "dataset['Japan'] = (origin == 3)*1.0\n",
        "dataset.tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zUWKgIH90AAd"
      },
      "source": [
        "#### 1.3.Divide data into the training set and the test set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "k8SKTlxn0AAd"
      },
      "outputs": [],
      "source": [
        "train_dataset = dataset.sample(frac=0.8,random_state=0)\n",
        "test_dataset = dataset.drop(train_dataset.index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "JXmch6MeG2pe",
        "outputId": "322422e2-0bba-4ecf-afd5-9d49654b613d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(314, 10)\n",
            "(78, 10)\n"
          ]
        }
      ],
      "source": [
        "print(train_dataset.shape)\n",
        "print(test_dataset.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z3zQA9dW0AAe"
      },
      "source": [
        "#### 1.4.Examine the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "id": "Z8fxkMXh0AAe",
        "outputId": "d769d5b3-f0bc-4295-b8c4-35e5b6a55f47"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7fa254b45f90>"
            ]
          },
          "execution_count": 158,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 20 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.pairplot(train_dataset[[\"MPG\", \"Cylinders\", \"Displacement\", \"Weight\"]], diag_kind=\"kde\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xnzqeg3_0AAe"
      },
      "source": [
        "#### 1.5.Check the statistics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "AuenlGaa0AAf",
        "outputId": "0be24250-49f9-4227-e0c2-7cf3d91c7ecd"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-3c04b477-54bb-408f-9e00-b5ff070a414c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Cylinders</th>\n",
              "      <td>314.0</td>\n",
              "      <td>5.477707</td>\n",
              "      <td>1.699788</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.00</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Displacement</th>\n",
              "      <td>314.0</td>\n",
              "      <td>195.318471</td>\n",
              "      <td>104.331589</td>\n",
              "      <td>68.0</td>\n",
              "      <td>105.50</td>\n",
              "      <td>151.0</td>\n",
              "      <td>265.75</td>\n",
              "      <td>455.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Horsepower</th>\n",
              "      <td>314.0</td>\n",
              "      <td>104.869427</td>\n",
              "      <td>38.096214</td>\n",
              "      <td>46.0</td>\n",
              "      <td>76.25</td>\n",
              "      <td>94.5</td>\n",
              "      <td>128.00</td>\n",
              "      <td>225.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weight</th>\n",
              "      <td>314.0</td>\n",
              "      <td>2990.251592</td>\n",
              "      <td>843.898596</td>\n",
              "      <td>1649.0</td>\n",
              "      <td>2256.50</td>\n",
              "      <td>2822.5</td>\n",
              "      <td>3608.00</td>\n",
              "      <td>5140.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Acceleration</th>\n",
              "      <td>314.0</td>\n",
              "      <td>15.559236</td>\n",
              "      <td>2.789230</td>\n",
              "      <td>8.0</td>\n",
              "      <td>13.80</td>\n",
              "      <td>15.5</td>\n",
              "      <td>17.20</td>\n",
              "      <td>24.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model Year</th>\n",
              "      <td>314.0</td>\n",
              "      <td>75.898089</td>\n",
              "      <td>3.675642</td>\n",
              "      <td>70.0</td>\n",
              "      <td>73.00</td>\n",
              "      <td>76.0</td>\n",
              "      <td>79.00</td>\n",
              "      <td>82.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>USA</th>\n",
              "      <td>314.0</td>\n",
              "      <td>0.624204</td>\n",
              "      <td>0.485101</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Europe</th>\n",
              "      <td>314.0</td>\n",
              "      <td>0.178344</td>\n",
              "      <td>0.383413</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Japan</th>\n",
              "      <td>314.0</td>\n",
              "      <td>0.197452</td>\n",
              "      <td>0.398712</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3c04b477-54bb-408f-9e00-b5ff070a414c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3c04b477-54bb-408f-9e00-b5ff070a414c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3c04b477-54bb-408f-9e00-b5ff070a414c');\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",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              count         mean         std     min      25%     50%  \\\n",
              "Cylinders     314.0     5.477707    1.699788     3.0     4.00     4.0   \n",
              "Displacement  314.0   195.318471  104.331589    68.0   105.50   151.0   \n",
              "Horsepower    314.0   104.869427   38.096214    46.0    76.25    94.5   \n",
              "Weight        314.0  2990.251592  843.898596  1649.0  2256.50  2822.5   \n",
              "Acceleration  314.0    15.559236    2.789230     8.0    13.80    15.5   \n",
              "Model Year    314.0    75.898089    3.675642    70.0    73.00    76.0   \n",
              "USA           314.0     0.624204    0.485101     0.0     0.00     1.0   \n",
              "Europe        314.0     0.178344    0.383413     0.0     0.00     0.0   \n",
              "Japan         314.0     0.197452    0.398712     0.0     0.00     0.0   \n",
              "\n",
              "                  75%     max  \n",
              "Cylinders        8.00     8.0  \n",
              "Displacement   265.75   455.0  \n",
              "Horsepower     128.00   225.0  \n",
              "Weight        3608.00  5140.0  \n",
              "Acceleration    17.20    24.8  \n",
              "Model Year      79.00    82.0  \n",
              "USA              1.00     1.0  \n",
              "Europe           0.00     1.0  \n",
              "Japan            0.00     1.0  "
            ]
          },
          "execution_count": 159,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_stats = train_dataset.describe()\n",
        "train_stats.pop(\"MPG\")\n",
        "train_stats = train_stats.transpose()\n",
        "train_stats"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9hzbVSV80AAf"
      },
      "source": [
        "#### 1.6.Split target values, or \"labels\", in feature"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "YufDLpzU0AAg"
      },
      "outputs": [],
      "source": [
        "train_labels = train_dataset.pop('MPG')\n",
        "test_labels = test_dataset.pop('MPG')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uE1j4gK40AAg"
      },
      "source": [
        "#### 1.7.Normalize data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "M-ci7soK0AAg"
      },
      "outputs": [],
      "source": [
        "def norm(x):\n",
        "    return (x - train_stats['mean']) / train_stats['std']\n",
        "normed_train_data = norm(train_dataset)\n",
        "normed_test_data = norm(test_dataset)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qR0dCl-00AAg"
      },
      "source": [
        "#### 1.8.Create the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "Pg1109ls0AAg"
      },
      "outputs": [],
      "source": [
        "# Make the sequential model with densely connected layer. The output layer returns the consecutive values.  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "_yXTq6bcAu8B",
        "outputId": "b5bb2b89-0cb9-4aef-e869-3fd09068c884"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "execution_count": 163,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(train_dataset.keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "c4I50fMa0AAh"
      },
      "outputs": [],
      "source": [
        "def build_model():\n",
        "    model = keras.Sequential([layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),\n",
        "                              layers.Dense(64, activation='relu'),\n",
        "                              layers.Dense(1, activation='linear')])\n",
        "    optimizer = tf.keras.optimizers.SGD(0.0001)\n",
        "    model.compile(loss='mse',optimizer=optimizer,metrics=['mse'])\n",
        "    return model\n",
        "\n",
        "model = build_model()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VRwRghzf0AAh"
      },
      "source": [
        "#### 1.9.Check the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "SafhxSKU0AAh",
        "outputId": "519b1450-8a5a-430b-9a63-2a04c600ce96"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_11\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_33 (Dense)            (None, 64)                640       \n",
            "                                                                 \n",
            " dense_34 (Dense)            (None, 64)                4160      \n",
            "                                                                 \n",
            " dense_35 (Dense)            (None, 64)                4160      \n",
            "                                                                 \n",
            " dense_36 (Dense)            (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 9,025\n",
            "Trainable params: 9,025\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "ODqXOxPi0AAi"
      },
      "outputs": [],
      "source": [
        "# Call the medel.predict mothod by arraying 10 samples of the train set into one batch."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "T8zDUXtU0AAi",
        "outputId": "ca8211b8-1aa5-4220-c27a-c72a0ce3bd98"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[ 0.06841837],\n",
              "       [-0.01547462],\n",
              "       [ 0.3882212 ],\n",
              "       [ 0.07071737],\n",
              "       [ 0.10949414],\n",
              "       [-0.03191379],\n",
              "       [ 0.07048041],\n",
              "       [ 0.11730431],\n",
              "       [ 0.05497625],\n",
              "       [-0.02719889]], dtype=float32)"
            ]
          },
          "execution_count": 167,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "example_batch = normed_train_data[:10]\n",
        "example_result = model.predict(example_batch)\n",
        "example_result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K55bIcAQ0AAj"
      },
      "source": [
        "#### 1.10.Train the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CtmZWFRM0AAj",
        "outputId": "fe413e35-1015-4445-b8dd-8311ad4e496e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/1000\n",
            "4/4 [==============================] - 1s 52ms/step - loss: 601.5663 - mse: 601.5663 - val_loss: 623.1442 - val_mse: 623.1442\n",
            "Epoch 2/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 594.1014 - mse: 594.1014 - val_loss: 615.2325 - val_mse: 615.2325\n",
            "Epoch 3/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 586.8716 - mse: 586.8716 - val_loss: 607.3946 - val_mse: 607.3946\n",
            "Epoch 4/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 579.6928 - mse: 579.6928 - val_loss: 599.5084 - val_mse: 599.5084\n",
            "Epoch 5/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 572.4600 - mse: 572.4600 - val_loss: 591.4442 - val_mse: 591.4442\n",
            "Epoch 6/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 565.0983 - mse: 565.0983 - val_loss: 583.3647 - val_mse: 583.3647\n",
            "Epoch 7/1000\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 557.6385 - mse: 557.6385 - val_loss: 575.1914 - val_mse: 575.1914\n",
            "Epoch 8/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 550.0632 - mse: 550.0632 - val_loss: 566.7375 - val_mse: 566.7375\n",
            "Epoch 9/1000\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 542.2466 - mse: 542.2466 - val_loss: 557.8817 - val_mse: 557.8817\n",
            "Epoch 10/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 534.0731 - mse: 534.0731 - val_loss: 548.7099 - val_mse: 548.7099\n",
            "Epoch 11/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 525.6002 - mse: 525.6002 - val_loss: 539.3561 - val_mse: 539.3561\n",
            "Epoch 12/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 516.8725 - mse: 516.8726 - val_loss: 529.5447 - val_mse: 529.5447\n",
            "Epoch 13/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 507.6894 - mse: 507.6894 - val_loss: 518.9836 - val_mse: 518.9836\n",
            "Epoch 14/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 497.8600 - mse: 497.8600 - val_loss: 507.9473 - val_mse: 507.9473\n",
            "Epoch 15/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 487.5115 - mse: 487.5115 - val_loss: 495.9451 - val_mse: 495.9451\n",
            "Epoch 16/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 476.3311 - mse: 476.3311 - val_loss: 483.6653 - val_mse: 483.6653\n",
            "Epoch 17/1000\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 464.7969 - mse: 464.7969 - val_loss: 470.2225 - val_mse: 470.2225\n",
            "Epoch 18/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 452.2519 - mse: 452.2519 - val_loss: 456.3181 - val_mse: 456.3181\n",
            "Epoch 19/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 439.1960 - mse: 439.1960 - val_loss: 441.5096 - val_mse: 441.5096\n",
            "Epoch 20/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 425.3276 - mse: 425.3276 - val_loss: 425.7714 - val_mse: 425.7714\n",
            "Epoch 21/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 410.5247 - mse: 410.5247 - val_loss: 408.9631 - val_mse: 408.9631\n",
            "Epoch 22/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 394.7630 - mse: 394.7630 - val_loss: 391.2150 - val_mse: 391.2150\n",
            "Epoch 23/1000\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 378.0710 - mse: 378.0710 - val_loss: 372.2904 - val_mse: 372.2904\n",
            "Epoch 24/1000\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 360.3298 - mse: 360.3298 - val_loss: 352.8555 - val_mse: 352.8555\n",
            "Epoch 25/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 341.9815 - mse: 341.9815 - val_loss: 332.3101 - val_mse: 332.3101\n",
            "Epoch 26/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 322.6563 - mse: 322.6563 - val_loss: 310.7114 - val_mse: 310.7114\n",
            "Epoch 27/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 302.3501 - mse: 302.3501 - val_loss: 288.6200 - val_mse: 288.6200\n",
            "Epoch 28/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 281.7094 - mse: 281.7094 - val_loss: 266.3358 - val_mse: 266.3358\n",
            "Epoch 29/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 260.9314 - mse: 260.9314 - val_loss: 244.4859 - val_mse: 244.4859\n",
            "Epoch 30/1000\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 240.3930 - mse: 240.3930 - val_loss: 222.1965 - val_mse: 222.1965\n",
            "Epoch 31/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 219.6557 - mse: 219.6557 - val_loss: 200.9622 - val_mse: 200.9622\n",
            "Epoch 32/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 199.6986 - mse: 199.6986 - val_loss: 180.1165 - val_mse: 180.1165\n",
            "Epoch 33/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 180.1763 - mse: 180.1763 - val_loss: 160.1438 - val_mse: 160.1438\n",
            "Epoch 34/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 161.4843 - mse: 161.4843 - val_loss: 141.8770 - val_mse: 141.8770\n",
            "Epoch 35/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 144.2896 - mse: 144.2896 - val_loss: 125.1052 - val_mse: 125.1052\n",
            "Epoch 36/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 128.4753 - mse: 128.4753 - val_loss: 110.3825 - val_mse: 110.3825\n",
            "Epoch 37/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 114.4981 - mse: 114.4981 - val_loss: 97.4740 - val_mse: 97.4740\n",
            "Epoch 38/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 102.0817 - mse: 102.0817 - val_loss: 86.2181 - val_mse: 86.2181\n",
            "Epoch 39/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 91.1268 - mse: 91.1268 - val_loss: 76.6842 - val_mse: 76.6842\n",
            "Epoch 40/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 81.7392 - mse: 81.7392 - val_loss: 68.8227 - val_mse: 68.8227\n",
            "Epoch 41/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 73.7701 - mse: 73.7701 - val_loss: 62.3446 - val_mse: 62.3446\n",
            "Epoch 42/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 67.0313 - mse: 67.0313 - val_loss: 57.0142 - val_mse: 57.0142\n",
            "Epoch 43/1000\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 61.3378 - mse: 61.3378 - val_loss: 52.7039 - val_mse: 52.7039\n",
            "Epoch 44/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 56.5799 - mse: 56.5799 - val_loss: 49.3180 - val_mse: 49.3180\n",
            "Epoch 45/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 52.6552 - mse: 52.6552 - val_loss: 46.5739 - val_mse: 46.5739\n",
            "Epoch 46/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 49.3543 - mse: 49.3543 - val_loss: 44.4486 - val_mse: 44.4486\n",
            "Epoch 47/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 46.6686 - mse: 46.6686 - val_loss: 42.7286 - val_mse: 42.7286\n",
            "Epoch 48/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 44.3465 - mse: 44.3465 - val_loss: 41.1351 - val_mse: 41.1351\n",
            "Epoch 49/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 42.1519 - mse: 42.1519 - val_loss: 39.9020 - val_mse: 39.9020\n",
            "Epoch 50/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 40.3292 - mse: 40.3292 - val_loss: 38.7851 - val_mse: 38.7851\n",
            "Epoch 51/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 38.6716 - mse: 38.6716 - val_loss: 37.8612 - val_mse: 37.8612\n",
            "Epoch 52/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 37.2761 - mse: 37.2761 - val_loss: 37.0218 - val_mse: 37.0218\n",
            "Epoch 53/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 35.9431 - mse: 35.9431 - val_loss: 36.2576 - val_mse: 36.2576\n",
            "Epoch 54/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 34.7319 - mse: 34.7319 - val_loss: 35.5717 - val_mse: 35.5717\n",
            "Epoch 55/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 33.7086 - mse: 33.7086 - val_loss: 34.8900 - val_mse: 34.8900\n",
            "Epoch 56/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 32.7148 - mse: 32.7148 - val_loss: 34.2479 - val_mse: 34.2479\n",
            "Epoch 57/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 31.7967 - mse: 31.7967 - val_loss: 33.6427 - val_mse: 33.6427\n",
            "Epoch 58/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 30.9438 - mse: 30.9438 - val_loss: 33.0635 - val_mse: 33.0635\n",
            "Epoch 59/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 30.1508 - mse: 30.1508 - val_loss: 32.4910 - val_mse: 32.4910\n",
            "Epoch 60/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 29.3642 - mse: 29.3642 - val_loss: 31.9475 - val_mse: 31.9475\n",
            "Epoch 61/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 28.6356 - mse: 28.6356 - val_loss: 31.4203 - val_mse: 31.4203\n",
            "Epoch 62/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 27.9415 - mse: 27.9415 - val_loss: 30.9125 - val_mse: 30.9125\n",
            "Epoch 63/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 27.3213 - mse: 27.3213 - val_loss: 30.4194 - val_mse: 30.4194\n",
            "Epoch 64/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 26.7247 - mse: 26.7247 - val_loss: 30.0102 - val_mse: 30.0102\n",
            "Epoch 65/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 26.1131 - mse: 26.1131 - val_loss: 29.5468 - val_mse: 29.5468\n",
            "Epoch 66/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 25.5743 - mse: 25.5743 - val_loss: 29.1393 - val_mse: 29.1393\n",
            "Epoch 67/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 25.0167 - mse: 25.0167 - val_loss: 28.6944 - val_mse: 28.6944\n",
            "Epoch 68/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 24.5362 - mse: 24.5362 - val_loss: 28.2681 - val_mse: 28.2681\n",
            "Epoch 69/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 24.0735 - mse: 24.0735 - val_loss: 27.8774 - val_mse: 27.8774\n",
            "Epoch 70/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 23.6091 - mse: 23.6091 - val_loss: 27.5205 - val_mse: 27.5205\n",
            "Epoch 71/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 23.1743 - mse: 23.1743 - val_loss: 27.1903 - val_mse: 27.1903\n",
            "Epoch 72/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 22.7478 - mse: 22.7478 - val_loss: 26.8417 - val_mse: 26.8417\n",
            "Epoch 73/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 22.3616 - mse: 22.3616 - val_loss: 26.4835 - val_mse: 26.4835\n",
            "Epoch 74/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 21.9928 - mse: 21.9928 - val_loss: 26.1740 - val_mse: 26.1740\n",
            "Epoch 75/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 21.6333 - mse: 21.6333 - val_loss: 25.8125 - val_mse: 25.8125\n",
            "Epoch 76/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 21.2874 - mse: 21.2874 - val_loss: 25.5104 - val_mse: 25.5104\n",
            "Epoch 77/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 20.9716 - mse: 20.9716 - val_loss: 25.2065 - val_mse: 25.2065\n",
            "Epoch 78/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 20.6587 - mse: 20.6587 - val_loss: 24.9747 - val_mse: 24.9747\n",
            "Epoch 79/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 20.3469 - mse: 20.3469 - val_loss: 24.6966 - val_mse: 24.6966\n",
            "Epoch 80/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 20.0525 - mse: 20.0525 - val_loss: 24.4144 - val_mse: 24.4144\n",
            "Epoch 81/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 19.7966 - mse: 19.7966 - val_loss: 24.1135 - val_mse: 24.1135\n",
            "Epoch 82/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 19.4892 - mse: 19.4892 - val_loss: 23.8387 - val_mse: 23.8387\n",
            "Epoch 83/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 19.2340 - mse: 19.2340 - val_loss: 23.5887 - val_mse: 23.5887\n",
            "Epoch 84/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 18.9653 - mse: 18.9653 - val_loss: 23.3242 - val_mse: 23.3242\n",
            "Epoch 85/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 18.7381 - mse: 18.7381 - val_loss: 23.0652 - val_mse: 23.0652\n",
            "Epoch 86/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 18.5060 - mse: 18.5060 - val_loss: 22.8421 - val_mse: 22.8421\n",
            "Epoch 87/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 18.2909 - mse: 18.2909 - val_loss: 22.6123 - val_mse: 22.6123\n",
            "Epoch 88/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 18.0698 - mse: 18.0698 - val_loss: 22.3901 - val_mse: 22.3901\n",
            "Epoch 89/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 17.8489 - mse: 17.8489 - val_loss: 22.1349 - val_mse: 22.1349\n",
            "Epoch 90/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 17.6532 - mse: 17.6532 - val_loss: 21.8893 - val_mse: 21.8893\n",
            "Epoch 91/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 17.4535 - mse: 17.4535 - val_loss: 21.6584 - val_mse: 21.6584\n",
            "Epoch 92/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 17.2729 - mse: 17.2729 - val_loss: 21.4728 - val_mse: 21.4728\n",
            "Epoch 93/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 17.1024 - mse: 17.1024 - val_loss: 21.2815 - val_mse: 21.2815\n",
            "Epoch 94/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 16.9204 - mse: 16.9204 - val_loss: 21.0904 - val_mse: 21.0904\n",
            "Epoch 95/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 16.7564 - mse: 16.7564 - val_loss: 20.8675 - val_mse: 20.8675\n",
            "Epoch 96/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 16.5958 - mse: 16.5958 - val_loss: 20.6494 - val_mse: 20.6494\n",
            "Epoch 97/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 16.4402 - mse: 16.4402 - val_loss: 20.4635 - val_mse: 20.4635\n",
            "Epoch 98/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 16.2844 - mse: 16.2844 - val_loss: 20.2915 - val_mse: 20.2915\n",
            "Epoch 99/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 16.1426 - mse: 16.1426 - val_loss: 20.1075 - val_mse: 20.1075\n",
            "Epoch 100/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 16.0050 - mse: 16.0050 - val_loss: 19.9236 - val_mse: 19.9236\n",
            "Epoch 101/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 15.8633 - mse: 15.8633 - val_loss: 19.7724 - val_mse: 19.7724\n",
            "Epoch 102/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 15.7362 - mse: 15.7362 - val_loss: 19.5696 - val_mse: 19.5696\n",
            "Epoch 103/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 15.6084 - mse: 15.6084 - val_loss: 19.4160 - val_mse: 19.4160\n",
            "Epoch 104/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 15.4901 - mse: 15.4901 - val_loss: 19.2618 - val_mse: 19.2618\n",
            "Epoch 105/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 15.3512 - mse: 15.3512 - val_loss: 19.0796 - val_mse: 19.0796\n",
            "Epoch 106/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 15.2342 - mse: 15.2342 - val_loss: 18.9452 - val_mse: 18.9452\n",
            "Epoch 107/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 15.1117 - mse: 15.1117 - val_loss: 18.7717 - val_mse: 18.7717\n",
            "Epoch 108/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 14.9926 - mse: 14.9926 - val_loss: 18.6165 - val_mse: 18.6165\n",
            "Epoch 109/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 14.8793 - mse: 14.8793 - val_loss: 18.4705 - val_mse: 18.4705\n",
            "Epoch 110/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 14.7753 - mse: 14.7753 - val_loss: 18.3231 - val_mse: 18.3231\n",
            "Epoch 111/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 14.6667 - mse: 14.6667 - val_loss: 18.1593 - val_mse: 18.1593\n",
            "Epoch 112/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 14.5610 - mse: 14.5610 - val_loss: 18.0008 - val_mse: 18.0008\n",
            "Epoch 113/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 14.4549 - mse: 14.4549 - val_loss: 17.8716 - val_mse: 17.8716\n",
            "Epoch 114/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 14.3678 - mse: 14.3678 - val_loss: 17.7286 - val_mse: 17.7286\n",
            "Epoch 115/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 14.2510 - mse: 14.2510 - val_loss: 17.5941 - val_mse: 17.5941\n",
            "Epoch 116/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 14.1742 - mse: 14.1742 - val_loss: 17.4595 - val_mse: 17.4595\n",
            "Epoch 117/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 14.0804 - mse: 14.0804 - val_loss: 17.3290 - val_mse: 17.3290\n",
            "Epoch 118/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 13.9763 - mse: 13.9763 - val_loss: 17.1918 - val_mse: 17.1918\n",
            "Epoch 119/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 13.8874 - mse: 13.8874 - val_loss: 17.0587 - val_mse: 17.0587\n",
            "Epoch 120/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 13.8102 - mse: 13.8102 - val_loss: 16.9473 - val_mse: 16.9473\n",
            "Epoch 121/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 13.7188 - mse: 13.7188 - val_loss: 16.8212 - val_mse: 16.8212\n",
            "Epoch 122/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 13.6293 - mse: 13.6293 - val_loss: 16.6993 - val_mse: 16.6993\n",
            "Epoch 123/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 13.5552 - mse: 13.5552 - val_loss: 16.5794 - val_mse: 16.5794\n",
            "Epoch 124/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 13.4748 - mse: 13.4748 - val_loss: 16.4737 - val_mse: 16.4737\n",
            "Epoch 125/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 13.3979 - mse: 13.3979 - val_loss: 16.3682 - val_mse: 16.3682\n",
            "Epoch 126/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 13.3214 - mse: 13.3214 - val_loss: 16.2479 - val_mse: 16.2479\n",
            "Epoch 127/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 13.2543 - mse: 13.2543 - val_loss: 16.1371 - val_mse: 16.1371\n",
            "Epoch 128/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 13.1702 - mse: 13.1702 - val_loss: 16.0034 - val_mse: 16.0034\n",
            "Epoch 129/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 13.0948 - mse: 13.0948 - val_loss: 15.8897 - val_mse: 15.8897\n",
            "Epoch 130/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 13.0279 - mse: 13.0279 - val_loss: 15.7705 - val_mse: 15.7705\n",
            "Epoch 131/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 12.9556 - mse: 12.9556 - val_loss: 15.6648 - val_mse: 15.6648\n",
            "Epoch 132/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 12.8939 - mse: 12.8939 - val_loss: 15.5786 - val_mse: 15.5786\n",
            "Epoch 133/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 12.8245 - mse: 12.8245 - val_loss: 15.4568 - val_mse: 15.4568\n",
            "Epoch 134/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 12.7643 - mse: 12.7643 - val_loss: 15.3574 - val_mse: 15.3574\n",
            "Epoch 135/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 12.6943 - mse: 12.6943 - val_loss: 15.2556 - val_mse: 15.2556\n",
            "Epoch 136/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 12.6427 - mse: 12.6427 - val_loss: 15.1469 - val_mse: 15.1469\n",
            "Epoch 137/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 12.5753 - mse: 12.5753 - val_loss: 15.0409 - val_mse: 15.0409\n",
            "Epoch 138/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 12.5167 - mse: 12.5167 - val_loss: 14.9379 - val_mse: 14.9379\n",
            "Epoch 139/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 12.4494 - mse: 12.4494 - val_loss: 14.8534 - val_mse: 14.8534\n",
            "Epoch 140/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 12.3987 - mse: 12.3987 - val_loss: 14.7682 - val_mse: 14.7682\n",
            "Epoch 141/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 12.3321 - mse: 12.3321 - val_loss: 14.6498 - val_mse: 14.6498\n",
            "Epoch 142/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 12.2753 - mse: 12.2753 - val_loss: 14.5597 - val_mse: 14.5597\n",
            "Epoch 143/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 12.2077 - mse: 12.2077 - val_loss: 14.4588 - val_mse: 14.4588\n",
            "Epoch 144/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 12.1573 - mse: 12.1573 - val_loss: 14.3793 - val_mse: 14.3793\n",
            "Epoch 145/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 12.1011 - mse: 12.1011 - val_loss: 14.3001 - val_mse: 14.3001\n",
            "Epoch 146/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 12.0495 - mse: 12.0495 - val_loss: 14.2119 - val_mse: 14.2119\n",
            "Epoch 147/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 11.9886 - mse: 11.9886 - val_loss: 14.1162 - val_mse: 14.1162\n",
            "Epoch 148/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 11.9378 - mse: 11.9378 - val_loss: 14.0329 - val_mse: 14.0329\n",
            "Epoch 149/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 11.9028 - mse: 11.9028 - val_loss: 13.9376 - val_mse: 13.9376\n",
            "Epoch 150/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 11.8433 - mse: 11.8433 - val_loss: 13.8615 - val_mse: 13.8615\n",
            "Epoch 151/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 11.7982 - mse: 11.7982 - val_loss: 13.7824 - val_mse: 13.7824\n",
            "Epoch 152/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 11.7363 - mse: 11.7363 - val_loss: 13.7009 - val_mse: 13.7009\n",
            "Epoch 153/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 11.6910 - mse: 11.6910 - val_loss: 13.6272 - val_mse: 13.6272\n",
            "Epoch 154/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 11.6622 - mse: 11.6622 - val_loss: 13.5429 - val_mse: 13.5429\n",
            "Epoch 155/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 11.5889 - mse: 11.5889 - val_loss: 13.4639 - val_mse: 13.4639\n",
            "Epoch 156/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 11.5415 - mse: 11.5415 - val_loss: 13.3933 - val_mse: 13.3933\n",
            "Epoch 157/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 11.4988 - mse: 11.4988 - val_loss: 13.3175 - val_mse: 13.3175\n",
            "Epoch 158/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 11.4488 - mse: 11.4488 - val_loss: 13.2300 - val_mse: 13.2300\n",
            "Epoch 159/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 11.4099 - mse: 11.4099 - val_loss: 13.1714 - val_mse: 13.1714\n",
            "Epoch 160/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 11.3746 - mse: 11.3746 - val_loss: 13.1005 - val_mse: 13.1005\n",
            "Epoch 161/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 11.3169 - mse: 11.3169 - val_loss: 13.0281 - val_mse: 13.0281\n",
            "Epoch 162/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 11.2767 - mse: 11.2767 - val_loss: 12.9499 - val_mse: 12.9499\n",
            "Epoch 163/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 11.2332 - mse: 11.2332 - val_loss: 12.8876 - val_mse: 12.8876\n",
            "Epoch 164/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 11.1968 - mse: 11.1968 - val_loss: 12.8260 - val_mse: 12.8260\n",
            "Epoch 165/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 11.1726 - mse: 11.1726 - val_loss: 12.7694 - val_mse: 12.7694\n",
            "Epoch 166/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 11.1153 - mse: 11.1153 - val_loss: 12.6883 - val_mse: 12.6883\n",
            "Epoch 167/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 11.0672 - mse: 11.0672 - val_loss: 12.6151 - val_mse: 12.6151\n",
            "Epoch 168/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 11.0366 - mse: 11.0366 - val_loss: 12.5617 - val_mse: 12.5617\n",
            "Epoch 169/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 10.9854 - mse: 10.9854 - val_loss: 12.4981 - val_mse: 12.4981\n",
            "Epoch 170/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 10.9563 - mse: 10.9563 - val_loss: 12.4442 - val_mse: 12.4442\n",
            "Epoch 171/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 10.9165 - mse: 10.9165 - val_loss: 12.3777 - val_mse: 12.3777\n",
            "Epoch 172/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 10.8775 - mse: 10.8775 - val_loss: 12.3049 - val_mse: 12.3049\n",
            "Epoch 173/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 10.8417 - mse: 10.8417 - val_loss: 12.2407 - val_mse: 12.2407\n",
            "Epoch 174/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 10.8102 - mse: 10.8102 - val_loss: 12.1936 - val_mse: 12.1936\n",
            "Epoch 175/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 10.7927 - mse: 10.7927 - val_loss: 12.1269 - val_mse: 12.1269\n",
            "Epoch 176/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 10.7384 - mse: 10.7384 - val_loss: 12.0718 - val_mse: 12.0718\n",
            "Epoch 177/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 10.7136 - mse: 10.7136 - val_loss: 12.0229 - val_mse: 12.0229\n",
            "Epoch 178/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 10.6780 - mse: 10.6780 - val_loss: 11.9623 - val_mse: 11.9623\n",
            "Epoch 179/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 10.6387 - mse: 10.6387 - val_loss: 11.9037 - val_mse: 11.9037\n",
            "Epoch 180/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 10.6057 - mse: 10.6057 - val_loss: 11.8586 - val_mse: 11.8586\n",
            "Epoch 181/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 10.5719 - mse: 10.5719 - val_loss: 11.8034 - val_mse: 11.8034\n",
            "Epoch 182/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 10.5451 - mse: 10.5451 - val_loss: 11.7543 - val_mse: 11.7543\n",
            "Epoch 183/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 10.5183 - mse: 10.5183 - val_loss: 11.7175 - val_mse: 11.7175\n",
            "Epoch 184/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 10.4925 - mse: 10.4925 - val_loss: 11.6653 - val_mse: 11.6653\n",
            "Epoch 185/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 10.4531 - mse: 10.4531 - val_loss: 11.6234 - val_mse: 11.6234\n",
            "Epoch 186/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 10.4230 - mse: 10.4230 - val_loss: 11.5685 - val_mse: 11.5685\n",
            "Epoch 187/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 10.3898 - mse: 10.3898 - val_loss: 11.5178 - val_mse: 11.5178\n",
            "Epoch 188/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 10.3644 - mse: 10.3644 - val_loss: 11.4750 - val_mse: 11.4750\n",
            "Epoch 189/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 10.3327 - mse: 10.3327 - val_loss: 11.4288 - val_mse: 11.4288\n",
            "Epoch 190/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 10.3149 - mse: 10.3149 - val_loss: 11.3784 - val_mse: 11.3784\n",
            "Epoch 191/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 10.2957 - mse: 10.2957 - val_loss: 11.3424 - val_mse: 11.3424\n",
            "Epoch 192/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 10.2635 - mse: 10.2635 - val_loss: 11.2957 - val_mse: 11.2957\n",
            "Epoch 193/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 10.2203 - mse: 10.2203 - val_loss: 11.2576 - val_mse: 11.2576\n",
            "Epoch 194/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 10.1946 - mse: 10.1946 - val_loss: 11.2088 - val_mse: 11.2088\n",
            "Epoch 195/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 10.1788 - mse: 10.1788 - val_loss: 11.1695 - val_mse: 11.1695\n",
            "Epoch 196/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 10.1436 - mse: 10.1436 - val_loss: 11.1274 - val_mse: 11.1274\n",
            "Epoch 197/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 10.1218 - mse: 10.1218 - val_loss: 11.0835 - val_mse: 11.0835\n",
            "Epoch 198/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 10.0946 - mse: 10.0946 - val_loss: 11.0411 - val_mse: 11.0411\n",
            "Epoch 199/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 10.0661 - mse: 10.0661 - val_loss: 11.0000 - val_mse: 11.0000\n",
            "Epoch 200/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 10.0478 - mse: 10.0478 - val_loss: 10.9625 - val_mse: 10.9625\n",
            "Epoch 201/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 10.0236 - mse: 10.0236 - val_loss: 10.9280 - val_mse: 10.9280\n",
            "Epoch 202/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 10.0027 - mse: 10.0027 - val_loss: 10.8904 - val_mse: 10.8904\n",
            "Epoch 203/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 9.9716 - mse: 9.9716 - val_loss: 10.8500 - val_mse: 10.8500\n",
            "Epoch 204/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 9.9603 - mse: 9.9603 - val_loss: 10.8000 - val_mse: 10.8000\n",
            "Epoch 205/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 9.9228 - mse: 9.9228 - val_loss: 10.7616 - val_mse: 10.7616\n",
            "Epoch 206/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 9.9016 - mse: 9.9016 - val_loss: 10.7300 - val_mse: 10.7300\n",
            "Epoch 207/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 9.8829 - mse: 9.8829 - val_loss: 10.7054 - val_mse: 10.7054\n",
            "Epoch 208/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 9.8565 - mse: 9.8565 - val_loss: 10.6727 - val_mse: 10.6727\n",
            "Epoch 209/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 9.8344 - mse: 9.8344 - val_loss: 10.6363 - val_mse: 10.6363\n",
            "Epoch 210/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 9.8151 - mse: 9.8151 - val_loss: 10.6033 - val_mse: 10.6033\n",
            "Epoch 211/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 9.7939 - mse: 9.7939 - val_loss: 10.5750 - val_mse: 10.5750\n",
            "Epoch 212/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 9.7752 - mse: 9.7752 - val_loss: 10.5393 - val_mse: 10.5393\n",
            "Epoch 213/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 9.7553 - mse: 9.7553 - val_loss: 10.4951 - val_mse: 10.4951\n",
            "Epoch 214/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 9.7290 - mse: 9.7290 - val_loss: 10.4643 - val_mse: 10.4643\n",
            "Epoch 215/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 9.7150 - mse: 9.7150 - val_loss: 10.4317 - val_mse: 10.4317\n",
            "Epoch 216/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 9.6899 - mse: 9.6899 - val_loss: 10.3995 - val_mse: 10.3995\n",
            "Epoch 217/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 9.6767 - mse: 9.6767 - val_loss: 10.3602 - val_mse: 10.3602\n",
            "Epoch 218/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 9.6517 - mse: 9.6517 - val_loss: 10.3326 - val_mse: 10.3326\n",
            "Epoch 219/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 9.6516 - mse: 9.6516 - val_loss: 10.3070 - val_mse: 10.3070\n",
            "Epoch 220/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 9.6133 - mse: 9.6133 - val_loss: 10.2780 - val_mse: 10.2780\n",
            "Epoch 221/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 9.5950 - mse: 9.5950 - val_loss: 10.2458 - val_mse: 10.2458\n",
            "Epoch 222/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 9.5715 - mse: 9.5715 - val_loss: 10.2205 - val_mse: 10.2205\n",
            "Epoch 223/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 9.5539 - mse: 9.5539 - val_loss: 10.1906 - val_mse: 10.1906\n",
            "Epoch 224/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 9.5358 - mse: 9.5358 - val_loss: 10.1603 - val_mse: 10.1603\n",
            "Epoch 225/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 9.5120 - mse: 9.5120 - val_loss: 10.1299 - val_mse: 10.1299\n",
            "Epoch 226/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 9.4981 - mse: 9.4981 - val_loss: 10.1032 - val_mse: 10.1032\n",
            "Epoch 227/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 9.4784 - mse: 9.4784 - val_loss: 10.0774 - val_mse: 10.0774\n",
            "Epoch 228/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 9.4747 - mse: 9.4747 - val_loss: 10.0573 - val_mse: 10.0573\n",
            "Epoch 229/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 9.4483 - mse: 9.4483 - val_loss: 10.0339 - val_mse: 10.0339\n",
            "Epoch 230/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 9.4294 - mse: 9.4294 - val_loss: 10.0064 - val_mse: 10.0064\n",
            "Epoch 231/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 9.4200 - mse: 9.4200 - val_loss: 9.9885 - val_mse: 9.9885\n",
            "Epoch 232/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 9.3934 - mse: 9.3934 - val_loss: 9.9628 - val_mse: 9.9628\n",
            "Epoch 233/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 9.3771 - mse: 9.3771 - val_loss: 9.9395 - val_mse: 9.9395\n",
            "Epoch 234/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 9.3759 - mse: 9.3759 - val_loss: 9.9239 - val_mse: 9.9239\n",
            "Epoch 235/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 9.3399 - mse: 9.3399 - val_loss: 9.9030 - val_mse: 9.9030\n",
            "Epoch 236/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 9.3269 - mse: 9.3269 - val_loss: 9.8810 - val_mse: 9.8810\n",
            "Epoch 237/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 9.3194 - mse: 9.3194 - val_loss: 9.8655 - val_mse: 9.8655\n",
            "Epoch 238/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 9.2900 - mse: 9.2900 - val_loss: 9.8385 - val_mse: 9.8385\n",
            "Epoch 239/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 9.2781 - mse: 9.2781 - val_loss: 9.8160 - val_mse: 9.8160\n",
            "Epoch 240/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 9.2644 - mse: 9.2644 - val_loss: 9.8024 - val_mse: 9.8024\n",
            "Epoch 241/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 9.2602 - mse: 9.2602 - val_loss: 9.7680 - val_mse: 9.7680\n",
            "Epoch 242/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 9.2342 - mse: 9.2342 - val_loss: 9.7495 - val_mse: 9.7495\n",
            "Epoch 243/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 9.2210 - mse: 9.2210 - val_loss: 9.7343 - val_mse: 9.7343\n",
            "Epoch 244/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 9.2086 - mse: 9.2086 - val_loss: 9.7090 - val_mse: 9.7090\n",
            "Epoch 245/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 9.1855 - mse: 9.1855 - val_loss: 9.6832 - val_mse: 9.6832\n",
            "Epoch 246/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 9.1901 - mse: 9.1901 - val_loss: 9.6639 - val_mse: 9.6639\n",
            "Epoch 247/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 9.1579 - mse: 9.1579 - val_loss: 9.6470 - val_mse: 9.6470\n",
            "Epoch 248/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 9.1495 - mse: 9.1495 - val_loss: 9.6318 - val_mse: 9.6318\n",
            "Epoch 249/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 9.1365 - mse: 9.1365 - val_loss: 9.6210 - val_mse: 9.6210\n",
            "Epoch 250/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 9.1203 - mse: 9.1203 - val_loss: 9.5896 - val_mse: 9.5896\n",
            "Epoch 251/1000\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 9.0963 - mse: 9.0963 - val_loss: 9.5724 - val_mse: 9.5724\n",
            "Epoch 252/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 9.0833 - mse: 9.0833 - val_loss: 9.5524 - val_mse: 9.5524\n",
            "Epoch 253/1000\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 9.0698 - mse: 9.0698 - val_loss: 9.5359 - val_mse: 9.5359\n",
            "Epoch 254/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 9.0631 - mse: 9.0631 - val_loss: 9.5154 - val_mse: 9.5154\n",
            "Epoch 255/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 9.0459 - mse: 9.0459 - val_loss: 9.4957 - val_mse: 9.4957\n",
            "Epoch 256/1000\n",
            "4/4 [==============================] - 0s 23ms/step - loss: 9.0307 - mse: 9.0307 - val_loss: 9.4802 - val_mse: 9.4802\n",
            "Epoch 257/1000\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 9.0150 - mse: 9.0150 - val_loss: 9.4681 - val_mse: 9.4681\n",
            "Epoch 258/1000\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 9.0069 - mse: 9.0069 - val_loss: 9.4435 - val_mse: 9.4435\n",
            "Epoch 259/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 8.9970 - mse: 8.9970 - val_loss: 9.4246 - val_mse: 9.4246\n",
            "Epoch 260/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 8.9755 - mse: 8.9755 - val_loss: 9.4025 - val_mse: 9.4025\n",
            "Epoch 261/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 8.9607 - mse: 8.9607 - val_loss: 9.3873 - val_mse: 9.3873\n",
            "Epoch 262/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 8.9502 - mse: 8.9502 - val_loss: 9.3732 - val_mse: 9.3732\n",
            "Epoch 263/1000\n",
            "4/4 [==============================] - 0s 23ms/step - loss: 8.9343 - mse: 8.9343 - val_loss: 9.3462 - val_mse: 9.3462\n",
            "Epoch 264/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 8.9225 - mse: 8.9225 - val_loss: 9.3291 - val_mse: 9.3291\n",
            "Epoch 265/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 8.9085 - mse: 8.9085 - val_loss: 9.3092 - val_mse: 9.3092\n",
            "Epoch 266/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 8.9050 - mse: 8.9050 - val_loss: 9.2959 - val_mse: 9.2959\n",
            "Epoch 267/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 8.8826 - mse: 8.8826 - val_loss: 9.2784 - val_mse: 9.2784\n",
            "Epoch 268/1000\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 8.8705 - mse: 8.8705 - val_loss: 9.2643 - val_mse: 9.2643\n",
            "Epoch 269/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 8.8608 - mse: 8.8608 - val_loss: 9.2486 - val_mse: 9.2486\n",
            "Epoch 270/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 8.8543 - mse: 8.8543 - val_loss: 9.2330 - val_mse: 9.2330\n",
            "Epoch 271/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 8.8432 - mse: 8.8432 - val_loss: 9.2175 - val_mse: 9.2175\n",
            "Epoch 272/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 8.8307 - mse: 8.8307 - val_loss: 9.2059 - val_mse: 9.2059\n",
            "Epoch 273/1000\n",
            "4/4 [==============================] - 0s 23ms/step - loss: 8.8306 - mse: 8.8306 - val_loss: 9.1934 - val_mse: 9.1934\n",
            "Epoch 274/1000\n",
            "4/4 [==============================] - 0s 23ms/step - loss: 8.8036 - mse: 8.8036 - val_loss: 9.1775 - val_mse: 9.1775\n",
            "Epoch 275/1000\n",
            "4/4 [==============================] - 0s 23ms/step - loss: 8.7953 - mse: 8.7953 - val_loss: 9.1644 - val_mse: 9.1644\n",
            "Epoch 276/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 8.7805 - mse: 8.7805 - val_loss: 9.1451 - val_mse: 9.1451\n",
            "Epoch 277/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 8.7775 - mse: 8.7775 - val_loss: 9.1342 - val_mse: 9.1342\n",
            "Epoch 278/1000\n",
            "4/4 [==============================] - 0s 28ms/step - loss: 8.7590 - mse: 8.7590 - val_loss: 9.1275 - val_mse: 9.1275\n",
            "Epoch 279/1000\n",
            "4/4 [==============================] - 0s 26ms/step - loss: 8.7506 - mse: 8.7506 - val_loss: 9.1173 - val_mse: 9.1173\n",
            "Epoch 280/1000\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 8.7434 - mse: 8.7434 - val_loss: 9.0976 - val_mse: 9.0976\n",
            "Epoch 281/1000\n",
            "4/4 [==============================] - 0s 26ms/step - loss: 8.7288 - mse: 8.7288 - val_loss: 9.0805 - val_mse: 9.0805\n",
            "Epoch 282/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 8.7233 - mse: 8.7233 - val_loss: 9.0653 - val_mse: 9.0653\n",
            "Epoch 283/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 8.7051 - mse: 8.7051 - val_loss: 9.0506 - val_mse: 9.0506\n",
            "Epoch 284/1000\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 8.6996 - mse: 8.6996 - val_loss: 9.0423 - val_mse: 9.0423\n",
            "Epoch 285/1000\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 8.6881 - mse: 8.6881 - val_loss: 9.0244 - val_mse: 9.0244\n",
            "Epoch 286/1000\n",
            "4/4 [==============================] - 0s 23ms/step - loss: 8.6851 - mse: 8.6851 - val_loss: 9.0176 - val_mse: 9.0176\n",
            "Epoch 287/1000\n",
            "4/4 [==============================] - 0s 26ms/step - loss: 8.6694 - mse: 8.6694 - val_loss: 9.0063 - val_mse: 9.0063\n",
            "Epoch 288/1000\n",
            "4/4 [==============================] - 0s 23ms/step - loss: 8.6680 - mse: 8.6680 - val_loss: 8.9990 - val_mse: 8.9990\n",
            "Epoch 289/1000\n",
            "4/4 [==============================] - 0s 30ms/step - loss: 8.6515 - mse: 8.6515 - val_loss: 8.9892 - val_mse: 8.9892\n",
            "Epoch 290/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 8.6594 - mse: 8.6594 - val_loss: 8.9783 - val_mse: 8.9783\n",
            "Epoch 291/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 8.6344 - mse: 8.6344 - val_loss: 8.9681 - val_mse: 8.9681\n",
            "Epoch 292/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 8.6386 - mse: 8.6386 - val_loss: 8.9595 - val_mse: 8.9595\n",
            "Epoch 293/1000\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 8.6125 - mse: 8.6125 - val_loss: 8.9442 - val_mse: 8.9442\n",
            "Epoch 294/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.6018 - mse: 8.6018 - val_loss: 8.9361 - val_mse: 8.9361\n",
            "Epoch 295/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.5931 - mse: 8.5931 - val_loss: 8.9317 - val_mse: 8.9317\n",
            "Epoch 296/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 8.5799 - mse: 8.5799 - val_loss: 8.9232 - val_mse: 8.9232\n",
            "Epoch 297/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 8.5793 - mse: 8.5793 - val_loss: 8.9181 - val_mse: 8.9181\n",
            "Epoch 298/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 8.5624 - mse: 8.5624 - val_loss: 8.9097 - val_mse: 8.9097\n",
            "Epoch 299/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 8.5624 - mse: 8.5624 - val_loss: 8.8993 - val_mse: 8.8993\n",
            "Epoch 300/1000\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 8.5489 - mse: 8.5489 - val_loss: 8.8856 - val_mse: 8.8856\n",
            "Epoch 301/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 8.5397 - mse: 8.5397 - val_loss: 8.8773 - val_mse: 8.8773\n",
            "Epoch 302/1000\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 8.5264 - mse: 8.5264 - val_loss: 8.8691 - val_mse: 8.8691\n",
            "Epoch 303/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.5196 - mse: 8.5196 - val_loss: 8.8627 - val_mse: 8.8627\n",
            "Epoch 304/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 8.5120 - mse: 8.5120 - val_loss: 8.8559 - val_mse: 8.8559\n",
            "Epoch 305/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.5043 - mse: 8.5043 - val_loss: 8.8453 - val_mse: 8.8453\n",
            "Epoch 306/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 8.4965 - mse: 8.4965 - val_loss: 8.8400 - val_mse: 8.8400\n",
            "Epoch 307/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 8.4804 - mse: 8.4804 - val_loss: 8.8264 - val_mse: 8.8264\n",
            "Epoch 308/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 8.4738 - mse: 8.4738 - val_loss: 8.8195 - val_mse: 8.8195\n",
            "Epoch 309/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 8.4813 - mse: 8.4813 - val_loss: 8.8131 - val_mse: 8.8131\n",
            "Epoch 310/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 8.4617 - mse: 8.4617 - val_loss: 8.8044 - val_mse: 8.8044\n",
            "Epoch 311/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.4589 - mse: 8.4589 - val_loss: 8.7927 - val_mse: 8.7927\n",
            "Epoch 312/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 8.4415 - mse: 8.4415 - val_loss: 8.7805 - val_mse: 8.7805\n",
            "Epoch 313/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.4311 - mse: 8.4311 - val_loss: 8.7739 - val_mse: 8.7739\n",
            "Epoch 314/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.4284 - mse: 8.4284 - val_loss: 8.7626 - val_mse: 8.7626\n",
            "Epoch 315/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 8.4162 - mse: 8.4162 - val_loss: 8.7562 - val_mse: 8.7562\n",
            "Epoch 316/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 8.4137 - mse: 8.4137 - val_loss: 8.7471 - val_mse: 8.7471\n",
            "Epoch 317/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 8.4052 - mse: 8.4052 - val_loss: 8.7361 - val_mse: 8.7361\n",
            "Epoch 318/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 8.3956 - mse: 8.3956 - val_loss: 8.7285 - val_mse: 8.7285\n",
            "Epoch 319/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 8.3963 - mse: 8.3963 - val_loss: 8.7202 - val_mse: 8.7202\n",
            "Epoch 320/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 8.3904 - mse: 8.3904 - val_loss: 8.7121 - val_mse: 8.7121\n",
            "Epoch 321/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.3793 - mse: 8.3793 - val_loss: 8.7080 - val_mse: 8.7080\n",
            "Epoch 322/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 8.3656 - mse: 8.3656 - val_loss: 8.7023 - val_mse: 8.7023\n",
            "Epoch 323/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.3586 - mse: 8.3586 - val_loss: 8.6946 - val_mse: 8.6946\n",
            "Epoch 324/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 8.3620 - mse: 8.3620 - val_loss: 8.6881 - val_mse: 8.6881\n",
            "Epoch 325/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 8.3475 - mse: 8.3475 - val_loss: 8.6802 - val_mse: 8.6802\n",
            "Epoch 326/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 8.3355 - mse: 8.3355 - val_loss: 8.6738 - val_mse: 8.6738\n",
            "Epoch 327/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 8.3227 - mse: 8.3227 - val_loss: 8.6608 - val_mse: 8.6608\n",
            "Epoch 328/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 8.3166 - mse: 8.3166 - val_loss: 8.6550 - val_mse: 8.6550\n",
            "Epoch 329/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 8.3241 - mse: 8.3241 - val_loss: 8.6506 - val_mse: 8.6506\n",
            "Epoch 330/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.3101 - mse: 8.3101 - val_loss: 8.6523 - val_mse: 8.6523\n",
            "Epoch 331/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 8.2975 - mse: 8.2975 - val_loss: 8.6451 - val_mse: 8.6451\n",
            "Epoch 332/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 8.2900 - mse: 8.2900 - val_loss: 8.6381 - val_mse: 8.6381\n",
            "Epoch 333/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 8.2885 - mse: 8.2885 - val_loss: 8.6287 - val_mse: 8.6287\n",
            "Epoch 334/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.2742 - mse: 8.2742 - val_loss: 8.6211 - val_mse: 8.6211\n",
            "Epoch 335/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.2691 - mse: 8.2691 - val_loss: 8.6132 - val_mse: 8.6132\n",
            "Epoch 336/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 8.2610 - mse: 8.2610 - val_loss: 8.6089 - val_mse: 8.6089\n",
            "Epoch 337/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 8.2526 - mse: 8.2526 - val_loss: 8.6062 - val_mse: 8.6062\n",
            "Epoch 338/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 8.2429 - mse: 8.2429 - val_loss: 8.6025 - val_mse: 8.6025\n",
            "Epoch 339/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 8.2373 - mse: 8.2373 - val_loss: 8.5944 - val_mse: 8.5944\n",
            "Epoch 340/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.2479 - mse: 8.2479 - val_loss: 8.5869 - val_mse: 8.5869\n",
            "Epoch 341/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.2300 - mse: 8.2300 - val_loss: 8.5871 - val_mse: 8.5871\n",
            "Epoch 342/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.2162 - mse: 8.2162 - val_loss: 8.5844 - val_mse: 8.5844\n",
            "Epoch 343/1000\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 8.2086 - mse: 8.2086 - val_loss: 8.5775 - val_mse: 8.5775\n",
            "Epoch 344/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 8.2107 - mse: 8.2107 - val_loss: 8.5701 - val_mse: 8.5701\n",
            "Epoch 345/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.2014 - mse: 8.2014 - val_loss: 8.5627 - val_mse: 8.5627\n",
            "Epoch 346/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.1984 - mse: 8.1984 - val_loss: 8.5605 - val_mse: 8.5605\n",
            "Epoch 347/1000\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 8.1867 - mse: 8.1867 - val_loss: 8.5579 - val_mse: 8.5579\n",
            "Epoch 348/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 8.1789 - mse: 8.1789 - val_loss: 8.5545 - val_mse: 8.5545\n",
            "Epoch 349/1000\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 8.1717 - mse: 8.1717 - val_loss: 8.5440 - val_mse: 8.5440\n",
            "Epoch 350/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 8.1768 - mse: 8.1768 - val_loss: 8.5418 - val_mse: 8.5418\n",
            "Epoch 351/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.1575 - mse: 8.1575 - val_loss: 8.5386 - val_mse: 8.5386\n",
            "Epoch 352/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.1627 - mse: 8.1627 - val_loss: 8.5408 - val_mse: 8.5408\n",
            "Epoch 353/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.1652 - mse: 8.1652 - val_loss: 8.5430 - val_mse: 8.5430\n",
            "Epoch 354/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 8.1497 - mse: 8.1497 - val_loss: 8.5417 - val_mse: 8.5417\n",
            "Epoch 355/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.1350 - mse: 8.1350 - val_loss: 8.5371 - val_mse: 8.5371\n",
            "Epoch 356/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 8.1300 - mse: 8.1300 - val_loss: 8.5286 - val_mse: 8.5286\n",
            "Epoch 357/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 8.1253 - mse: 8.1253 - val_loss: 8.5224 - val_mse: 8.5224\n",
            "Epoch 358/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 8.1201 - mse: 8.1201 - val_loss: 8.5210 - val_mse: 8.5210\n",
            "Epoch 359/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 8.1102 - mse: 8.1102 - val_loss: 8.5143 - val_mse: 8.5143\n",
            "Epoch 360/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 8.1122 - mse: 8.1122 - val_loss: 8.5148 - val_mse: 8.5148\n",
            "Epoch 361/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 8.0963 - mse: 8.0963 - val_loss: 8.5091 - val_mse: 8.5091\n",
            "Epoch 362/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 8.1077 - mse: 8.1077 - val_loss: 8.5052 - val_mse: 8.5052\n",
            "Epoch 363/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 8.0998 - mse: 8.0998 - val_loss: 8.5022 - val_mse: 8.5022\n",
            "Epoch 364/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.0880 - mse: 8.0880 - val_loss: 8.4943 - val_mse: 8.4943\n",
            "Epoch 365/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 8.0766 - mse: 8.0766 - val_loss: 8.4915 - val_mse: 8.4915\n",
            "Epoch 366/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 8.0793 - mse: 8.0793 - val_loss: 8.4873 - val_mse: 8.4873\n",
            "Epoch 367/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 8.0638 - mse: 8.0638 - val_loss: 8.4897 - val_mse: 8.4897\n",
            "Epoch 368/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.0667 - mse: 8.0667 - val_loss: 8.4818 - val_mse: 8.4818\n",
            "Epoch 369/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.0501 - mse: 8.0501 - val_loss: 8.4744 - val_mse: 8.4744\n",
            "Epoch 370/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 8.0466 - mse: 8.0466 - val_loss: 8.4690 - val_mse: 8.4690\n",
            "Epoch 371/1000\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 8.0519 - mse: 8.0519 - val_loss: 8.4647 - val_mse: 8.4647\n",
            "Epoch 372/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 8.0452 - mse: 8.0452 - val_loss: 8.4613 - val_mse: 8.4613\n",
            "Epoch 373/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.0319 - mse: 8.0319 - val_loss: 8.4592 - val_mse: 8.4592\n",
            "Epoch 374/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.0377 - mse: 8.0377 - val_loss: 8.4518 - val_mse: 8.4518\n",
            "Epoch 375/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 8.0255 - mse: 8.0255 - val_loss: 8.4479 - val_mse: 8.4479\n",
            "Epoch 376/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 8.0136 - mse: 8.0136 - val_loss: 8.4437 - val_mse: 8.4437\n",
            "Epoch 377/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 8.0091 - mse: 8.0091 - val_loss: 8.4380 - val_mse: 8.4380\n",
            "Epoch 378/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.0133 - mse: 8.0133 - val_loss: 8.4294 - val_mse: 8.4294\n",
            "Epoch 379/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.9957 - mse: 7.9957 - val_loss: 8.4214 - val_mse: 8.4214\n",
            "Epoch 380/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 7.9917 - mse: 7.9917 - val_loss: 8.4154 - val_mse: 8.4154\n",
            "Epoch 381/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.9919 - mse: 7.9919 - val_loss: 8.4088 - val_mse: 8.4088\n",
            "Epoch 382/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.9823 - mse: 7.9823 - val_loss: 8.4083 - val_mse: 8.4083\n",
            "Epoch 383/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.9850 - mse: 7.9850 - val_loss: 8.4070 - val_mse: 8.4070\n",
            "Epoch 384/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.9758 - mse: 7.9758 - val_loss: 8.4077 - val_mse: 8.4077\n",
            "Epoch 385/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.9720 - mse: 7.9720 - val_loss: 8.4069 - val_mse: 8.4069\n",
            "Epoch 386/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.9596 - mse: 7.9596 - val_loss: 8.4099 - val_mse: 8.4099\n",
            "Epoch 387/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.9561 - mse: 7.9561 - val_loss: 8.4093 - val_mse: 8.4093\n",
            "Epoch 388/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 7.9600 - mse: 7.9600 - val_loss: 8.4014 - val_mse: 8.4014\n",
            "Epoch 389/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.9539 - mse: 7.9539 - val_loss: 8.3984 - val_mse: 8.3984\n",
            "Epoch 390/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 7.9540 - mse: 7.9540 - val_loss: 8.3955 - val_mse: 8.3955\n",
            "Epoch 391/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.9510 - mse: 7.9510 - val_loss: 8.3933 - val_mse: 8.3933\n",
            "Epoch 392/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.9361 - mse: 7.9361 - val_loss: 8.3895 - val_mse: 8.3895\n",
            "Epoch 393/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.9357 - mse: 7.9357 - val_loss: 8.3875 - val_mse: 8.3875\n",
            "Epoch 394/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.9185 - mse: 7.9185 - val_loss: 8.3893 - val_mse: 8.3893\n",
            "Epoch 395/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 7.9372 - mse: 7.9372 - val_loss: 8.3940 - val_mse: 8.3940\n",
            "Epoch 396/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.9117 - mse: 7.9117 - val_loss: 8.3878 - val_mse: 8.3878\n",
            "Epoch 397/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.9181 - mse: 7.9181 - val_loss: 8.3837 - val_mse: 8.3837\n",
            "Epoch 398/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 7.9016 - mse: 7.9016 - val_loss: 8.3846 - val_mse: 8.3846\n",
            "Epoch 399/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.8920 - mse: 7.8920 - val_loss: 8.3779 - val_mse: 8.3779\n",
            "Epoch 400/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.8966 - mse: 7.8966 - val_loss: 8.3694 - val_mse: 8.3694\n",
            "Epoch 401/1000\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 7.8932 - mse: 7.8932 - val_loss: 8.3670 - val_mse: 8.3670\n",
            "Epoch 402/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.8873 - mse: 7.8873 - val_loss: 8.3607 - val_mse: 8.3607\n",
            "Epoch 403/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.8844 - mse: 7.8844 - val_loss: 8.3556 - val_mse: 8.3556\n",
            "Epoch 404/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.8831 - mse: 7.8831 - val_loss: 8.3564 - val_mse: 8.3564\n",
            "Epoch 405/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.8693 - mse: 7.8693 - val_loss: 8.3592 - val_mse: 8.3592\n",
            "Epoch 406/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.8643 - mse: 7.8643 - val_loss: 8.3567 - val_mse: 8.3567\n",
            "Epoch 407/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.8687 - mse: 7.8687 - val_loss: 8.3511 - val_mse: 8.3511\n",
            "Epoch 408/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.8521 - mse: 7.8521 - val_loss: 8.3469 - val_mse: 8.3469\n",
            "Epoch 409/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 7.8526 - mse: 7.8526 - val_loss: 8.3486 - val_mse: 8.3486\n",
            "Epoch 410/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.8474 - mse: 7.8474 - val_loss: 8.3449 - val_mse: 8.3449\n",
            "Epoch 411/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.8413 - mse: 7.8413 - val_loss: 8.3397 - val_mse: 8.3397\n",
            "Epoch 412/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.8409 - mse: 7.8409 - val_loss: 8.3363 - val_mse: 8.3363\n",
            "Epoch 413/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.8338 - mse: 7.8338 - val_loss: 8.3302 - val_mse: 8.3302\n",
            "Epoch 414/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.8300 - mse: 7.8300 - val_loss: 8.3288 - val_mse: 8.3288\n",
            "Epoch 415/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.8280 - mse: 7.8280 - val_loss: 8.3251 - val_mse: 8.3251\n",
            "Epoch 416/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.8192 - mse: 7.8192 - val_loss: 8.3183 - val_mse: 8.3183\n",
            "Epoch 417/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.8209 - mse: 7.8209 - val_loss: 8.3180 - val_mse: 8.3180\n",
            "Epoch 418/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.8124 - mse: 7.8124 - val_loss: 8.3115 - val_mse: 8.3115\n",
            "Epoch 419/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.8186 - mse: 7.8186 - val_loss: 8.3148 - val_mse: 8.3148\n",
            "Epoch 420/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.8000 - mse: 7.8000 - val_loss: 8.3124 - val_mse: 8.3124\n",
            "Epoch 421/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 7.8049 - mse: 7.8049 - val_loss: 8.3111 - val_mse: 8.3111\n",
            "Epoch 422/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.7956 - mse: 7.7956 - val_loss: 8.3118 - val_mse: 8.3118\n",
            "Epoch 423/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.7926 - mse: 7.7926 - val_loss: 8.3107 - val_mse: 8.3107\n",
            "Epoch 424/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.7863 - mse: 7.7863 - val_loss: 8.3032 - val_mse: 8.3032\n",
            "Epoch 425/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.7889 - mse: 7.7889 - val_loss: 8.3044 - val_mse: 8.3044\n",
            "Epoch 426/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.7792 - mse: 7.7792 - val_loss: 8.3027 - val_mse: 8.3027\n",
            "Epoch 427/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.7710 - mse: 7.7710 - val_loss: 8.3002 - val_mse: 8.3002\n",
            "Epoch 428/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.7856 - mse: 7.7856 - val_loss: 8.3037 - val_mse: 8.3037\n",
            "Epoch 429/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.7787 - mse: 7.7787 - val_loss: 8.3079 - val_mse: 8.3079\n",
            "Epoch 430/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.7601 - mse: 7.7601 - val_loss: 8.3096 - val_mse: 8.3096\n",
            "Epoch 431/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.7588 - mse: 7.7588 - val_loss: 8.3081 - val_mse: 8.3081\n",
            "Epoch 432/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.7516 - mse: 7.7516 - val_loss: 8.3077 - val_mse: 8.3077\n",
            "Epoch 433/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.7473 - mse: 7.7473 - val_loss: 8.3078 - val_mse: 8.3078\n",
            "Epoch 434/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.7429 - mse: 7.7429 - val_loss: 8.3056 - val_mse: 8.3056\n",
            "Epoch 435/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.7394 - mse: 7.7394 - val_loss: 8.3040 - val_mse: 8.3040\n",
            "Epoch 436/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.7374 - mse: 7.7374 - val_loss: 8.3076 - val_mse: 8.3076\n",
            "Epoch 437/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.7319 - mse: 7.7319 - val_loss: 8.3076 - val_mse: 8.3076\n",
            "Epoch 438/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.7358 - mse: 7.7358 - val_loss: 8.3014 - val_mse: 8.3014\n",
            "Epoch 439/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.7316 - mse: 7.7316 - val_loss: 8.3051 - val_mse: 8.3051\n",
            "Epoch 440/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.7234 - mse: 7.7234 - val_loss: 8.3027 - val_mse: 8.3027\n",
            "Epoch 441/1000\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 7.7267 - mse: 7.7267 - val_loss: 8.2962 - val_mse: 8.2962\n",
            "Epoch 442/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.7251 - mse: 7.7251 - val_loss: 8.2950 - val_mse: 8.2950\n",
            "Epoch 443/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.7091 - mse: 7.7091 - val_loss: 8.2911 - val_mse: 8.2911\n",
            "Epoch 444/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.7054 - mse: 7.7054 - val_loss: 8.2885 - val_mse: 8.2885\n",
            "Epoch 445/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.7093 - mse: 7.7093 - val_loss: 8.2899 - val_mse: 8.2899\n",
            "Epoch 446/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.6984 - mse: 7.6984 - val_loss: 8.2864 - val_mse: 8.2864\n",
            "Epoch 447/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.6926 - mse: 7.6926 - val_loss: 8.2829 - val_mse: 8.2829\n",
            "Epoch 448/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.6994 - mse: 7.6994 - val_loss: 8.2776 - val_mse: 8.2776\n",
            "Epoch 449/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.6937 - mse: 7.6937 - val_loss: 8.2762 - val_mse: 8.2762\n",
            "Epoch 450/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.6828 - mse: 7.6828 - val_loss: 8.2771 - val_mse: 8.2771\n",
            "Epoch 451/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 7.6857 - mse: 7.6857 - val_loss: 8.2705 - val_mse: 8.2705\n",
            "Epoch 452/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.6837 - mse: 7.6837 - val_loss: 8.2632 - val_mse: 8.2632\n",
            "Epoch 453/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.6747 - mse: 7.6747 - val_loss: 8.2633 - val_mse: 8.2633\n",
            "Epoch 454/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.6759 - mse: 7.6759 - val_loss: 8.2675 - val_mse: 8.2675\n",
            "Epoch 455/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 7.6698 - mse: 7.6698 - val_loss: 8.2605 - val_mse: 8.2605\n",
            "Epoch 456/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.6664 - mse: 7.6664 - val_loss: 8.2557 - val_mse: 8.2557\n",
            "Epoch 457/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.6617 - mse: 7.6617 - val_loss: 8.2536 - val_mse: 8.2536\n",
            "Epoch 458/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.6613 - mse: 7.6613 - val_loss: 8.2517 - val_mse: 8.2517\n",
            "Epoch 459/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.6516 - mse: 7.6516 - val_loss: 8.2508 - val_mse: 8.2508\n",
            "Epoch 460/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.6612 - mse: 7.6612 - val_loss: 8.2556 - val_mse: 8.2556\n",
            "Epoch 461/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.6536 - mse: 7.6536 - val_loss: 8.2600 - val_mse: 8.2600\n",
            "Epoch 462/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.6453 - mse: 7.6453 - val_loss: 8.2523 - val_mse: 8.2523\n",
            "Epoch 463/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.6404 - mse: 7.6404 - val_loss: 8.2453 - val_mse: 8.2453\n",
            "Epoch 464/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.6408 - mse: 7.6408 - val_loss: 8.2401 - val_mse: 8.2401\n",
            "Epoch 465/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.6347 - mse: 7.6347 - val_loss: 8.2412 - val_mse: 8.2412\n",
            "Epoch 466/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.6309 - mse: 7.6309 - val_loss: 8.2433 - val_mse: 8.2433\n",
            "Epoch 467/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.6374 - mse: 7.6374 - val_loss: 8.2471 - val_mse: 8.2471\n",
            "Epoch 468/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.6271 - mse: 7.6271 - val_loss: 8.2458 - val_mse: 8.2458\n",
            "Epoch 469/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.6263 - mse: 7.6263 - val_loss: 8.2482 - val_mse: 8.2482\n",
            "Epoch 470/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.6162 - mse: 7.6162 - val_loss: 8.2436 - val_mse: 8.2436\n",
            "Epoch 471/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.6121 - mse: 7.6121 - val_loss: 8.2436 - val_mse: 8.2436\n",
            "Epoch 472/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.6069 - mse: 7.6069 - val_loss: 8.2382 - val_mse: 8.2382\n",
            "Epoch 473/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.6206 - mse: 7.6206 - val_loss: 8.2398 - val_mse: 8.2398\n",
            "Epoch 474/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.6065 - mse: 7.6065 - val_loss: 8.2405 - val_mse: 8.2405\n",
            "Epoch 475/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.5994 - mse: 7.5994 - val_loss: 8.2363 - val_mse: 8.2363\n",
            "Epoch 476/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.6077 - mse: 7.6077 - val_loss: 8.2312 - val_mse: 8.2312\n",
            "Epoch 477/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.5947 - mse: 7.5947 - val_loss: 8.2308 - val_mse: 8.2308\n",
            "Epoch 478/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.5932 - mse: 7.5932 - val_loss: 8.2271 - val_mse: 8.2271\n",
            "Epoch 479/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.5838 - mse: 7.5838 - val_loss: 8.2256 - val_mse: 8.2256\n",
            "Epoch 480/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.5900 - mse: 7.5900 - val_loss: 8.2324 - val_mse: 8.2324\n",
            "Epoch 481/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.5816 - mse: 7.5816 - val_loss: 8.2360 - val_mse: 8.2360\n",
            "Epoch 482/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.5774 - mse: 7.5774 - val_loss: 8.2303 - val_mse: 8.2303\n",
            "Epoch 483/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.5853 - mse: 7.5853 - val_loss: 8.2318 - val_mse: 8.2318\n",
            "Epoch 484/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.5851 - mse: 7.5851 - val_loss: 8.2322 - val_mse: 8.2322\n",
            "Epoch 485/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.5693 - mse: 7.5693 - val_loss: 8.2340 - val_mse: 8.2340\n",
            "Epoch 486/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.5698 - mse: 7.5698 - val_loss: 8.2348 - val_mse: 8.2348\n",
            "Epoch 487/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.5696 - mse: 7.5696 - val_loss: 8.2334 - val_mse: 8.2334\n",
            "Epoch 488/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.5600 - mse: 7.5600 - val_loss: 8.2348 - val_mse: 8.2348\n",
            "Epoch 489/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.5558 - mse: 7.5558 - val_loss: 8.2375 - val_mse: 8.2375\n",
            "Epoch 490/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.5515 - mse: 7.5515 - val_loss: 8.2369 - val_mse: 8.2369\n",
            "Epoch 491/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.5540 - mse: 7.5540 - val_loss: 8.2362 - val_mse: 8.2362\n",
            "Epoch 492/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.5477 - mse: 7.5477 - val_loss: 8.2356 - val_mse: 8.2356\n",
            "Epoch 493/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.5480 - mse: 7.5480 - val_loss: 8.2346 - val_mse: 8.2346\n",
            "Epoch 494/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 7.5501 - mse: 7.5501 - val_loss: 8.2373 - val_mse: 8.2373\n",
            "Epoch 495/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.5366 - mse: 7.5366 - val_loss: 8.2348 - val_mse: 8.2348\n",
            "Epoch 496/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.5365 - mse: 7.5365 - val_loss: 8.2370 - val_mse: 8.2370\n",
            "Epoch 497/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.5403 - mse: 7.5403 - val_loss: 8.2426 - val_mse: 8.2426\n",
            "Epoch 498/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.5343 - mse: 7.5343 - val_loss: 8.2400 - val_mse: 8.2400\n",
            "Epoch 499/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.5302 - mse: 7.5302 - val_loss: 8.2411 - val_mse: 8.2411\n",
            "Epoch 500/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.5272 - mse: 7.5272 - val_loss: 8.2377 - val_mse: 8.2377\n",
            "Epoch 501/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.5287 - mse: 7.5287 - val_loss: 8.2390 - val_mse: 8.2390\n",
            "Epoch 502/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.5271 - mse: 7.5271 - val_loss: 8.2414 - val_mse: 8.2414\n",
            "Epoch 503/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.5149 - mse: 7.5149 - val_loss: 8.2399 - val_mse: 8.2399\n",
            "Epoch 504/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.5166 - mse: 7.5166 - val_loss: 8.2395 - val_mse: 8.2395\n",
            "Epoch 505/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.5137 - mse: 7.5137 - val_loss: 8.2308 - val_mse: 8.2308\n",
            "Epoch 506/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.5066 - mse: 7.5066 - val_loss: 8.2255 - val_mse: 8.2255\n",
            "Epoch 507/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.5032 - mse: 7.5032 - val_loss: 8.2229 - val_mse: 8.2229\n",
            "Epoch 508/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.5035 - mse: 7.5035 - val_loss: 8.2229 - val_mse: 8.2229\n",
            "Epoch 509/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.5124 - mse: 7.5124 - val_loss: 8.2236 - val_mse: 8.2236\n",
            "Epoch 510/1000\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 7.4963 - mse: 7.4963 - val_loss: 8.2244 - val_mse: 8.2244\n",
            "Epoch 511/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.4982 - mse: 7.4982 - val_loss: 8.2216 - val_mse: 8.2216\n",
            "Epoch 512/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.5102 - mse: 7.5102 - val_loss: 8.2197 - val_mse: 8.2197\n",
            "Epoch 513/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.4899 - mse: 7.4899 - val_loss: 8.2210 - val_mse: 8.2210\n",
            "Epoch 514/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.4902 - mse: 7.4902 - val_loss: 8.2196 - val_mse: 8.2196\n",
            "Epoch 515/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.4850 - mse: 7.4850 - val_loss: 8.2214 - val_mse: 8.2214\n",
            "Epoch 516/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.4806 - mse: 7.4806 - val_loss: 8.2195 - val_mse: 8.2195\n",
            "Epoch 517/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.4816 - mse: 7.4816 - val_loss: 8.2196 - val_mse: 8.2196\n",
            "Epoch 518/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.4781 - mse: 7.4781 - val_loss: 8.2191 - val_mse: 8.2191\n",
            "Epoch 519/1000\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 7.4731 - mse: 7.4731 - val_loss: 8.2176 - val_mse: 8.2176\n",
            "Epoch 520/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.4705 - mse: 7.4705 - val_loss: 8.2183 - val_mse: 8.2183\n",
            "Epoch 521/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.4733 - mse: 7.4733 - val_loss: 8.2194 - val_mse: 8.2194\n",
            "Epoch 522/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.4647 - mse: 7.4647 - val_loss: 8.2150 - val_mse: 8.2150\n",
            "Epoch 523/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.4635 - mse: 7.4635 - val_loss: 8.2151 - val_mse: 8.2151\n",
            "Epoch 524/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.4623 - mse: 7.4623 - val_loss: 8.2165 - val_mse: 8.2165\n",
            "Epoch 525/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.4617 - mse: 7.4617 - val_loss: 8.2184 - val_mse: 8.2184\n",
            "Epoch 526/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.4541 - mse: 7.4541 - val_loss: 8.2162 - val_mse: 8.2162\n",
            "Epoch 527/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.4597 - mse: 7.4597 - val_loss: 8.2164 - val_mse: 8.2164\n",
            "Epoch 528/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.4550 - mse: 7.4550 - val_loss: 8.2136 - val_mse: 8.2136\n",
            "Epoch 529/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.4536 - mse: 7.4536 - val_loss: 8.2108 - val_mse: 8.2108\n",
            "Epoch 530/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.4479 - mse: 7.4479 - val_loss: 8.2136 - val_mse: 8.2136\n",
            "Epoch 531/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.4460 - mse: 7.4460 - val_loss: 8.2168 - val_mse: 8.2168\n",
            "Epoch 532/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.4492 - mse: 7.4492 - val_loss: 8.2126 - val_mse: 8.2126\n",
            "Epoch 533/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.4402 - mse: 7.4402 - val_loss: 8.2112 - val_mse: 8.2112\n",
            "Epoch 534/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.4377 - mse: 7.4377 - val_loss: 8.2111 - val_mse: 8.2111\n",
            "Epoch 535/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.4399 - mse: 7.4399 - val_loss: 8.2130 - val_mse: 8.2130\n",
            "Epoch 536/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.4321 - mse: 7.4321 - val_loss: 8.2139 - val_mse: 8.2139\n",
            "Epoch 537/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.4351 - mse: 7.4351 - val_loss: 8.2134 - val_mse: 8.2134\n",
            "Epoch 538/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 7.4256 - mse: 7.4256 - val_loss: 8.2158 - val_mse: 8.2158\n",
            "Epoch 539/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.4311 - mse: 7.4311 - val_loss: 8.2143 - val_mse: 8.2143\n",
            "Epoch 540/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.4236 - mse: 7.4236 - val_loss: 8.2065 - val_mse: 8.2065\n",
            "Epoch 541/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.4170 - mse: 7.4170 - val_loss: 8.2041 - val_mse: 8.2041\n",
            "Epoch 542/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.4206 - mse: 7.4206 - val_loss: 8.2060 - val_mse: 8.2060\n",
            "Epoch 543/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.4282 - mse: 7.4282 - val_loss: 8.2084 - val_mse: 8.2084\n",
            "Epoch 544/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.4177 - mse: 7.4177 - val_loss: 8.2162 - val_mse: 8.2162\n",
            "Epoch 545/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.4059 - mse: 7.4059 - val_loss: 8.2146 - val_mse: 8.2146\n",
            "Epoch 546/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.4214 - mse: 7.4214 - val_loss: 8.2152 - val_mse: 8.2152\n",
            "Epoch 547/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.4081 - mse: 7.4081 - val_loss: 8.2150 - val_mse: 8.2150\n",
            "Epoch 548/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.4188 - mse: 7.4188 - val_loss: 8.2134 - val_mse: 8.2134\n",
            "Epoch 549/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.4078 - mse: 7.4078 - val_loss: 8.2145 - val_mse: 8.2145\n",
            "Epoch 550/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.4043 - mse: 7.4043 - val_loss: 8.2110 - val_mse: 8.2110\n",
            "Epoch 551/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.3948 - mse: 7.3948 - val_loss: 8.2112 - val_mse: 8.2112\n",
            "Epoch 552/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.3947 - mse: 7.3947 - val_loss: 8.2101 - val_mse: 8.2101\n",
            "Epoch 553/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.3948 - mse: 7.3948 - val_loss: 8.2068 - val_mse: 8.2068\n",
            "Epoch 554/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.3901 - mse: 7.3901 - val_loss: 8.2066 - val_mse: 8.2066\n",
            "Epoch 555/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.3881 - mse: 7.3881 - val_loss: 8.2082 - val_mse: 8.2082\n",
            "Epoch 556/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.3805 - mse: 7.3805 - val_loss: 8.2072 - val_mse: 8.2072\n",
            "Epoch 557/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.3921 - mse: 7.3921 - val_loss: 8.2073 - val_mse: 8.2073\n",
            "Epoch 558/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.3806 - mse: 7.3806 - val_loss: 8.2042 - val_mse: 8.2042\n",
            "Epoch 559/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.3812 - mse: 7.3812 - val_loss: 8.2021 - val_mse: 8.2021\n",
            "Epoch 560/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.3730 - mse: 7.3730 - val_loss: 8.2037 - val_mse: 8.2037\n",
            "Epoch 561/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.3808 - mse: 7.3808 - val_loss: 8.2014 - val_mse: 8.2014\n",
            "Epoch 562/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.3703 - mse: 7.3703 - val_loss: 8.2062 - val_mse: 8.2062\n",
            "Epoch 563/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.3684 - mse: 7.3684 - val_loss: 8.2075 - val_mse: 8.2075\n",
            "Epoch 564/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.3649 - mse: 7.3649 - val_loss: 8.2083 - val_mse: 8.2083\n",
            "Epoch 565/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.3652 - mse: 7.3652 - val_loss: 8.2073 - val_mse: 8.2073\n",
            "Epoch 566/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.3713 - mse: 7.3713 - val_loss: 8.2051 - val_mse: 8.2051\n",
            "Epoch 567/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.3561 - mse: 7.3561 - val_loss: 8.2040 - val_mse: 8.2040\n",
            "Epoch 568/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.3573 - mse: 7.3573 - val_loss: 8.2080 - val_mse: 8.2080\n",
            "Epoch 569/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.3534 - mse: 7.3534 - val_loss: 8.2074 - val_mse: 8.2074\n",
            "Epoch 570/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.3523 - mse: 7.3523 - val_loss: 8.2067 - val_mse: 8.2067\n",
            "Epoch 571/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.3509 - mse: 7.3509 - val_loss: 8.2084 - val_mse: 8.2084\n",
            "Epoch 572/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.3455 - mse: 7.3455 - val_loss: 8.2083 - val_mse: 8.2083\n",
            "Epoch 573/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.3471 - mse: 7.3471 - val_loss: 8.2055 - val_mse: 8.2055\n",
            "Epoch 574/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.3439 - mse: 7.3439 - val_loss: 8.2043 - val_mse: 8.2043\n",
            "Epoch 575/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.3401 - mse: 7.3401 - val_loss: 8.2045 - val_mse: 8.2045\n",
            "Epoch 576/1000\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 7.3422 - mse: 7.3422 - val_loss: 8.2034 - val_mse: 8.2034\n",
            "Epoch 577/1000\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 7.3362 - mse: 7.3362 - val_loss: 8.2062 - val_mse: 8.2062\n",
            "Epoch 578/1000\n",
            "4/4 [==============================] - 0s 22ms/step - loss: 7.3372 - mse: 7.3372 - val_loss: 8.2067 - val_mse: 8.2067\n",
            "Epoch 579/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.3359 - mse: 7.3359 - val_loss: 8.2029 - val_mse: 8.2029\n",
            "Epoch 580/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.3306 - mse: 7.3306 - val_loss: 8.2026 - val_mse: 8.2026\n",
            "Epoch 581/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.3303 - mse: 7.3303 - val_loss: 8.2008 - val_mse: 8.2008\n",
            "Epoch 582/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.3294 - mse: 7.3294 - val_loss: 8.2048 - val_mse: 8.2048\n",
            "Epoch 583/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.3285 - mse: 7.3285 - val_loss: 8.2040 - val_mse: 8.2040\n",
            "Epoch 584/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.3247 - mse: 7.3247 - val_loss: 8.2113 - val_mse: 8.2113\n",
            "Epoch 585/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.3192 - mse: 7.3192 - val_loss: 8.2091 - val_mse: 8.2091\n",
            "Epoch 586/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.3188 - mse: 7.3188 - val_loss: 8.2088 - val_mse: 8.2088\n",
            "Epoch 587/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.3138 - mse: 7.3138 - val_loss: 8.2093 - val_mse: 8.2093\n",
            "Epoch 588/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.3232 - mse: 7.3232 - val_loss: 8.2107 - val_mse: 8.2107\n",
            "Epoch 589/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.3149 - mse: 7.3149 - val_loss: 8.2135 - val_mse: 8.2135\n",
            "Epoch 590/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.3139 - mse: 7.3139 - val_loss: 8.2144 - val_mse: 8.2144\n",
            "Epoch 591/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.3077 - mse: 7.3077 - val_loss: 8.2183 - val_mse: 8.2183\n",
            "Epoch 592/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.3066 - mse: 7.3066 - val_loss: 8.2186 - val_mse: 8.2186\n",
            "Epoch 593/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.3059 - mse: 7.3059 - val_loss: 8.2152 - val_mse: 8.2152\n",
            "Epoch 594/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.3009 - mse: 7.3009 - val_loss: 8.2159 - val_mse: 8.2159\n",
            "Epoch 595/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.3007 - mse: 7.3007 - val_loss: 8.2171 - val_mse: 8.2171\n",
            "Epoch 596/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 7.3041 - mse: 7.3041 - val_loss: 8.2209 - val_mse: 8.2209\n",
            "Epoch 597/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.2969 - mse: 7.2969 - val_loss: 8.2237 - val_mse: 8.2237\n",
            "Epoch 598/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 7.3098 - mse: 7.3098 - val_loss: 8.2285 - val_mse: 8.2285\n",
            "Epoch 599/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.2935 - mse: 7.2935 - val_loss: 8.2260 - val_mse: 8.2260\n",
            "Epoch 600/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.2943 - mse: 7.2943 - val_loss: 8.2253 - val_mse: 8.2253\n",
            "Epoch 601/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.2875 - mse: 7.2875 - val_loss: 8.2195 - val_mse: 8.2195\n",
            "Epoch 602/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.2921 - mse: 7.2921 - val_loss: 8.2183 - val_mse: 8.2183\n",
            "Epoch 603/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.2830 - mse: 7.2830 - val_loss: 8.2198 - val_mse: 8.2198\n",
            "Epoch 604/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.2969 - mse: 7.2969 - val_loss: 8.2164 - val_mse: 8.2164\n",
            "Epoch 605/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.2810 - mse: 7.2810 - val_loss: 8.2158 - val_mse: 8.2158\n",
            "Epoch 606/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.2805 - mse: 7.2805 - val_loss: 8.2159 - val_mse: 8.2159\n",
            "Epoch 607/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.2773 - mse: 7.2773 - val_loss: 8.2158 - val_mse: 8.2158\n",
            "Epoch 608/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.2830 - mse: 7.2830 - val_loss: 8.2186 - val_mse: 8.2186\n",
            "Epoch 609/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.2711 - mse: 7.2711 - val_loss: 8.2221 - val_mse: 8.2221\n",
            "Epoch 610/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.2732 - mse: 7.2732 - val_loss: 8.2237 - val_mse: 8.2237\n",
            "Epoch 611/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.2684 - mse: 7.2684 - val_loss: 8.2215 - val_mse: 8.2215\n",
            "Epoch 612/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.2669 - mse: 7.2669 - val_loss: 8.2207 - val_mse: 8.2207\n",
            "Epoch 613/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.2639 - mse: 7.2639 - val_loss: 8.2218 - val_mse: 8.2218\n",
            "Epoch 614/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.2652 - mse: 7.2652 - val_loss: 8.2211 - val_mse: 8.2211\n",
            "Epoch 615/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.2653 - mse: 7.2653 - val_loss: 8.2211 - val_mse: 8.2211\n",
            "Epoch 616/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.2576 - mse: 7.2576 - val_loss: 8.2213 - val_mse: 8.2213\n",
            "Epoch 617/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.2724 - mse: 7.2724 - val_loss: 8.2253 - val_mse: 8.2253\n",
            "Epoch 618/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.2656 - mse: 7.2656 - val_loss: 8.2336 - val_mse: 8.2336\n",
            "Epoch 619/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.2543 - mse: 7.2543 - val_loss: 8.2348 - val_mse: 8.2348\n",
            "Epoch 620/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.2519 - mse: 7.2519 - val_loss: 8.2309 - val_mse: 8.2309\n",
            "Epoch 621/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.2463 - mse: 7.2463 - val_loss: 8.2307 - val_mse: 8.2307\n",
            "Epoch 622/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.2462 - mse: 7.2462 - val_loss: 8.2281 - val_mse: 8.2281\n",
            "Epoch 623/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.2473 - mse: 7.2473 - val_loss: 8.2319 - val_mse: 8.2319\n",
            "Epoch 624/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.2481 - mse: 7.2481 - val_loss: 8.2309 - val_mse: 8.2309\n",
            "Epoch 625/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.2451 - mse: 7.2451 - val_loss: 8.2297 - val_mse: 8.2297\n",
            "Epoch 626/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.2648 - mse: 7.2648 - val_loss: 8.2310 - val_mse: 8.2310\n",
            "Epoch 627/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.2345 - mse: 7.2345 - val_loss: 8.2290 - val_mse: 8.2290\n",
            "Epoch 628/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.2454 - mse: 7.2454 - val_loss: 8.2308 - val_mse: 8.2308\n",
            "Epoch 629/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.2438 - mse: 7.2438 - val_loss: 8.2299 - val_mse: 8.2299\n",
            "Epoch 630/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.2404 - mse: 7.2404 - val_loss: 8.2252 - val_mse: 8.2252\n",
            "Epoch 631/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.2366 - mse: 7.2366 - val_loss: 8.2313 - val_mse: 8.2313\n",
            "Epoch 632/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.2331 - mse: 7.2331 - val_loss: 8.2249 - val_mse: 8.2249\n",
            "Epoch 633/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.2274 - mse: 7.2274 - val_loss: 8.2241 - val_mse: 8.2241\n",
            "Epoch 634/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.2257 - mse: 7.2257 - val_loss: 8.2277 - val_mse: 8.2277\n",
            "Epoch 635/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.2217 - mse: 7.2217 - val_loss: 8.2267 - val_mse: 8.2267\n",
            "Epoch 636/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.2225 - mse: 7.2225 - val_loss: 8.2315 - val_mse: 8.2315\n",
            "Epoch 637/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.2304 - mse: 7.2304 - val_loss: 8.2266 - val_mse: 8.2266\n",
            "Epoch 638/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.2200 - mse: 7.2200 - val_loss: 8.2303 - val_mse: 8.2303\n",
            "Epoch 639/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.2159 - mse: 7.2159 - val_loss: 8.2320 - val_mse: 8.2320\n",
            "Epoch 640/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.2197 - mse: 7.2197 - val_loss: 8.2335 - val_mse: 8.2335\n",
            "Epoch 641/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.2128 - mse: 7.2128 - val_loss: 8.2387 - val_mse: 8.2387\n",
            "Epoch 642/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.2112 - mse: 7.2112 - val_loss: 8.2374 - val_mse: 8.2374\n",
            "Epoch 643/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.2113 - mse: 7.2113 - val_loss: 8.2367 - val_mse: 8.2367\n",
            "Epoch 644/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.2089 - mse: 7.2089 - val_loss: 8.2379 - val_mse: 8.2379\n",
            "Epoch 645/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.2099 - mse: 7.2099 - val_loss: 8.2364 - val_mse: 8.2364\n",
            "Epoch 646/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.2085 - mse: 7.2085 - val_loss: 8.2346 - val_mse: 8.2346\n",
            "Epoch 647/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.2041 - mse: 7.2041 - val_loss: 8.2312 - val_mse: 8.2312\n",
            "Epoch 648/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.2026 - mse: 7.2026 - val_loss: 8.2346 - val_mse: 8.2346\n",
            "Epoch 649/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.1987 - mse: 7.1987 - val_loss: 8.2304 - val_mse: 8.2304\n",
            "Epoch 650/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.2060 - mse: 7.2060 - val_loss: 8.2321 - val_mse: 8.2321\n",
            "Epoch 651/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.1968 - mse: 7.1968 - val_loss: 8.2344 - val_mse: 8.2344\n",
            "Epoch 652/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.1996 - mse: 7.1996 - val_loss: 8.2378 - val_mse: 8.2378\n",
            "Epoch 653/1000\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 7.2117 - mse: 7.2117 - val_loss: 8.2381 - val_mse: 8.2381\n",
            "Epoch 654/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.1911 - mse: 7.1911 - val_loss: 8.2360 - val_mse: 8.2360\n",
            "Epoch 655/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.1943 - mse: 7.1943 - val_loss: 8.2361 - val_mse: 8.2361\n",
            "Epoch 656/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.1942 - mse: 7.1942 - val_loss: 8.2386 - val_mse: 8.2386\n",
            "Epoch 657/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.1871 - mse: 7.1871 - val_loss: 8.2382 - val_mse: 8.2382\n",
            "Epoch 658/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.1832 - mse: 7.1832 - val_loss: 8.2433 - val_mse: 8.2433\n",
            "Epoch 659/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.1802 - mse: 7.1802 - val_loss: 8.2443 - val_mse: 8.2443\n",
            "Epoch 660/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.1854 - mse: 7.1854 - val_loss: 8.2432 - val_mse: 8.2432\n",
            "Epoch 661/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.1759 - mse: 7.1759 - val_loss: 8.2381 - val_mse: 8.2381\n",
            "Epoch 662/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.1848 - mse: 7.1848 - val_loss: 8.2362 - val_mse: 8.2362\n",
            "Epoch 663/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.1740 - mse: 7.1740 - val_loss: 8.2365 - val_mse: 8.2365\n",
            "Epoch 664/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.1867 - mse: 7.1867 - val_loss: 8.2365 - val_mse: 8.2365\n",
            "Epoch 665/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.1797 - mse: 7.1797 - val_loss: 8.2354 - val_mse: 8.2354\n",
            "Epoch 666/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.1769 - mse: 7.1769 - val_loss: 8.2355 - val_mse: 8.2355\n",
            "Epoch 667/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.1661 - mse: 7.1661 - val_loss: 8.2309 - val_mse: 8.2309\n",
            "Epoch 668/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.1762 - mse: 7.1762 - val_loss: 8.2204 - val_mse: 8.2204\n",
            "Epoch 669/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.1625 - mse: 7.1625 - val_loss: 8.2206 - val_mse: 8.2206\n",
            "Epoch 670/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.1702 - mse: 7.1702 - val_loss: 8.2198 - val_mse: 8.2198\n",
            "Epoch 671/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.1692 - mse: 7.1692 - val_loss: 8.2272 - val_mse: 8.2272\n",
            "Epoch 672/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.1676 - mse: 7.1676 - val_loss: 8.2360 - val_mse: 8.2360\n",
            "Epoch 673/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.1627 - mse: 7.1627 - val_loss: 8.2369 - val_mse: 8.2369\n",
            "Epoch 674/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.1643 - mse: 7.1643 - val_loss: 8.2403 - val_mse: 8.2403\n",
            "Epoch 675/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.1651 - mse: 7.1651 - val_loss: 8.2425 - val_mse: 8.2425\n",
            "Epoch 676/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.1575 - mse: 7.1575 - val_loss: 8.2414 - val_mse: 8.2414\n",
            "Epoch 677/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.1491 - mse: 7.1491 - val_loss: 8.2378 - val_mse: 8.2378\n",
            "Epoch 678/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.1504 - mse: 7.1504 - val_loss: 8.2365 - val_mse: 8.2365\n",
            "Epoch 679/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.1541 - mse: 7.1541 - val_loss: 8.2411 - val_mse: 8.2411\n",
            "Epoch 680/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.1509 - mse: 7.1509 - val_loss: 8.2421 - val_mse: 8.2421\n",
            "Epoch 681/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.1441 - mse: 7.1441 - val_loss: 8.2413 - val_mse: 8.2413\n",
            "Epoch 682/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.1440 - mse: 7.1440 - val_loss: 8.2457 - val_mse: 8.2457\n",
            "Epoch 683/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.1486 - mse: 7.1486 - val_loss: 8.2504 - val_mse: 8.2504\n",
            "Epoch 684/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.1399 - mse: 7.1399 - val_loss: 8.2494 - val_mse: 8.2494\n",
            "Epoch 685/1000\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 7.1372 - mse: 7.1372 - val_loss: 8.2469 - val_mse: 8.2469\n",
            "Epoch 686/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.1513 - mse: 7.1513 - val_loss: 8.2508 - val_mse: 8.2508\n",
            "Epoch 687/1000\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 7.1339 - mse: 7.1339 - val_loss: 8.2524 - val_mse: 8.2524\n",
            "Epoch 688/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.1562 - mse: 7.1562 - val_loss: 8.2565 - val_mse: 8.2565\n",
            "Epoch 689/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.1318 - mse: 7.1318 - val_loss: 8.2614 - val_mse: 8.2614\n",
            "Epoch 690/1000\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 7.1321 - mse: 7.1321 - val_loss: 8.2619 - val_mse: 8.2619\n",
            "Epoch 691/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.1358 - mse: 7.1358 - val_loss: 8.2624 - val_mse: 8.2624\n",
            "Epoch 692/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.1368 - mse: 7.1368 - val_loss: 8.2624 - val_mse: 8.2624\n",
            "Epoch 693/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.1340 - mse: 7.1340 - val_loss: 8.2609 - val_mse: 8.2609\n",
            "Epoch 694/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 7.1268 - mse: 7.1268 - val_loss: 8.2540 - val_mse: 8.2540\n",
            "Epoch 695/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.1227 - mse: 7.1227 - val_loss: 8.2530 - val_mse: 8.2530\n",
            "Epoch 696/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.1223 - mse: 7.1223 - val_loss: 8.2547 - val_mse: 8.2547\n",
            "Epoch 697/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.1241 - mse: 7.1241 - val_loss: 8.2522 - val_mse: 8.2522\n",
            "Epoch 698/1000\n",
            "4/4 [==============================] - 0s 23ms/step - loss: 7.1175 - mse: 7.1175 - val_loss: 8.2556 - val_mse: 8.2556\n",
            "Epoch 699/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.1193 - mse: 7.1193 - val_loss: 8.2546 - val_mse: 8.2546\n",
            "Epoch 700/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.1132 - mse: 7.1132 - val_loss: 8.2566 - val_mse: 8.2566\n",
            "Epoch 701/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.1320 - mse: 7.1320 - val_loss: 8.2563 - val_mse: 8.2563\n",
            "Epoch 702/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 7.1244 - mse: 7.1244 - val_loss: 8.2542 - val_mse: 8.2542\n",
            "Epoch 703/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.1178 - mse: 7.1178 - val_loss: 8.2528 - val_mse: 8.2528\n",
            "Epoch 704/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.1206 - mse: 7.1206 - val_loss: 8.2475 - val_mse: 8.2475\n",
            "Epoch 705/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.1114 - mse: 7.1114 - val_loss: 8.2459 - val_mse: 8.2459\n",
            "Epoch 706/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.1089 - mse: 7.1089 - val_loss: 8.2413 - val_mse: 8.2413\n",
            "Epoch 707/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.1123 - mse: 7.1123 - val_loss: 8.2375 - val_mse: 8.2375\n",
            "Epoch 708/1000\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 7.1072 - mse: 7.1072 - val_loss: 8.2371 - val_mse: 8.2371\n",
            "Epoch 709/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.1070 - mse: 7.1070 - val_loss: 8.2335 - val_mse: 8.2335\n",
            "Epoch 710/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.0985 - mse: 7.0985 - val_loss: 8.2335 - val_mse: 8.2335\n",
            "Epoch 711/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.1064 - mse: 7.1064 - val_loss: 8.2351 - val_mse: 8.2351\n",
            "Epoch 712/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.0999 - mse: 7.0999 - val_loss: 8.2368 - val_mse: 8.2368\n",
            "Epoch 713/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.1119 - mse: 7.1119 - val_loss: 8.2313 - val_mse: 8.2313\n",
            "Epoch 714/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.1015 - mse: 7.1015 - val_loss: 8.2308 - val_mse: 8.2308\n",
            "Epoch 715/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.0985 - mse: 7.0985 - val_loss: 8.2315 - val_mse: 8.2315\n",
            "Epoch 716/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.0967 - mse: 7.0967 - val_loss: 8.2301 - val_mse: 8.2301\n",
            "Epoch 717/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.0939 - mse: 7.0939 - val_loss: 8.2289 - val_mse: 8.2289\n",
            "Epoch 718/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.0937 - mse: 7.0937 - val_loss: 8.2319 - val_mse: 8.2319\n",
            "Epoch 719/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.0873 - mse: 7.0873 - val_loss: 8.2322 - val_mse: 8.2322\n",
            "Epoch 720/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.1016 - mse: 7.1016 - val_loss: 8.2286 - val_mse: 8.2286\n",
            "Epoch 721/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.0858 - mse: 7.0858 - val_loss: 8.2250 - val_mse: 8.2250\n",
            "Epoch 722/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.0895 - mse: 7.0895 - val_loss: 8.2219 - val_mse: 8.2219\n",
            "Epoch 723/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.0837 - mse: 7.0837 - val_loss: 8.2210 - val_mse: 8.2210\n",
            "Epoch 724/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.0858 - mse: 7.0858 - val_loss: 8.2226 - val_mse: 8.2226\n",
            "Epoch 725/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.0825 - mse: 7.0825 - val_loss: 8.2256 - val_mse: 8.2256\n",
            "Epoch 726/1000\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 7.0797 - mse: 7.0797 - val_loss: 8.2248 - val_mse: 8.2248\n",
            "Epoch 727/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.0755 - mse: 7.0755 - val_loss: 8.2193 - val_mse: 8.2193\n",
            "Epoch 728/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.0816 - mse: 7.0816 - val_loss: 8.2191 - val_mse: 8.2191\n",
            "Epoch 729/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.0762 - mse: 7.0762 - val_loss: 8.2192 - val_mse: 8.2192\n",
            "Epoch 730/1000\n",
            "4/4 [==============================] - 0s 22ms/step - loss: 7.0725 - mse: 7.0725 - val_loss: 8.2205 - val_mse: 8.2205\n",
            "Epoch 731/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.0727 - mse: 7.0727 - val_loss: 8.2211 - val_mse: 8.2211\n",
            "Epoch 732/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 7.0883 - mse: 7.0883 - val_loss: 8.2170 - val_mse: 8.2170\n",
            "Epoch 733/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.0736 - mse: 7.0736 - val_loss: 8.2213 - val_mse: 8.2213\n",
            "Epoch 734/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.0721 - mse: 7.0721 - val_loss: 8.2220 - val_mse: 8.2220\n",
            "Epoch 735/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.0714 - mse: 7.0714 - val_loss: 8.2252 - val_mse: 8.2252\n",
            "Epoch 736/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.0737 - mse: 7.0737 - val_loss: 8.2244 - val_mse: 8.2244\n",
            "Epoch 737/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.0646 - mse: 7.0646 - val_loss: 8.2263 - val_mse: 8.2263\n",
            "Epoch 738/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.0767 - mse: 7.0767 - val_loss: 8.2246 - val_mse: 8.2246\n",
            "Epoch 739/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.0671 - mse: 7.0671 - val_loss: 8.2258 - val_mse: 8.2258\n",
            "Epoch 740/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.0694 - mse: 7.0694 - val_loss: 8.2345 - val_mse: 8.2345\n",
            "Epoch 741/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.0589 - mse: 7.0589 - val_loss: 8.2331 - val_mse: 8.2331\n",
            "Epoch 742/1000\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 7.0571 - mse: 7.0571 - val_loss: 8.2335 - val_mse: 8.2335\n",
            "Epoch 743/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.0567 - mse: 7.0567 - val_loss: 8.2375 - val_mse: 8.2375\n",
            "Epoch 744/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.0541 - mse: 7.0541 - val_loss: 8.2325 - val_mse: 8.2325\n",
            "Epoch 745/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.0547 - mse: 7.0547 - val_loss: 8.2370 - val_mse: 8.2370\n",
            "Epoch 746/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.0510 - mse: 7.0510 - val_loss: 8.2434 - val_mse: 8.2434\n",
            "Epoch 747/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.0525 - mse: 7.0525 - val_loss: 8.2437 - val_mse: 8.2437\n",
            "Epoch 748/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.0682 - mse: 7.0682 - val_loss: 8.2432 - val_mse: 8.2432\n",
            "Epoch 749/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 7.0702 - mse: 7.0702 - val_loss: 8.2466 - val_mse: 8.2466\n",
            "Epoch 750/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.0488 - mse: 7.0488 - val_loss: 8.2412 - val_mse: 8.2412\n",
            "Epoch 751/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.0448 - mse: 7.0448 - val_loss: 8.2404 - val_mse: 8.2404\n",
            "Epoch 752/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.0588 - mse: 7.0588 - val_loss: 8.2436 - val_mse: 8.2436\n",
            "Epoch 753/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.0403 - mse: 7.0403 - val_loss: 8.2409 - val_mse: 8.2409\n",
            "Epoch 754/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.0456 - mse: 7.0456 - val_loss: 8.2386 - val_mse: 8.2386\n",
            "Epoch 755/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.0426 - mse: 7.0426 - val_loss: 8.2345 - val_mse: 8.2345\n",
            "Epoch 756/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.0398 - mse: 7.0398 - val_loss: 8.2339 - val_mse: 8.2339\n",
            "Epoch 757/1000\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 7.0384 - mse: 7.0384 - val_loss: 8.2353 - val_mse: 8.2353\n",
            "Epoch 758/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.0369 - mse: 7.0369 - val_loss: 8.2350 - val_mse: 8.2350\n",
            "Epoch 759/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.0336 - mse: 7.0336 - val_loss: 8.2355 - val_mse: 8.2355\n",
            "Epoch 760/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.0367 - mse: 7.0367 - val_loss: 8.2380 - val_mse: 8.2380\n",
            "Epoch 761/1000\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 7.0332 - mse: 7.0332 - val_loss: 8.2381 - val_mse: 8.2381\n",
            "Epoch 762/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.0289 - mse: 7.0289 - val_loss: 8.2381 - val_mse: 8.2381\n",
            "Epoch 763/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.0300 - mse: 7.0300 - val_loss: 8.2354 - val_mse: 8.2354\n",
            "Epoch 764/1000\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 7.0279 - mse: 7.0279 - val_loss: 8.2375 - val_mse: 8.2375\n",
            "Epoch 765/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.0218 - mse: 7.0218 - val_loss: 8.2423 - val_mse: 8.2423\n",
            "Epoch 766/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 7.0275 - mse: 7.0275 - val_loss: 8.2419 - val_mse: 8.2419\n",
            "Epoch 767/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.0192 - mse: 7.0192 - val_loss: 8.2437 - val_mse: 8.2437\n",
            "Epoch 768/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.0179 - mse: 7.0179 - val_loss: 8.2423 - val_mse: 8.2423\n",
            "Epoch 769/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.0209 - mse: 7.0209 - val_loss: 8.2387 - val_mse: 8.2387\n",
            "Epoch 770/1000\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 7.0183 - mse: 7.0183 - val_loss: 8.2345 - val_mse: 8.2345\n",
            "Epoch 771/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.0195 - mse: 7.0195 - val_loss: 8.2342 - val_mse: 8.2342\n",
            "Epoch 772/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.0165 - mse: 7.0165 - val_loss: 8.2310 - val_mse: 8.2310\n",
            "Epoch 773/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.0177 - mse: 7.0177 - val_loss: 8.2289 - val_mse: 8.2289\n",
            "Epoch 774/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.0146 - mse: 7.0146 - val_loss: 8.2308 - val_mse: 8.2308\n",
            "Epoch 775/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.0117 - mse: 7.0117 - val_loss: 8.2313 - val_mse: 8.2313\n",
            "Epoch 776/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 7.0146 - mse: 7.0146 - val_loss: 8.2290 - val_mse: 8.2290\n",
            "Epoch 777/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.0150 - mse: 7.0150 - val_loss: 8.2329 - val_mse: 8.2329\n",
            "Epoch 778/1000\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 7.0204 - mse: 7.0204 - val_loss: 8.2294 - val_mse: 8.2294\n",
            "Epoch 779/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.0169 - mse: 7.0169 - val_loss: 8.2305 - val_mse: 8.2305\n",
            "Epoch 780/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.0158 - mse: 7.0158 - val_loss: 8.2287 - val_mse: 8.2287\n",
            "Epoch 781/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 7.0059 - mse: 7.0059 - val_loss: 8.2314 - val_mse: 8.2314\n",
            "Epoch 782/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 7.0026 - mse: 7.0026 - val_loss: 8.2318 - val_mse: 8.2318\n",
            "Epoch 783/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 7.0063 - mse: 7.0063 - val_loss: 8.2289 - val_mse: 8.2289\n",
            "Epoch 784/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.9983 - mse: 6.9983 - val_loss: 8.2317 - val_mse: 8.2317\n",
            "Epoch 785/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 7.0227 - mse: 7.0227 - val_loss: 8.2275 - val_mse: 8.2275\n",
            "Epoch 786/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.9992 - mse: 6.9992 - val_loss: 8.2315 - val_mse: 8.2315\n",
            "Epoch 787/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.9997 - mse: 6.9997 - val_loss: 8.2372 - val_mse: 8.2372\n",
            "Epoch 788/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.9940 - mse: 6.9940 - val_loss: 8.2395 - val_mse: 8.2395\n",
            "Epoch 789/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.9985 - mse: 6.9985 - val_loss: 8.2426 - val_mse: 8.2426\n",
            "Epoch 790/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.9993 - mse: 6.9993 - val_loss: 8.2386 - val_mse: 8.2386\n",
            "Epoch 791/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.9937 - mse: 6.9937 - val_loss: 8.2397 - val_mse: 8.2397\n",
            "Epoch 792/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.9949 - mse: 6.9949 - val_loss: 8.2457 - val_mse: 8.2457\n",
            "Epoch 793/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.9918 - mse: 6.9918 - val_loss: 8.2403 - val_mse: 8.2403\n",
            "Epoch 794/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.9940 - mse: 6.9940 - val_loss: 8.2389 - val_mse: 8.2389\n",
            "Epoch 795/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.9945 - mse: 6.9945 - val_loss: 8.2400 - val_mse: 8.2400\n",
            "Epoch 796/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.9927 - mse: 6.9927 - val_loss: 8.2435 - val_mse: 8.2435\n",
            "Epoch 797/1000\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 6.9847 - mse: 6.9847 - val_loss: 8.2435 - val_mse: 8.2435\n",
            "Epoch 798/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.9833 - mse: 6.9833 - val_loss: 8.2440 - val_mse: 8.2440\n",
            "Epoch 799/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.9796 - mse: 6.9796 - val_loss: 8.2406 - val_mse: 8.2406\n",
            "Epoch 800/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.9883 - mse: 6.9883 - val_loss: 8.2437 - val_mse: 8.2437\n",
            "Epoch 801/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 6.9901 - mse: 6.9901 - val_loss: 8.2470 - val_mse: 8.2470\n",
            "Epoch 802/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.9751 - mse: 6.9751 - val_loss: 8.2463 - val_mse: 8.2463\n",
            "Epoch 803/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.9781 - mse: 6.9781 - val_loss: 8.2465 - val_mse: 8.2465\n",
            "Epoch 804/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.9735 - mse: 6.9735 - val_loss: 8.2507 - val_mse: 8.2507\n",
            "Epoch 805/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.9775 - mse: 6.9775 - val_loss: 8.2521 - val_mse: 8.2521\n",
            "Epoch 806/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.9723 - mse: 6.9723 - val_loss: 8.2524 - val_mse: 8.2524\n",
            "Epoch 807/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.9815 - mse: 6.9815 - val_loss: 8.2519 - val_mse: 8.2519\n",
            "Epoch 808/1000\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 6.9780 - mse: 6.9780 - val_loss: 8.2550 - val_mse: 8.2550\n",
            "Epoch 809/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.9705 - mse: 6.9705 - val_loss: 8.2553 - val_mse: 8.2553\n",
            "Epoch 810/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.9700 - mse: 6.9700 - val_loss: 8.2533 - val_mse: 8.2533\n",
            "Epoch 811/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.9670 - mse: 6.9670 - val_loss: 8.2532 - val_mse: 8.2532\n",
            "Epoch 812/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.9808 - mse: 6.9808 - val_loss: 8.2554 - val_mse: 8.2554\n",
            "Epoch 813/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.9650 - mse: 6.9650 - val_loss: 8.2539 - val_mse: 8.2539\n",
            "Epoch 814/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.9600 - mse: 6.9600 - val_loss: 8.2534 - val_mse: 8.2534\n",
            "Epoch 815/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.9654 - mse: 6.9654 - val_loss: 8.2521 - val_mse: 8.2521\n",
            "Epoch 816/1000\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 6.9756 - mse: 6.9756 - val_loss: 8.2526 - val_mse: 8.2526\n",
            "Epoch 817/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.9593 - mse: 6.9593 - val_loss: 8.2580 - val_mse: 8.2580\n",
            "Epoch 818/1000\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 6.9654 - mse: 6.9654 - val_loss: 8.2575 - val_mse: 8.2575\n",
            "Epoch 819/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.9562 - mse: 6.9562 - val_loss: 8.2576 - val_mse: 8.2576\n",
            "Epoch 820/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.9539 - mse: 6.9539 - val_loss: 8.2591 - val_mse: 8.2591\n",
            "Epoch 821/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.9523 - mse: 6.9523 - val_loss: 8.2566 - val_mse: 8.2566\n",
            "Epoch 822/1000\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 6.9517 - mse: 6.9517 - val_loss: 8.2570 - val_mse: 8.2570\n",
            "Epoch 823/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.9619 - mse: 6.9619 - val_loss: 8.2552 - val_mse: 8.2552\n",
            "Epoch 824/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.9596 - mse: 6.9596 - val_loss: 8.2532 - val_mse: 8.2532\n",
            "Epoch 825/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.9474 - mse: 6.9474 - val_loss: 8.2556 - val_mse: 8.2556\n",
            "Epoch 826/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 6.9469 - mse: 6.9469 - val_loss: 8.2580 - val_mse: 8.2580\n",
            "Epoch 827/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.9490 - mse: 6.9490 - val_loss: 8.2598 - val_mse: 8.2598\n",
            "Epoch 828/1000\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 6.9415 - mse: 6.9415 - val_loss: 8.2619 - val_mse: 8.2619\n",
            "Epoch 829/1000\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 6.9430 - mse: 6.9430 - val_loss: 8.2658 - val_mse: 8.2658\n",
            "Epoch 830/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.9520 - mse: 6.9520 - val_loss: 8.2672 - val_mse: 8.2672\n",
            "Epoch 831/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.9395 - mse: 6.9395 - val_loss: 8.2643 - val_mse: 8.2643\n",
            "Epoch 832/1000\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 6.9388 - mse: 6.9388 - val_loss: 8.2629 - val_mse: 8.2629\n",
            "Epoch 833/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 6.9438 - mse: 6.9438 - val_loss: 8.2620 - val_mse: 8.2620\n",
            "Epoch 834/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.9440 - mse: 6.9440 - val_loss: 8.2667 - val_mse: 8.2667\n",
            "Epoch 835/1000\n",
            "4/4 [==============================] - 0s 23ms/step - loss: 6.9415 - mse: 6.9415 - val_loss: 8.2677 - val_mse: 8.2677\n",
            "Epoch 836/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.9407 - mse: 6.9407 - val_loss: 8.2637 - val_mse: 8.2637\n",
            "Epoch 837/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.9341 - mse: 6.9341 - val_loss: 8.2660 - val_mse: 8.2660\n",
            "Epoch 838/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.9427 - mse: 6.9427 - val_loss: 8.2645 - val_mse: 8.2645\n",
            "Epoch 839/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.9371 - mse: 6.9371 - val_loss: 8.2568 - val_mse: 8.2568\n",
            "Epoch 840/1000\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 6.9318 - mse: 6.9318 - val_loss: 8.2520 - val_mse: 8.2520\n",
            "Epoch 841/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.9312 - mse: 6.9312 - val_loss: 8.2524 - val_mse: 8.2524\n",
            "Epoch 842/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.9254 - mse: 6.9254 - val_loss: 8.2558 - val_mse: 8.2558\n",
            "Epoch 843/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.9235 - mse: 6.9235 - val_loss: 8.2548 - val_mse: 8.2548\n",
            "Epoch 844/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.9278 - mse: 6.9278 - val_loss: 8.2539 - val_mse: 8.2539\n",
            "Epoch 845/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.9264 - mse: 6.9264 - val_loss: 8.2533 - val_mse: 8.2533\n",
            "Epoch 846/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.9298 - mse: 6.9298 - val_loss: 8.2581 - val_mse: 8.2581\n",
            "Epoch 847/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.9214 - mse: 6.9214 - val_loss: 8.2589 - val_mse: 8.2589\n",
            "Epoch 848/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.9197 - mse: 6.9197 - val_loss: 8.2595 - val_mse: 8.2595\n",
            "Epoch 849/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.9173 - mse: 6.9173 - val_loss: 8.2550 - val_mse: 8.2550\n",
            "Epoch 850/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.9176 - mse: 6.9176 - val_loss: 8.2568 - val_mse: 8.2568\n",
            "Epoch 851/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.9231 - mse: 6.9231 - val_loss: 8.2522 - val_mse: 8.2522\n",
            "Epoch 852/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.9162 - mse: 6.9162 - val_loss: 8.2514 - val_mse: 8.2514\n",
            "Epoch 853/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.9141 - mse: 6.9141 - val_loss: 8.2539 - val_mse: 8.2539\n",
            "Epoch 854/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.9121 - mse: 6.9121 - val_loss: 8.2513 - val_mse: 8.2513\n",
            "Epoch 855/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.9148 - mse: 6.9148 - val_loss: 8.2550 - val_mse: 8.2550\n",
            "Epoch 856/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.9228 - mse: 6.9228 - val_loss: 8.2537 - val_mse: 8.2537\n",
            "Epoch 857/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.9133 - mse: 6.9133 - val_loss: 8.2600 - val_mse: 8.2600\n",
            "Epoch 858/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.9073 - mse: 6.9073 - val_loss: 8.2599 - val_mse: 8.2599\n",
            "Epoch 859/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.9043 - mse: 6.9043 - val_loss: 8.2612 - val_mse: 8.2612\n",
            "Epoch 860/1000\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 6.9105 - mse: 6.9105 - val_loss: 8.2634 - val_mse: 8.2634\n",
            "Epoch 861/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.9073 - mse: 6.9073 - val_loss: 8.2629 - val_mse: 8.2629\n",
            "Epoch 862/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.9049 - mse: 6.9049 - val_loss: 8.2648 - val_mse: 8.2648\n",
            "Epoch 863/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.9018 - mse: 6.9018 - val_loss: 8.2609 - val_mse: 8.2609\n",
            "Epoch 864/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.9103 - mse: 6.9103 - val_loss: 8.2577 - val_mse: 8.2577\n",
            "Epoch 865/1000\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 6.8998 - mse: 6.8998 - val_loss: 8.2566 - val_mse: 8.2566\n",
            "Epoch 866/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.9008 - mse: 6.9008 - val_loss: 8.2553 - val_mse: 8.2553\n",
            "Epoch 867/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.8970 - mse: 6.8970 - val_loss: 8.2624 - val_mse: 8.2624\n",
            "Epoch 868/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 6.8995 - mse: 6.8995 - val_loss: 8.2615 - val_mse: 8.2615\n",
            "Epoch 869/1000\n",
            "4/4 [==============================] - 0s 22ms/step - loss: 6.8954 - mse: 6.8954 - val_loss: 8.2675 - val_mse: 8.2675\n",
            "Epoch 870/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.8949 - mse: 6.8949 - val_loss: 8.2646 - val_mse: 8.2646\n",
            "Epoch 871/1000\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 6.8934 - mse: 6.8934 - val_loss: 8.2608 - val_mse: 8.2608\n",
            "Epoch 872/1000\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 6.8981 - mse: 6.8981 - val_loss: 8.2653 - val_mse: 8.2653\n",
            "Epoch 873/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.8889 - mse: 6.8889 - val_loss: 8.2651 - val_mse: 8.2651\n",
            "Epoch 874/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.8960 - mse: 6.8960 - val_loss: 8.2658 - val_mse: 8.2658\n",
            "Epoch 875/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.8928 - mse: 6.8928 - val_loss: 8.2666 - val_mse: 8.2666\n",
            "Epoch 876/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.8882 - mse: 6.8882 - val_loss: 8.2616 - val_mse: 8.2616\n",
            "Epoch 877/1000\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 6.8861 - mse: 6.8861 - val_loss: 8.2620 - val_mse: 8.2620\n",
            "Epoch 878/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 6.8849 - mse: 6.8849 - val_loss: 8.2606 - val_mse: 8.2606\n",
            "Epoch 879/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.8855 - mse: 6.8855 - val_loss: 8.2605 - val_mse: 8.2605\n",
            "Epoch 880/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.8841 - mse: 6.8841 - val_loss: 8.2632 - val_mse: 8.2632\n",
            "Epoch 881/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.8898 - mse: 6.8898 - val_loss: 8.2681 - val_mse: 8.2681\n",
            "Epoch 882/1000\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 6.8912 - mse: 6.8912 - val_loss: 8.2681 - val_mse: 8.2681\n",
            "Epoch 883/1000\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 6.8837 - mse: 6.8837 - val_loss: 8.2615 - val_mse: 8.2615\n",
            "Epoch 884/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 6.8813 - mse: 6.8813 - val_loss: 8.2619 - val_mse: 8.2619\n",
            "Epoch 885/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.8775 - mse: 6.8775 - val_loss: 8.2674 - val_mse: 8.2674\n",
            "Epoch 886/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.8821 - mse: 6.8821 - val_loss: 8.2658 - val_mse: 8.2658\n",
            "Epoch 887/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.8804 - mse: 6.8804 - val_loss: 8.2639 - val_mse: 8.2639\n",
            "Epoch 888/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 6.8739 - mse: 6.8739 - val_loss: 8.2675 - val_mse: 8.2675\n",
            "Epoch 889/1000\n",
            "4/4 [==============================] - 0s 23ms/step - loss: 6.8737 - mse: 6.8737 - val_loss: 8.2641 - val_mse: 8.2641\n",
            "Epoch 890/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.8706 - mse: 6.8706 - val_loss: 8.2645 - val_mse: 8.2645\n",
            "Epoch 891/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.8734 - mse: 6.8734 - val_loss: 8.2629 - val_mse: 8.2629\n",
            "Epoch 892/1000\n",
            "4/4 [==============================] - 0s 22ms/step - loss: 6.8753 - mse: 6.8753 - val_loss: 8.2640 - val_mse: 8.2640\n",
            "Epoch 893/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.8775 - mse: 6.8775 - val_loss: 8.2628 - val_mse: 8.2628\n",
            "Epoch 894/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.8687 - mse: 6.8687 - val_loss: 8.2623 - val_mse: 8.2623\n",
            "Epoch 895/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.8676 - mse: 6.8676 - val_loss: 8.2624 - val_mse: 8.2624\n",
            "Epoch 896/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.8749 - mse: 6.8749 - val_loss: 8.2565 - val_mse: 8.2565\n",
            "Epoch 897/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.8641 - mse: 6.8641 - val_loss: 8.2585 - val_mse: 8.2585\n",
            "Epoch 898/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.8603 - mse: 6.8603 - val_loss: 8.2547 - val_mse: 8.2547\n",
            "Epoch 899/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.8624 - mse: 6.8624 - val_loss: 8.2520 - val_mse: 8.2520\n",
            "Epoch 900/1000\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 6.8738 - mse: 6.8738 - val_loss: 8.2516 - val_mse: 8.2516\n",
            "Epoch 901/1000\n",
            "4/4 [==============================] - 0s 22ms/step - loss: 6.8611 - mse: 6.8611 - val_loss: 8.2499 - val_mse: 8.2499\n",
            "Epoch 902/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.8700 - mse: 6.8700 - val_loss: 8.2547 - val_mse: 8.2547\n",
            "Epoch 903/1000\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 6.8536 - mse: 6.8536 - val_loss: 8.2497 - val_mse: 8.2497\n",
            "Epoch 904/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.8645 - mse: 6.8645 - val_loss: 8.2442 - val_mse: 8.2442\n",
            "Epoch 905/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 6.8507 - mse: 6.8507 - val_loss: 8.2425 - val_mse: 8.2425\n",
            "Epoch 906/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.8568 - mse: 6.8568 - val_loss: 8.2407 - val_mse: 8.2407\n",
            "Epoch 907/1000\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 6.8584 - mse: 6.8584 - val_loss: 8.2410 - val_mse: 8.2410\n",
            "Epoch 908/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.8539 - mse: 6.8539 - val_loss: 8.2399 - val_mse: 8.2399\n",
            "Epoch 909/1000\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 6.8534 - mse: 6.8534 - val_loss: 8.2397 - val_mse: 8.2397\n",
            "Epoch 910/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 6.8520 - mse: 6.8520 - val_loss: 8.2430 - val_mse: 8.2430\n",
            "Epoch 911/1000\n",
            "4/4 [==============================] - 0s 22ms/step - loss: 6.8499 - mse: 6.8499 - val_loss: 8.2449 - val_mse: 8.2449\n",
            "Epoch 912/1000\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 6.8505 - mse: 6.8505 - val_loss: 8.2445 - val_mse: 8.2445\n",
            "Epoch 913/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.8445 - mse: 6.8445 - val_loss: 8.2444 - val_mse: 8.2444\n",
            "Epoch 914/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.8486 - mse: 6.8486 - val_loss: 8.2454 - val_mse: 8.2454\n",
            "Epoch 915/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.8519 - mse: 6.8519 - val_loss: 8.2437 - val_mse: 8.2437\n",
            "Epoch 916/1000\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 6.8529 - mse: 6.8529 - val_loss: 8.2433 - val_mse: 8.2433\n",
            "Epoch 917/1000\n",
            "4/4 [==============================] - 0s 23ms/step - loss: 6.8409 - mse: 6.8409 - val_loss: 8.2371 - val_mse: 8.2371\n",
            "Epoch 918/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.8451 - mse: 6.8451 - val_loss: 8.2355 - val_mse: 8.2355\n",
            "Epoch 919/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 6.8402 - mse: 6.8402 - val_loss: 8.2390 - val_mse: 8.2390\n",
            "Epoch 920/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.8465 - mse: 6.8465 - val_loss: 8.2397 - val_mse: 8.2397\n",
            "Epoch 921/1000\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 6.8367 - mse: 6.8367 - val_loss: 8.2383 - val_mse: 8.2383\n",
            "Epoch 922/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 6.8403 - mse: 6.8403 - val_loss: 8.2420 - val_mse: 8.2420\n",
            "Epoch 923/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.8538 - mse: 6.8538 - val_loss: 8.2454 - val_mse: 8.2454\n",
            "Epoch 924/1000\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 6.8418 - mse: 6.8418 - val_loss: 8.2473 - val_mse: 8.2473\n",
            "Epoch 925/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.8364 - mse: 6.8364 - val_loss: 8.2479 - val_mse: 8.2479\n",
            "Epoch 926/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.8341 - mse: 6.8341 - val_loss: 8.2510 - val_mse: 8.2510\n",
            "Epoch 927/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.8319 - mse: 6.8319 - val_loss: 8.2474 - val_mse: 8.2474\n",
            "Epoch 928/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.8323 - mse: 6.8323 - val_loss: 8.2450 - val_mse: 8.2450\n",
            "Epoch 929/1000\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 6.8253 - mse: 6.8253 - val_loss: 8.2484 - val_mse: 8.2484\n",
            "Epoch 930/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.8309 - mse: 6.8309 - val_loss: 8.2500 - val_mse: 8.2500\n",
            "Epoch 931/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.8259 - mse: 6.8259 - val_loss: 8.2544 - val_mse: 8.2544\n",
            "Epoch 932/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.8294 - mse: 6.8294 - val_loss: 8.2594 - val_mse: 8.2594\n",
            "Epoch 933/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.8219 - mse: 6.8219 - val_loss: 8.2606 - val_mse: 8.2606\n",
            "Epoch 934/1000\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 6.8289 - mse: 6.8289 - val_loss: 8.2596 - val_mse: 8.2596\n",
            "Epoch 935/1000\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 6.8264 - mse: 6.8264 - val_loss: 8.2598 - val_mse: 8.2598\n",
            "Epoch 936/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.8209 - mse: 6.8209 - val_loss: 8.2561 - val_mse: 8.2561\n",
            "Epoch 937/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.8187 - mse: 6.8187 - val_loss: 8.2593 - val_mse: 8.2593\n",
            "Epoch 938/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.8253 - mse: 6.8253 - val_loss: 8.2580 - val_mse: 8.2580\n",
            "Epoch 939/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.8190 - mse: 6.8190 - val_loss: 8.2598 - val_mse: 8.2598\n",
            "Epoch 940/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.8132 - mse: 6.8132 - val_loss: 8.2641 - val_mse: 8.2641\n",
            "Epoch 941/1000\n",
            "4/4 [==============================] - 0s 24ms/step - loss: 6.8130 - mse: 6.8130 - val_loss: 8.2623 - val_mse: 8.2623\n",
            "Epoch 942/1000\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 6.8203 - mse: 6.8203 - val_loss: 8.2653 - val_mse: 8.2653\n",
            "Epoch 943/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.8150 - mse: 6.8150 - val_loss: 8.2648 - val_mse: 8.2648\n",
            "Epoch 944/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.8189 - mse: 6.8189 - val_loss: 8.2595 - val_mse: 8.2595\n",
            "Epoch 945/1000\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 6.8118 - mse: 6.8118 - val_loss: 8.2588 - val_mse: 8.2588\n",
            "Epoch 946/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 6.8183 - mse: 6.8183 - val_loss: 8.2599 - val_mse: 8.2599\n",
            "Epoch 947/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.8153 - mse: 6.8153 - val_loss: 8.2628 - val_mse: 8.2628\n",
            "Epoch 948/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.8058 - mse: 6.8058 - val_loss: 8.2633 - val_mse: 8.2633\n",
            "Epoch 949/1000\n",
            "4/4 [==============================] - 0s 23ms/step - loss: 6.8130 - mse: 6.8130 - val_loss: 8.2632 - val_mse: 8.2632\n",
            "Epoch 950/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.8160 - mse: 6.8160 - val_loss: 8.2639 - val_mse: 8.2639\n",
            "Epoch 951/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.8088 - mse: 6.8088 - val_loss: 8.2623 - val_mse: 8.2623\n",
            "Epoch 952/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.8109 - mse: 6.8109 - val_loss: 8.2657 - val_mse: 8.2657\n",
            "Epoch 953/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.8055 - mse: 6.8055 - val_loss: 8.2615 - val_mse: 8.2615\n",
            "Epoch 954/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.8207 - mse: 6.8207 - val_loss: 8.2633 - val_mse: 8.2633\n",
            "Epoch 955/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.8017 - mse: 6.8017 - val_loss: 8.2646 - val_mse: 8.2646\n",
            "Epoch 956/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.7999 - mse: 6.7999 - val_loss: 8.2629 - val_mse: 8.2629\n",
            "Epoch 957/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.7973 - mse: 6.7973 - val_loss: 8.2641 - val_mse: 8.2641\n",
            "Epoch 958/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.7986 - mse: 6.7986 - val_loss: 8.2648 - val_mse: 8.2648\n",
            "Epoch 959/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.8004 - mse: 6.8004 - val_loss: 8.2645 - val_mse: 8.2645\n",
            "Epoch 960/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.8008 - mse: 6.8008 - val_loss: 8.2631 - val_mse: 8.2631\n",
            "Epoch 961/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.8114 - mse: 6.8114 - val_loss: 8.2681 - val_mse: 8.2681\n",
            "Epoch 962/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.7918 - mse: 6.7918 - val_loss: 8.2646 - val_mse: 8.2646\n",
            "Epoch 963/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.8029 - mse: 6.8029 - val_loss: 8.2654 - val_mse: 8.2654\n",
            "Epoch 964/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.7943 - mse: 6.7943 - val_loss: 8.2646 - val_mse: 8.2646\n",
            "Epoch 965/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.7882 - mse: 6.7882 - val_loss: 8.2611 - val_mse: 8.2611\n",
            "Epoch 966/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.7939 - mse: 6.7939 - val_loss: 8.2562 - val_mse: 8.2562\n",
            "Epoch 967/1000\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 6.7885 - mse: 6.7885 - val_loss: 8.2560 - val_mse: 8.2560\n",
            "Epoch 968/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.7858 - mse: 6.7858 - val_loss: 8.2545 - val_mse: 8.2545\n",
            "Epoch 969/1000\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 6.7914 - mse: 6.7914 - val_loss: 8.2566 - val_mse: 8.2566\n",
            "Epoch 970/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.7915 - mse: 6.7915 - val_loss: 8.2564 - val_mse: 8.2564\n",
            "Epoch 971/1000\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 6.7971 - mse: 6.7971 - val_loss: 8.2560 - val_mse: 8.2560\n",
            "Epoch 972/1000\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 6.7897 - mse: 6.7897 - val_loss: 8.2559 - val_mse: 8.2559\n",
            "Epoch 973/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 6.7816 - mse: 6.7816 - val_loss: 8.2538 - val_mse: 8.2538\n",
            "Epoch 974/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.7791 - mse: 6.7791 - val_loss: 8.2557 - val_mse: 8.2557\n",
            "Epoch 975/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.7820 - mse: 6.7820 - val_loss: 8.2515 - val_mse: 8.2515\n",
            "Epoch 976/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.7830 - mse: 6.7830 - val_loss: 8.2537 - val_mse: 8.2537\n",
            "Epoch 977/1000\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 6.7787 - mse: 6.7787 - val_loss: 8.2511 - val_mse: 8.2511\n",
            "Epoch 978/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.7872 - mse: 6.7872 - val_loss: 8.2531 - val_mse: 8.2531\n",
            "Epoch 979/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.7731 - mse: 6.7731 - val_loss: 8.2525 - val_mse: 8.2525\n",
            "Epoch 980/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.7730 - mse: 6.7730 - val_loss: 8.2598 - val_mse: 8.2598\n",
            "Epoch 981/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.7722 - mse: 6.7722 - val_loss: 8.2650 - val_mse: 8.2650\n",
            "Epoch 982/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.7734 - mse: 6.7734 - val_loss: 8.2619 - val_mse: 8.2619\n",
            "Epoch 983/1000\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 6.7710 - mse: 6.7710 - val_loss: 8.2585 - val_mse: 8.2585\n",
            "Epoch 984/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.7771 - mse: 6.7771 - val_loss: 8.2531 - val_mse: 8.2531\n",
            "Epoch 985/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.7723 - mse: 6.7723 - val_loss: 8.2522 - val_mse: 8.2522\n",
            "Epoch 986/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.7688 - mse: 6.7688 - val_loss: 8.2540 - val_mse: 8.2540\n",
            "Epoch 987/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.7707 - mse: 6.7707 - val_loss: 8.2555 - val_mse: 8.2555\n",
            "Epoch 988/1000\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 6.7691 - mse: 6.7691 - val_loss: 8.2543 - val_mse: 8.2543\n",
            "Epoch 989/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.7801 - mse: 6.7801 - val_loss: 8.2566 - val_mse: 8.2566\n",
            "Epoch 990/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.7642 - mse: 6.7642 - val_loss: 8.2588 - val_mse: 8.2588\n",
            "Epoch 991/1000\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 6.7633 - mse: 6.7633 - val_loss: 8.2609 - val_mse: 8.2609\n",
            "Epoch 992/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.7670 - mse: 6.7670 - val_loss: 8.2586 - val_mse: 8.2586\n",
            "Epoch 993/1000\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 6.7708 - mse: 6.7708 - val_loss: 8.2584 - val_mse: 8.2584\n",
            "Epoch 994/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.7635 - mse: 6.7635 - val_loss: 8.2523 - val_mse: 8.2523\n",
            "Epoch 995/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.7596 - mse: 6.7596 - val_loss: 8.2460 - val_mse: 8.2460\n",
            "Epoch 996/1000\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 6.7596 - mse: 6.7596 - val_loss: 8.2495 - val_mse: 8.2495\n",
            "Epoch 997/1000\n",
            "4/4 [==============================] - 0s 22ms/step - loss: 6.7607 - mse: 6.7607 - val_loss: 8.2505 - val_mse: 8.2505\n",
            "Epoch 998/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.7607 - mse: 6.7607 - val_loss: 8.2494 - val_mse: 8.2494\n",
            "Epoch 999/1000\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 6.7542 - mse: 6.7542 - val_loss: 8.2487 - val_mse: 8.2487\n",
            "Epoch 1000/1000\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 6.7519 - mse: 6.7519 - val_loss: 8.2516 - val_mse: 8.2516\n"
          ]
        }
      ],
      "source": [
        "\n",
        "\n",
        "EPOCHS = 1000\n",
        "\n",
        "history = model.fit(normed_train_data, train_labels,epochs=EPOCHS, validation_split = 0.2, batch_size=70)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bWdjnEz20AAj"
      },
      "outputs": [],
      "source": [
        "# Visualize the performance of training process using the statistics in the history object."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "API1LTPT0AAk",
        "outputId": "31fed467-a67a-4136-eec0-16a149abdc84"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-350e17ed-910e-4323-a581-0680ca54f6a2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>mse</th>\n",
              "      <th>val_loss</th>\n",
              "      <th>val_mse</th>\n",
              "      <th>epoch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>6.759580</td>\n",
              "      <td>6.759580</td>\n",
              "      <td>8.249475</td>\n",
              "      <td>8.249475</td>\n",
              "      <td>995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>6.760664</td>\n",
              "      <td>6.760664</td>\n",
              "      <td>8.250474</td>\n",
              "      <td>8.250474</td>\n",
              "      <td>996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>6.760729</td>\n",
              "      <td>6.760729</td>\n",
              "      <td>8.249434</td>\n",
              "      <td>8.249434</td>\n",
              "      <td>997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>6.754203</td>\n",
              "      <td>6.754203</td>\n",
              "      <td>8.248668</td>\n",
              "      <td>8.248668</td>\n",
              "      <td>998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>6.751943</td>\n",
              "      <td>6.751943</td>\n",
              "      <td>8.251564</td>\n",
              "      <td>8.251564</td>\n",
              "      <td>999</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-350e17ed-910e-4323-a581-0680ca54f6a2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-350e17ed-910e-4323-a581-0680ca54f6a2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-350e17ed-910e-4323-a581-0680ca54f6a2');\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",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         loss       mse  val_loss   val_mse  epoch\n",
              "995  6.759580  6.759580  8.249475  8.249475    995\n",
              "996  6.760664  6.760664  8.250474  8.250474    996\n",
              "997  6.760729  6.760729  8.249434  8.249434    997\n",
              "998  6.754203  6.754203  8.248668  8.248668    998\n",
              "999  6.751943  6.751943  8.251564  8.251564    999"
            ]
          },
          "execution_count": 70,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "hist = pd.DataFrame(history.history)\n",
        "hist['epoch'] = history.epoch\n",
        "hist.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HD4v8ifIJiNL",
        "outputId": "53e0744c-0cf5-4b94-a912-1e3f870dba66"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d567c452-0712-4301-a7c2-70433c19ed80\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>mse</th>\n",
              "      <th>val_loss</th>\n",
              "      <th>val_mse</th>\n",
              "      <th>epoch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>601.566284</td>\n",
              "      <td>601.566284</td>\n",
              "      <td>623.144165</td>\n",
              "      <td>623.144165</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>594.101440</td>\n",
              "      <td>594.101440</td>\n",
              "      <td>615.232483</td>\n",
              "      <td>615.232483</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>586.871643</td>\n",
              "      <td>586.871643</td>\n",
              "      <td>607.394592</td>\n",
              "      <td>607.394592</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>579.692810</td>\n",
              "      <td>579.692810</td>\n",
              "      <td>599.508423</td>\n",
              "      <td>599.508423</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>572.460022</td>\n",
              "      <td>572.460022</td>\n",
              "      <td>591.444153</td>\n",
              "      <td>591.444153</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d567c452-0712-4301-a7c2-70433c19ed80')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d567c452-0712-4301-a7c2-70433c19ed80 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d567c452-0712-4301-a7c2-70433c19ed80');\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",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         loss         mse    val_loss     val_mse  epoch\n",
              "0  601.566284  601.566284  623.144165  623.144165      0\n",
              "1  594.101440  594.101440  615.232483  615.232483      1\n",
              "2  586.871643  586.871643  607.394592  607.394592      2\n",
              "3  579.692810  579.692810  599.508423  599.508423      3\n",
              "4  572.460022  572.460022  591.444153  591.444153      4"
            ]
          },
          "execution_count": 71,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "hist.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "rDxErXnF0AAk",
        "outputId": "28005fce-4aa2-4562-cff8-6849bdd4b91c"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x864 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_history(history):\n",
        "    hist = pd.DataFrame(history.history)\n",
        "    hist['epoch'] = history.epoch\n",
        "    \n",
        "    plt.figure(figsize=(8,12))\n",
        "\n",
        "    \n",
        "    plt.subplot(2,1,2)\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Mean Square Error [$MPG^2$]')\n",
        "    plt.plot(hist['epoch'], hist['mse'],label='Train Error')\n",
        "    plt.plot(hist['epoch'], hist['val_mse'],label = 'Val Error')\n",
        "    plt.ylim([0,20])\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "plot_history(history)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4xysQbzS0AAk"
      },
      "outputs": [],
      "source": [
        "# By modifying the model.fit method, let trainig automatically stop when varification does not improve.  \n",
        "# Use EarlyStopping callback to check the training process at every epoch.\n",
        "# Automatically stop training when there is no more improvement over the specified number of epochs.  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "XA9uX6Tq0AAl",
        "outputId": "e0938524-fecb-425e-95b2-00cf75f0e32b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/1000\n",
            "8/8 [==============================] - 1s 27ms/step - loss: 566.3007 - mse: 566.3007 - val_loss: 550.4825 - val_mse: 550.4825\n",
            "Epoch 2/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 476.7901 - mse: 476.7901 - val_loss: 374.4702 - val_mse: 374.4702\n",
            "Epoch 3/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 224.0224 - mse: 224.0224 - val_loss: 50.3379 - val_mse: 50.3379\n",
            "Epoch 4/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 43.9015 - mse: 43.9015 - val_loss: 24.1080 - val_mse: 24.1080\n",
            "Epoch 5/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 24.6070 - mse: 24.6070 - val_loss: 17.5354 - val_mse: 17.5354\n",
            "Epoch 6/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 17.3946 - mse: 17.3946 - val_loss: 15.3647 - val_mse: 15.3647\n",
            "Epoch 7/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 14.6849 - mse: 14.6849 - val_loss: 14.9637 - val_mse: 14.9637\n",
            "Epoch 8/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 13.5190 - mse: 13.5190 - val_loss: 13.1074 - val_mse: 13.1074\n",
            "Epoch 9/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 12.8116 - mse: 12.8116 - val_loss: 12.3554 - val_mse: 12.3554\n",
            "Epoch 10/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 11.8544 - mse: 11.8544 - val_loss: 12.1169 - val_mse: 12.1169\n",
            "Epoch 11/1000\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 11.5230 - mse: 11.5230 - val_loss: 11.2165 - val_mse: 11.2165\n",
            "Epoch 12/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 11.0457 - mse: 11.0457 - val_loss: 10.7726 - val_mse: 10.7726\n",
            "Epoch 13/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 10.4928 - mse: 10.4928 - val_loss: 10.3826 - val_mse: 10.3826\n",
            "Epoch 14/1000\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 10.3295 - mse: 10.3295 - val_loss: 10.0768 - val_mse: 10.0768\n",
            "Epoch 15/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 9.9471 - mse: 9.9471 - val_loss: 9.7221 - val_mse: 9.7221\n",
            "Epoch 16/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 9.8973 - mse: 9.8973 - val_loss: 9.5774 - val_mse: 9.5774\n",
            "Epoch 17/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 9.2942 - mse: 9.2942 - val_loss: 9.7318 - val_mse: 9.7318\n",
            "Epoch 18/1000\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 9.1955 - mse: 9.1955 - val_loss: 9.2739 - val_mse: 9.2739\n",
            "Epoch 19/1000\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 9.1870 - mse: 9.1870 - val_loss: 9.1521 - val_mse: 9.1521\n",
            "Epoch 20/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 8.8695 - mse: 8.8695 - val_loss: 9.4272 - val_mse: 9.4272\n",
            "Epoch 21/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 8.9823 - mse: 8.9823 - val_loss: 8.9991 - val_mse: 8.9991\n",
            "Epoch 22/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 8.5167 - mse: 8.5167 - val_loss: 8.9401 - val_mse: 8.9401\n",
            "Epoch 23/1000\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 8.3937 - mse: 8.3937 - val_loss: 8.8969 - val_mse: 8.8969\n",
            "Epoch 24/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 8.5753 - mse: 8.5753 - val_loss: 8.8579 - val_mse: 8.8579\n",
            "Epoch 25/1000\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 8.3047 - mse: 8.3047 - val_loss: 8.7679 - val_mse: 8.7679\n",
            "Epoch 26/1000\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 8.1515 - mse: 8.1515 - val_loss: 8.9269 - val_mse: 8.9269\n",
            "Epoch 27/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 8.2240 - mse: 8.2240 - val_loss: 8.8001 - val_mse: 8.8001\n",
            "Epoch 28/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 7.8533 - mse: 7.8533 - val_loss: 8.6467 - val_mse: 8.6467\n",
            "Epoch 29/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 7.7880 - mse: 7.7880 - val_loss: 8.6662 - val_mse: 8.6662\n",
            "Epoch 30/1000\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 7.7099 - mse: 7.7099 - val_loss: 8.9105 - val_mse: 8.9105\n",
            "Epoch 31/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 7.7648 - mse: 7.7648 - val_loss: 8.8593 - val_mse: 8.8593\n",
            "Epoch 32/1000\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 7.7203 - mse: 7.7203 - val_loss: 9.3319 - val_mse: 9.3319\n",
            "Epoch 33/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 7.6428 - mse: 7.6428 - val_loss: 8.7399 - val_mse: 8.7399\n",
            "Epoch 34/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 7.4728 - mse: 7.4728 - val_loss: 9.1154 - val_mse: 9.1154\n",
            "Epoch 35/1000\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 7.6212 - mse: 7.6212 - val_loss: 8.6650 - val_mse: 8.6650\n",
            "Epoch 36/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 7.5244 - mse: 7.5244 - val_loss: 8.6883 - val_mse: 8.6883\n",
            "Epoch 37/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 7.4727 - mse: 7.4727 - val_loss: 8.6957 - val_mse: 8.6957\n",
            "Epoch 38/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 7.3634 - mse: 7.3634 - val_loss: 8.6524 - val_mse: 8.6524\n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x864 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model = build_model()\n",
        "\n",
        "# The 'patience' parameter is the epoch number to be used for checkking improvement. \n",
        "early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)\n",
        "\n",
        "history = model.fit(normed_train_data, train_labels, epochs=EPOCHS,\n",
        "                    validation_split = 0.2, callbacks=[early_stop])\n",
        "\n",
        "plot_history(history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bj5tE08V0AAl"
      },
      "source": [
        "#### 1.11.Check the performance of model training set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Dpf29xH0AAl",
        "outputId": "d91d88fb-254f-4cc1-a7ce-713f8ef6243b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3/3 - 0s - loss: 6.3495 - mse: 6.3495 - 24ms/epoch - 8ms/step\n",
            "Mean square error of test set:  6.35 MPG\n"
          ]
        }
      ],
      "source": [
        "loss, mse = model.evaluate(normed_test_data, test_labels, verbose=2)\n",
        "\n",
        "print(\"Mean square error of test set: {:5.2f} MPG\".format(mse))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5sw3f3IO0AAm"
      },
      "source": [
        "#### 1.12. Prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "Y3mnmCHA0AAm",
        "outputId": "42ccc8cd-ca93-4253-a8ec-3ef973886c2b"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "test_predictions = model.predict(normed_test_data).flatten()\n",
        "\n",
        "plt.scatter(test_labels, test_predictions)\n",
        "plt.xlabel('True Values [MPG]')\n",
        "plt.ylabel('Predictions [MPG]')\n",
        "plt.axis('equal')\n",
        "plt.axis('square')\n",
        "plt.xlim([0,plt.xlim()[1]])\n",
        "plt.ylim([0,plt.ylim()[1]])\n",
        "_ = plt.plot([-100, 100], [-100, 100])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "Bmz0FN-R0AAn"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}