regressao_tensorflow.ipynb

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  "metadata": {
    "colab": {
      "name": "regressao_tensorflow.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMP5iJY/x2SsrhgohBWgb2S",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Willian-R/Google_Colab/blob/master/regressao_tensorflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KNYC8RQLM_S3"
      },
      "source": [
        "# Regressão: Preveja consumo de combustível"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WRV_NSYvNG-G"
      },
      "source": [
        "Em um problema de regressão, o objetivo é prever as saídas (*outputs*) de um valor contínuo, como um preço ou probabilidade. Em contraste de problemas de classificação, onde temos o propósito de escolher uma classe em uma lista de classificações (por exemplo, se uma imagem contem uma maçã ou laranja, assim reconhecendo qual fruta é representada na imagem).\n",
        "\n",
        "Este notebook usa a clássica base de dados Auto MPG e constrói um modelo para prever a economia de combustíveis de automóveis do final dos anos 1970, inicio dos anos 1980. Para isso, forneceremos um modelo com descrição de vários automóveis desse período. Essa descrição inclui atributos como: cilindros, deslocamento, potência do motor e peso. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DcVNpR5WOmpM"
      },
      "source": [
        "# Use seaborn para pairplot\n",
        "!pip install -q seaborn"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TSxG__0ROrR1",
        "outputId": "db5a9773-445f-4901-a126-88a21c3d69d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!pip install --upgrade pip"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: pip in /usr/local/lib/python3.6/dist-packages (20.2.3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R21aaSA-PIf1",
        "outputId": "857b6649-fc32-4f51-9de8-f50e98a3d7bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "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__)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.3.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gRmG6qUyPmjM"
      },
      "source": [
        "## Base de dados Auto MPG"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dS1O7yFYPrMc"
      },
      "source": [
        "A base de dados está disponível em UCI Machine Learning Repository."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VN9kTEMBP0rs"
      },
      "source": [
        "### Pegando os dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5KQEcspLP38E"
      },
      "source": [
        "Primeiro baixe a base de dados dos automóveis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AGm3mOeFP7SE",
        "outputId": "7c31909a-98f4-47a6-bc2c-5e70583b813b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "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"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/root/.keras/datasets/auto-mpg.data'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r964WomBQMsk"
      },
      "source": [
        "Utilizando o pandas, importe os dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Woia2SNFQQNz",
        "outputId": "36b5e037-025b-4748-e1a8-962e82e32856",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight', 'Acceleration', 'Model Year', 'Origin']\n",
        "raw_dataset = pd.read_csv(dataset_path, names=column_names, na_values='?', comment='\\t', sep=' ', skipinitialspace=True)\n",
        "dataset = raw_dataset.copy()\n",
        "dataset.tail()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MPG</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>Displacement</th>\n",
              "      <th>Horsepower</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Acceleration</th>\n",
              "      <th>Model Year</th>\n",
              "      <th>Origin</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>27.0</td>\n",
              "      <td>4</td>\n",
              "      <td>140.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>2790.0</td>\n",
              "      <td>15.6</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>44.0</td>\n",
              "      <td>4</td>\n",
              "      <td>97.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2130.0</td>\n",
              "      <td>24.6</td>\n",
              "      <td>82</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>32.0</td>\n",
              "      <td>4</td>\n",
              "      <td>135.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>2295.0</td>\n",
              "      <td>11.6</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>28.0</td>\n",
              "      <td>4</td>\n",
              "      <td>120.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>2625.0</td>\n",
              "      <td>18.6</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>31.0</td>\n",
              "      <td>4</td>\n",
              "      <td>119.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>2720.0</td>\n",
              "      <td>19.4</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      MPG  Cylinders  Displacement  ...  Acceleration  Model Year  Origin\n",
              "393  27.0          4         140.0  ...          15.6          82       1\n",
              "394  44.0          4          97.0  ...          24.6          82       2\n",
              "395  32.0          4         135.0  ...          11.6          82       1\n",
              "396  28.0          4         120.0  ...          18.6          82       1\n",
              "397  31.0          4         119.0  ...          19.4          82       1\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BBVhY_ySRCKW"
      },
      "source": [
        "### Limpe os dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4sg8phntRFIc"
      },
      "source": [
        "Esta base contém alguns valores não conhecidos (*unknown*)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nmAhQi0kRL4L",
        "outputId": "324a678b-f22e-4153-88d4-db57679abb7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "dataset.isna().sum()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRm3B5CSRS58"
      },
      "source": [
        "Para continuar, remova as linhas com esses valores não conhecidos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_LcQKq72RPbk"
      },
      "source": [
        "dataset = dataset.dropna()"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0mOARJMCRfTj"
      },
      "source": [
        "A coluna \"Origin\" é uma coluna categórica e não numérica. Logo converta para *one-hot*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EJSrCx2oRepc"
      },
      "source": [
        "origin = dataset.pop('Origin')"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C1XzeOYjRr8T",
        "outputId": "14dbbd80-4dbf-42b9-c65c-7ce5227284e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "dataset['USA'] = (origin == 1)*1.0\n",
        "dataset['Europe'] = (origin == 2)*1.0\n",
        "dataset['Japan'] = (origin == 3)*1.0\n",
        "dataset.tail()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MPG</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>Displacement</th>\n",
              "      <th>Horsepower</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Acceleration</th>\n",
              "      <th>Model Year</th>\n",
              "      <th>USA</th>\n",
              "      <th>Europe</th>\n",
              "      <th>Japan</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>27.0</td>\n",
              "      <td>4</td>\n",
              "      <td>140.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>2790.0</td>\n",
              "      <td>15.6</td>\n",
              "      <td>82</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>44.0</td>\n",
              "      <td>4</td>\n",
              "      <td>97.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2130.0</td>\n",
              "      <td>24.6</td>\n",
              "      <td>82</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>32.0</td>\n",
              "      <td>4</td>\n",
              "      <td>135.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>2295.0</td>\n",
              "      <td>11.6</td>\n",
              "      <td>82</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>28.0</td>\n",
              "      <td>4</td>\n",
              "      <td>120.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>2625.0</td>\n",
              "      <td>18.6</td>\n",
              "      <td>82</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>31.0</td>\n",
              "      <td>4</td>\n",
              "      <td>119.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>2720.0</td>\n",
              "      <td>19.4</td>\n",
              "      <td>82</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      MPG  Cylinders  Displacement  Horsepower  ...  Model Year  USA  Europe  Japan\n",
              "393  27.0          4         140.0        86.0  ...          82  1.0     0.0    0.0\n",
              "394  44.0          4          97.0        52.0  ...          82  0.0     1.0    0.0\n",
              "395  32.0          4         135.0        84.0  ...          82  1.0     0.0    0.0\n",
              "396  28.0          4         120.0        79.0  ...          82  1.0     0.0    0.0\n",
              "397  31.0          4         119.0        82.0  ...          82  1.0     0.0    0.0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ajFhESl2R_qT"
      },
      "source": [
        "### Separando dados de treinamento e teste"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "laWiUEbCSDRM"
      },
      "source": [
        "Agora separe os dados em um conjunto de treinamento e outro para teste.\n",
        "\n",
        "Iremos utilizar o conjunto de teste no final da análise do modelo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RNkeFBg9R66j"
      },
      "source": [
        "train_dataset = dataset.sample(frac=0.8, random_state=0)\n",
        "test_dataset = dataset.drop(train_dataset.index)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lJw6nAc7Scrz"
      },
      "source": [
        "### Inspecione os dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eAR2CT5VSgOr"
      },
      "source": [
        "Dê uma rápida olhada em como está a distribuição de algumas colunas do conjunto de treinamento"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CgDAJSIcSZni",
        "outputId": "aedd0426-bc73-420f-d03b-9ca21c1dd278",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        }
      },
      "source": [
        "sns.pairplot(train_dataset[['MPG', 'Cylinders', 'Displacement', 'Weight']], diag_kind='kde')"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f3290c62518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TwSVPjBmS7jJ"
      },
      "source": [
        "Repare na visão geral das estatísticas:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-0MFpYgBSymy",
        "outputId": "08c4eae1-6272-49c0-fd05-2c7003c61342",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        }
      },
      "source": [
        "train_stats = train_dataset.describe()\n",
        "train_stats.pop('MPG')\n",
        "train_stats = train_stats.transpose()\n",
        "train_stats"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Cylinders</th>\n",
              "      <td>314.0</td>\n",
              "      <td>5.477707</td>\n",
              "      <td>1.699788</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.00</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Displacement</th>\n",
              "      <td>314.0</td>\n",
              "      <td>195.318471</td>\n",
              "      <td>104.331589</td>\n",
              "      <td>68.0</td>\n",
              "      <td>105.50</td>\n",
              "      <td>151.0</td>\n",
              "      <td>265.75</td>\n",
              "      <td>455.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Horsepower</th>\n",
              "      <td>314.0</td>\n",
              "      <td>104.869427</td>\n",
              "      <td>38.096214</td>\n",
              "      <td>46.0</td>\n",
              "      <td>76.25</td>\n",
              "      <td>94.5</td>\n",
              "      <td>128.00</td>\n",
              "      <td>225.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weight</th>\n",
              "      <td>314.0</td>\n",
              "      <td>2990.251592</td>\n",
              "      <td>843.898596</td>\n",
              "      <td>1649.0</td>\n",
              "      <td>2256.50</td>\n",
              "      <td>2822.5</td>\n",
              "      <td>3608.00</td>\n",
              "      <td>5140.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Acceleration</th>\n",
              "      <td>314.0</td>\n",
              "      <td>15.559236</td>\n",
              "      <td>2.789230</td>\n",
              "      <td>8.0</td>\n",
              "      <td>13.80</td>\n",
              "      <td>15.5</td>\n",
              "      <td>17.20</td>\n",
              "      <td>24.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model Year</th>\n",
              "      <td>314.0</td>\n",
              "      <td>75.898089</td>\n",
              "      <td>3.675642</td>\n",
              "      <td>70.0</td>\n",
              "      <td>73.00</td>\n",
              "      <td>76.0</td>\n",
              "      <td>79.00</td>\n",
              "      <td>82.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>USA</th>\n",
              "      <td>314.0</td>\n",
              "      <td>0.624204</td>\n",
              "      <td>0.485101</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Europe</th>\n",
              "      <td>314.0</td>\n",
              "      <td>0.178344</td>\n",
              "      <td>0.383413</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Japan</th>\n",
              "      <td>314.0</td>\n",
              "      <td>0.197452</td>\n",
              "      <td>0.398712</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              count         mean         std  ...     50%      75%     max\n",
              "Cylinders     314.0     5.477707    1.699788  ...     4.0     8.00     8.0\n",
              "Displacement  314.0   195.318471  104.331589  ...   151.0   265.75   455.0\n",
              "Horsepower    314.0   104.869427   38.096214  ...    94.5   128.00   225.0\n",
              "Weight        314.0  2990.251592  843.898596  ...  2822.5  3608.00  5140.0\n",
              "Acceleration  314.0    15.559236    2.789230  ...    15.5    17.20    24.8\n",
              "Model Year    314.0    75.898089    3.675642  ...    76.0    79.00    82.0\n",
              "USA           314.0     0.624204    0.485101  ...     1.0     1.00     1.0\n",
              "Europe        314.0     0.178344    0.383413  ...     0.0     0.00     1.0\n",
              "Japan         314.0     0.197452    0.398712  ...     0.0     0.00     1.0\n",
              "\n",
              "[9 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qKet6OWMTVm6"
      },
      "source": [
        "### Separe features de labels"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-vnX1dQsTYfK"
      },
      "source": [
        "Separe o valor alvo (*labels*), das features. Esta label é o valor no qual o modelo é treinado para prever."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kno8fQhuTQp7"
      },
      "source": [
        "train_labels = train_dataset.pop('MPG')\n",
        "test_labels = test_dataset.pop('MPG')"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jUQt89LuTtSL"
      },
      "source": [
        "### Normalize os dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qpCyWGgITvmS"
      },
      "source": [
        "Olhe novamente para o bloco **`train_stats`** acima e note quão diferente é a variação de feature.\n",
        "\n",
        "Uma boa prática é normalizar as *features* que usam diferentes escalas e intervalos. Apesar do modelo poder convergir sem a normalização, isso torna o treinamento mais difícil, e o resultado do modelo dependente da escolha das unidades de entrada."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YtB7kgo1Tsgi"
      },
      "source": [
        "def norm(x):\n",
        "  return (x - train_stats['mean'])/train_stats['std']\n",
        "\n",
        "normed_train_data = norm(train_dataset)\n",
        "normed_test_data = norm(test_dataset)"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QAch2LM9UoIR"
      },
      "source": [
        "O dado normalizado é o que nós usaremos para treinar o modelo.\n",
        "\n",
        "**Atenção**: A estatística usada para normalizar as entradas aqui (média e desvio padrão) precisa ser aplicada em qualquer outro dado que alimenta o modelo, junto com a condificação *one-hot* que fizemos anteriormente. Isso inclui o conjunto de teste, assim como os dados que o modelo usará em produção."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NEhD0AgGVIWC"
      },
      "source": [
        "## O Modelo\n",
        "\n",
        "### Construindo o modelo\n",
        "\n",
        "Vamos construir o modelo. Aqui usaremos o modelo `Sequential` com duas camadas *densely connected*(completamente conectadas), e a camada de saída que retorna um único valor contínuo. Os passos de construção do modelo são agrupados em uma função, `build_model`, já que criaremos um segundo modelo mais tarde."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HZXAK5FlUnDh"
      },
      "source": [
        "def build_model():\n",
        "  model = keras.Sequential([\n",
        "                           layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),\n",
        "                           layers.Dense(64, activation='relu'),\n",
        "                           layers.Dense(1)\n",
        "  ])\n",
        "\n",
        "  optimizer = tf.keras.optimizers.RMSprop(0.001)\n",
        "\n",
        "  model.compile(loss='mse',\n",
        "                optimizer=optimizer,\n",
        "                metrics=['mae', 'mse'])\n",
        "  return model"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_1g02iifWVI0"
      },
      "source": [
        "model = build_model()"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N74cC5-2WzWN"
      },
      "source": [
        "### Examine o modelo\n",
        "\n",
        "Use o método `.summary` para exibir uma descrição simples do modelo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "abUvvjURWXs5",
        "outputId": "70916ccc-bdf6-4a28-95f7-de190b4a9bcf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_3 (Dense)              (None, 64)                640       \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 64)                4160      \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 4,865\n",
            "Trainable params: 4,865\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pnBJbqBgXDFA"
      },
      "source": [
        "Agora teste o modelo. Pegue um batch de 10 exemplos do conjunto de treinamento e chame `model.predict`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sjRjlSZTW-A5",
        "outputId": "02c6cfed-0ce4-4bd0-a051-db3e1c42c0a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "example_batch = normed_train_data[:10]\n",
        "example_result = model.predict(example_batch)\n",
        "example_result"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.48333117],\n",
              "       [ 0.03885733],\n",
              "       [-0.10828911],\n",
              "       [ 0.22597007],\n",
              "       [ 0.36553174],\n",
              "       [-0.02771195],\n",
              "       [ 0.34122953],\n",
              "       [-0.10749023],\n",
              "       [-0.05767351],\n",
              "       [ 0.41032773]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OYd4imZpXcB4"
      },
      "source": [
        "Parece que está funcionando e ele produz o resultado de forma e tipo esperados."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p0lqvA2CXiqY"
      },
      "source": [
        "### Treinando o modelo\n",
        "\n",
        "Treine o modelo com 1000 épocas, e grave a acurácia do treinamento e da validação em um onjeto `history`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OG9rFzHPXXOQ",
        "outputId": "1ecf4dc2-d3cd-4c60-c9c3-27c82e96882c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "history = model.fit(\n",
        "    normed_train_data, train_labels,\n",
        "    epochs=1000, validation_split=0.2, verbose=1)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/1000\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 559.4042 - mae: 22.4564 - mse: 559.4042 - val_loss: 551.5887 - val_mae: 22.2947 - val_mse: 551.5887\n",
            "Epoch 2/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 507.5626 - mae: 21.3673 - mse: 507.5626 - val_loss: 495.6733 - val_mae: 21.0935 - val_mse: 495.6733\n",
            "Epoch 3/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 454.3885 - mae: 20.1581 - mse: 454.3885 - val_loss: 436.1595 - val_mae: 19.7112 - val_mse: 436.1595\n",
            "Epoch 4/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 396.0215 - mae: 18.7399 - mse: 396.0215 - val_loss: 369.5404 - val_mae: 18.0423 - val_mse: 369.5404\n",
            "Epoch 5/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 333.1198 - mae: 17.0679 - mse: 333.1198 - val_loss: 299.8284 - val_mae: 16.1116 - val_mse: 299.8284\n",
            "Epoch 6/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 268.2729 - mae: 15.1589 - mse: 268.2729 - val_loss: 231.8850 - val_mae: 13.9407 - val_mse: 231.8850\n",
            "Epoch 7/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 207.5091 - mae: 13.0433 - mse: 207.5091 - val_loss: 170.3292 - val_mae: 11.6183 - val_mse: 170.3292\n",
            "Epoch 8/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 154.0841 - mae: 10.8667 - mse: 154.0841 - val_loss: 120.1593 - val_mae: 9.3626 - val_mse: 120.1593\n",
            "Epoch 9/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 111.5860 - mae: 8.8260 - mse: 111.5860 - val_loss: 83.3956 - val_mae: 7.5181 - val_mse: 83.3956\n",
            "Epoch 10/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 80.9240 - mae: 7.2465 - mse: 80.9240 - val_loss: 60.3007 - val_mae: 6.2865 - val_mse: 60.3007\n",
            "Epoch 11/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 61.0043 - mae: 6.2249 - mse: 61.0043 - val_loss: 46.8892 - val_mae: 5.6293 - val_mse: 46.8892\n",
            "Epoch 12/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 47.7082 - mae: 5.5317 - mse: 47.7082 - val_loss: 38.1351 - val_mae: 5.1554 - val_mse: 38.1351\n",
            "Epoch 13/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 37.8834 - mae: 4.9537 - mse: 37.8834 - val_loss: 31.9841 - val_mae: 4.7423 - val_mse: 31.9841\n",
            "Epoch 14/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 30.3906 - mae: 4.3825 - mse: 30.3906 - val_loss: 26.9319 - val_mae: 4.3264 - val_mse: 26.9319\n",
            "Epoch 15/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 24.7292 - mae: 3.9172 - mse: 24.7292 - val_loss: 23.1687 - val_mae: 3.9571 - val_mse: 23.1687\n",
            "Epoch 16/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 20.3768 - mae: 3.5109 - mse: 20.3768 - val_loss: 19.9830 - val_mae: 3.6661 - val_mse: 19.9830\n",
            "Epoch 17/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 17.3241 - mae: 3.2088 - mse: 17.3241 - val_loss: 17.7245 - val_mae: 3.4205 - val_mse: 17.7245\n",
            "Epoch 18/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 15.2360 - mae: 3.0300 - mse: 15.2360 - val_loss: 15.9518 - val_mae: 3.2751 - val_mse: 15.9518\n",
            "Epoch 19/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 13.8516 - mae: 2.8407 - mse: 13.8516 - val_loss: 14.3436 - val_mae: 3.0877 - val_mse: 14.3436\n",
            "Epoch 20/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 12.5737 - mae: 2.6639 - mse: 12.5737 - val_loss: 13.1825 - val_mae: 2.9793 - val_mse: 13.1825\n",
            "Epoch 21/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 11.4640 - mae: 2.5733 - mse: 11.4640 - val_loss: 12.6209 - val_mae: 2.9830 - val_mse: 12.6209\n",
            "Epoch 22/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 10.8597 - mae: 2.4775 - mse: 10.8597 - val_loss: 11.4089 - val_mae: 2.6929 - val_mse: 11.4089\n",
            "Epoch 23/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 10.1540 - mae: 2.3682 - mse: 10.1540 - val_loss: 10.9047 - val_mae: 2.6176 - val_mse: 10.9047\n",
            "Epoch 24/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 9.7982 - mae: 2.3065 - mse: 9.7982 - val_loss: 10.3223 - val_mae: 2.5887 - val_mse: 10.3223\n",
            "Epoch 25/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 9.5828 - mae: 2.2385 - mse: 9.5828 - val_loss: 9.8608 - val_mae: 2.4636 - val_mse: 9.8608\n",
            "Epoch 26/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 8.8000 - mae: 2.1767 - mse: 8.8000 - val_loss: 9.8934 - val_mae: 2.5446 - val_mse: 9.8934\n",
            "Epoch 27/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 8.9355 - mae: 2.1693 - mse: 8.9355 - val_loss: 9.3574 - val_mae: 2.3921 - val_mse: 9.3574\n",
            "Epoch 28/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 8.4625 - mae: 2.1219 - mse: 8.4625 - val_loss: 9.2047 - val_mae: 2.3818 - val_mse: 9.2047\n",
            "Epoch 29/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 8.4014 - mae: 2.0798 - mse: 8.4014 - val_loss: 9.0648 - val_mae: 2.3688 - val_mse: 9.0648\n",
            "Epoch 30/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 8.2817 - mae: 2.0976 - mse: 8.2817 - val_loss: 9.1529 - val_mae: 2.4030 - val_mse: 9.1529\n",
            "Epoch 31/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 7.9465 - mae: 2.0163 - mse: 7.9465 - val_loss: 9.0922 - val_mae: 2.2900 - val_mse: 9.0922\n",
            "Epoch 32/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 7.9168 - mae: 2.0326 - mse: 7.9168 - val_loss: 8.8100 - val_mae: 2.2935 - val_mse: 8.8100\n",
            "Epoch 33/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 7.7703 - mae: 2.0269 - mse: 7.7703 - val_loss: 8.9281 - val_mae: 2.3273 - val_mse: 8.9281\n",
            "Epoch 34/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 7.7432 - mae: 2.0108 - mse: 7.7432 - val_loss: 9.1882 - val_mae: 2.2652 - val_mse: 9.1882\n",
            "Epoch 35/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 7.6631 - mae: 2.0201 - mse: 7.6631 - val_loss: 9.2128 - val_mae: 2.4225 - val_mse: 9.2128\n",
            "Epoch 36/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 7.5260 - mae: 1.9599 - mse: 7.5260 - val_loss: 8.6619 - val_mae: 2.2589 - val_mse: 8.6619\n",
            "Epoch 37/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 7.5592 - mae: 1.9699 - mse: 7.5592 - val_loss: 8.9899 - val_mae: 2.4084 - val_mse: 8.9899\n",
            "Epoch 38/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 7.5236 - mae: 1.9547 - mse: 7.5236 - val_loss: 8.6690 - val_mae: 2.2371 - val_mse: 8.6690\n",
            "Epoch 39/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 7.4044 - mae: 1.9474 - mse: 7.4044 - val_loss: 8.6951 - val_mae: 2.2517 - val_mse: 8.6951\n",
            "Epoch 40/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 7.3403 - mae: 1.9345 - mse: 7.3403 - val_loss: 9.1784 - val_mae: 2.2093 - val_mse: 9.1784\n",
            "Epoch 41/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 7.1877 - mae: 1.9580 - mse: 7.1877 - val_loss: 8.8483 - val_mae: 2.3149 - val_mse: 8.8483\n",
            "Epoch 42/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 7.2838 - mae: 1.9330 - mse: 7.2838 - val_loss: 8.8390 - val_mae: 2.2133 - val_mse: 8.8390\n",
            "Epoch 43/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 7.5261 - mae: 1.9641 - mse: 7.5261 - val_loss: 8.6725 - val_mae: 2.2100 - val_mse: 8.6725\n",
            "Epoch 44/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 7.2245 - mae: 1.9475 - mse: 7.2245 - val_loss: 8.6666 - val_mae: 2.2356 - val_mse: 8.6666\n",
            "Epoch 45/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 7.1547 - mae: 1.9073 - mse: 7.1547 - val_loss: 8.7519 - val_mae: 2.2084 - val_mse: 8.7519\n",
            "Epoch 46/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 7.0987 - mae: 1.9278 - mse: 7.0987 - val_loss: 8.5865 - val_mae: 2.2180 - val_mse: 8.5865\n",
            "Epoch 47/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 7.1506 - mae: 1.9339 - mse: 7.1506 - val_loss: 8.7139 - val_mae: 2.2890 - val_mse: 8.7139\n",
            "Epoch 48/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 7.1956 - mae: 1.9100 - mse: 7.1956 - val_loss: 8.6196 - val_mae: 2.2554 - val_mse: 8.6196\n",
            "Epoch 49/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.9613 - mae: 1.8868 - mse: 6.9613 - val_loss: 8.7500 - val_mae: 2.2124 - val_mse: 8.7500\n",
            "Epoch 50/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 7.0692 - mae: 1.9096 - mse: 7.0692 - val_loss: 8.6134 - val_mae: 2.2737 - val_mse: 8.6134\n",
            "Epoch 51/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.9049 - mae: 1.8879 - mse: 6.9049 - val_loss: 8.7875 - val_mae: 2.3036 - val_mse: 8.7875\n",
            "Epoch 52/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 7.1247 - mae: 1.8969 - mse: 7.1247 - val_loss: 8.7917 - val_mae: 2.3043 - val_mse: 8.7917\n",
            "Epoch 53/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.8576 - mae: 1.8492 - mse: 6.8576 - val_loss: 8.8440 - val_mae: 2.2127 - val_mse: 8.8440\n",
            "Epoch 54/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 7.0277 - mae: 1.9009 - mse: 7.0277 - val_loss: 8.8183 - val_mae: 2.3382 - val_mse: 8.8183\n",
            "Epoch 55/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.9921 - mae: 1.8771 - mse: 6.9921 - val_loss: 8.5874 - val_mae: 2.2531 - val_mse: 8.5874\n",
            "Epoch 56/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.8620 - mae: 1.8803 - mse: 6.8620 - val_loss: 8.9448 - val_mae: 2.1963 - val_mse: 8.9448\n",
            "Epoch 57/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.8246 - mae: 1.8847 - mse: 6.8246 - val_loss: 8.8868 - val_mae: 2.1803 - val_mse: 8.8868\n",
            "Epoch 58/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.8974 - mae: 1.8776 - mse: 6.8974 - val_loss: 8.5033 - val_mae: 2.1901 - val_mse: 8.5033\n",
            "Epoch 59/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.8076 - mae: 1.8698 - mse: 6.8076 - val_loss: 8.7204 - val_mae: 2.3412 - val_mse: 8.7204\n",
            "Epoch 60/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.6841 - mae: 1.8533 - mse: 6.6841 - val_loss: 8.5998 - val_mae: 2.2974 - val_mse: 8.5998\n",
            "Epoch 61/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.8972 - mae: 1.8471 - mse: 6.8972 - val_loss: 8.6870 - val_mae: 2.2545 - val_mse: 8.6870\n",
            "Epoch 62/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.6972 - mae: 1.8500 - mse: 6.6972 - val_loss: 8.8563 - val_mae: 2.1955 - val_mse: 8.8563\n",
            "Epoch 63/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.6959 - mae: 1.8598 - mse: 6.6959 - val_loss: 8.8143 - val_mae: 2.1853 - val_mse: 8.8143\n",
            "Epoch 64/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.7802 - mae: 1.8897 - mse: 6.7802 - val_loss: 8.4828 - val_mae: 2.2272 - val_mse: 8.4828\n",
            "Epoch 65/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.4208 - mae: 1.8013 - mse: 6.4208 - val_loss: 9.4069 - val_mae: 2.2209 - val_mse: 9.4069\n",
            "Epoch 66/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.7869 - mae: 1.8907 - mse: 6.7869 - val_loss: 8.5645 - val_mae: 2.2668 - val_mse: 8.5645\n",
            "Epoch 67/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.5771 - mae: 1.8081 - mse: 6.5771 - val_loss: 8.8931 - val_mae: 2.1943 - val_mse: 8.8931\n",
            "Epoch 68/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.6410 - mae: 1.8416 - mse: 6.6410 - val_loss: 8.6179 - val_mae: 2.2089 - val_mse: 8.6179\n",
            "Epoch 69/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.5867 - mae: 1.8268 - mse: 6.5867 - val_loss: 8.5815 - val_mae: 2.2405 - val_mse: 8.5815\n",
            "Epoch 70/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.5515 - mae: 1.8097 - mse: 6.5515 - val_loss: 8.4554 - val_mae: 2.2301 - val_mse: 8.4554\n",
            "Epoch 71/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.6319 - mae: 1.8221 - mse: 6.6319 - val_loss: 8.7093 - val_mae: 2.2235 - val_mse: 8.7093\n",
            "Epoch 72/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.6185 - mae: 1.8319 - mse: 6.6185 - val_loss: 8.5486 - val_mae: 2.2308 - val_mse: 8.5486\n",
            "Epoch 73/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.3628 - mae: 1.7780 - mse: 6.3628 - val_loss: 8.4584 - val_mae: 2.2104 - val_mse: 8.4584\n",
            "Epoch 74/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.5406 - mae: 1.8185 - mse: 6.5406 - val_loss: 8.4705 - val_mae: 2.2829 - val_mse: 8.4705\n",
            "Epoch 75/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.8257 - mae: 1.8297 - mse: 6.8257 - val_loss: 8.4605 - val_mae: 2.2394 - val_mse: 8.4605\n",
            "Epoch 76/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.5334 - mae: 1.8229 - mse: 6.5334 - val_loss: 8.6109 - val_mae: 2.2387 - val_mse: 8.6109\n",
            "Epoch 77/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.4634 - mae: 1.8124 - mse: 6.4634 - val_loss: 8.7176 - val_mae: 2.3285 - val_mse: 8.7176\n",
            "Epoch 78/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.4170 - mae: 1.8021 - mse: 6.4170 - val_loss: 8.8518 - val_mae: 2.3333 - val_mse: 8.8518\n",
            "Epoch 79/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.7804 - mae: 1.8395 - mse: 6.7804 - val_loss: 8.6456 - val_mae: 2.2305 - val_mse: 8.6456\n",
            "Epoch 80/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.4637 - mae: 1.8209 - mse: 6.4637 - val_loss: 8.6830 - val_mae: 2.2059 - val_mse: 8.6830\n",
            "Epoch 81/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.5264 - mae: 1.8038 - mse: 6.5264 - val_loss: 8.6367 - val_mae: 2.1964 - val_mse: 8.6367\n",
            "Epoch 82/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.2479 - mae: 1.8024 - mse: 6.2479 - val_loss: 8.7881 - val_mae: 2.3170 - val_mse: 8.7881\n",
            "Epoch 83/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.6323 - mae: 1.8061 - mse: 6.6323 - val_loss: 8.6231 - val_mae: 2.2126 - val_mse: 8.6231\n",
            "Epoch 84/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.2742 - mae: 1.8012 - mse: 6.2742 - val_loss: 8.6668 - val_mae: 2.2183 - val_mse: 8.6668\n",
            "Epoch 85/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.1833 - mae: 1.7555 - mse: 6.1833 - val_loss: 8.7331 - val_mae: 2.2923 - val_mse: 8.7331\n",
            "Epoch 86/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.1963 - mae: 1.7641 - mse: 6.1963 - val_loss: 9.0236 - val_mae: 2.2047 - val_mse: 9.0236\n",
            "Epoch 87/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.4264 - mae: 1.7875 - mse: 6.4264 - val_loss: 8.8707 - val_mae: 2.1979 - val_mse: 8.8707\n",
            "Epoch 88/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.5899 - mae: 1.8373 - mse: 6.5899 - val_loss: 8.4793 - val_mae: 2.2289 - val_mse: 8.4793\n",
            "Epoch 89/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.3998 - mae: 1.7983 - mse: 6.3998 - val_loss: 8.5131 - val_mae: 2.2115 - val_mse: 8.5131\n",
            "Epoch 90/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.3845 - mae: 1.7736 - mse: 6.3845 - val_loss: 9.1586 - val_mae: 2.2084 - val_mse: 9.1586\n",
            "Epoch 91/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.4017 - mae: 1.8040 - mse: 6.4017 - val_loss: 8.3980 - val_mae: 2.2368 - val_mse: 8.3980\n",
            "Epoch 92/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.1265 - mae: 1.7367 - mse: 6.1265 - val_loss: 8.6208 - val_mae: 2.1724 - val_mse: 8.6208\n",
            "Epoch 93/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.2474 - mae: 1.7770 - mse: 6.2474 - val_loss: 8.3316 - val_mae: 2.1751 - val_mse: 8.3316\n",
            "Epoch 94/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.3071 - mae: 1.7794 - mse: 6.3071 - val_loss: 8.4427 - val_mae: 2.2074 - val_mse: 8.4427\n",
            "Epoch 95/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.1964 - mae: 1.7556 - mse: 6.1964 - val_loss: 8.6027 - val_mae: 2.2948 - val_mse: 8.6027\n",
            "Epoch 96/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.2193 - mae: 1.7814 - mse: 6.2193 - val_loss: 8.6321 - val_mae: 2.2208 - val_mse: 8.6321\n",
            "Epoch 97/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.1314 - mae: 1.7441 - mse: 6.1314 - val_loss: 8.8551 - val_mae: 2.2125 - val_mse: 8.8551\n",
            "Epoch 98/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.5227 - mae: 1.7938 - mse: 6.5227 - val_loss: 8.7678 - val_mae: 2.2395 - val_mse: 8.7678\n",
            "Epoch 99/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.0843 - mae: 1.7518 - mse: 6.0843 - val_loss: 8.9540 - val_mae: 2.3636 - val_mse: 8.9540\n",
            "Epoch 100/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 6.1532 - mae: 1.7504 - mse: 6.1532 - val_loss: 8.5709 - val_mae: 2.1710 - val_mse: 8.5709\n",
            "Epoch 101/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.9977 - mae: 1.7622 - mse: 5.9977 - val_loss: 8.4217 - val_mae: 2.2733 - val_mse: 8.4217\n",
            "Epoch 102/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.3069 - mae: 1.7572 - mse: 6.3069 - val_loss: 8.4788 - val_mae: 2.2413 - val_mse: 8.4788\n",
            "Epoch 103/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.1983 - mae: 1.7184 - mse: 6.1983 - val_loss: 8.7167 - val_mae: 2.2088 - val_mse: 8.7167\n",
            "Epoch 104/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.2172 - mae: 1.7782 - mse: 6.2172 - val_loss: 8.5332 - val_mae: 2.2383 - val_mse: 8.5332\n",
            "Epoch 105/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.1582 - mae: 1.7508 - mse: 6.1582 - val_loss: 8.4979 - val_mae: 2.2536 - val_mse: 8.4979\n",
            "Epoch 106/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.1445 - mae: 1.7363 - mse: 6.1445 - val_loss: 8.7744 - val_mae: 2.1886 - val_mse: 8.7744\n",
            "Epoch 107/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.0922 - mae: 1.7563 - mse: 6.0922 - val_loss: 8.9893 - val_mae: 2.1897 - val_mse: 8.9893\n",
            "Epoch 108/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.1975 - mae: 1.7653 - mse: 6.1975 - val_loss: 8.5777 - val_mae: 2.2594 - val_mse: 8.5777\n",
            "Epoch 109/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.0310 - mae: 1.7414 - mse: 6.0310 - val_loss: 8.4927 - val_mae: 2.2473 - val_mse: 8.4927\n",
            "Epoch 110/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.9438 - mae: 1.7420 - mse: 5.9438 - val_loss: 8.5398 - val_mae: 2.2938 - val_mse: 8.5398\n",
            "Epoch 111/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.1334 - mae: 1.7500 - mse: 6.1334 - val_loss: 8.5532 - val_mae: 2.2619 - val_mse: 8.5532\n",
            "Epoch 112/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.1941 - mae: 1.7488 - mse: 6.1941 - val_loss: 8.5313 - val_mae: 2.2626 - val_mse: 8.5313\n",
            "Epoch 113/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.1475 - mae: 1.7266 - mse: 6.1475 - val_loss: 8.6550 - val_mae: 2.1810 - val_mse: 8.6550\n",
            "Epoch 114/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.0159 - mae: 1.7389 - mse: 6.0159 - val_loss: 8.4830 - val_mae: 2.1962 - val_mse: 8.4830\n",
            "Epoch 115/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.2216 - mae: 1.7423 - mse: 6.2216 - val_loss: 8.5850 - val_mae: 2.1841 - val_mse: 8.5850\n",
            "Epoch 116/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.1152 - mae: 1.7308 - mse: 6.1152 - val_loss: 8.4678 - val_mae: 2.2243 - val_mse: 8.4678\n",
            "Epoch 117/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.8331 - mae: 1.6940 - mse: 5.8331 - val_loss: 8.5123 - val_mae: 2.2860 - val_mse: 8.5123\n",
            "Epoch 118/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.0268 - mae: 1.6892 - mse: 6.0268 - val_loss: 8.6860 - val_mae: 2.2090 - val_mse: 8.6860\n",
            "Epoch 119/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.1896 - mae: 1.7578 - mse: 6.1896 - val_loss: 8.6128 - val_mae: 2.2360 - val_mse: 8.6128\n",
            "Epoch 120/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.0962 - mae: 1.7574 - mse: 6.0962 - val_loss: 8.4596 - val_mae: 2.2570 - val_mse: 8.4596\n",
            "Epoch 121/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.9248 - mae: 1.7024 - mse: 5.9248 - val_loss: 8.4323 - val_mae: 2.2211 - val_mse: 8.4323\n",
            "Epoch 122/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 5.9840 - mae: 1.7144 - mse: 5.9840 - val_loss: 8.4646 - val_mae: 2.2295 - val_mse: 8.4646\n",
            "Epoch 123/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.0190 - mae: 1.7226 - mse: 6.0190 - val_loss: 8.8517 - val_mae: 2.1897 - val_mse: 8.8517\n",
            "Epoch 124/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.8207 - mae: 1.6907 - mse: 5.8207 - val_loss: 8.5067 - val_mae: 2.2813 - val_mse: 8.5067\n",
            "Epoch 125/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.0271 - mae: 1.7091 - mse: 6.0271 - val_loss: 8.6077 - val_mae: 2.2401 - val_mse: 8.6077\n",
            "Epoch 126/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.8159 - mae: 1.6965 - mse: 5.8159 - val_loss: 8.4167 - val_mae: 2.2553 - val_mse: 8.4167\n",
            "Epoch 127/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.0905 - mae: 1.6959 - mse: 6.0905 - val_loss: 8.3973 - val_mae: 2.1979 - val_mse: 8.3973\n",
            "Epoch 128/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.8512 - mae: 1.7018 - mse: 5.8512 - val_loss: 9.4337 - val_mae: 2.2145 - val_mse: 9.4337\n",
            "Epoch 129/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.0035 - mae: 1.7783 - mse: 6.0035 - val_loss: 8.3860 - val_mae: 2.2091 - val_mse: 8.3860\n",
            "Epoch 130/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.7603 - mae: 1.6816 - mse: 5.7603 - val_loss: 8.4269 - val_mae: 2.2459 - val_mse: 8.4269\n",
            "Epoch 131/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.8628 - mae: 1.7130 - mse: 5.8628 - val_loss: 8.4779 - val_mae: 2.1799 - val_mse: 8.4779\n",
            "Epoch 132/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.0752 - mae: 1.7453 - mse: 6.0752 - val_loss: 8.5676 - val_mae: 2.2534 - val_mse: 8.5676\n",
            "Epoch 133/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.8579 - mae: 1.6743 - mse: 5.8579 - val_loss: 8.5782 - val_mae: 2.2158 - val_mse: 8.5782\n",
            "Epoch 134/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.8792 - mae: 1.7241 - mse: 5.8792 - val_loss: 8.4979 - val_mae: 2.2452 - val_mse: 8.4979\n",
            "Epoch 135/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.9559 - mae: 1.6682 - mse: 5.9559 - val_loss: 8.6849 - val_mae: 2.2133 - val_mse: 8.6849\n",
            "Epoch 136/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.7604 - mae: 1.6874 - mse: 5.7604 - val_loss: 8.5870 - val_mae: 2.1730 - val_mse: 8.5870\n",
            "Epoch 137/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.7888 - mae: 1.6854 - mse: 5.7888 - val_loss: 8.5380 - val_mae: 2.2026 - val_mse: 8.5380\n",
            "Epoch 138/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.7655 - mae: 1.6973 - mse: 5.7655 - val_loss: 8.4224 - val_mae: 2.2009 - val_mse: 8.4224\n",
            "Epoch 139/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.8873 - mae: 1.7071 - mse: 5.8873 - val_loss: 8.4666 - val_mae: 2.2468 - val_mse: 8.4666\n",
            "Epoch 140/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.8551 - mae: 1.6891 - mse: 5.8551 - val_loss: 8.6628 - val_mae: 2.2051 - val_mse: 8.6628\n",
            "Epoch 141/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.8610 - mae: 1.6732 - mse: 5.8610 - val_loss: 8.7204 - val_mae: 2.3121 - val_mse: 8.7204\n",
            "Epoch 142/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.5845 - mae: 1.6347 - mse: 5.5845 - val_loss: 9.1302 - val_mae: 2.1892 - val_mse: 9.1302\n",
            "Epoch 143/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.9020 - mae: 1.7080 - mse: 5.9020 - val_loss: 8.4833 - val_mae: 2.2312 - val_mse: 8.4833\n",
            "Epoch 144/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.6844 - mae: 1.6956 - mse: 5.6844 - val_loss: 8.4613 - val_mae: 2.2545 - val_mse: 8.4613\n",
            "Epoch 145/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.5434 - mae: 1.6495 - mse: 5.5434 - val_loss: 8.6017 - val_mae: 2.3276 - val_mse: 8.6017\n",
            "Epoch 146/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 5.9655 - mae: 1.6888 - mse: 5.9655 - val_loss: 8.5601 - val_mae: 2.2466 - val_mse: 8.5601\n",
            "Epoch 147/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.5853 - mae: 1.6723 - mse: 5.5853 - val_loss: 8.8309 - val_mae: 2.3318 - val_mse: 8.8309\n",
            "Epoch 148/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.5509 - mae: 1.6409 - mse: 5.5509 - val_loss: 8.6563 - val_mae: 2.1799 - val_mse: 8.6563\n",
            "Epoch 149/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.7121 - mae: 1.6805 - mse: 5.7121 - val_loss: 8.6834 - val_mae: 2.3038 - val_mse: 8.6834\n",
            "Epoch 150/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.7763 - mae: 1.6624 - mse: 5.7763 - val_loss: 8.7428 - val_mae: 2.2289 - val_mse: 8.7428\n",
            "Epoch 151/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.6937 - mae: 1.6887 - mse: 5.6937 - val_loss: 8.6020 - val_mae: 2.2499 - val_mse: 8.6020\n",
            "Epoch 152/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.8473 - mae: 1.6743 - mse: 5.8473 - val_loss: 8.6533 - val_mae: 2.2609 - val_mse: 8.6533\n",
            "Epoch 153/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.7136 - mae: 1.6988 - mse: 5.7136 - val_loss: 8.9157 - val_mae: 2.3465 - val_mse: 8.9157\n",
            "Epoch 154/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.6499 - mae: 1.6476 - mse: 5.6499 - val_loss: 8.7185 - val_mae: 2.2118 - val_mse: 8.7185\n",
            "Epoch 155/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.5927 - mae: 1.6641 - mse: 5.5927 - val_loss: 8.5907 - val_mae: 2.2574 - val_mse: 8.5907\n",
            "Epoch 156/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.6058 - mae: 1.6419 - mse: 5.6058 - val_loss: 8.7702 - val_mae: 2.3190 - val_mse: 8.7702\n",
            "Epoch 157/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.5980 - mae: 1.6364 - mse: 5.5980 - val_loss: 9.2456 - val_mae: 2.2147 - val_mse: 9.2456\n",
            "Epoch 158/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.9722 - mae: 1.7444 - mse: 5.9722 - val_loss: 8.4947 - val_mae: 2.2620 - val_mse: 8.4947\n",
            "Epoch 159/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.6389 - mae: 1.6476 - mse: 5.6389 - val_loss: 8.7021 - val_mae: 2.2927 - val_mse: 8.7021\n",
            "Epoch 160/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.4948 - mae: 1.6249 - mse: 5.4948 - val_loss: 9.1404 - val_mae: 2.2075 - val_mse: 9.1404\n",
            "Epoch 161/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.8775 - mae: 1.6764 - mse: 5.8775 - val_loss: 8.7615 - val_mae: 2.2146 - val_mse: 8.7615\n",
            "Epoch 162/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.5098 - mae: 1.6667 - mse: 5.5098 - val_loss: 8.6409 - val_mae: 2.2869 - val_mse: 8.6409\n",
            "Epoch 163/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.5967 - mae: 1.6918 - mse: 5.5967 - val_loss: 8.7240 - val_mae: 2.2961 - val_mse: 8.7240\n",
            "Epoch 164/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.4545 - mae: 1.6289 - mse: 5.4545 - val_loss: 8.7598 - val_mae: 2.2892 - val_mse: 8.7598\n",
            "Epoch 165/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.6482 - mae: 1.6558 - mse: 5.6482 - val_loss: 8.6548 - val_mae: 2.2092 - val_mse: 8.6548\n",
            "Epoch 166/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.5065 - mae: 1.6487 - mse: 5.5065 - val_loss: 9.0639 - val_mae: 2.2655 - val_mse: 9.0639\n",
            "Epoch 167/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.4658 - mae: 1.6475 - mse: 5.4658 - val_loss: 8.8136 - val_mae: 2.2689 - val_mse: 8.8136\n",
            "Epoch 168/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.7360 - mae: 1.6401 - mse: 5.7360 - val_loss: 8.8299 - val_mae: 2.3225 - val_mse: 8.8299\n",
            "Epoch 169/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.6883 - mae: 1.6555 - mse: 5.6883 - val_loss: 8.6089 - val_mae: 2.2794 - val_mse: 8.6089\n",
            "Epoch 170/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.5008 - mae: 1.6459 - mse: 5.5008 - val_loss: 8.7785 - val_mae: 2.2186 - val_mse: 8.7785\n",
            "Epoch 171/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.5963 - mae: 1.6683 - mse: 5.5963 - val_loss: 8.7721 - val_mae: 2.2004 - val_mse: 8.7721\n",
            "Epoch 172/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.3631 - mae: 1.6225 - mse: 5.3631 - val_loss: 9.3396 - val_mae: 2.2359 - val_mse: 9.3396\n",
            "Epoch 173/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.6551 - mae: 1.6510 - mse: 5.6551 - val_loss: 8.7507 - val_mae: 2.2098 - val_mse: 8.7507\n",
            "Epoch 174/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.5632 - mae: 1.6710 - mse: 5.5632 - val_loss: 8.7564 - val_mae: 2.3055 - val_mse: 8.7564\n",
            "Epoch 175/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.3743 - mae: 1.5920 - mse: 5.3743 - val_loss: 8.9508 - val_mae: 2.1998 - val_mse: 8.9508\n",
            "Epoch 176/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.5013 - mae: 1.6860 - mse: 5.5013 - val_loss: 8.5928 - val_mae: 2.2541 - val_mse: 8.5928\n",
            "Epoch 177/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.4712 - mae: 1.6507 - mse: 5.4712 - val_loss: 8.5518 - val_mae: 2.2122 - val_mse: 8.5518\n",
            "Epoch 178/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 5.3679 - mae: 1.6187 - mse: 5.3679 - val_loss: 8.6167 - val_mae: 2.1770 - val_mse: 8.6167\n",
            "Epoch 179/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.5669 - mae: 1.6600 - mse: 5.5669 - val_loss: 8.5351 - val_mae: 2.2050 - val_mse: 8.5351\n",
            "Epoch 180/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.7007 - mae: 1.6834 - mse: 5.7007 - val_loss: 8.5434 - val_mae: 2.2309 - val_mse: 8.5434\n",
            "Epoch 181/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.4539 - mae: 1.6245 - mse: 5.4539 - val_loss: 8.9939 - val_mae: 2.2123 - val_mse: 8.9939\n",
            "Epoch 182/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 5.3509 - mae: 1.6229 - mse: 5.3509 - val_loss: 8.7817 - val_mae: 2.2222 - val_mse: 8.7817\n",
            "Epoch 183/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.3331 - mae: 1.6022 - mse: 5.3331 - val_loss: 8.8004 - val_mae: 2.2623 - val_mse: 8.8004\n",
            "Epoch 184/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.5174 - mae: 1.6681 - mse: 5.5174 - val_loss: 8.8513 - val_mae: 2.3242 - val_mse: 8.8513\n",
            "Epoch 185/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.3584 - mae: 1.6027 - mse: 5.3584 - val_loss: 8.7520 - val_mae: 2.3051 - val_mse: 8.7520\n",
            "Epoch 186/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.6503 - mae: 1.6647 - mse: 5.6503 - val_loss: 8.5092 - val_mae: 2.2267 - val_mse: 8.5092\n",
            "Epoch 187/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.6220 - mae: 1.6817 - mse: 5.6220 - val_loss: 8.7811 - val_mae: 2.3005 - val_mse: 8.7811\n",
            "Epoch 188/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.3534 - mae: 1.5851 - mse: 5.3534 - val_loss: 9.1242 - val_mae: 2.2104 - val_mse: 9.1242\n",
            "Epoch 189/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.3193 - mae: 1.6310 - mse: 5.3193 - val_loss: 8.7057 - val_mae: 2.2537 - val_mse: 8.7057\n",
            "Epoch 190/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.4810 - mae: 1.6467 - mse: 5.4810 - val_loss: 8.6831 - val_mae: 2.2522 - val_mse: 8.6831\n",
            "Epoch 191/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.3300 - mae: 1.6175 - mse: 5.3300 - val_loss: 8.6619 - val_mae: 2.2141 - val_mse: 8.6619\n",
            "Epoch 192/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.3324 - mae: 1.6379 - mse: 5.3324 - val_loss: 9.2196 - val_mae: 2.3863 - val_mse: 9.2196\n",
            "Epoch 193/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.4559 - mae: 1.6402 - mse: 5.4559 - val_loss: 9.4115 - val_mae: 2.4333 - val_mse: 9.4115\n",
            "Epoch 194/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.2332 - mae: 1.5934 - mse: 5.2332 - val_loss: 9.4647 - val_mae: 2.2239 - val_mse: 9.4647\n",
            "Epoch 195/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.7948 - mae: 1.7019 - mse: 5.7948 - val_loss: 8.7094 - val_mae: 2.2569 - val_mse: 8.7094\n",
            "Epoch 196/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.2954 - mae: 1.6015 - mse: 5.2954 - val_loss: 8.6647 - val_mae: 2.2565 - val_mse: 8.6647\n",
            "Epoch 197/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.3177 - mae: 1.6181 - mse: 5.3177 - val_loss: 9.2033 - val_mae: 2.3886 - val_mse: 9.2033\n",
            "Epoch 198/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.4302 - mae: 1.6096 - mse: 5.4302 - val_loss: 8.7404 - val_mae: 2.2038 - val_mse: 8.7404\n",
            "Epoch 199/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.4220 - mae: 1.6034 - mse: 5.4220 - val_loss: 8.8173 - val_mae: 2.2679 - val_mse: 8.8173\n",
            "Epoch 200/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.5624 - mae: 1.6337 - mse: 5.5624 - val_loss: 8.8244 - val_mae: 2.2240 - val_mse: 8.8244\n",
            "Epoch 201/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.2932 - mae: 1.5885 - mse: 5.2932 - val_loss: 8.7795 - val_mae: 2.2460 - val_mse: 8.7795\n",
            "Epoch 202/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.2787 - mae: 1.6106 - mse: 5.2787 - val_loss: 8.7816 - val_mae: 2.2894 - val_mse: 8.7816\n",
            "Epoch 203/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.3334 - mae: 1.6188 - mse: 5.3334 - val_loss: 9.5917 - val_mae: 2.4333 - val_mse: 9.5917\n",
            "Epoch 204/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 5.6409 - mae: 1.6269 - mse: 5.6409 - val_loss: 9.0084 - val_mae: 2.3445 - val_mse: 9.0084\n",
            "Epoch 205/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.3340 - mae: 1.5967 - mse: 5.3340 - val_loss: 8.9183 - val_mae: 2.2357 - val_mse: 8.9183\n",
            "Epoch 206/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.4332 - mae: 1.6150 - mse: 5.4332 - val_loss: 9.1029 - val_mae: 2.2085 - val_mse: 9.1029\n",
            "Epoch 207/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.2675 - mae: 1.6002 - mse: 5.2675 - val_loss: 8.7007 - val_mae: 2.2346 - val_mse: 8.7007\n",
            "Epoch 208/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.2801 - mae: 1.6233 - mse: 5.2801 - val_loss: 8.7110 - val_mae: 2.2447 - val_mse: 8.7110\n",
            "Epoch 209/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.4253 - mae: 1.6280 - mse: 5.4253 - val_loss: 8.8276 - val_mae: 2.2929 - val_mse: 8.8276\n",
            "Epoch 210/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 5.2418 - mae: 1.5785 - mse: 5.2418 - val_loss: 8.8925 - val_mae: 2.2289 - val_mse: 8.8925\n",
            "Epoch 211/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.3917 - mae: 1.6237 - mse: 5.3917 - val_loss: 8.9825 - val_mae: 2.2630 - val_mse: 8.9825\n",
            "Epoch 212/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.1069 - mae: 1.5809 - mse: 5.1069 - val_loss: 9.2657 - val_mae: 2.3741 - val_mse: 9.2657\n",
            "Epoch 213/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.3496 - mae: 1.6214 - mse: 5.3496 - val_loss: 9.0780 - val_mae: 2.3459 - val_mse: 9.0780\n",
            "Epoch 214/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.3863 - mae: 1.6082 - mse: 5.3863 - val_loss: 8.7744 - val_mae: 2.2513 - val_mse: 8.7744\n",
            "Epoch 215/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.1846 - mae: 1.5709 - mse: 5.1846 - val_loss: 8.7776 - val_mae: 2.2467 - val_mse: 8.7776\n",
            "Epoch 216/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.2699 - mae: 1.6111 - mse: 5.2699 - val_loss: 8.8782 - val_mae: 2.2286 - val_mse: 8.8782\n",
            "Epoch 217/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.4309 - mae: 1.6120 - mse: 5.4309 - val_loss: 8.8720 - val_mae: 2.2088 - val_mse: 8.8720\n",
            "Epoch 218/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.3923 - mae: 1.6210 - mse: 5.3923 - val_loss: 8.8393 - val_mae: 2.2447 - val_mse: 8.8393\n",
            "Epoch 219/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.2418 - mae: 1.6059 - mse: 5.2418 - val_loss: 8.8219 - val_mae: 2.2904 - val_mse: 8.8219\n",
            "Epoch 220/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.1447 - mae: 1.5874 - mse: 5.1447 - val_loss: 9.1720 - val_mae: 2.3777 - val_mse: 9.1720\n",
            "Epoch 221/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.3502 - mae: 1.5929 - mse: 5.3502 - val_loss: 8.9293 - val_mae: 2.2620 - val_mse: 8.9293\n",
            "Epoch 222/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.2474 - mae: 1.5986 - mse: 5.2474 - val_loss: 8.8643 - val_mae: 2.2967 - val_mse: 8.8643\n",
            "Epoch 223/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 5.3435 - mae: 1.5747 - mse: 5.3435 - val_loss: 8.8375 - val_mae: 2.2648 - val_mse: 8.8375\n",
            "Epoch 224/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.1470 - mae: 1.5876 - mse: 5.1470 - val_loss: 8.7797 - val_mae: 2.2674 - val_mse: 8.7797\n",
            "Epoch 225/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.2833 - mae: 1.6015 - mse: 5.2833 - val_loss: 8.7387 - val_mae: 2.2426 - val_mse: 8.7387\n",
            "Epoch 226/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.0817 - mae: 1.5405 - mse: 5.0817 - val_loss: 8.8172 - val_mae: 2.2688 - val_mse: 8.8172\n",
            "Epoch 227/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.2517 - mae: 1.5659 - mse: 5.2517 - val_loss: 9.2886 - val_mae: 2.2205 - val_mse: 9.2886\n",
            "Epoch 228/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.2872 - mae: 1.6328 - mse: 5.2872 - val_loss: 9.1297 - val_mae: 2.2486 - val_mse: 9.1297\n",
            "Epoch 229/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.1832 - mae: 1.5789 - mse: 5.1832 - val_loss: 8.8767 - val_mae: 2.2218 - val_mse: 8.8767\n",
            "Epoch 230/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 5.1938 - mae: 1.5688 - mse: 5.1938 - val_loss: 8.9461 - val_mae: 2.2542 - val_mse: 8.9461\n",
            "Epoch 231/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.3394 - mae: 1.6041 - mse: 5.3394 - val_loss: 9.0648 - val_mae: 2.2066 - val_mse: 9.0648\n",
            "Epoch 232/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.2702 - mae: 1.5940 - mse: 5.2702 - val_loss: 8.8857 - val_mae: 2.2765 - val_mse: 8.8857\n",
            "Epoch 233/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 5.2514 - mae: 1.6066 - mse: 5.2514 - val_loss: 8.9362 - val_mae: 2.2836 - val_mse: 8.9362\n",
            "Epoch 234/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.1802 - mae: 1.5502 - mse: 5.1802 - val_loss: 8.7983 - val_mae: 2.2217 - val_mse: 8.7983\n",
            "Epoch 235/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.1151 - mae: 1.6038 - mse: 5.1151 - val_loss: 8.8808 - val_mae: 2.2979 - val_mse: 8.8808\n",
            "Epoch 236/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.2281 - mae: 1.5838 - mse: 5.2281 - val_loss: 8.8610 - val_mae: 2.2998 - val_mse: 8.8610\n",
            "Epoch 237/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.2461 - mae: 1.5730 - mse: 5.2461 - val_loss: 8.7119 - val_mae: 2.2264 - val_mse: 8.7119\n",
            "Epoch 238/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.0847 - mae: 1.5546 - mse: 5.0847 - val_loss: 9.1049 - val_mae: 2.3486 - val_mse: 9.1049\n",
            "Epoch 239/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.4615 - mae: 1.5742 - mse: 5.4615 - val_loss: 9.0216 - val_mae: 2.2392 - val_mse: 9.0216\n",
            "Epoch 240/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.1801 - mae: 1.5794 - mse: 5.1801 - val_loss: 8.6225 - val_mae: 2.2290 - val_mse: 8.6225\n",
            "Epoch 241/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.1356 - mae: 1.5738 - mse: 5.1356 - val_loss: 8.8483 - val_mae: 2.2912 - val_mse: 8.8483\n",
            "Epoch 242/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 5.1507 - mae: 1.5609 - mse: 5.1507 - val_loss: 8.8293 - val_mae: 2.2510 - val_mse: 8.8293\n",
            "Epoch 243/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.9655 - mae: 1.5299 - mse: 4.9655 - val_loss: 8.9293 - val_mae: 2.3022 - val_mse: 8.9293\n",
            "Epoch 244/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.1537 - mae: 1.5484 - mse: 5.1537 - val_loss: 8.7153 - val_mae: 2.2935 - val_mse: 8.7153\n",
            "Epoch 245/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.1727 - mae: 1.5671 - mse: 5.1727 - val_loss: 8.7805 - val_mae: 2.2380 - val_mse: 8.7805\n",
            "Epoch 246/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.3733 - mae: 1.5752 - mse: 5.3733 - val_loss: 9.1738 - val_mae: 2.2247 - val_mse: 9.1738\n",
            "Epoch 247/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 5.1895 - mae: 1.5752 - mse: 5.1895 - val_loss: 8.8638 - val_mae: 2.2252 - val_mse: 8.8638\n",
            "Epoch 248/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.1084 - mae: 1.5575 - mse: 5.1084 - val_loss: 8.8970 - val_mae: 2.2746 - val_mse: 8.8970\n",
            "Epoch 249/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.1295 - mae: 1.5442 - mse: 5.1295 - val_loss: 8.8995 - val_mae: 2.2283 - val_mse: 8.8995\n",
            "Epoch 250/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.2846 - mae: 1.5856 - mse: 5.2846 - val_loss: 8.8017 - val_mae: 2.2575 - val_mse: 8.8017\n",
            "Epoch 251/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.0502 - mae: 1.5259 - mse: 5.0502 - val_loss: 9.2649 - val_mae: 2.2228 - val_mse: 9.2649\n",
            "Epoch 252/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.0149 - mae: 1.5207 - mse: 5.0149 - val_loss: 8.9459 - val_mae: 2.2606 - val_mse: 8.9459\n",
            "Epoch 253/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.1990 - mae: 1.5946 - mse: 5.1990 - val_loss: 9.3107 - val_mae: 2.3873 - val_mse: 9.3107\n",
            "Epoch 254/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.1033 - mae: 1.5381 - mse: 5.1033 - val_loss: 8.8873 - val_mae: 2.2535 - val_mse: 8.8873\n",
            "Epoch 255/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 5.1054 - mae: 1.5786 - mse: 5.1054 - val_loss: 9.2410 - val_mae: 2.2497 - val_mse: 9.2410\n",
            "Epoch 256/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.0668 - mae: 1.5199 - mse: 5.0668 - val_loss: 9.0092 - val_mae: 2.2026 - val_mse: 9.0092\n",
            "Epoch 257/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.0341 - mae: 1.5593 - mse: 5.0341 - val_loss: 9.0284 - val_mae: 2.2758 - val_mse: 9.0284\n",
            "Epoch 258/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.1281 - mae: 1.5990 - mse: 5.1281 - val_loss: 8.8888 - val_mae: 2.3063 - val_mse: 8.8888\n",
            "Epoch 259/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.9613 - mae: 1.5120 - mse: 4.9613 - val_loss: 9.2285 - val_mae: 2.2227 - val_mse: 9.2285\n",
            "Epoch 260/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.1232 - mae: 1.5714 - mse: 5.1232 - val_loss: 9.0605 - val_mae: 2.2177 - val_mse: 9.0605\n",
            "Epoch 261/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.2256 - mae: 1.5758 - mse: 5.2256 - val_loss: 8.7742 - val_mae: 2.2938 - val_mse: 8.7742\n",
            "Epoch 262/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.0202 - mae: 1.5608 - mse: 5.0202 - val_loss: 8.9353 - val_mae: 2.2520 - val_mse: 8.9353\n",
            "Epoch 263/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.9814 - mae: 1.5271 - mse: 4.9814 - val_loss: 9.0284 - val_mae: 2.2611 - val_mse: 9.0284\n",
            "Epoch 264/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.0695 - mae: 1.5349 - mse: 5.0695 - val_loss: 9.2279 - val_mae: 2.2470 - val_mse: 9.2279\n",
            "Epoch 265/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.2205 - mae: 1.5568 - mse: 5.2205 - val_loss: 9.0675 - val_mae: 2.2343 - val_mse: 9.0675\n",
            "Epoch 266/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.9549 - mae: 1.5295 - mse: 4.9549 - val_loss: 9.0735 - val_mae: 2.2171 - val_mse: 9.0735\n",
            "Epoch 267/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.0497 - mae: 1.5535 - mse: 5.0497 - val_loss: 9.0689 - val_mae: 2.2697 - val_mse: 9.0689\n",
            "Epoch 268/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.9015 - mae: 1.5269 - mse: 4.9015 - val_loss: 9.2612 - val_mae: 2.3287 - val_mse: 9.2612\n",
            "Epoch 269/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.1104 - mae: 1.5411 - mse: 5.1104 - val_loss: 9.4686 - val_mae: 2.2441 - val_mse: 9.4686\n",
            "Epoch 270/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.0855 - mae: 1.5461 - mse: 5.0855 - val_loss: 8.8798 - val_mae: 2.2767 - val_mse: 8.8798\n",
            "Epoch 271/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.9789 - mae: 1.5361 - mse: 4.9789 - val_loss: 9.2497 - val_mae: 2.2989 - val_mse: 9.2497\n",
            "Epoch 272/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.9789 - mae: 1.5623 - mse: 4.9789 - val_loss: 9.1341 - val_mae: 2.3178 - val_mse: 9.1341\n",
            "Epoch 273/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.1191 - mae: 1.5318 - mse: 5.1191 - val_loss: 9.2377 - val_mae: 2.2936 - val_mse: 9.2377\n",
            "Epoch 274/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 5.0477 - mae: 1.5522 - mse: 5.0477 - val_loss: 9.3335 - val_mae: 2.2434 - val_mse: 9.3335\n",
            "Epoch 275/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.0977 - mae: 1.5311 - mse: 5.0977 - val_loss: 8.9807 - val_mae: 2.2694 - val_mse: 8.9807\n",
            "Epoch 276/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.9434 - mae: 1.5272 - mse: 4.9434 - val_loss: 9.2477 - val_mae: 2.2756 - val_mse: 9.2477\n",
            "Epoch 277/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.9502 - mae: 1.5598 - mse: 4.9502 - val_loss: 9.2544 - val_mae: 2.3026 - val_mse: 9.2544\n",
            "Epoch 278/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.0099 - mae: 1.5428 - mse: 5.0099 - val_loss: 9.4720 - val_mae: 2.3360 - val_mse: 9.4720\n",
            "Epoch 279/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.2011 - mae: 1.5731 - mse: 5.2011 - val_loss: 9.1390 - val_mae: 2.2770 - val_mse: 9.1390\n",
            "Epoch 280/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.9611 - mae: 1.5354 - mse: 4.9611 - val_loss: 9.4278 - val_mae: 2.3308 - val_mse: 9.4278\n",
            "Epoch 281/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.2032 - mae: 1.5404 - mse: 5.2032 - val_loss: 9.3788 - val_mae: 2.2599 - val_mse: 9.3788\n",
            "Epoch 282/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.8489 - mae: 1.5189 - mse: 4.8489 - val_loss: 9.2406 - val_mae: 2.2450 - val_mse: 9.2406\n",
            "Epoch 283/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.9363 - mae: 1.5159 - mse: 4.9363 - val_loss: 9.7159 - val_mae: 2.2678 - val_mse: 9.7159\n",
            "Epoch 284/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.9845 - mae: 1.5175 - mse: 4.9845 - val_loss: 9.0196 - val_mae: 2.2172 - val_mse: 9.0196\n",
            "Epoch 285/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.7976 - mae: 1.5026 - mse: 4.7976 - val_loss: 9.1626 - val_mae: 2.3227 - val_mse: 9.1626\n",
            "Epoch 286/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.1453 - mae: 1.5906 - mse: 5.1453 - val_loss: 9.2600 - val_mae: 2.3145 - val_mse: 9.2600\n",
            "Epoch 287/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 5.0049 - mae: 1.5637 - mse: 5.0049 - val_loss: 9.0923 - val_mae: 2.2486 - val_mse: 9.0923\n",
            "Epoch 288/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 5.2078 - mae: 1.5625 - mse: 5.2078 - val_loss: 9.0691 - val_mae: 2.2593 - val_mse: 9.0691\n",
            "Epoch 289/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.1828 - mae: 1.5719 - mse: 5.1828 - val_loss: 9.2263 - val_mae: 2.2709 - val_mse: 9.2263\n",
            "Epoch 290/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.8642 - mae: 1.5180 - mse: 4.8642 - val_loss: 9.5368 - val_mae: 2.2542 - val_mse: 9.5368\n",
            "Epoch 291/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.8086 - mae: 1.5030 - mse: 4.8086 - val_loss: 9.1623 - val_mae: 2.2654 - val_mse: 9.1623\n",
            "Epoch 292/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.7108 - mae: 1.5387 - mse: 4.7108 - val_loss: 9.2391 - val_mae: 2.2418 - val_mse: 9.2391\n",
            "Epoch 293/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.7807 - mae: 1.5128 - mse: 4.7807 - val_loss: 9.3146 - val_mae: 2.3523 - val_mse: 9.3146\n",
            "Epoch 294/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.8672 - mae: 1.5121 - mse: 4.8672 - val_loss: 9.1181 - val_mae: 2.2875 - val_mse: 9.1181\n",
            "Epoch 295/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.9200 - mae: 1.5024 - mse: 4.9200 - val_loss: 9.0232 - val_mae: 2.2490 - val_mse: 9.0232\n",
            "Epoch 296/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.8560 - mae: 1.5196 - mse: 4.8560 - val_loss: 9.0136 - val_mae: 2.2994 - val_mse: 9.0136\n",
            "Epoch 297/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.0346 - mae: 1.5498 - mse: 5.0346 - val_loss: 9.3396 - val_mae: 2.3133 - val_mse: 9.3396\n",
            "Epoch 298/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.9029 - mae: 1.5043 - mse: 4.9029 - val_loss: 9.2340 - val_mae: 2.2767 - val_mse: 9.2340\n",
            "Epoch 299/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.8929 - mae: 1.5014 - mse: 4.8929 - val_loss: 9.0699 - val_mae: 2.2599 - val_mse: 9.0699\n",
            "Epoch 300/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.1504 - mae: 1.5393 - mse: 5.1504 - val_loss: 9.2322 - val_mae: 2.3409 - val_mse: 9.2322\n",
            "Epoch 301/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.8989 - mae: 1.5095 - mse: 4.8989 - val_loss: 9.2234 - val_mae: 2.2767 - val_mse: 9.2234\n",
            "Epoch 302/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.0114 - mae: 1.5605 - mse: 5.0114 - val_loss: 9.1568 - val_mae: 2.2259 - val_mse: 9.1568\n",
            "Epoch 303/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.9875 - mae: 1.5432 - mse: 4.9875 - val_loss: 9.1117 - val_mae: 2.3295 - val_mse: 9.1117\n",
            "Epoch 304/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.6868 - mae: 1.4608 - mse: 4.6868 - val_loss: 9.1417 - val_mae: 2.2376 - val_mse: 9.1417\n",
            "Epoch 305/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.7921 - mae: 1.4960 - mse: 4.7921 - val_loss: 9.1331 - val_mae: 2.2649 - val_mse: 9.1331\n",
            "Epoch 306/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.7302 - mae: 1.4883 - mse: 4.7302 - val_loss: 9.0625 - val_mae: 2.2613 - val_mse: 9.0625\n",
            "Epoch 307/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.8197 - mae: 1.5154 - mse: 4.8197 - val_loss: 9.6979 - val_mae: 2.2866 - val_mse: 9.6979\n",
            "Epoch 308/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.8308 - mae: 1.5240 - mse: 4.8308 - val_loss: 9.3806 - val_mae: 2.3637 - val_mse: 9.3806\n",
            "Epoch 309/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.8222 - mae: 1.5075 - mse: 4.8222 - val_loss: 9.2073 - val_mae: 2.2628 - val_mse: 9.2073\n",
            "Epoch 310/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.6734 - mae: 1.4640 - mse: 4.6734 - val_loss: 8.9396 - val_mae: 2.2340 - val_mse: 8.9396\n",
            "Epoch 311/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.8368 - mae: 1.5340 - mse: 4.8368 - val_loss: 9.1208 - val_mae: 2.2911 - val_mse: 9.1208\n",
            "Epoch 312/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.8012 - mae: 1.4849 - mse: 4.8012 - val_loss: 9.2201 - val_mae: 2.2516 - val_mse: 9.2201\n",
            "Epoch 313/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.8897 - mae: 1.5326 - mse: 4.8897 - val_loss: 9.0624 - val_mae: 2.2481 - val_mse: 9.0624\n",
            "Epoch 314/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.8530 - mae: 1.5077 - mse: 4.8530 - val_loss: 9.7359 - val_mae: 2.2851 - val_mse: 9.7359\n",
            "Epoch 315/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.7319 - mae: 1.5105 - mse: 4.7319 - val_loss: 9.5958 - val_mae: 2.3015 - val_mse: 9.5958\n",
            "Epoch 316/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.7852 - mae: 1.5168 - mse: 4.7852 - val_loss: 8.9556 - val_mae: 2.2215 - val_mse: 8.9556\n",
            "Epoch 317/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.7521 - mae: 1.5205 - mse: 4.7521 - val_loss: 9.6327 - val_mae: 2.2733 - val_mse: 9.6327\n",
            "Epoch 318/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.9612 - mae: 1.4916 - mse: 4.9612 - val_loss: 9.5137 - val_mae: 2.2783 - val_mse: 9.5137\n",
            "Epoch 319/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.7396 - mae: 1.5343 - mse: 4.7396 - val_loss: 9.3236 - val_mae: 2.3447 - val_mse: 9.3236\n",
            "Epoch 320/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.8378 - mae: 1.5098 - mse: 4.8378 - val_loss: 9.3249 - val_mae: 2.2498 - val_mse: 9.3249\n",
            "Epoch 321/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.7063 - mae: 1.4777 - mse: 4.7063 - val_loss: 9.4068 - val_mae: 2.3421 - val_mse: 9.4068\n",
            "Epoch 322/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.7796 - mae: 1.5107 - mse: 4.7796 - val_loss: 9.2358 - val_mae: 2.2910 - val_mse: 9.2358\n",
            "Epoch 323/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.8151 - mae: 1.4833 - mse: 4.8151 - val_loss: 9.3325 - val_mae: 2.3228 - val_mse: 9.3325\n",
            "Epoch 324/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.8854 - mae: 1.4963 - mse: 4.8854 - val_loss: 9.2346 - val_mae: 2.3170 - val_mse: 9.2346\n",
            "Epoch 325/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.7024 - mae: 1.4746 - mse: 4.7024 - val_loss: 9.2596 - val_mae: 2.2460 - val_mse: 9.2596\n",
            "Epoch 326/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.7117 - mae: 1.4865 - mse: 4.7117 - val_loss: 9.4291 - val_mae: 2.3590 - val_mse: 9.4291\n",
            "Epoch 327/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.6264 - mae: 1.5033 - mse: 4.6264 - val_loss: 9.2748 - val_mae: 2.3471 - val_mse: 9.2748\n",
            "Epoch 328/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.9148 - mae: 1.5056 - mse: 4.9148 - val_loss: 9.1828 - val_mae: 2.2902 - val_mse: 9.1828\n",
            "Epoch 329/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.6920 - mae: 1.4826 - mse: 4.6920 - val_loss: 9.2809 - val_mae: 2.3425 - val_mse: 9.2809\n",
            "Epoch 330/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.8328 - mae: 1.4930 - mse: 4.8328 - val_loss: 9.0456 - val_mae: 2.2358 - val_mse: 9.0456\n",
            "Epoch 331/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.7641 - mae: 1.4552 - mse: 4.7641 - val_loss: 9.1056 - val_mae: 2.2742 - val_mse: 9.1056\n",
            "Epoch 332/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.8328 - mae: 1.4912 - mse: 4.8328 - val_loss: 9.2721 - val_mae: 2.3126 - val_mse: 9.2721\n",
            "Epoch 333/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.7103 - mae: 1.4449 - mse: 4.7103 - val_loss: 9.3145 - val_mae: 2.2568 - val_mse: 9.3145\n",
            "Epoch 334/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.6360 - mae: 1.5225 - mse: 4.6360 - val_loss: 9.3367 - val_mae: 2.3091 - val_mse: 9.3367\n",
            "Epoch 335/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.6600 - mae: 1.4749 - mse: 4.6600 - val_loss: 9.2600 - val_mae: 2.2526 - val_mse: 9.2600\n",
            "Epoch 336/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.7404 - mae: 1.4704 - mse: 4.7404 - val_loss: 9.9418 - val_mae: 2.3169 - val_mse: 9.9418\n",
            "Epoch 337/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.6729 - mae: 1.4919 - mse: 4.6729 - val_loss: 9.3102 - val_mae: 2.2786 - val_mse: 9.3102\n",
            "Epoch 338/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.7912 - mae: 1.4834 - mse: 4.7912 - val_loss: 9.3426 - val_mae: 2.2883 - val_mse: 9.3426\n",
            "Epoch 339/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.6603 - mae: 1.4720 - mse: 4.6603 - val_loss: 9.2888 - val_mae: 2.3117 - val_mse: 9.2888\n",
            "Epoch 340/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.5981 - mae: 1.4640 - mse: 4.5981 - val_loss: 9.2190 - val_mae: 2.2855 - val_mse: 9.2190\n",
            "Epoch 341/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.8079 - mae: 1.4824 - mse: 4.8079 - val_loss: 9.4786 - val_mae: 2.2797 - val_mse: 9.4786\n",
            "Epoch 342/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.6753 - mae: 1.4782 - mse: 4.6753 - val_loss: 9.3185 - val_mae: 2.2876 - val_mse: 9.3185\n",
            "Epoch 343/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.6809 - mae: 1.4835 - mse: 4.6809 - val_loss: 9.3073 - val_mae: 2.3307 - val_mse: 9.3073\n",
            "Epoch 344/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.7415 - mae: 1.5143 - mse: 4.7415 - val_loss: 9.2979 - val_mae: 2.3200 - val_mse: 9.2979\n",
            "Epoch 345/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.7570 - mae: 1.4891 - mse: 4.7570 - val_loss: 9.5256 - val_mae: 2.3160 - val_mse: 9.5256\n",
            "Epoch 346/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.5732 - mae: 1.4543 - mse: 4.5732 - val_loss: 9.5002 - val_mae: 2.3627 - val_mse: 9.5002\n",
            "Epoch 347/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.6924 - mae: 1.4680 - mse: 4.6924 - val_loss: 9.5052 - val_mae: 2.3446 - val_mse: 9.5052\n",
            "Epoch 348/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.6492 - mae: 1.4732 - mse: 4.6492 - val_loss: 9.3144 - val_mae: 2.3139 - val_mse: 9.3144\n",
            "Epoch 349/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.4878 - mae: 1.4419 - mse: 4.4878 - val_loss: 9.4494 - val_mae: 2.2626 - val_mse: 9.4494\n",
            "Epoch 350/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.6311 - mae: 1.4796 - mse: 4.6311 - val_loss: 9.5171 - val_mae: 2.2690 - val_mse: 9.5171\n",
            "Epoch 351/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.6262 - mae: 1.4623 - mse: 4.6262 - val_loss: 9.2622 - val_mae: 2.2796 - val_mse: 9.2622\n",
            "Epoch 352/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.6732 - mae: 1.4634 - mse: 4.6732 - val_loss: 9.2381 - val_mae: 2.2961 - val_mse: 9.2381\n",
            "Epoch 353/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.5711 - mae: 1.4737 - mse: 4.5711 - val_loss: 9.9968 - val_mae: 2.4301 - val_mse: 9.9968\n",
            "Epoch 354/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.7050 - mae: 1.4794 - mse: 4.7050 - val_loss: 9.6636 - val_mae: 2.2836 - val_mse: 9.6636\n",
            "Epoch 355/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.7690 - mae: 1.4936 - mse: 4.7690 - val_loss: 9.4092 - val_mae: 2.2750 - val_mse: 9.4092\n",
            "Epoch 356/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.5040 - mae: 1.4583 - mse: 4.5040 - val_loss: 9.6899 - val_mae: 2.3955 - val_mse: 9.6899\n",
            "Epoch 357/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.5918 - mae: 1.4928 - mse: 4.5918 - val_loss: 9.4682 - val_mae: 2.3194 - val_mse: 9.4682\n",
            "Epoch 358/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.5814 - mae: 1.4554 - mse: 4.5814 - val_loss: 9.2755 - val_mae: 2.2821 - val_mse: 9.2755\n",
            "Epoch 359/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.5823 - mae: 1.4542 - mse: 4.5823 - val_loss: 9.2680 - val_mae: 2.3104 - val_mse: 9.2680\n",
            "Epoch 360/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.5143 - mae: 1.4302 - mse: 4.5143 - val_loss: 9.7398 - val_mae: 2.3007 - val_mse: 9.7398\n",
            "Epoch 361/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.6826 - mae: 1.4650 - mse: 4.6826 - val_loss: 9.3641 - val_mae: 2.3193 - val_mse: 9.3641\n",
            "Epoch 362/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.5931 - mae: 1.4572 - mse: 4.5931 - val_loss: 9.3883 - val_mae: 2.3200 - val_mse: 9.3883\n",
            "Epoch 363/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.6057 - mae: 1.4591 - mse: 4.6057 - val_loss: 9.5567 - val_mae: 2.3052 - val_mse: 9.5567\n",
            "Epoch 364/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.4871 - mae: 1.4202 - mse: 4.4871 - val_loss: 9.6458 - val_mae: 2.2868 - val_mse: 9.6458\n",
            "Epoch 365/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.6319 - mae: 1.4753 - mse: 4.6319 - val_loss: 9.3256 - val_mae: 2.2951 - val_mse: 9.3256\n",
            "Epoch 366/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.5364 - mae: 1.4446 - mse: 4.5364 - val_loss: 9.4417 - val_mae: 2.2619 - val_mse: 9.4417\n",
            "Epoch 367/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.5170 - mae: 1.4875 - mse: 4.5170 - val_loss: 9.0393 - val_mae: 2.2447 - val_mse: 9.0393\n",
            "Epoch 368/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.4840 - mae: 1.4347 - mse: 4.4840 - val_loss: 9.3793 - val_mae: 2.3136 - val_mse: 9.3793\n",
            "Epoch 369/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.6200 - mae: 1.4677 - mse: 4.6200 - val_loss: 9.7009 - val_mae: 2.3943 - val_mse: 9.7009\n",
            "Epoch 370/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.5260 - mae: 1.4526 - mse: 4.5260 - val_loss: 9.5774 - val_mae: 2.3486 - val_mse: 9.5774\n",
            "Epoch 371/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.4803 - mae: 1.4445 - mse: 4.4803 - val_loss: 9.4099 - val_mae: 2.3252 - val_mse: 9.4099\n",
            "Epoch 372/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.5049 - mae: 1.4354 - mse: 4.5049 - val_loss: 9.1987 - val_mae: 2.2508 - val_mse: 9.1987\n",
            "Epoch 373/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.3270 - mae: 1.4302 - mse: 4.3270 - val_loss: 9.5654 - val_mae: 2.3378 - val_mse: 9.5654\n",
            "Epoch 374/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.5112 - mae: 1.4401 - mse: 4.5112 - val_loss: 9.7468 - val_mae: 2.3747 - val_mse: 9.7468\n",
            "Epoch 375/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.6922 - mae: 1.4486 - mse: 4.6922 - val_loss: 9.5550 - val_mae: 2.2742 - val_mse: 9.5550\n",
            "Epoch 376/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.4388 - mae: 1.4600 - mse: 4.4388 - val_loss: 9.5448 - val_mae: 2.3089 - val_mse: 9.5448\n",
            "Epoch 377/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.4691 - mae: 1.4314 - mse: 4.4691 - val_loss: 9.1603 - val_mae: 2.2565 - val_mse: 9.1603\n",
            "Epoch 378/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.4082 - mae: 1.4318 - mse: 4.4082 - val_loss: 9.3231 - val_mae: 2.2791 - val_mse: 9.3231\n",
            "Epoch 379/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 4.7418 - mae: 1.4847 - mse: 4.7418 - val_loss: 9.2326 - val_mae: 2.2671 - val_mse: 9.2326\n",
            "Epoch 380/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.4225 - mae: 1.4387 - mse: 4.4225 - val_loss: 9.1489 - val_mae: 2.2911 - val_mse: 9.1489\n",
            "Epoch 381/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.5971 - mae: 1.4658 - mse: 4.5971 - val_loss: 9.5144 - val_mae: 2.3419 - val_mse: 9.5144\n",
            "Epoch 382/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.5560 - mae: 1.4485 - mse: 4.5560 - val_loss: 9.3073 - val_mae: 2.2918 - val_mse: 9.3073\n",
            "Epoch 383/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.5884 - mae: 1.4612 - mse: 4.5884 - val_loss: 9.5014 - val_mae: 2.3292 - val_mse: 9.5014\n",
            "Epoch 384/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.6762 - mae: 1.4913 - mse: 4.6762 - val_loss: 9.4640 - val_mae: 2.3278 - val_mse: 9.4640\n",
            "Epoch 385/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.3747 - mae: 1.4048 - mse: 4.3747 - val_loss: 9.5952 - val_mae: 2.2802 - val_mse: 9.5952\n",
            "Epoch 386/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.4026 - mae: 1.4615 - mse: 4.4026 - val_loss: 9.6602 - val_mae: 2.4039 - val_mse: 9.6602\n",
            "Epoch 387/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.5216 - mae: 1.4170 - mse: 4.5216 - val_loss: 9.2106 - val_mae: 2.2683 - val_mse: 9.2106\n",
            "Epoch 388/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.4536 - mae: 1.4361 - mse: 4.4536 - val_loss: 9.4136 - val_mae: 2.3024 - val_mse: 9.4136\n",
            "Epoch 389/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.3993 - mae: 1.4108 - mse: 4.3993 - val_loss: 9.4501 - val_mae: 2.2692 - val_mse: 9.4501\n",
            "Epoch 390/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.3401 - mae: 1.4052 - mse: 4.3401 - val_loss: 9.5913 - val_mae: 2.2899 - val_mse: 9.5913\n",
            "Epoch 391/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.4030 - mae: 1.4216 - mse: 4.4030 - val_loss: 9.4180 - val_mae: 2.2964 - val_mse: 9.4180\n",
            "Epoch 392/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.4035 - mae: 1.4106 - mse: 4.4035 - val_loss: 9.9061 - val_mae: 2.3356 - val_mse: 9.9061\n",
            "Epoch 393/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.3257 - mae: 1.4291 - mse: 4.3257 - val_loss: 10.2914 - val_mae: 2.3482 - val_mse: 10.2914\n",
            "Epoch 394/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.5784 - mae: 1.5110 - mse: 4.5784 - val_loss: 9.1921 - val_mae: 2.2681 - val_mse: 9.1921\n",
            "Epoch 395/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.3136 - mae: 1.4004 - mse: 4.3136 - val_loss: 10.6740 - val_mae: 2.3838 - val_mse: 10.6740\n",
            "Epoch 396/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.5837 - mae: 1.4581 - mse: 4.5837 - val_loss: 9.3642 - val_mae: 2.3104 - val_mse: 9.3642\n",
            "Epoch 397/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.3457 - mae: 1.4258 - mse: 4.3457 - val_loss: 9.3445 - val_mae: 2.3062 - val_mse: 9.3445\n",
            "Epoch 398/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.4519 - mae: 1.4167 - mse: 4.4519 - val_loss: 9.9099 - val_mae: 2.4033 - val_mse: 9.9099\n",
            "Epoch 399/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.3203 - mae: 1.4249 - mse: 4.3203 - val_loss: 9.2546 - val_mae: 2.2633 - val_mse: 9.2546\n",
            "Epoch 400/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.4706 - mae: 1.4422 - mse: 4.4706 - val_loss: 9.3925 - val_mae: 2.3303 - val_mse: 9.3925\n",
            "Epoch 401/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 4.4063 - mae: 1.4419 - mse: 4.4063 - val_loss: 9.4318 - val_mae: 2.3288 - val_mse: 9.4318\n",
            "Epoch 402/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.4531 - mae: 1.4124 - mse: 4.4531 - val_loss: 9.5133 - val_mae: 2.3163 - val_mse: 9.5133\n",
            "Epoch 403/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.3479 - mae: 1.4084 - mse: 4.3479 - val_loss: 9.7234 - val_mae: 2.2930 - val_mse: 9.7234\n",
            "Epoch 404/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.3897 - mae: 1.4116 - mse: 4.3897 - val_loss: 9.3415 - val_mae: 2.2966 - val_mse: 9.3415\n",
            "Epoch 405/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.2405 - mae: 1.3777 - mse: 4.2405 - val_loss: 9.6320 - val_mae: 2.3727 - val_mse: 9.6320\n",
            "Epoch 406/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.1670 - mae: 1.3953 - mse: 4.1670 - val_loss: 9.2603 - val_mae: 2.2526 - val_mse: 9.2603\n",
            "Epoch 407/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.5191 - mae: 1.4305 - mse: 4.5191 - val_loss: 9.4324 - val_mae: 2.2743 - val_mse: 9.4324\n",
            "Epoch 408/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.4143 - mae: 1.4160 - mse: 4.4143 - val_loss: 9.3908 - val_mae: 2.2988 - val_mse: 9.3908\n",
            "Epoch 409/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.3741 - mae: 1.4088 - mse: 4.3741 - val_loss: 9.6271 - val_mae: 2.3766 - val_mse: 9.6271\n",
            "Epoch 410/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.3805 - mae: 1.3990 - mse: 4.3805 - val_loss: 9.6127 - val_mae: 2.3701 - val_mse: 9.6127\n",
            "Epoch 411/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.6256 - mae: 1.4423 - mse: 4.6256 - val_loss: 9.4122 - val_mae: 2.3142 - val_mse: 9.4122\n",
            "Epoch 412/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.3002 - mae: 1.3951 - mse: 4.3002 - val_loss: 9.4199 - val_mae: 2.2902 - val_mse: 9.4199\n",
            "Epoch 413/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 4.5620 - mae: 1.4571 - mse: 4.5620 - val_loss: 9.6581 - val_mae: 2.2991 - val_mse: 9.6581\n",
            "Epoch 414/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.1611 - mae: 1.4141 - mse: 4.1611 - val_loss: 9.4911 - val_mae: 2.3458 - val_mse: 9.4911\n",
            "Epoch 415/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.1289 - mae: 1.3827 - mse: 4.1289 - val_loss: 9.8737 - val_mae: 2.4278 - val_mse: 9.8737\n",
            "Epoch 416/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.3474 - mae: 1.4396 - mse: 4.3474 - val_loss: 9.5269 - val_mae: 2.3262 - val_mse: 9.5269\n",
            "Epoch 417/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.2757 - mae: 1.3963 - mse: 4.2757 - val_loss: 9.8317 - val_mae: 2.3252 - val_mse: 9.8317\n",
            "Epoch 418/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.5034 - mae: 1.4430 - mse: 4.5034 - val_loss: 9.4169 - val_mae: 2.3239 - val_mse: 9.4169\n",
            "Epoch 419/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.6537 - mae: 1.4711 - mse: 4.6537 - val_loss: 9.7054 - val_mae: 2.3878 - val_mse: 9.7054\n",
            "Epoch 420/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.4034 - mae: 1.4046 - mse: 4.4034 - val_loss: 10.0286 - val_mae: 2.3417 - val_mse: 10.0286\n",
            "Epoch 421/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.1835 - mae: 1.3854 - mse: 4.1835 - val_loss: 9.4597 - val_mae: 2.2789 - val_mse: 9.4597\n",
            "Epoch 422/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.5924 - mae: 1.4326 - mse: 4.5924 - val_loss: 9.3403 - val_mae: 2.2891 - val_mse: 9.3403\n",
            "Epoch 423/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.2102 - mae: 1.3855 - mse: 4.2102 - val_loss: 9.4384 - val_mae: 2.2887 - val_mse: 9.4384\n",
            "Epoch 424/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.4267 - mae: 1.4420 - mse: 4.4267 - val_loss: 9.8085 - val_mae: 2.3887 - val_mse: 9.8085\n",
            "Epoch 425/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.4060 - mae: 1.4311 - mse: 4.4060 - val_loss: 9.7343 - val_mae: 2.3445 - val_mse: 9.7343\n",
            "Epoch 426/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.1967 - mae: 1.3617 - mse: 4.1967 - val_loss: 9.4961 - val_mae: 2.3025 - val_mse: 9.4961\n",
            "Epoch 427/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.2776 - mae: 1.4034 - mse: 4.2776 - val_loss: 10.2133 - val_mae: 2.3527 - val_mse: 10.2133\n",
            "Epoch 428/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.2339 - mae: 1.3801 - mse: 4.2339 - val_loss: 9.5144 - val_mae: 2.2817 - val_mse: 9.5144\n",
            "Epoch 429/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.2985 - mae: 1.3970 - mse: 4.2985 - val_loss: 9.7193 - val_mae: 2.3658 - val_mse: 9.7193\n",
            "Epoch 430/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.1490 - mae: 1.3828 - mse: 4.1490 - val_loss: 10.5610 - val_mae: 2.3831 - val_mse: 10.5610\n",
            "Epoch 431/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.4265 - mae: 1.4262 - mse: 4.4265 - val_loss: 9.4072 - val_mae: 2.2967 - val_mse: 9.4072\n",
            "Epoch 432/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.3273 - mae: 1.4203 - mse: 4.3273 - val_loss: 9.5973 - val_mae: 2.3095 - val_mse: 9.5973\n",
            "Epoch 433/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.3158 - mae: 1.4122 - mse: 4.3158 - val_loss: 9.4162 - val_mae: 2.3070 - val_mse: 9.4162\n",
            "Epoch 434/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.2849 - mae: 1.3896 - mse: 4.2849 - val_loss: 9.4665 - val_mae: 2.3052 - val_mse: 9.4665\n",
            "Epoch 435/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.2250 - mae: 1.3830 - mse: 4.2250 - val_loss: 9.5590 - val_mae: 2.3367 - val_mse: 9.5590\n",
            "Epoch 436/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.2646 - mae: 1.4038 - mse: 4.2646 - val_loss: 10.2629 - val_mae: 2.3446 - val_mse: 10.2629\n",
            "Epoch 437/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.2593 - mae: 1.4258 - mse: 4.2593 - val_loss: 9.7092 - val_mae: 2.3483 - val_mse: 9.7092\n",
            "Epoch 438/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.2329 - mae: 1.3875 - mse: 4.2329 - val_loss: 9.3674 - val_mae: 2.2857 - val_mse: 9.3674\n",
            "Epoch 439/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.1580 - mae: 1.3763 - mse: 4.1580 - val_loss: 9.8544 - val_mae: 2.3166 - val_mse: 9.8544\n",
            "Epoch 440/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.3830 - mae: 1.4302 - mse: 4.3830 - val_loss: 9.6885 - val_mae: 2.3584 - val_mse: 9.6885\n",
            "Epoch 441/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.4497 - mae: 1.4481 - mse: 4.4497 - val_loss: 9.5097 - val_mae: 2.2862 - val_mse: 9.5097\n",
            "Epoch 442/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.2144 - mae: 1.4029 - mse: 4.2144 - val_loss: 9.4420 - val_mae: 2.3070 - val_mse: 9.4420\n",
            "Epoch 443/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.2217 - mae: 1.3886 - mse: 4.2217 - val_loss: 9.5984 - val_mae: 2.3369 - val_mse: 9.5984\n",
            "Epoch 444/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.1652 - mae: 1.3813 - mse: 4.1652 - val_loss: 9.9523 - val_mae: 2.4006 - val_mse: 9.9523\n",
            "Epoch 445/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.3244 - mae: 1.4233 - mse: 4.3244 - val_loss: 9.7657 - val_mae: 2.3217 - val_mse: 9.7657\n",
            "Epoch 446/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.3153 - mae: 1.3807 - mse: 4.3153 - val_loss: 9.7354 - val_mae: 2.3432 - val_mse: 9.7354\n",
            "Epoch 447/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.1976 - mae: 1.3803 - mse: 4.1976 - val_loss: 9.7009 - val_mae: 2.3223 - val_mse: 9.7009\n",
            "Epoch 448/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.2087 - mae: 1.4111 - mse: 4.2087 - val_loss: 10.3970 - val_mae: 2.3738 - val_mse: 10.3970\n",
            "Epoch 449/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.4883 - mae: 1.4422 - mse: 4.4883 - val_loss: 9.5564 - val_mae: 2.3111 - val_mse: 9.5564\n",
            "Epoch 450/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.0889 - mae: 1.3654 - mse: 4.0889 - val_loss: 9.8000 - val_mae: 2.3234 - val_mse: 9.8000\n",
            "Epoch 451/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.1831 - mae: 1.3963 - mse: 4.1831 - val_loss: 10.2656 - val_mae: 2.3644 - val_mse: 10.2656\n",
            "Epoch 452/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.3169 - mae: 1.4064 - mse: 4.3169 - val_loss: 10.0484 - val_mae: 2.3724 - val_mse: 10.0484\n",
            "Epoch 453/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.1341 - mae: 1.3778 - mse: 4.1341 - val_loss: 9.4936 - val_mae: 2.3159 - val_mse: 9.4936\n",
            "Epoch 454/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.4847 - mae: 1.4027 - mse: 4.4847 - val_loss: 9.5073 - val_mae: 2.2978 - val_mse: 9.5073\n",
            "Epoch 455/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.1534 - mae: 1.3686 - mse: 4.1534 - val_loss: 9.9476 - val_mae: 2.3607 - val_mse: 9.9476\n",
            "Epoch 456/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.4194 - mae: 1.4428 - mse: 4.4194 - val_loss: 9.7699 - val_mae: 2.3319 - val_mse: 9.7699\n",
            "Epoch 457/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.1043 - mae: 1.3762 - mse: 4.1043 - val_loss: 9.5937 - val_mae: 2.3147 - val_mse: 9.5937\n",
            "Epoch 458/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.2406 - mae: 1.3793 - mse: 4.2406 - val_loss: 9.6398 - val_mae: 2.3275 - val_mse: 9.6398\n",
            "Epoch 459/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.2227 - mae: 1.3778 - mse: 4.2227 - val_loss: 9.6902 - val_mae: 2.3088 - val_mse: 9.6902\n",
            "Epoch 460/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.2565 - mae: 1.4423 - mse: 4.2565 - val_loss: 9.7345 - val_mae: 2.3736 - val_mse: 9.7345\n",
            "Epoch 461/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.1048 - mae: 1.3733 - mse: 4.1048 - val_loss: 9.6799 - val_mae: 2.3029 - val_mse: 9.6799\n",
            "Epoch 462/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.2605 - mae: 1.3697 - mse: 4.2605 - val_loss: 9.4993 - val_mae: 2.3209 - val_mse: 9.4993\n",
            "Epoch 463/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 4.0620 - mae: 1.3592 - mse: 4.0620 - val_loss: 9.5511 - val_mae: 2.3259 - val_mse: 9.5511\n",
            "Epoch 464/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.0555 - mae: 1.3581 - mse: 4.0555 - val_loss: 9.5647 - val_mae: 2.3285 - val_mse: 9.5647\n",
            "Epoch 465/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.0327 - mae: 1.3497 - mse: 4.0327 - val_loss: 9.7753 - val_mae: 2.3109 - val_mse: 9.7753\n",
            "Epoch 466/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.0975 - mae: 1.3935 - mse: 4.0975 - val_loss: 9.8232 - val_mae: 2.3612 - val_mse: 9.8232\n",
            "Epoch 467/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.2764 - mae: 1.4043 - mse: 4.2764 - val_loss: 9.4752 - val_mae: 2.3324 - val_mse: 9.4752\n",
            "Epoch 468/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.0776 - mae: 1.3644 - mse: 4.0776 - val_loss: 9.0659 - val_mae: 2.2641 - val_mse: 9.0659\n",
            "Epoch 469/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.0232 - mae: 1.3413 - mse: 4.0232 - val_loss: 9.5930 - val_mae: 2.3162 - val_mse: 9.5930\n",
            "Epoch 470/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.3197 - mae: 1.4011 - mse: 4.3197 - val_loss: 9.4442 - val_mae: 2.2971 - val_mse: 9.4442\n",
            "Epoch 471/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.0033 - mae: 1.3699 - mse: 4.0033 - val_loss: 9.8541 - val_mae: 2.3431 - val_mse: 9.8541\n",
            "Epoch 472/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.0416 - mae: 1.3483 - mse: 4.0416 - val_loss: 10.1873 - val_mae: 2.3972 - val_mse: 10.1873\n",
            "Epoch 473/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.2645 - mae: 1.4457 - mse: 4.2645 - val_loss: 9.4468 - val_mae: 2.2973 - val_mse: 9.4468\n",
            "Epoch 474/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.0931 - mae: 1.3610 - mse: 4.0931 - val_loss: 9.7681 - val_mae: 2.3214 - val_mse: 9.7681\n",
            "Epoch 475/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.3607 - mae: 1.3928 - mse: 4.3607 - val_loss: 9.7815 - val_mae: 2.3371 - val_mse: 9.7815\n",
            "Epoch 476/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.2648 - mae: 1.4071 - mse: 4.2648 - val_loss: 9.6976 - val_mae: 2.3507 - val_mse: 9.6976\n",
            "Epoch 477/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.9327 - mae: 1.3301 - mse: 3.9327 - val_loss: 9.9164 - val_mae: 2.3590 - val_mse: 9.9164\n",
            "Epoch 478/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 4.1036 - mae: 1.3835 - mse: 4.1036 - val_loss: 10.4308 - val_mae: 2.3687 - val_mse: 10.4308\n",
            "Epoch 479/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.1766 - mae: 1.4195 - mse: 4.1766 - val_loss: 10.0216 - val_mae: 2.3624 - val_mse: 10.0216\n",
            "Epoch 480/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.0164 - mae: 1.3590 - mse: 4.0164 - val_loss: 9.5067 - val_mae: 2.3337 - val_mse: 9.5067\n",
            "Epoch 481/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.9984 - mae: 1.3538 - mse: 3.9984 - val_loss: 9.7702 - val_mae: 2.3273 - val_mse: 9.7702\n",
            "Epoch 482/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.9496 - mae: 1.3774 - mse: 3.9496 - val_loss: 10.1230 - val_mae: 2.3575 - val_mse: 10.1230\n",
            "Epoch 483/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.0417 - mae: 1.4018 - mse: 4.0417 - val_loss: 9.8701 - val_mae: 2.3941 - val_mse: 9.8701\n",
            "Epoch 484/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.0900 - mae: 1.3605 - mse: 4.0900 - val_loss: 9.4075 - val_mae: 2.3089 - val_mse: 9.4075\n",
            "Epoch 485/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.0684 - mae: 1.3620 - mse: 4.0684 - val_loss: 9.9569 - val_mae: 2.3571 - val_mse: 9.9569\n",
            "Epoch 486/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.0480 - mae: 1.3670 - mse: 4.0480 - val_loss: 9.8520 - val_mae: 2.3432 - val_mse: 9.8520\n",
            "Epoch 487/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.9988 - mae: 1.3685 - mse: 3.9988 - val_loss: 10.0154 - val_mae: 2.3950 - val_mse: 10.0154\n",
            "Epoch 488/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.2641 - mae: 1.3699 - mse: 4.2641 - val_loss: 9.8920 - val_mae: 2.3599 - val_mse: 9.8920\n",
            "Epoch 489/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.0804 - mae: 1.3507 - mse: 4.0804 - val_loss: 9.4024 - val_mae: 2.3044 - val_mse: 9.4024\n",
            "Epoch 490/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.0482 - mae: 1.3712 - mse: 4.0482 - val_loss: 9.8008 - val_mae: 2.3718 - val_mse: 9.8008\n",
            "Epoch 491/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.1845 - mae: 1.3749 - mse: 4.1845 - val_loss: 9.8635 - val_mae: 2.3408 - val_mse: 9.8635\n",
            "Epoch 492/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.9447 - mae: 1.3543 - mse: 3.9447 - val_loss: 9.7683 - val_mae: 2.3204 - val_mse: 9.7683\n",
            "Epoch 493/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.9642 - mae: 1.3644 - mse: 3.9642 - val_loss: 9.8675 - val_mae: 2.3697 - val_mse: 9.8675\n",
            "Epoch 494/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 4.1985 - mae: 1.3995 - mse: 4.1985 - val_loss: 9.8137 - val_mae: 2.3367 - val_mse: 9.8137\n",
            "Epoch 495/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.0221 - mae: 1.3739 - mse: 4.0221 - val_loss: 9.7559 - val_mae: 2.3419 - val_mse: 9.7559\n",
            "Epoch 496/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.1007 - mae: 1.3563 - mse: 4.1007 - val_loss: 9.9511 - val_mae: 2.3578 - val_mse: 9.9511\n",
            "Epoch 497/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.1821 - mae: 1.3743 - mse: 4.1821 - val_loss: 9.6731 - val_mae: 2.3342 - val_mse: 9.6731\n",
            "Epoch 498/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.9516 - mae: 1.3424 - mse: 3.9516 - val_loss: 10.1721 - val_mae: 2.4280 - val_mse: 10.1721\n",
            "Epoch 499/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.1412 - mae: 1.3705 - mse: 4.1412 - val_loss: 9.7084 - val_mae: 2.3317 - val_mse: 9.7084\n",
            "Epoch 500/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.0827 - mae: 1.3500 - mse: 4.0827 - val_loss: 9.6986 - val_mae: 2.3399 - val_mse: 9.6986\n",
            "Epoch 501/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.9279 - mae: 1.3339 - mse: 3.9279 - val_loss: 10.3211 - val_mae: 2.3642 - val_mse: 10.3211\n",
            "Epoch 502/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.0962 - mae: 1.4015 - mse: 4.0962 - val_loss: 9.5132 - val_mae: 2.3088 - val_mse: 9.5132\n",
            "Epoch 503/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.1543 - mae: 1.3720 - mse: 4.1543 - val_loss: 9.7352 - val_mae: 2.3375 - val_mse: 9.7352\n",
            "Epoch 504/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 4.0218 - mae: 1.3610 - mse: 4.0218 - val_loss: 9.6411 - val_mae: 2.3327 - val_mse: 9.6411\n",
            "Epoch 505/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.0959 - mae: 1.3803 - mse: 4.0959 - val_loss: 9.8416 - val_mae: 2.3262 - val_mse: 9.8416\n",
            "Epoch 506/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.8576 - mae: 1.3387 - mse: 3.8576 - val_loss: 10.1257 - val_mae: 2.3620 - val_mse: 10.1257\n",
            "Epoch 507/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.9183 - mae: 1.3393 - mse: 3.9183 - val_loss: 9.9286 - val_mae: 2.3844 - val_mse: 9.9286\n",
            "Epoch 508/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.9983 - mae: 1.3552 - mse: 3.9983 - val_loss: 9.9167 - val_mae: 2.3579 - val_mse: 9.9167\n",
            "Epoch 509/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.0841 - mae: 1.3778 - mse: 4.0841 - val_loss: 9.8782 - val_mae: 2.3495 - val_mse: 9.8782\n",
            "Epoch 510/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.8715 - mae: 1.3218 - mse: 3.8715 - val_loss: 10.0815 - val_mae: 2.3497 - val_mse: 10.0815\n",
            "Epoch 511/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.1539 - mae: 1.3313 - mse: 4.1539 - val_loss: 10.2634 - val_mae: 2.3640 - val_mse: 10.2634\n",
            "Epoch 512/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.9775 - mae: 1.3429 - mse: 3.9775 - val_loss: 9.7861 - val_mae: 2.3315 - val_mse: 9.7861\n",
            "Epoch 513/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.1415 - mae: 1.3892 - mse: 4.1415 - val_loss: 9.8832 - val_mae: 2.3845 - val_mse: 9.8832\n",
            "Epoch 514/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.9306 - mae: 1.3240 - mse: 3.9306 - val_loss: 10.0543 - val_mae: 2.3875 - val_mse: 10.0543\n",
            "Epoch 515/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.0156 - mae: 1.3951 - mse: 4.0156 - val_loss: 9.6518 - val_mae: 2.3313 - val_mse: 9.6518\n",
            "Epoch 516/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.8522 - mae: 1.3413 - mse: 3.8522 - val_loss: 9.9805 - val_mae: 2.3621 - val_mse: 9.9805\n",
            "Epoch 517/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.8706 - mae: 1.3090 - mse: 3.8706 - val_loss: 9.9278 - val_mae: 2.3508 - val_mse: 9.9278\n",
            "Epoch 518/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.0626 - mae: 1.3727 - mse: 4.0626 - val_loss: 9.5197 - val_mae: 2.3018 - val_mse: 9.5197\n",
            "Epoch 519/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.9599 - mae: 1.3589 - mse: 3.9599 - val_loss: 9.8743 - val_mae: 2.3518 - val_mse: 9.8743\n",
            "Epoch 520/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.9414 - mae: 1.3392 - mse: 3.9414 - val_loss: 10.0257 - val_mae: 2.3542 - val_mse: 10.0257\n",
            "Epoch 521/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.8731 - mae: 1.3003 - mse: 3.8731 - val_loss: 10.0663 - val_mae: 2.3827 - val_mse: 10.0663\n",
            "Epoch 522/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.0876 - mae: 1.3722 - mse: 4.0876 - val_loss: 10.0825 - val_mae: 2.3993 - val_mse: 10.0825\n",
            "Epoch 523/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.0132 - mae: 1.3989 - mse: 4.0132 - val_loss: 9.7953 - val_mae: 2.3584 - val_mse: 9.7953\n",
            "Epoch 524/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.9630 - mae: 1.3544 - mse: 3.9630 - val_loss: 9.9333 - val_mae: 2.3543 - val_mse: 9.9333\n",
            "Epoch 525/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.8721 - mae: 1.3280 - mse: 3.8721 - val_loss: 9.7072 - val_mae: 2.3315 - val_mse: 9.7072\n",
            "Epoch 526/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.1200 - mae: 1.3632 - mse: 4.1200 - val_loss: 10.0909 - val_mae: 2.3935 - val_mse: 10.0909\n",
            "Epoch 527/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.0001 - mae: 1.3406 - mse: 4.0001 - val_loss: 9.9781 - val_mae: 2.3565 - val_mse: 9.9781\n",
            "Epoch 528/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.1169 - mae: 1.3921 - mse: 4.1169 - val_loss: 9.7458 - val_mae: 2.3401 - val_mse: 9.7458\n",
            "Epoch 529/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.8061 - mae: 1.3273 - mse: 3.8061 - val_loss: 9.9584 - val_mae: 2.3480 - val_mse: 9.9584\n",
            "Epoch 530/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.0185 - mae: 1.3607 - mse: 4.0185 - val_loss: 9.7241 - val_mae: 2.3404 - val_mse: 9.7241\n",
            "Epoch 531/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.8740 - mae: 1.3111 - mse: 3.8740 - val_loss: 9.7134 - val_mae: 2.3209 - val_mse: 9.7134\n",
            "Epoch 532/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.8532 - mae: 1.3362 - mse: 3.8532 - val_loss: 9.7451 - val_mae: 2.3415 - val_mse: 9.7451\n",
            "Epoch 533/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.8164 - mae: 1.3003 - mse: 3.8164 - val_loss: 9.9108 - val_mae: 2.3523 - val_mse: 9.9108\n",
            "Epoch 534/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.1176 - mae: 1.3553 - mse: 4.1176 - val_loss: 9.9948 - val_mae: 2.3692 - val_mse: 9.9948\n",
            "Epoch 535/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.9942 - mae: 1.3437 - mse: 3.9942 - val_loss: 10.0928 - val_mae: 2.3784 - val_mse: 10.0928\n",
            "Epoch 536/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.9778 - mae: 1.3536 - mse: 3.9778 - val_loss: 10.1955 - val_mae: 2.4455 - val_mse: 10.1955\n",
            "Epoch 537/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.9382 - mae: 1.3406 - mse: 3.9382 - val_loss: 9.8593 - val_mae: 2.3533 - val_mse: 9.8593\n",
            "Epoch 538/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.8895 - mae: 1.3296 - mse: 3.8895 - val_loss: 9.8946 - val_mae: 2.3476 - val_mse: 9.8946\n",
            "Epoch 539/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.9268 - mae: 1.3347 - mse: 3.9268 - val_loss: 10.0960 - val_mae: 2.3574 - val_mse: 10.0960\n",
            "Epoch 540/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.0099 - mae: 1.3822 - mse: 4.0099 - val_loss: 10.0413 - val_mae: 2.3788 - val_mse: 10.0413\n",
            "Epoch 541/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 3.7889 - mae: 1.2988 - mse: 3.7889 - val_loss: 9.6937 - val_mae: 2.3325 - val_mse: 9.6937\n",
            "Epoch 542/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.7887 - mae: 1.2881 - mse: 3.7887 - val_loss: 9.9749 - val_mae: 2.3788 - val_mse: 9.9749\n",
            "Epoch 543/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.9627 - mae: 1.3464 - mse: 3.9627 - val_loss: 9.8079 - val_mae: 2.3397 - val_mse: 9.8079\n",
            "Epoch 544/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.7654 - mae: 1.3148 - mse: 3.7654 - val_loss: 10.2273 - val_mae: 2.3827 - val_mse: 10.2273\n",
            "Epoch 545/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.0412 - mae: 1.4308 - mse: 4.0412 - val_loss: 9.9722 - val_mae: 2.3889 - val_mse: 9.9722\n",
            "Epoch 546/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.7647 - mae: 1.2925 - mse: 3.7647 - val_loss: 10.1347 - val_mae: 2.3800 - val_mse: 10.1347\n",
            "Epoch 547/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.7513 - mae: 1.3379 - mse: 3.7513 - val_loss: 10.2189 - val_mae: 2.3864 - val_mse: 10.2189\n",
            "Epoch 548/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.0798 - mae: 1.3701 - mse: 4.0798 - val_loss: 10.3157 - val_mae: 2.3888 - val_mse: 10.3157\n",
            "Epoch 549/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.7441 - mae: 1.2882 - mse: 3.7441 - val_loss: 10.1604 - val_mae: 2.3841 - val_mse: 10.1604\n",
            "Epoch 550/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.8352 - mae: 1.3405 - mse: 3.8352 - val_loss: 10.0235 - val_mae: 2.3623 - val_mse: 10.0235\n",
            "Epoch 551/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.8248 - mae: 1.3060 - mse: 3.8248 - val_loss: 10.2571 - val_mae: 2.3766 - val_mse: 10.2571\n",
            "Epoch 552/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.6147 - mae: 1.3074 - mse: 3.6147 - val_loss: 11.0605 - val_mae: 2.5467 - val_mse: 11.0605\n",
            "Epoch 553/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.9585 - mae: 1.3364 - mse: 3.9585 - val_loss: 10.2222 - val_mae: 2.3867 - val_mse: 10.2222\n",
            "Epoch 554/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.9601 - mae: 1.3009 - mse: 3.9601 - val_loss: 10.4322 - val_mae: 2.3943 - val_mse: 10.4322\n",
            "Epoch 555/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.8130 - mae: 1.3563 - mse: 3.8130 - val_loss: 10.1664 - val_mae: 2.3778 - val_mse: 10.1664\n",
            "Epoch 556/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.8088 - mae: 1.3315 - mse: 3.8088 - val_loss: 9.8815 - val_mae: 2.3696 - val_mse: 9.8815\n",
            "Epoch 557/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.8317 - mae: 1.3030 - mse: 3.8317 - val_loss: 10.1699 - val_mae: 2.4176 - val_mse: 10.1699\n",
            "Epoch 558/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 3.7695 - mae: 1.3113 - mse: 3.7695 - val_loss: 9.7654 - val_mae: 2.3410 - val_mse: 9.7654\n",
            "Epoch 559/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.8672 - mae: 1.3277 - mse: 3.8672 - val_loss: 10.2369 - val_mae: 2.3899 - val_mse: 10.2369\n",
            "Epoch 560/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.8311 - mae: 1.3018 - mse: 3.8311 - val_loss: 10.1315 - val_mae: 2.3779 - val_mse: 10.1315\n",
            "Epoch 561/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.0384 - mae: 1.3671 - mse: 4.0384 - val_loss: 10.0072 - val_mae: 2.3798 - val_mse: 10.0072\n",
            "Epoch 562/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.7907 - mae: 1.3137 - mse: 3.7907 - val_loss: 10.5137 - val_mae: 2.3975 - val_mse: 10.5137\n",
            "Epoch 563/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.9554 - mae: 1.3358 - mse: 3.9554 - val_loss: 9.8587 - val_mae: 2.3474 - val_mse: 9.8587\n",
            "Epoch 564/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.7027 - mae: 1.2815 - mse: 3.7027 - val_loss: 10.7522 - val_mae: 2.4236 - val_mse: 10.7522\n",
            "Epoch 565/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.1200 - mae: 1.3742 - mse: 4.1200 - val_loss: 10.3021 - val_mae: 2.4175 - val_mse: 10.3021\n",
            "Epoch 566/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.7615 - mae: 1.3182 - mse: 3.7615 - val_loss: 10.3466 - val_mae: 2.4535 - val_mse: 10.3466\n",
            "Epoch 567/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.7204 - mae: 1.3053 - mse: 3.7204 - val_loss: 10.1181 - val_mae: 2.4037 - val_mse: 10.1181\n",
            "Epoch 568/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.6428 - mae: 1.2930 - mse: 3.6428 - val_loss: 9.9128 - val_mae: 2.3605 - val_mse: 9.9128\n",
            "Epoch 569/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.8189 - mae: 1.3297 - mse: 3.8189 - val_loss: 9.9765 - val_mae: 2.3631 - val_mse: 9.9765\n",
            "Epoch 570/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.7915 - mae: 1.3431 - mse: 3.7915 - val_loss: 10.2715 - val_mae: 2.4114 - val_mse: 10.2715\n",
            "Epoch 571/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.6979 - mae: 1.3146 - mse: 3.6979 - val_loss: 10.1267 - val_mae: 2.4266 - val_mse: 10.1267\n",
            "Epoch 572/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.7665 - mae: 1.3121 - mse: 3.7665 - val_loss: 10.1052 - val_mae: 2.3829 - val_mse: 10.1052\n",
            "Epoch 573/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.7113 - mae: 1.3060 - mse: 3.7113 - val_loss: 10.0958 - val_mae: 2.3810 - val_mse: 10.0958\n",
            "Epoch 574/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.7803 - mae: 1.3423 - mse: 3.7803 - val_loss: 10.0887 - val_mae: 2.3681 - val_mse: 10.0887\n",
            "Epoch 575/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 4.0306 - mae: 1.3738 - mse: 4.0306 - val_loss: 9.9336 - val_mae: 2.3499 - val_mse: 9.9336\n",
            "Epoch 576/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.6666 - mae: 1.3014 - mse: 3.6666 - val_loss: 10.0254 - val_mae: 2.3628 - val_mse: 10.0254\n",
            "Epoch 577/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.7239 - mae: 1.2983 - mse: 3.7239 - val_loss: 10.1020 - val_mae: 2.3798 - val_mse: 10.1020\n",
            "Epoch 578/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.9213 - mae: 1.3371 - mse: 3.9213 - val_loss: 9.8345 - val_mae: 2.3543 - val_mse: 9.8345\n",
            "Epoch 579/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.8024 - mae: 1.2734 - mse: 3.8024 - val_loss: 10.1723 - val_mae: 2.3658 - val_mse: 10.1723\n",
            "Epoch 580/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.7717 - mae: 1.3098 - mse: 3.7717 - val_loss: 9.9956 - val_mae: 2.3632 - val_mse: 9.9956\n",
            "Epoch 581/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.7487 - mae: 1.3195 - mse: 3.7487 - val_loss: 10.1775 - val_mae: 2.3886 - val_mse: 10.1775\n",
            "Epoch 582/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.7662 - mae: 1.2737 - mse: 3.7662 - val_loss: 10.3027 - val_mae: 2.3937 - val_mse: 10.3027\n",
            "Epoch 583/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.8889 - mae: 1.3535 - mse: 3.8889 - val_loss: 10.1080 - val_mae: 2.3765 - val_mse: 10.1080\n",
            "Epoch 584/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.6950 - mae: 1.2778 - mse: 3.6950 - val_loss: 10.1559 - val_mae: 2.3930 - val_mse: 10.1559\n",
            "Epoch 585/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.5600 - mae: 1.2505 - mse: 3.5600 - val_loss: 10.2008 - val_mae: 2.3854 - val_mse: 10.2008\n",
            "Epoch 586/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.5803 - mae: 1.2857 - mse: 3.5803 - val_loss: 10.8055 - val_mae: 2.5163 - val_mse: 10.8055\n",
            "Epoch 587/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.7378 - mae: 1.2963 - mse: 3.7378 - val_loss: 10.2756 - val_mae: 2.4209 - val_mse: 10.2756\n",
            "Epoch 588/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.7054 - mae: 1.2972 - mse: 3.7054 - val_loss: 10.5341 - val_mae: 2.4722 - val_mse: 10.5341\n",
            "Epoch 589/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.9460 - mae: 1.3355 - mse: 3.9460 - val_loss: 9.9711 - val_mae: 2.3758 - val_mse: 9.9711\n",
            "Epoch 590/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.0220 - mae: 1.3227 - mse: 4.0220 - val_loss: 10.0685 - val_mae: 2.3832 - val_mse: 10.0685\n",
            "Epoch 591/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 3.5150 - mae: 1.2510 - mse: 3.5150 - val_loss: 10.0446 - val_mae: 2.3769 - val_mse: 10.0446\n",
            "Epoch 592/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.7939 - mae: 1.3257 - mse: 3.7939 - val_loss: 9.8170 - val_mae: 2.3638 - val_mse: 9.8170\n",
            "Epoch 593/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.6703 - mae: 1.2884 - mse: 3.6703 - val_loss: 10.1654 - val_mae: 2.4207 - val_mse: 10.1654\n",
            "Epoch 594/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.8005 - mae: 1.3250 - mse: 3.8005 - val_loss: 10.3622 - val_mae: 2.4100 - val_mse: 10.3622\n",
            "Epoch 595/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.5483 - mae: 1.2646 - mse: 3.5483 - val_loss: 9.8670 - val_mae: 2.3535 - val_mse: 9.8670\n",
            "Epoch 596/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.5737 - mae: 1.3179 - mse: 3.5737 - val_loss: 10.3323 - val_mae: 2.4226 - val_mse: 10.3323\n",
            "Epoch 597/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.7112 - mae: 1.2980 - mse: 3.7112 - val_loss: 10.3507 - val_mae: 2.4120 - val_mse: 10.3507\n",
            "Epoch 598/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.8474 - mae: 1.3381 - mse: 3.8474 - val_loss: 10.1039 - val_mae: 2.3916 - val_mse: 10.1039\n",
            "Epoch 599/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.6363 - mae: 1.3006 - mse: 3.6363 - val_loss: 10.1620 - val_mae: 2.3921 - val_mse: 10.1620\n",
            "Epoch 600/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.6825 - mae: 1.2756 - mse: 3.6825 - val_loss: 10.2995 - val_mae: 2.4048 - val_mse: 10.2995\n",
            "Epoch 601/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.6839 - mae: 1.2999 - mse: 3.6839 - val_loss: 10.3362 - val_mae: 2.4172 - val_mse: 10.3362\n",
            "Epoch 602/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.7590 - mae: 1.2939 - mse: 3.7590 - val_loss: 10.1629 - val_mae: 2.3894 - val_mse: 10.1629\n",
            "Epoch 603/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 3.7141 - mae: 1.2914 - mse: 3.7141 - val_loss: 10.0385 - val_mae: 2.3771 - val_mse: 10.0385\n",
            "Epoch 604/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.6936 - mae: 1.2898 - mse: 3.6936 - val_loss: 10.0172 - val_mae: 2.3776 - val_mse: 10.0172\n",
            "Epoch 605/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.7062 - mae: 1.3214 - mse: 3.7062 - val_loss: 10.1587 - val_mae: 2.4131 - val_mse: 10.1587\n",
            "Epoch 606/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.5225 - mae: 1.2565 - mse: 3.5225 - val_loss: 10.5479 - val_mae: 2.4243 - val_mse: 10.5479\n",
            "Epoch 607/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.5997 - mae: 1.2768 - mse: 3.5997 - val_loss: 11.6523 - val_mae: 2.5202 - val_mse: 11.6523\n",
            "Epoch 608/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.8920 - mae: 1.3445 - mse: 3.8920 - val_loss: 10.4410 - val_mae: 2.4285 - val_mse: 10.4410\n",
            "Epoch 609/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.7035 - mae: 1.2638 - mse: 3.7035 - val_loss: 10.4328 - val_mae: 2.4531 - val_mse: 10.4328\n",
            "Epoch 610/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.7458 - mae: 1.3196 - mse: 3.7458 - val_loss: 10.1072 - val_mae: 2.3934 - val_mse: 10.1072\n",
            "Epoch 611/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.7377 - mae: 1.3386 - mse: 3.7377 - val_loss: 10.2515 - val_mae: 2.4093 - val_mse: 10.2515\n",
            "Epoch 612/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.5661 - mae: 1.2514 - mse: 3.5661 - val_loss: 10.1072 - val_mae: 2.3863 - val_mse: 10.1072\n",
            "Epoch 613/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 4.0179 - mae: 1.3576 - mse: 4.0179 - val_loss: 10.8055 - val_mae: 2.4415 - val_mse: 10.8055\n",
            "Epoch 614/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.6174 - mae: 1.3482 - mse: 3.6174 - val_loss: 10.2702 - val_mae: 2.4367 - val_mse: 10.2702\n",
            "Epoch 615/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.7206 - mae: 1.2735 - mse: 3.7206 - val_loss: 9.9773 - val_mae: 2.3565 - val_mse: 9.9773\n",
            "Epoch 616/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.5961 - mae: 1.2936 - mse: 3.5961 - val_loss: 10.3968 - val_mae: 2.3978 - val_mse: 10.3968\n",
            "Epoch 617/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.7900 - mae: 1.2999 - mse: 3.7900 - val_loss: 10.3160 - val_mae: 2.4094 - val_mse: 10.3160\n",
            "Epoch 618/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.5024 - mae: 1.2599 - mse: 3.5024 - val_loss: 10.1088 - val_mae: 2.3869 - val_mse: 10.1088\n",
            "Epoch 619/1000\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 3.7746 - mae: 1.2956 - mse: 3.7746 - val_loss: 10.2997 - val_mae: 2.4158 - val_mse: 10.2997\n",
            "Epoch 620/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.4921 - mae: 1.2295 - mse: 3.4921 - val_loss: 11.1642 - val_mae: 2.4950 - val_mse: 11.1642\n",
            "Epoch 621/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.5608 - mae: 1.3286 - mse: 3.5608 - val_loss: 10.1886 - val_mae: 2.3992 - val_mse: 10.1886\n",
            "Epoch 622/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.5031 - mae: 1.2384 - mse: 3.5031 - val_loss: 10.3160 - val_mae: 2.4108 - val_mse: 10.3160\n",
            "Epoch 623/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.6327 - mae: 1.2474 - mse: 3.6327 - val_loss: 9.7294 - val_mae: 2.3273 - val_mse: 9.7294\n",
            "Epoch 624/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.5205 - mae: 1.2689 - mse: 3.5205 - val_loss: 9.8660 - val_mae: 2.3497 - val_mse: 9.8660\n",
            "Epoch 625/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.6603 - mae: 1.2705 - mse: 3.6603 - val_loss: 10.1345 - val_mae: 2.4088 - val_mse: 10.1345\n",
            "Epoch 626/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.5122 - mae: 1.2716 - mse: 3.5122 - val_loss: 10.6107 - val_mae: 2.4668 - val_mse: 10.6107\n",
            "Epoch 627/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.5741 - mae: 1.2515 - mse: 3.5741 - val_loss: 10.3510 - val_mae: 2.4097 - val_mse: 10.3510\n",
            "Epoch 628/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.5771 - mae: 1.3001 - mse: 3.5771 - val_loss: 10.1983 - val_mae: 2.3996 - val_mse: 10.1983\n",
            "Epoch 629/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.6136 - mae: 1.2576 - mse: 3.6136 - val_loss: 10.5458 - val_mae: 2.4621 - val_mse: 10.5458\n",
            "Epoch 630/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.5324 - mae: 1.2759 - mse: 3.5324 - val_loss: 10.3815 - val_mae: 2.4380 - val_mse: 10.3815\n",
            "Epoch 631/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.8409 - mae: 1.3082 - mse: 3.8409 - val_loss: 10.0955 - val_mae: 2.4044 - val_mse: 10.0955\n",
            "Epoch 632/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.5484 - mae: 1.2627 - mse: 3.5484 - val_loss: 10.1505 - val_mae: 2.4139 - val_mse: 10.1505\n",
            "Epoch 633/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.5325 - mae: 1.2395 - mse: 3.5325 - val_loss: 10.2451 - val_mae: 2.4171 - val_mse: 10.2451\n",
            "Epoch 634/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.5586 - mae: 1.2447 - mse: 3.5586 - val_loss: 11.0250 - val_mae: 2.4804 - val_mse: 11.0250\n",
            "Epoch 635/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.6180 - mae: 1.3109 - mse: 3.6180 - val_loss: 10.3884 - val_mae: 2.4385 - val_mse: 10.3884\n",
            "Epoch 636/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.3652 - mae: 1.2438 - mse: 3.3652 - val_loss: 10.4253 - val_mae: 2.4114 - val_mse: 10.4253\n",
            "Epoch 637/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.5851 - mae: 1.3069 - mse: 3.5851 - val_loss: 10.4357 - val_mae: 2.4303 - val_mse: 10.4357\n",
            "Epoch 638/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.5755 - mae: 1.2797 - mse: 3.5755 - val_loss: 10.2237 - val_mae: 2.3942 - val_mse: 10.2237\n",
            "Epoch 639/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.9027 - mae: 1.3204 - mse: 3.9027 - val_loss: 10.2290 - val_mae: 2.4144 - val_mse: 10.2290\n",
            "Epoch 640/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.6265 - mae: 1.2972 - mse: 3.6265 - val_loss: 10.3520 - val_mae: 2.4180 - val_mse: 10.3520\n",
            "Epoch 641/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.5296 - mae: 1.2669 - mse: 3.5296 - val_loss: 10.6031 - val_mae: 2.4368 - val_mse: 10.6031\n",
            "Epoch 642/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.5379 - mae: 1.2891 - mse: 3.5379 - val_loss: 10.2727 - val_mae: 2.4044 - val_mse: 10.2727\n",
            "Epoch 643/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.4917 - mae: 1.2829 - mse: 3.4917 - val_loss: 10.2618 - val_mae: 2.4090 - val_mse: 10.2618\n",
            "Epoch 644/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.4597 - mae: 1.2687 - mse: 3.4597 - val_loss: 10.3157 - val_mae: 2.4274 - val_mse: 10.3157\n",
            "Epoch 645/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.6122 - mae: 1.2853 - mse: 3.6122 - val_loss: 10.3903 - val_mae: 2.4356 - val_mse: 10.3903\n",
            "Epoch 646/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 3.7668 - mae: 1.2698 - mse: 3.7668 - val_loss: 10.3909 - val_mae: 2.4295 - val_mse: 10.3909\n",
            "Epoch 647/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.4789 - mae: 1.2517 - mse: 3.4789 - val_loss: 10.3910 - val_mae: 2.4248 - val_mse: 10.3910\n",
            "Epoch 648/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.4907 - mae: 1.2445 - mse: 3.4907 - val_loss: 10.3077 - val_mae: 2.4022 - val_mse: 10.3077\n",
            "Epoch 649/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.7459 - mae: 1.3127 - mse: 3.7459 - val_loss: 10.4140 - val_mae: 2.4481 - val_mse: 10.4140\n",
            "Epoch 650/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.5901 - mae: 1.2512 - mse: 3.5901 - val_loss: 10.9537 - val_mae: 2.4881 - val_mse: 10.9537\n",
            "Epoch 651/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 3.4978 - mae: 1.2546 - mse: 3.4978 - val_loss: 9.9780 - val_mae: 2.3834 - val_mse: 9.9780\n",
            "Epoch 652/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.4373 - mae: 1.2213 - mse: 3.4373 - val_loss: 10.5740 - val_mae: 2.4487 - val_mse: 10.5740\n",
            "Epoch 653/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.7256 - mae: 1.2787 - mse: 3.7256 - val_loss: 10.6560 - val_mae: 2.4603 - val_mse: 10.6560\n",
            "Epoch 654/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.4296 - mae: 1.2249 - mse: 3.4296 - val_loss: 10.4809 - val_mae: 2.4510 - val_mse: 10.4809\n",
            "Epoch 655/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.4391 - mae: 1.2661 - mse: 3.4391 - val_loss: 10.7169 - val_mae: 2.4631 - val_mse: 10.7169\n",
            "Epoch 656/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.5720 - mae: 1.2778 - mse: 3.5720 - val_loss: 10.6141 - val_mae: 2.4530 - val_mse: 10.6141\n",
            "Epoch 657/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.4092 - mae: 1.1977 - mse: 3.4092 - val_loss: 10.5129 - val_mae: 2.4259 - val_mse: 10.5129\n",
            "Epoch 658/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.6253 - mae: 1.2938 - mse: 3.6253 - val_loss: 10.4766 - val_mae: 2.4397 - val_mse: 10.4766\n",
            "Epoch 659/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.4637 - mae: 1.2683 - mse: 3.4637 - val_loss: 10.4533 - val_mae: 2.4474 - val_mse: 10.4533\n",
            "Epoch 660/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.4919 - mae: 1.2517 - mse: 3.4919 - val_loss: 10.3021 - val_mae: 2.4261 - val_mse: 10.3021\n",
            "Epoch 661/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.5526 - mae: 1.3019 - mse: 3.5526 - val_loss: 10.3208 - val_mae: 2.4164 - val_mse: 10.3208\n",
            "Epoch 662/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.6777 - mae: 1.2841 - mse: 3.6777 - val_loss: 10.4710 - val_mae: 2.4327 - val_mse: 10.4710\n",
            "Epoch 663/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.6048 - mae: 1.2923 - mse: 3.6048 - val_loss: 10.6524 - val_mae: 2.4652 - val_mse: 10.6524\n",
            "Epoch 664/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3515 - mae: 1.2200 - mse: 3.3515 - val_loss: 10.3354 - val_mae: 2.4182 - val_mse: 10.3354\n",
            "Epoch 665/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.3203 - mae: 1.2325 - mse: 3.3203 - val_loss: 10.1527 - val_mae: 2.4047 - val_mse: 10.1527\n",
            "Epoch 666/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.4188 - mae: 1.2373 - mse: 3.4188 - val_loss: 10.1427 - val_mae: 2.4107 - val_mse: 10.1427\n",
            "Epoch 667/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.6109 - mae: 1.2403 - mse: 3.6109 - val_loss: 10.4257 - val_mae: 2.4450 - val_mse: 10.4257\n",
            "Epoch 668/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.4935 - mae: 1.2223 - mse: 3.4935 - val_loss: 10.4203 - val_mae: 2.4345 - val_mse: 10.4203\n",
            "Epoch 669/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.4698 - mae: 1.2372 - mse: 3.4698 - val_loss: 10.3390 - val_mae: 2.4260 - val_mse: 10.3390\n",
            "Epoch 670/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.4778 - mae: 1.2625 - mse: 3.4778 - val_loss: 10.2901 - val_mae: 2.4182 - val_mse: 10.2901\n",
            "Epoch 671/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.4998 - mae: 1.2474 - mse: 3.4998 - val_loss: 10.3408 - val_mae: 2.4305 - val_mse: 10.3408\n",
            "Epoch 672/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.5765 - mae: 1.2622 - mse: 3.5765 - val_loss: 10.3747 - val_mae: 2.4233 - val_mse: 10.3747\n",
            "Epoch 673/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.5831 - mae: 1.2676 - mse: 3.5831 - val_loss: 10.6727 - val_mae: 2.5081 - val_mse: 10.6727\n",
            "Epoch 674/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 3.5763 - mae: 1.2810 - mse: 3.5763 - val_loss: 10.2997 - val_mae: 2.4096 - val_mse: 10.2997\n",
            "Epoch 675/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.4738 - mae: 1.2441 - mse: 3.4738 - val_loss: 10.4173 - val_mae: 2.4363 - val_mse: 10.4173\n",
            "Epoch 676/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 3.3440 - mae: 1.2401 - mse: 3.3440 - val_loss: 10.6300 - val_mae: 2.4551 - val_mse: 10.6300\n",
            "Epoch 677/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.3286 - mae: 1.2161 - mse: 3.3286 - val_loss: 10.1352 - val_mae: 2.4014 - val_mse: 10.1352\n",
            "Epoch 678/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.4181 - mae: 1.2066 - mse: 3.4181 - val_loss: 11.1268 - val_mae: 2.4883 - val_mse: 11.1268\n",
            "Epoch 679/1000\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 3.5052 - mae: 1.2472 - mse: 3.5052 - val_loss: 10.4105 - val_mae: 2.4515 - val_mse: 10.4105\n",
            "Epoch 680/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 3.5655 - mae: 1.2660 - mse: 3.5655 - val_loss: 10.3789 - val_mae: 2.4168 - val_mse: 10.3789\n",
            "Epoch 681/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 3.6244 - mae: 1.2975 - mse: 3.6244 - val_loss: 10.6640 - val_mae: 2.4870 - val_mse: 10.6640\n",
            "Epoch 682/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.3043 - mae: 1.2182 - mse: 3.3043 - val_loss: 10.4038 - val_mae: 2.4542 - val_mse: 10.4038\n",
            "Epoch 683/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.4967 - mae: 1.2546 - mse: 3.4967 - val_loss: 10.1453 - val_mae: 2.3980 - val_mse: 10.1453\n",
            "Epoch 684/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.3016 - mae: 1.2031 - mse: 3.3016 - val_loss: 11.4999 - val_mae: 2.5652 - val_mse: 11.4999\n",
            "Epoch 685/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.4088 - mae: 1.2480 - mse: 3.4088 - val_loss: 10.5637 - val_mae: 2.4385 - val_mse: 10.5637\n",
            "Epoch 686/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.5005 - mae: 1.2184 - mse: 3.5005 - val_loss: 10.2378 - val_mae: 2.4248 - val_mse: 10.2378\n",
            "Epoch 687/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3929 - mae: 1.2223 - mse: 3.3929 - val_loss: 10.3844 - val_mae: 2.4433 - val_mse: 10.3844\n",
            "Epoch 688/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.3444 - mae: 1.2300 - mse: 3.3444 - val_loss: 10.8707 - val_mae: 2.4637 - val_mse: 10.8707\n",
            "Epoch 689/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.4695 - mae: 1.2950 - mse: 3.4695 - val_loss: 10.9507 - val_mae: 2.4814 - val_mse: 10.9507\n",
            "Epoch 690/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.5550 - mae: 1.2549 - mse: 3.5550 - val_loss: 10.9512 - val_mae: 2.4814 - val_mse: 10.9512\n",
            "Epoch 691/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.4131 - mae: 1.2513 - mse: 3.4131 - val_loss: 10.5225 - val_mae: 2.4678 - val_mse: 10.5225\n",
            "Epoch 692/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3968 - mae: 1.2280 - mse: 3.3968 - val_loss: 10.3059 - val_mae: 2.4073 - val_mse: 10.3059\n",
            "Epoch 693/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3331 - mae: 1.2360 - mse: 3.3331 - val_loss: 10.6048 - val_mae: 2.4434 - val_mse: 10.6048\n",
            "Epoch 694/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.4616 - mae: 1.2492 - mse: 3.4616 - val_loss: 10.2950 - val_mae: 2.4357 - val_mse: 10.2950\n",
            "Epoch 695/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3629 - mae: 1.2083 - mse: 3.3629 - val_loss: 10.4010 - val_mae: 2.4401 - val_mse: 10.4010\n",
            "Epoch 696/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.6217 - mae: 1.2757 - mse: 3.6217 - val_loss: 11.0334 - val_mae: 2.5284 - val_mse: 11.0334\n",
            "Epoch 697/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.4322 - mae: 1.2384 - mse: 3.4322 - val_loss: 10.3149 - val_mae: 2.4165 - val_mse: 10.3149\n",
            "Epoch 698/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.2296 - mae: 1.2062 - mse: 3.2296 - val_loss: 10.7760 - val_mae: 2.4947 - val_mse: 10.7760\n",
            "Epoch 699/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 3.5173 - mae: 1.2463 - mse: 3.5173 - val_loss: 10.6002 - val_mae: 2.4605 - val_mse: 10.6002\n",
            "Epoch 700/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3128 - mae: 1.2189 - mse: 3.3128 - val_loss: 10.2098 - val_mae: 2.4043 - val_mse: 10.2098\n",
            "Epoch 701/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.3737 - mae: 1.2166 - mse: 3.3737 - val_loss: 10.7839 - val_mae: 2.4862 - val_mse: 10.7839\n",
            "Epoch 702/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.2877 - mae: 1.2286 - mse: 3.2877 - val_loss: 10.3664 - val_mae: 2.4247 - val_mse: 10.3664\n",
            "Epoch 703/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.5642 - mae: 1.2920 - mse: 3.5642 - val_loss: 10.4442 - val_mae: 2.4607 - val_mse: 10.4442\n",
            "Epoch 704/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3467 - mae: 1.2044 - mse: 3.3467 - val_loss: 10.3817 - val_mae: 2.4384 - val_mse: 10.3817\n",
            "Epoch 705/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.4375 - mae: 1.2128 - mse: 3.4375 - val_loss: 10.6393 - val_mae: 2.4600 - val_mse: 10.6393\n",
            "Epoch 706/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.2325 - mae: 1.2331 - mse: 3.2325 - val_loss: 10.3478 - val_mae: 2.4227 - val_mse: 10.3478\n",
            "Epoch 707/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.2669 - mae: 1.1866 - mse: 3.2669 - val_loss: 10.1389 - val_mae: 2.4052 - val_mse: 10.1389\n",
            "Epoch 708/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.5170 - mae: 1.2664 - mse: 3.5170 - val_loss: 10.5950 - val_mae: 2.4465 - val_mse: 10.5950\n",
            "Epoch 709/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.1875 - mae: 1.1843 - mse: 3.1875 - val_loss: 10.9794 - val_mae: 2.5022 - val_mse: 10.9794\n",
            "Epoch 710/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3471 - mae: 1.2497 - mse: 3.3471 - val_loss: 10.4046 - val_mae: 2.4284 - val_mse: 10.4046\n",
            "Epoch 711/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3982 - mae: 1.2404 - mse: 3.3982 - val_loss: 10.7411 - val_mae: 2.5050 - val_mse: 10.7411\n",
            "Epoch 712/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.4309 - mae: 1.2148 - mse: 3.4309 - val_loss: 10.5698 - val_mae: 2.4768 - val_mse: 10.5698\n",
            "Epoch 713/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.5904 - mae: 1.2511 - mse: 3.5904 - val_loss: 10.3049 - val_mae: 2.4157 - val_mse: 10.3049\n",
            "Epoch 714/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.2865 - mae: 1.2038 - mse: 3.2865 - val_loss: 10.7244 - val_mae: 2.4870 - val_mse: 10.7244\n",
            "Epoch 715/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.6955 - mae: 1.2614 - mse: 3.6955 - val_loss: 10.3929 - val_mae: 2.4154 - val_mse: 10.3929\n",
            "Epoch 716/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 3.3908 - mae: 1.2217 - mse: 3.3908 - val_loss: 10.4517 - val_mae: 2.4339 - val_mse: 10.4517\n",
            "Epoch 717/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.2220 - mae: 1.1947 - mse: 3.2220 - val_loss: 10.7098 - val_mae: 2.4927 - val_mse: 10.7098\n",
            "Epoch 718/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.1988 - mae: 1.2297 - mse: 3.1988 - val_loss: 10.8098 - val_mae: 2.5008 - val_mse: 10.8098\n",
            "Epoch 719/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3128 - mae: 1.1981 - mse: 3.3128 - val_loss: 10.2506 - val_mae: 2.4178 - val_mse: 10.2506\n",
            "Epoch 720/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.2888 - mae: 1.2519 - mse: 3.2888 - val_loss: 10.9232 - val_mae: 2.5010 - val_mse: 10.9232\n",
            "Epoch 721/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3150 - mae: 1.1825 - mse: 3.3150 - val_loss: 11.3507 - val_mae: 2.5685 - val_mse: 11.3507\n",
            "Epoch 722/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.4231 - mae: 1.2423 - mse: 3.4231 - val_loss: 10.8863 - val_mae: 2.4815 - val_mse: 10.8863\n",
            "Epoch 723/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.2636 - mae: 1.2245 - mse: 3.2636 - val_loss: 10.8155 - val_mae: 2.4903 - val_mse: 10.8155\n",
            "Epoch 724/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.4652 - mae: 1.2366 - mse: 3.4652 - val_loss: 10.2563 - val_mae: 2.4098 - val_mse: 10.2563\n",
            "Epoch 725/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3147 - mae: 1.2275 - mse: 3.3147 - val_loss: 10.2294 - val_mae: 2.4092 - val_mse: 10.2294\n",
            "Epoch 726/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 3.4950 - mae: 1.2470 - mse: 3.4950 - val_loss: 10.7954 - val_mae: 2.4853 - val_mse: 10.7954\n",
            "Epoch 727/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.2717 - mae: 1.1977 - mse: 3.2717 - val_loss: 10.7111 - val_mae: 2.4828 - val_mse: 10.7111\n",
            "Epoch 728/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.2505 - mae: 1.2051 - mse: 3.2505 - val_loss: 10.7330 - val_mae: 2.4643 - val_mse: 10.7330\n",
            "Epoch 729/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.4152 - mae: 1.2098 - mse: 3.4152 - val_loss: 10.7125 - val_mae: 2.4400 - val_mse: 10.7125\n",
            "Epoch 730/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.2482 - mae: 1.1913 - mse: 3.2482 - val_loss: 10.5095 - val_mae: 2.4462 - val_mse: 10.5095\n",
            "Epoch 731/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.4965 - mae: 1.2290 - mse: 3.4965 - val_loss: 10.7285 - val_mae: 2.4765 - val_mse: 10.7285\n",
            "Epoch 732/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3075 - mae: 1.2134 - mse: 3.3075 - val_loss: 10.4072 - val_mae: 2.4373 - val_mse: 10.4072\n",
            "Epoch 733/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.2680 - mae: 1.1684 - mse: 3.2680 - val_loss: 10.4802 - val_mae: 2.4470 - val_mse: 10.4802\n",
            "Epoch 734/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.2429 - mae: 1.2075 - mse: 3.2429 - val_loss: 10.6194 - val_mae: 2.4767 - val_mse: 10.6194\n",
            "Epoch 735/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3333 - mae: 1.1860 - mse: 3.3333 - val_loss: 11.3457 - val_mae: 2.5072 - val_mse: 11.3457\n",
            "Epoch 736/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.2048 - mae: 1.2170 - mse: 3.2048 - val_loss: 11.0117 - val_mae: 2.5422 - val_mse: 11.0117\n",
            "Epoch 737/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.5870 - mae: 1.2182 - mse: 3.5870 - val_loss: 10.6616 - val_mae: 2.4644 - val_mse: 10.6616\n",
            "Epoch 738/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.1607 - mae: 1.1700 - mse: 3.1607 - val_loss: 11.1330 - val_mae: 2.4886 - val_mse: 11.1330\n",
            "Epoch 739/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.1707 - mae: 1.2141 - mse: 3.1707 - val_loss: 10.9574 - val_mae: 2.4832 - val_mse: 10.9574\n",
            "Epoch 740/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.2246 - mae: 1.1921 - mse: 3.2246 - val_loss: 10.9099 - val_mae: 2.4739 - val_mse: 10.9099\n",
            "Epoch 741/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.3250 - mae: 1.2336 - mse: 3.3250 - val_loss: 10.4097 - val_mae: 2.4346 - val_mse: 10.4097\n",
            "Epoch 742/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.4274 - mae: 1.2115 - mse: 3.4274 - val_loss: 10.6708 - val_mae: 2.4799 - val_mse: 10.6708\n",
            "Epoch 743/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.2985 - mae: 1.2255 - mse: 3.2985 - val_loss: 11.1089 - val_mae: 2.5569 - val_mse: 11.1089\n",
            "Epoch 744/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.2607 - mae: 1.1845 - mse: 3.2607 - val_loss: 10.7115 - val_mae: 2.4356 - val_mse: 10.7115\n",
            "Epoch 745/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3267 - mae: 1.1931 - mse: 3.3267 - val_loss: 10.4038 - val_mae: 2.4468 - val_mse: 10.4038\n",
            "Epoch 746/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.1585 - mae: 1.1642 - mse: 3.1585 - val_loss: 10.8494 - val_mae: 2.4864 - val_mse: 10.8494\n",
            "Epoch 747/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3610 - mae: 1.2157 - mse: 3.3610 - val_loss: 11.0491 - val_mae: 2.5345 - val_mse: 11.0491\n",
            "Epoch 748/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.3290 - mae: 1.2131 - mse: 3.3290 - val_loss: 10.9031 - val_mae: 2.5096 - val_mse: 10.9031\n",
            "Epoch 749/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.2999 - mae: 1.2105 - mse: 3.2999 - val_loss: 10.4837 - val_mae: 2.4498 - val_mse: 10.4837\n",
            "Epoch 750/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.2428 - mae: 1.1979 - mse: 3.2428 - val_loss: 11.0010 - val_mae: 2.4812 - val_mse: 11.0010\n",
            "Epoch 751/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.2030 - mae: 1.2067 - mse: 3.2030 - val_loss: 10.4551 - val_mae: 2.4358 - val_mse: 10.4551\n",
            "Epoch 752/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.0430 - mae: 1.2072 - mse: 3.0430 - val_loss: 10.7076 - val_mae: 2.4876 - val_mse: 10.7076\n",
            "Epoch 753/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3868 - mae: 1.2302 - mse: 3.3868 - val_loss: 10.3379 - val_mae: 2.4370 - val_mse: 10.3379\n",
            "Epoch 754/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.0347 - mae: 1.1682 - mse: 3.0347 - val_loss: 10.7882 - val_mae: 2.5191 - val_mse: 10.7882\n",
            "Epoch 755/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3578 - mae: 1.2072 - mse: 3.3578 - val_loss: 10.4614 - val_mae: 2.4346 - val_mse: 10.4614\n",
            "Epoch 756/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.2371 - mae: 1.1913 - mse: 3.2371 - val_loss: 10.4788 - val_mae: 2.4273 - val_mse: 10.4788\n",
            "Epoch 757/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3171 - mae: 1.2285 - mse: 3.3171 - val_loss: 10.7271 - val_mae: 2.4630 - val_mse: 10.7271\n",
            "Epoch 758/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3457 - mae: 1.1976 - mse: 3.3457 - val_loss: 10.4432 - val_mae: 2.4312 - val_mse: 10.4432\n",
            "Epoch 759/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 3.2927 - mae: 1.1998 - mse: 3.2927 - val_loss: 10.7124 - val_mae: 2.4699 - val_mse: 10.7124\n",
            "Epoch 760/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.1905 - mae: 1.1694 - mse: 3.1905 - val_loss: 10.8335 - val_mae: 2.4800 - val_mse: 10.8335\n",
            "Epoch 761/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.2887 - mae: 1.1824 - mse: 3.2887 - val_loss: 10.9367 - val_mae: 2.4777 - val_mse: 10.9367\n",
            "Epoch 762/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.1684 - mae: 1.1878 - mse: 3.1684 - val_loss: 10.6680 - val_mae: 2.4659 - val_mse: 10.6680\n",
            "Epoch 763/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.2193 - mae: 1.1524 - mse: 3.2193 - val_loss: 11.0277 - val_mae: 2.4945 - val_mse: 11.0277\n",
            "Epoch 764/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.5633 - mae: 1.2774 - mse: 3.5633 - val_loss: 10.7255 - val_mae: 2.4846 - val_mse: 10.7255\n",
            "Epoch 765/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.1461 - mae: 1.1636 - mse: 3.1461 - val_loss: 10.9897 - val_mae: 2.5185 - val_mse: 10.9897\n",
            "Epoch 766/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3035 - mae: 1.2109 - mse: 3.3035 - val_loss: 10.8403 - val_mae: 2.4680 - val_mse: 10.8403\n",
            "Epoch 767/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.2954 - mae: 1.2106 - mse: 3.2954 - val_loss: 10.8814 - val_mae: 2.4739 - val_mse: 10.8814\n",
            "Epoch 768/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.2231 - mae: 1.1805 - mse: 3.2231 - val_loss: 10.6898 - val_mae: 2.4846 - val_mse: 10.6898\n",
            "Epoch 769/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.1089 - mae: 1.1413 - mse: 3.1089 - val_loss: 11.1712 - val_mae: 2.5078 - val_mse: 11.1712\n",
            "Epoch 770/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3882 - mae: 1.2277 - mse: 3.3882 - val_loss: 10.7167 - val_mae: 2.4577 - val_mse: 10.7167\n",
            "Epoch 771/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.1920 - mae: 1.1962 - mse: 3.1920 - val_loss: 10.3988 - val_mae: 2.4271 - val_mse: 10.3988\n",
            "Epoch 772/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.2914 - mae: 1.2027 - mse: 3.2914 - val_loss: 10.9797 - val_mae: 2.5253 - val_mse: 10.9797\n",
            "Epoch 773/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 3.1819 - mae: 1.1987 - mse: 3.1819 - val_loss: 10.5892 - val_mae: 2.4533 - val_mse: 10.5892\n",
            "Epoch 774/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.2158 - mae: 1.1710 - mse: 3.2158 - val_loss: 10.8067 - val_mae: 2.4741 - val_mse: 10.8067\n",
            "Epoch 775/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.1745 - mae: 1.1886 - mse: 3.1745 - val_loss: 11.0043 - val_mae: 2.5277 - val_mse: 11.0043\n",
            "Epoch 776/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.2037 - mae: 1.1849 - mse: 3.2037 - val_loss: 10.6792 - val_mae: 2.4540 - val_mse: 10.6792\n",
            "Epoch 777/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.1138 - mae: 1.1858 - mse: 3.1138 - val_loss: 10.8787 - val_mae: 2.4892 - val_mse: 10.8787\n",
            "Epoch 778/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.5143 - mae: 1.2797 - mse: 3.5143 - val_loss: 10.3154 - val_mae: 2.4220 - val_mse: 10.3154\n",
            "Epoch 779/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.1148 - mae: 1.1520 - mse: 3.1148 - val_loss: 10.7530 - val_mae: 2.4807 - val_mse: 10.7530\n",
            "Epoch 780/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 3.0711 - mae: 1.1673 - mse: 3.0711 - val_loss: 10.6619 - val_mae: 2.4372 - val_mse: 10.6619\n",
            "Epoch 781/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.4803 - mae: 1.2438 - mse: 3.4803 - val_loss: 10.8092 - val_mae: 2.4719 - val_mse: 10.8092\n",
            "Epoch 782/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.2168 - mae: 1.1828 - mse: 3.2168 - val_loss: 10.7106 - val_mae: 2.4883 - val_mse: 10.7106\n",
            "Epoch 783/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.0813 - mae: 1.1733 - mse: 3.0813 - val_loss: 10.7662 - val_mae: 2.4670 - val_mse: 10.7662\n",
            "Epoch 784/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.2036 - mae: 1.1743 - mse: 3.2036 - val_loss: 10.9075 - val_mae: 2.4942 - val_mse: 10.9075\n",
            "Epoch 785/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.1576 - mae: 1.1862 - mse: 3.1576 - val_loss: 10.8871 - val_mae: 2.5010 - val_mse: 10.8871\n",
            "Epoch 786/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.1357 - mae: 1.1561 - mse: 3.1357 - val_loss: 10.8674 - val_mae: 2.4919 - val_mse: 10.8674\n",
            "Epoch 787/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3736 - mae: 1.2081 - mse: 3.3736 - val_loss: 10.7958 - val_mae: 2.4607 - val_mse: 10.7958\n",
            "Epoch 788/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.1343 - mae: 1.1379 - mse: 3.1343 - val_loss: 11.1995 - val_mae: 2.5284 - val_mse: 11.1995\n",
            "Epoch 789/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.1592 - mae: 1.1839 - mse: 3.1592 - val_loss: 11.1640 - val_mae: 2.5112 - val_mse: 11.1640\n",
            "Epoch 790/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3740 - mae: 1.2172 - mse: 3.3740 - val_loss: 10.7253 - val_mae: 2.4856 - val_mse: 10.7253\n",
            "Epoch 791/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3030 - mae: 1.2110 - mse: 3.3030 - val_loss: 10.8231 - val_mae: 2.5034 - val_mse: 10.8231\n",
            "Epoch 792/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.0126 - mae: 1.1491 - mse: 3.0126 - val_loss: 11.0230 - val_mae: 2.4981 - val_mse: 11.0230\n",
            "Epoch 793/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.2684 - mae: 1.2458 - mse: 3.2684 - val_loss: 10.8293 - val_mae: 2.4689 - val_mse: 10.8293\n",
            "Epoch 794/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.0207 - mae: 1.1398 - mse: 3.0207 - val_loss: 10.9394 - val_mae: 2.5194 - val_mse: 10.9394\n",
            "Epoch 795/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.2666 - mae: 1.1621 - mse: 3.2666 - val_loss: 11.7247 - val_mae: 2.5673 - val_mse: 11.7247\n",
            "Epoch 796/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 3.2922 - mae: 1.1955 - mse: 3.2922 - val_loss: 11.1206 - val_mae: 2.5176 - val_mse: 11.1206\n",
            "Epoch 797/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.3419 - mae: 1.1881 - mse: 3.3419 - val_loss: 10.6170 - val_mae: 2.4514 - val_mse: 10.6170\n",
            "Epoch 798/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 3.2242 - mae: 1.2384 - mse: 3.2242 - val_loss: 10.6750 - val_mae: 2.4566 - val_mse: 10.6750\n",
            "Epoch 799/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.2289 - mae: 1.2048 - mse: 3.2289 - val_loss: 10.6661 - val_mae: 2.4835 - val_mse: 10.6661\n",
            "Epoch 800/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.1982 - mae: 1.1761 - mse: 3.1982 - val_loss: 10.7689 - val_mae: 2.4946 - val_mse: 10.7689\n",
            "Epoch 801/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.0821 - mae: 1.1270 - mse: 3.0821 - val_loss: 10.8408 - val_mae: 2.5072 - val_mse: 10.8408\n",
            "Epoch 802/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.1274 - mae: 1.1643 - mse: 3.1274 - val_loss: 10.6532 - val_mae: 2.4686 - val_mse: 10.6532\n",
            "Epoch 803/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.2249 - mae: 1.1783 - mse: 3.2249 - val_loss: 10.4230 - val_mae: 2.4601 - val_mse: 10.4230\n",
            "Epoch 804/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.0318 - mae: 1.1361 - mse: 3.0318 - val_loss: 11.0986 - val_mae: 2.5153 - val_mse: 11.0986\n",
            "Epoch 805/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.1454 - mae: 1.2170 - mse: 3.1454 - val_loss: 10.5655 - val_mae: 2.4695 - val_mse: 10.5655\n",
            "Epoch 806/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.2294 - mae: 1.1650 - mse: 3.2294 - val_loss: 10.7638 - val_mae: 2.4867 - val_mse: 10.7638\n",
            "Epoch 807/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.1606 - mae: 1.1897 - mse: 3.1606 - val_loss: 10.7272 - val_mae: 2.4752 - val_mse: 10.7272\n",
            "Epoch 808/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.3350 - mae: 1.2088 - mse: 3.3350 - val_loss: 10.4772 - val_mae: 2.4617 - val_mse: 10.4772\n",
            "Epoch 809/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.1706 - mae: 1.1710 - mse: 3.1706 - val_loss: 10.5356 - val_mae: 2.4453 - val_mse: 10.5356\n",
            "Epoch 810/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.0573 - mae: 1.1300 - mse: 3.0573 - val_loss: 10.6795 - val_mae: 2.4790 - val_mse: 10.6795\n",
            "Epoch 811/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9842 - mae: 1.1421 - mse: 2.9842 - val_loss: 10.6716 - val_mae: 2.4599 - val_mse: 10.6716\n",
            "Epoch 812/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.9970 - mae: 1.1418 - mse: 2.9970 - val_loss: 10.5422 - val_mae: 2.4683 - val_mse: 10.5422\n",
            "Epoch 813/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.1142 - mae: 1.1766 - mse: 3.1142 - val_loss: 10.8224 - val_mae: 2.4741 - val_mse: 10.8224\n",
            "Epoch 814/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.0637 - mae: 1.1790 - mse: 3.0637 - val_loss: 10.5584 - val_mae: 2.4542 - val_mse: 10.5584\n",
            "Epoch 815/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.1028 - mae: 1.1148 - mse: 3.1028 - val_loss: 11.0052 - val_mae: 2.4939 - val_mse: 11.0052\n",
            "Epoch 816/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.0328 - mae: 1.1697 - mse: 3.0328 - val_loss: 11.2101 - val_mae: 2.5354 - val_mse: 11.2101\n",
            "Epoch 817/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.1017 - mae: 1.1586 - mse: 3.1017 - val_loss: 11.0456 - val_mae: 2.4973 - val_mse: 11.0456\n",
            "Epoch 818/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.3305 - mae: 1.1898 - mse: 3.3305 - val_loss: 11.5002 - val_mae: 2.5451 - val_mse: 11.5002\n",
            "Epoch 819/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.0298 - mae: 1.1562 - mse: 3.0298 - val_loss: 10.8908 - val_mae: 2.5137 - val_mse: 10.8908\n",
            "Epoch 820/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.4715 - mae: 1.2005 - mse: 3.4715 - val_loss: 10.4962 - val_mae: 2.4580 - val_mse: 10.4962\n",
            "Epoch 821/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.0565 - mae: 1.1303 - mse: 3.0565 - val_loss: 10.6886 - val_mae: 2.4769 - val_mse: 10.6886\n",
            "Epoch 822/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.0663 - mae: 1.1711 - mse: 3.0663 - val_loss: 10.6915 - val_mae: 2.4787 - val_mse: 10.6915\n",
            "Epoch 823/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.0132 - mae: 1.1261 - mse: 3.0132 - val_loss: 10.4849 - val_mae: 2.4346 - val_mse: 10.4849\n",
            "Epoch 824/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.0078 - mae: 1.1590 - mse: 3.0078 - val_loss: 11.2160 - val_mae: 2.4980 - val_mse: 11.2160\n",
            "Epoch 825/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.1366 - mae: 1.1710 - mse: 3.1366 - val_loss: 10.5139 - val_mae: 2.4342 - val_mse: 10.5139\n",
            "Epoch 826/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9350 - mae: 1.1248 - mse: 2.9350 - val_loss: 11.1480 - val_mae: 2.5413 - val_mse: 11.1480\n",
            "Epoch 827/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.0083 - mae: 1.1428 - mse: 3.0083 - val_loss: 11.0220 - val_mae: 2.5520 - val_mse: 11.0220\n",
            "Epoch 828/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3288 - mae: 1.1934 - mse: 3.3288 - val_loss: 10.7451 - val_mae: 2.4788 - val_mse: 10.7451\n",
            "Epoch 829/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.4188 - mae: 1.2084 - mse: 3.4188 - val_loss: 11.0322 - val_mae: 2.5119 - val_mse: 11.0322\n",
            "Epoch 830/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.0414 - mae: 1.1426 - mse: 3.0414 - val_loss: 11.4122 - val_mae: 2.5382 - val_mse: 11.4122\n",
            "Epoch 831/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.0691 - mae: 1.1626 - mse: 3.0691 - val_loss: 10.9707 - val_mae: 2.5072 - val_mse: 10.9707\n",
            "Epoch 832/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.0630 - mae: 1.1656 - mse: 3.0630 - val_loss: 10.7618 - val_mae: 2.5004 - val_mse: 10.7618\n",
            "Epoch 833/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.0242 - mae: 1.1387 - mse: 3.0242 - val_loss: 11.1802 - val_mae: 2.5168 - val_mse: 11.1802\n",
            "Epoch 834/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 2.9088 - mae: 1.1281 - mse: 2.9088 - val_loss: 10.8372 - val_mae: 2.5092 - val_mse: 10.8372\n",
            "Epoch 835/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.1419 - mae: 1.1662 - mse: 3.1419 - val_loss: 11.1709 - val_mae: 2.5671 - val_mse: 11.1709\n",
            "Epoch 836/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.0969 - mae: 1.1661 - mse: 3.0969 - val_loss: 10.9208 - val_mae: 2.4801 - val_mse: 10.9208\n",
            "Epoch 837/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.0893 - mae: 1.1394 - mse: 3.0893 - val_loss: 11.3197 - val_mae: 2.5390 - val_mse: 11.3197\n",
            "Epoch 838/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.8932 - mae: 1.1311 - mse: 2.8932 - val_loss: 10.6875 - val_mae: 2.4744 - val_mse: 10.6875\n",
            "Epoch 839/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.1754 - mae: 1.1543 - mse: 3.1754 - val_loss: 10.6132 - val_mae: 2.4491 - val_mse: 10.6132\n",
            "Epoch 840/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.3302 - mae: 1.1754 - mse: 3.3302 - val_loss: 10.8391 - val_mae: 2.4765 - val_mse: 10.8391\n",
            "Epoch 841/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.0021 - mae: 1.1418 - mse: 3.0021 - val_loss: 11.0930 - val_mae: 2.5089 - val_mse: 11.0930\n",
            "Epoch 842/1000\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 3.0832 - mae: 1.1596 - mse: 3.0832 - val_loss: 10.7473 - val_mae: 2.4783 - val_mse: 10.7473\n",
            "Epoch 843/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.8660 - mae: 1.1105 - mse: 2.8660 - val_loss: 10.6461 - val_mae: 2.4678 - val_mse: 10.6461\n",
            "Epoch 844/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.3446 - mae: 1.1771 - mse: 3.3446 - val_loss: 10.6924 - val_mae: 2.4528 - val_mse: 10.6924\n",
            "Epoch 845/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.1474 - mae: 1.1704 - mse: 3.1474 - val_loss: 11.2120 - val_mae: 2.5620 - val_mse: 11.2120\n",
            "Epoch 846/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.1403 - mae: 1.1318 - mse: 3.1403 - val_loss: 10.6835 - val_mae: 2.4862 - val_mse: 10.6835\n",
            "Epoch 847/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.0781 - mae: 1.1440 - mse: 3.0781 - val_loss: 10.9279 - val_mae: 2.4802 - val_mse: 10.9279\n",
            "Epoch 848/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.9825 - mae: 1.1565 - mse: 2.9825 - val_loss: 10.5205 - val_mae: 2.4415 - val_mse: 10.5205\n",
            "Epoch 849/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9669 - mae: 1.1267 - mse: 2.9669 - val_loss: 10.9797 - val_mae: 2.5187 - val_mse: 10.9797\n",
            "Epoch 850/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.9865 - mae: 1.1482 - mse: 2.9865 - val_loss: 11.0906 - val_mae: 2.5270 - val_mse: 11.0906\n",
            "Epoch 851/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.0215 - mae: 1.1410 - mse: 3.0215 - val_loss: 11.0125 - val_mae: 2.4945 - val_mse: 11.0125\n",
            "Epoch 852/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.0442 - mae: 1.1427 - mse: 3.0442 - val_loss: 11.0161 - val_mae: 2.5411 - val_mse: 11.0161\n",
            "Epoch 853/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.9741 - mae: 1.1498 - mse: 2.9741 - val_loss: 10.9365 - val_mae: 2.4982 - val_mse: 10.9365\n",
            "Epoch 854/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.0651 - mae: 1.1704 - mse: 3.0651 - val_loss: 10.9844 - val_mae: 2.5474 - val_mse: 10.9844\n",
            "Epoch 855/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 3.0577 - mae: 1.1207 - mse: 3.0577 - val_loss: 10.3405 - val_mae: 2.4178 - val_mse: 10.3405\n",
            "Epoch 856/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 2.9304 - mae: 1.1033 - mse: 2.9304 - val_loss: 10.7407 - val_mae: 2.4840 - val_mse: 10.7407\n",
            "Epoch 857/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.0534 - mae: 1.1661 - mse: 3.0534 - val_loss: 10.9929 - val_mae: 2.5147 - val_mse: 10.9929\n",
            "Epoch 858/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9141 - mae: 1.1089 - mse: 2.9141 - val_loss: 11.0106 - val_mae: 2.4944 - val_mse: 11.0106\n",
            "Epoch 859/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9264 - mae: 1.1360 - mse: 2.9264 - val_loss: 11.0147 - val_mae: 2.5019 - val_mse: 11.0147\n",
            "Epoch 860/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.0067 - mae: 1.1639 - mse: 3.0067 - val_loss: 10.8155 - val_mae: 2.4911 - val_mse: 10.8155\n",
            "Epoch 861/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.8880 - mae: 1.0897 - mse: 2.8880 - val_loss: 10.8606 - val_mae: 2.4637 - val_mse: 10.8606\n",
            "Epoch 862/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9087 - mae: 1.1608 - mse: 2.9087 - val_loss: 10.8848 - val_mae: 2.4869 - val_mse: 10.8848\n",
            "Epoch 863/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.0131 - mae: 1.1760 - mse: 3.0131 - val_loss: 11.1626 - val_mae: 2.5205 - val_mse: 11.1626\n",
            "Epoch 864/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.8529 - mae: 1.1220 - mse: 2.8529 - val_loss: 11.3618 - val_mae: 2.5637 - val_mse: 11.3618\n",
            "Epoch 865/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.2065 - mae: 1.1635 - mse: 3.2065 - val_loss: 10.9301 - val_mae: 2.5100 - val_mse: 10.9301\n",
            "Epoch 866/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9963 - mae: 1.1151 - mse: 2.9963 - val_loss: 10.7866 - val_mae: 2.4890 - val_mse: 10.7866\n",
            "Epoch 867/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.8457 - mae: 1.1074 - mse: 2.8457 - val_loss: 11.4353 - val_mae: 2.5706 - val_mse: 11.4353\n",
            "Epoch 868/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9893 - mae: 1.1591 - mse: 2.9893 - val_loss: 10.8576 - val_mae: 2.4737 - val_mse: 10.8576\n",
            "Epoch 869/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.0096 - mae: 1.1549 - mse: 3.0096 - val_loss: 10.6812 - val_mae: 2.4768 - val_mse: 10.6812\n",
            "Epoch 870/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9233 - mae: 1.1525 - mse: 2.9233 - val_loss: 11.6555 - val_mae: 2.5965 - val_mse: 11.6555\n",
            "Epoch 871/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9833 - mae: 1.1742 - mse: 2.9833 - val_loss: 11.0425 - val_mae: 2.4881 - val_mse: 11.0425\n",
            "Epoch 872/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.8844 - mae: 1.1259 - mse: 2.8844 - val_loss: 10.7868 - val_mae: 2.4446 - val_mse: 10.7868\n",
            "Epoch 873/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.9633 - mae: 1.1218 - mse: 2.9633 - val_loss: 11.3069 - val_mae: 2.5501 - val_mse: 11.3069\n",
            "Epoch 874/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9550 - mae: 1.1257 - mse: 2.9550 - val_loss: 10.9058 - val_mae: 2.4986 - val_mse: 10.9058\n",
            "Epoch 875/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9878 - mae: 1.1555 - mse: 2.9878 - val_loss: 11.7394 - val_mae: 2.5585 - val_mse: 11.7394\n",
            "Epoch 876/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9713 - mae: 1.1646 - mse: 2.9713 - val_loss: 10.8410 - val_mae: 2.4734 - val_mse: 10.8410\n",
            "Epoch 877/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.1274 - mae: 1.1464 - mse: 3.1274 - val_loss: 10.9407 - val_mae: 2.4775 - val_mse: 10.9407\n",
            "Epoch 878/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9070 - mae: 1.1006 - mse: 2.9070 - val_loss: 11.1082 - val_mae: 2.4945 - val_mse: 11.1082\n",
            "Epoch 879/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.8121 - mae: 1.0997 - mse: 2.8121 - val_loss: 10.6773 - val_mae: 2.4820 - val_mse: 10.6773\n",
            "Epoch 880/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.7883 - mae: 1.0966 - mse: 2.7883 - val_loss: 10.7512 - val_mae: 2.4978 - val_mse: 10.7512\n",
            "Epoch 881/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.0602 - mae: 1.1714 - mse: 3.0602 - val_loss: 10.6031 - val_mae: 2.4377 - val_mse: 10.6031\n",
            "Epoch 882/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.0024 - mae: 1.1563 - mse: 3.0024 - val_loss: 10.6674 - val_mae: 2.4708 - val_mse: 10.6674\n",
            "Epoch 883/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.0302 - mae: 1.1335 - mse: 3.0302 - val_loss: 10.9852 - val_mae: 2.4998 - val_mse: 10.9852\n",
            "Epoch 884/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9243 - mae: 1.1375 - mse: 2.9243 - val_loss: 10.5278 - val_mae: 2.4476 - val_mse: 10.5278\n",
            "Epoch 885/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.7479 - mae: 1.0839 - mse: 2.7479 - val_loss: 11.2704 - val_mae: 2.5508 - val_mse: 11.2704\n",
            "Epoch 886/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9066 - mae: 1.1355 - mse: 2.9066 - val_loss: 10.4250 - val_mae: 2.4138 - val_mse: 10.4250\n",
            "Epoch 887/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.9213 - mae: 1.1472 - mse: 2.9213 - val_loss: 10.8747 - val_mae: 2.4719 - val_mse: 10.8747\n",
            "Epoch 888/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.7965 - mae: 1.0883 - mse: 2.7965 - val_loss: 10.7865 - val_mae: 2.4529 - val_mse: 10.7865\n",
            "Epoch 889/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.0208 - mae: 1.1512 - mse: 3.0208 - val_loss: 10.8286 - val_mae: 2.4676 - val_mse: 10.8286\n",
            "Epoch 890/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9468 - mae: 1.1239 - mse: 2.9468 - val_loss: 11.2537 - val_mae: 2.5122 - val_mse: 11.2537\n",
            "Epoch 891/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.0509 - mae: 1.1472 - mse: 3.0509 - val_loss: 11.0838 - val_mae: 2.5178 - val_mse: 11.0838\n",
            "Epoch 892/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.8619 - mae: 1.0929 - mse: 2.8619 - val_loss: 10.8979 - val_mae: 2.4842 - val_mse: 10.8979\n",
            "Epoch 893/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.8584 - mae: 1.1343 - mse: 2.8584 - val_loss: 10.7617 - val_mae: 2.4456 - val_mse: 10.7617\n",
            "Epoch 894/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9203 - mae: 1.1091 - mse: 2.9203 - val_loss: 10.5074 - val_mae: 2.4307 - val_mse: 10.5074\n",
            "Epoch 895/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9229 - mae: 1.1397 - mse: 2.9229 - val_loss: 10.8558 - val_mae: 2.4743 - val_mse: 10.8558\n",
            "Epoch 896/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.8727 - mae: 1.1099 - mse: 2.8727 - val_loss: 10.9666 - val_mae: 2.4982 - val_mse: 10.9666\n",
            "Epoch 897/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.0863 - mae: 1.1367 - mse: 3.0863 - val_loss: 10.8023 - val_mae: 2.4754 - val_mse: 10.8023\n",
            "Epoch 898/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.8819 - mae: 1.1271 - mse: 2.8819 - val_loss: 10.9107 - val_mae: 2.4789 - val_mse: 10.9107\n",
            "Epoch 899/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.0014 - mae: 1.1295 - mse: 3.0014 - val_loss: 11.3717 - val_mae: 2.5679 - val_mse: 11.3717\n",
            "Epoch 900/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.8756 - mae: 1.1220 - mse: 2.8756 - val_loss: 10.8918 - val_mae: 2.4936 - val_mse: 10.8918\n",
            "Epoch 901/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9203 - mae: 1.1059 - mse: 2.9203 - val_loss: 10.7999 - val_mae: 2.4730 - val_mse: 10.7999\n",
            "Epoch 902/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.8541 - mae: 1.1071 - mse: 2.8541 - val_loss: 10.7268 - val_mae: 2.4530 - val_mse: 10.7268\n",
            "Epoch 903/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.8561 - mae: 1.1092 - mse: 2.8561 - val_loss: 11.0328 - val_mae: 2.5184 - val_mse: 11.0328\n",
            "Epoch 904/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9345 - mae: 1.1780 - mse: 2.9345 - val_loss: 10.9565 - val_mae: 2.4893 - val_mse: 10.9565\n",
            "Epoch 905/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.8228 - mae: 1.1075 - mse: 2.8228 - val_loss: 11.0370 - val_mae: 2.4854 - val_mse: 11.0370\n",
            "Epoch 906/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9519 - mae: 1.1285 - mse: 2.9519 - val_loss: 10.9918 - val_mae: 2.4686 - val_mse: 10.9918\n",
            "Epoch 907/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.8057 - mae: 1.0791 - mse: 2.8057 - val_loss: 11.3111 - val_mae: 2.5211 - val_mse: 11.3111\n",
            "Epoch 908/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.8476 - mae: 1.1172 - mse: 2.8476 - val_loss: 10.9546 - val_mae: 2.5025 - val_mse: 10.9546\n",
            "Epoch 909/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.7924 - mae: 1.1007 - mse: 2.7924 - val_loss: 10.7706 - val_mae: 2.4709 - val_mse: 10.7706\n",
            "Epoch 910/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.0375 - mae: 1.1433 - mse: 3.0375 - val_loss: 10.7696 - val_mae: 2.4454 - val_mse: 10.7696\n",
            "Epoch 911/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.8472 - mae: 1.1052 - mse: 2.8472 - val_loss: 10.8646 - val_mae: 2.4601 - val_mse: 10.8646\n",
            "Epoch 912/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.9948 - mae: 1.1522 - mse: 2.9948 - val_loss: 11.0897 - val_mae: 2.5222 - val_mse: 11.0897\n",
            "Epoch 913/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9101 - mae: 1.1320 - mse: 2.9101 - val_loss: 11.4008 - val_mae: 2.5526 - val_mse: 11.4008\n",
            "Epoch 914/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.7294 - mae: 1.0800 - mse: 2.7294 - val_loss: 10.8980 - val_mae: 2.4521 - val_mse: 10.8980\n",
            "Epoch 915/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.8837 - mae: 1.1171 - mse: 2.8837 - val_loss: 11.0225 - val_mae: 2.4934 - val_mse: 11.0225\n",
            "Epoch 916/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.7592 - mae: 1.0781 - mse: 2.7592 - val_loss: 10.9931 - val_mae: 2.4867 - val_mse: 10.9931\n",
            "Epoch 917/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9013 - mae: 1.1165 - mse: 2.9013 - val_loss: 10.5725 - val_mae: 2.4487 - val_mse: 10.5725\n",
            "Epoch 918/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.7955 - mae: 1.0663 - mse: 2.7955 - val_loss: 10.6749 - val_mae: 2.4502 - val_mse: 10.6749\n",
            "Epoch 919/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.1050 - mae: 1.2008 - mse: 3.1050 - val_loss: 10.9819 - val_mae: 2.5103 - val_mse: 10.9819\n",
            "Epoch 920/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9305 - mae: 1.1507 - mse: 2.9305 - val_loss: 10.8025 - val_mae: 2.4805 - val_mse: 10.8025\n",
            "Epoch 921/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.7441 - mae: 1.0818 - mse: 2.7441 - val_loss: 11.4781 - val_mae: 2.5160 - val_mse: 11.4781\n",
            "Epoch 922/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.7634 - mae: 1.0553 - mse: 2.7634 - val_loss: 11.2111 - val_mae: 2.4824 - val_mse: 11.2111\n",
            "Epoch 923/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.8091 - mae: 1.0908 - mse: 2.8091 - val_loss: 10.8455 - val_mae: 2.4486 - val_mse: 10.8455\n",
            "Epoch 924/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.7795 - mae: 1.1337 - mse: 2.7795 - val_loss: 10.7735 - val_mae: 2.4765 - val_mse: 10.7735\n",
            "Epoch 925/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.7779 - mae: 1.0890 - mse: 2.7779 - val_loss: 11.1746 - val_mae: 2.4990 - val_mse: 11.1746\n",
            "Epoch 926/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.8842 - mae: 1.1483 - mse: 2.8842 - val_loss: 10.7727 - val_mae: 2.4589 - val_mse: 10.7727\n",
            "Epoch 927/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 3.0147 - mae: 1.1007 - mse: 3.0147 - val_loss: 10.8399 - val_mae: 2.4873 - val_mse: 10.8399\n",
            "Epoch 928/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.7658 - mae: 1.0916 - mse: 2.7658 - val_loss: 11.3096 - val_mae: 2.5108 - val_mse: 11.3096\n",
            "Epoch 929/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.8706 - mae: 1.1479 - mse: 2.8706 - val_loss: 11.0622 - val_mae: 2.4932 - val_mse: 11.0622\n",
            "Epoch 930/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.7679 - mae: 1.1346 - mse: 2.7679 - val_loss: 10.7130 - val_mae: 2.4975 - val_mse: 10.7130\n",
            "Epoch 931/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 3.0058 - mae: 1.1069 - mse: 3.0058 - val_loss: 10.7759 - val_mae: 2.4896 - val_mse: 10.7759\n",
            "Epoch 932/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.8534 - mae: 1.1106 - mse: 2.8534 - val_loss: 10.7567 - val_mae: 2.4748 - val_mse: 10.7567\n",
            "Epoch 933/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.8920 - mae: 1.1135 - mse: 2.8920 - val_loss: 10.6692 - val_mae: 2.4590 - val_mse: 10.6692\n",
            "Epoch 934/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.6525 - mae: 1.0722 - mse: 2.6525 - val_loss: 11.1857 - val_mae: 2.5082 - val_mse: 11.1857\n",
            "Epoch 935/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 2.9250 - mae: 1.1270 - mse: 2.9250 - val_loss: 11.1786 - val_mae: 2.4871 - val_mse: 11.1786\n",
            "Epoch 936/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.8066 - mae: 1.0996 - mse: 2.8066 - val_loss: 10.9262 - val_mae: 2.4963 - val_mse: 10.9262\n",
            "Epoch 937/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.7719 - mae: 1.1256 - mse: 2.7719 - val_loss: 10.9638 - val_mae: 2.5068 - val_mse: 10.9638\n",
            "Epoch 938/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9988 - mae: 1.1100 - mse: 2.9988 - val_loss: 11.4330 - val_mae: 2.5763 - val_mse: 11.4330\n",
            "Epoch 939/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.7219 - mae: 1.0952 - mse: 2.7219 - val_loss: 11.2686 - val_mae: 2.5480 - val_mse: 11.2686\n",
            "Epoch 940/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.8618 - mae: 1.1452 - mse: 2.8618 - val_loss: 10.8456 - val_mae: 2.4520 - val_mse: 10.8456\n",
            "Epoch 941/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.7498 - mae: 1.0861 - mse: 2.7498 - val_loss: 11.0530 - val_mae: 2.4774 - val_mse: 11.0530\n",
            "Epoch 942/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.7297 - mae: 1.0635 - mse: 2.7297 - val_loss: 11.0705 - val_mae: 2.5115 - val_mse: 11.0705\n",
            "Epoch 943/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.8695 - mae: 1.1062 - mse: 2.8695 - val_loss: 11.0465 - val_mae: 2.4898 - val_mse: 11.0465\n",
            "Epoch 944/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.6556 - mae: 1.0656 - mse: 2.6556 - val_loss: 11.3733 - val_mae: 2.5031 - val_mse: 11.3733\n",
            "Epoch 945/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.7181 - mae: 1.0974 - mse: 2.7181 - val_loss: 11.1907 - val_mae: 2.5115 - val_mse: 11.1907\n",
            "Epoch 946/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.7481 - mae: 1.0953 - mse: 2.7481 - val_loss: 10.8466 - val_mae: 2.4951 - val_mse: 10.8466\n",
            "Epoch 947/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.9654 - mae: 1.0956 - mse: 2.9654 - val_loss: 10.7031 - val_mae: 2.4340 - val_mse: 10.7031\n",
            "Epoch 948/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.9357 - mae: 1.1584 - mse: 2.9357 - val_loss: 11.0777 - val_mae: 2.4833 - val_mse: 11.0777\n",
            "Epoch 949/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.6758 - mae: 1.0510 - mse: 2.6758 - val_loss: 11.1672 - val_mae: 2.5549 - val_mse: 11.1672\n",
            "Epoch 950/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.8264 - mae: 1.1162 - mse: 2.8264 - val_loss: 11.1596 - val_mae: 2.5239 - val_mse: 11.1596\n",
            "Epoch 951/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.7502 - mae: 1.0849 - mse: 2.7502 - val_loss: 11.5792 - val_mae: 2.5278 - val_mse: 11.5792\n",
            "Epoch 952/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.7936 - mae: 1.1026 - mse: 2.7936 - val_loss: 11.1559 - val_mae: 2.5216 - val_mse: 11.1559\n",
            "Epoch 953/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 2.7725 - mae: 1.0793 - mse: 2.7725 - val_loss: 10.8253 - val_mae: 2.4474 - val_mse: 10.8253\n",
            "Epoch 954/1000\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 2.6637 - mae: 1.0619 - mse: 2.6637 - val_loss: 11.1648 - val_mae: 2.5248 - val_mse: 11.1648\n",
            "Epoch 955/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.6489 - mae: 1.0390 - mse: 2.6489 - val_loss: 10.8243 - val_mae: 2.4615 - val_mse: 10.8243\n",
            "Epoch 956/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.6527 - mae: 1.0706 - mse: 2.6527 - val_loss: 11.1978 - val_mae: 2.5137 - val_mse: 11.1978\n",
            "Epoch 957/1000\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 2.7967 - mae: 1.1408 - mse: 2.7967 - val_loss: 10.8762 - val_mae: 2.4678 - val_mse: 10.8762\n",
            "Epoch 958/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.7341 - mae: 1.0928 - mse: 2.7341 - val_loss: 11.3238 - val_mae: 2.5146 - val_mse: 11.3238\n",
            "Epoch 959/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.9024 - mae: 1.1370 - mse: 2.9024 - val_loss: 11.5428 - val_mae: 2.5512 - val_mse: 11.5428\n",
            "Epoch 960/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.7090 - mae: 1.0780 - mse: 2.7090 - val_loss: 11.2140 - val_mae: 2.5409 - val_mse: 11.2140\n",
            "Epoch 961/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.7092 - mae: 1.0900 - mse: 2.7092 - val_loss: 11.1988 - val_mae: 2.5019 - val_mse: 11.1988\n",
            "Epoch 962/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.6928 - mae: 1.0582 - mse: 2.6928 - val_loss: 11.9188 - val_mae: 2.5343 - val_mse: 11.9188\n",
            "Epoch 963/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.7211 - mae: 1.0637 - mse: 2.7211 - val_loss: 11.7628 - val_mae: 2.5986 - val_mse: 11.7628\n",
            "Epoch 964/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.7912 - mae: 1.1110 - mse: 2.7912 - val_loss: 11.5254 - val_mae: 2.5952 - val_mse: 11.5254\n",
            "Epoch 965/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.8370 - mae: 1.1295 - mse: 2.8370 - val_loss: 11.2502 - val_mae: 2.5299 - val_mse: 11.2502\n",
            "Epoch 966/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.9498 - mae: 1.1167 - mse: 2.9498 - val_loss: 10.9189 - val_mae: 2.4943 - val_mse: 10.9189\n",
            "Epoch 967/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.7817 - mae: 1.0943 - mse: 2.7817 - val_loss: 11.4892 - val_mae: 2.5712 - val_mse: 11.4892\n",
            "Epoch 968/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.8451 - mae: 1.0821 - mse: 2.8451 - val_loss: 11.2352 - val_mae: 2.4873 - val_mse: 11.2352\n",
            "Epoch 969/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.6684 - mae: 1.0657 - mse: 2.6684 - val_loss: 11.8311 - val_mae: 2.5464 - val_mse: 11.8311\n",
            "Epoch 970/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.8769 - mae: 1.1292 - mse: 2.8769 - val_loss: 10.7550 - val_mae: 2.4480 - val_mse: 10.7550\n",
            "Epoch 971/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.6020 - mae: 1.0546 - mse: 2.6020 - val_loss: 10.8558 - val_mae: 2.4493 - val_mse: 10.8558\n",
            "Epoch 972/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.8228 - mae: 1.1002 - mse: 2.8228 - val_loss: 10.8430 - val_mae: 2.4619 - val_mse: 10.8430\n",
            "Epoch 973/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.6993 - mae: 1.0919 - mse: 2.6993 - val_loss: 10.8723 - val_mae: 2.4437 - val_mse: 10.8723\n",
            "Epoch 974/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.7366 - mae: 1.0787 - mse: 2.7366 - val_loss: 10.8279 - val_mae: 2.4865 - val_mse: 10.8279\n",
            "Epoch 975/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.6640 - mae: 1.0881 - mse: 2.6640 - val_loss: 11.1566 - val_mae: 2.5311 - val_mse: 11.1566\n",
            "Epoch 976/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.7932 - mae: 1.1076 - mse: 2.7932 - val_loss: 11.3598 - val_mae: 2.5666 - val_mse: 11.3598\n",
            "Epoch 977/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.4648 - mae: 1.0607 - mse: 2.4648 - val_loss: 10.6460 - val_mae: 2.4302 - val_mse: 10.6460\n",
            "Epoch 978/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.8249 - mae: 1.0791 - mse: 2.8249 - val_loss: 10.7409 - val_mae: 2.4421 - val_mse: 10.7409\n",
            "Epoch 979/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.7049 - mae: 1.0685 - mse: 2.7049 - val_loss: 11.4538 - val_mae: 2.5800 - val_mse: 11.4538\n",
            "Epoch 980/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.8391 - mae: 1.1126 - mse: 2.8391 - val_loss: 10.7199 - val_mae: 2.4588 - val_mse: 10.7199\n",
            "Epoch 981/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.6132 - mae: 1.0711 - mse: 2.6132 - val_loss: 11.3272 - val_mae: 2.5119 - val_mse: 11.3272\n",
            "Epoch 982/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.6083 - mae: 1.0669 - mse: 2.6083 - val_loss: 12.0159 - val_mae: 2.6123 - val_mse: 12.0159\n",
            "Epoch 983/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.6248 - mae: 1.1117 - mse: 2.6248 - val_loss: 11.3194 - val_mae: 2.5033 - val_mse: 11.3194\n",
            "Epoch 984/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.7086 - mae: 1.0754 - mse: 2.7086 - val_loss: 11.6523 - val_mae: 2.5842 - val_mse: 11.6523\n",
            "Epoch 985/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.6321 - mae: 1.0868 - mse: 2.6321 - val_loss: 11.2217 - val_mae: 2.4936 - val_mse: 11.2217\n",
            "Epoch 986/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.8753 - mae: 1.1050 - mse: 2.8753 - val_loss: 11.1219 - val_mae: 2.4908 - val_mse: 11.1219\n",
            "Epoch 987/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.7423 - mae: 1.0880 - mse: 2.7423 - val_loss: 10.8234 - val_mae: 2.4544 - val_mse: 10.8234\n",
            "Epoch 988/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.7700 - mae: 1.1098 - mse: 2.7700 - val_loss: 11.0383 - val_mae: 2.5016 - val_mse: 11.0383\n",
            "Epoch 989/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.6126 - mae: 1.0865 - mse: 2.6126 - val_loss: 11.2549 - val_mae: 2.5339 - val_mse: 11.2549\n",
            "Epoch 990/1000\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.6268 - mae: 1.0587 - mse: 2.6268 - val_loss: 11.4277 - val_mae: 2.5764 - val_mse: 11.4277\n",
            "Epoch 991/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.7539 - mae: 1.0632 - mse: 2.7539 - val_loss: 10.8554 - val_mae: 2.4564 - val_mse: 10.8554\n",
            "Epoch 992/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.6112 - mae: 1.0716 - mse: 2.6112 - val_loss: 11.3069 - val_mae: 2.5435 - val_mse: 11.3069\n",
            "Epoch 993/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.5730 - mae: 1.0336 - mse: 2.5730 - val_loss: 11.0958 - val_mae: 2.4761 - val_mse: 11.0958\n",
            "Epoch 994/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.6405 - mae: 1.0493 - mse: 2.6405 - val_loss: 11.0268 - val_mae: 2.4987 - val_mse: 11.0268\n",
            "Epoch 995/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.8498 - mae: 1.0906 - mse: 2.8498 - val_loss: 11.1813 - val_mae: 2.4974 - val_mse: 11.1813\n",
            "Epoch 996/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.8314 - mae: 1.0897 - mse: 2.8314 - val_loss: 11.3095 - val_mae: 2.5455 - val_mse: 11.3095\n",
            "Epoch 997/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.5967 - mae: 1.0946 - mse: 2.5967 - val_loss: 11.0461 - val_mae: 2.4801 - val_mse: 11.0461\n",
            "Epoch 998/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.7445 - mae: 1.0697 - mse: 2.7445 - val_loss: 10.9589 - val_mae: 2.4841 - val_mse: 10.9589\n",
            "Epoch 999/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.5884 - mae: 1.0809 - mse: 2.5884 - val_loss: 10.8066 - val_mae: 2.4713 - val_mse: 10.8066\n",
            "Epoch 1000/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.6349 - mae: 1.0557 - mse: 2.6349 - val_loss: 10.9197 - val_mae: 2.4666 - val_mse: 10.9197\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1650rCkPYh3I"
      },
      "source": [
        "Visualize o progresso do modelo de treinamento usando os estados armazenados no objeto `history`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b56CgfRqYGBY",
        "outputId": "202d71e7-e593-4f16-dc44-e01f3a628b03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "hist = pd.DataFrame(history.history)\n",
        "hist['epoch'] = history.epoch\n",
        "hist.tail()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>mae</th>\n",
              "      <th>mse</th>\n",
              "      <th>val_loss</th>\n",
              "      <th>val_mae</th>\n",
              "      <th>val_mse</th>\n",
              "      <th>epoch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>2.831383</td>\n",
              "      <td>1.089699</td>\n",
              "      <td>2.831383</td>\n",
              "      <td>11.309529</td>\n",
              "      <td>2.545530</td>\n",
              "      <td>11.309529</td>\n",
              "      <td>995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>2.596737</td>\n",
              "      <td>1.094568</td>\n",
              "      <td>2.596737</td>\n",
              "      <td>11.046123</td>\n",
              "      <td>2.480128</td>\n",
              "      <td>11.046123</td>\n",
              "      <td>996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>2.744481</td>\n",
              "      <td>1.069669</td>\n",
              "      <td>2.744481</td>\n",
              "      <td>10.958874</td>\n",
              "      <td>2.484052</td>\n",
              "      <td>10.958874</td>\n",
              "      <td>997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>2.588399</td>\n",
              "      <td>1.080856</td>\n",
              "      <td>2.588399</td>\n",
              "      <td>10.806558</td>\n",
              "      <td>2.471273</td>\n",
              "      <td>10.806558</td>\n",
              "      <td>998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>2.634868</td>\n",
              "      <td>1.055669</td>\n",
              "      <td>2.634868</td>\n",
              "      <td>10.919705</td>\n",
              "      <td>2.466554</td>\n",
              "      <td>10.919705</td>\n",
              "      <td>999</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         loss       mae       mse   val_loss   val_mae    val_mse  epoch\n",
              "995  2.831383  1.089699  2.831383  11.309529  2.545530  11.309529    995\n",
              "996  2.596737  1.094568  2.596737  11.046123  2.480128  11.046123    996\n",
              "997  2.744481  1.069669  2.744481  10.958874  2.484052  10.958874    997\n",
              "998  2.588399  1.080856  2.588399  10.806558  2.471273  10.806558    998\n",
              "999  2.634868  1.055669  2.634868  10.919705  2.466554  10.919705    999"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V8NA2I2lY2UQ",
        "outputId": "ab5e787a-a8bb-454d-94a1-a0863d1fbcaf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        }
      },
      "source": [
        "def plot_history(history):\n",
        "  hist = pd.DataFrame(history.history)\n",
        "  hist['epoch'] = history.epoch\n",
        "\n",
        "  plt.figure()\n",
        "  plt.xlabel('Época')\n",
        "  plt.ylabel('Média erro absoluto [MPG]')\n",
        "  plt.plot(hist['epoch'], hist['mae'], label='Train Error')\n",
        "  plt.plot(hist['epoch'], hist['val_mae'], label='Val Error')\n",
        "  plt.ylim([0,5])\n",
        "  plt.legend()\n",
        "\n",
        "  plt.figure()\n",
        "  plt.xlabel('Época')\n",
        "  plt.ylabel('Erro médio quadrado [$MPG^2$]')\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)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+DRyJ67x+DMgDrlPVYaoaM0EDIF8S8KklOTTGxK5IZo5nqeq/gGFAd+BXERlcaSWroQrjEoi3pipjTAwLq6lKRI7BTf7rhWuaagXsBppXXtFqpkKxwGGMiW3h9nF8B8wF3gb+o6qrK6tANZ36EkjAmqqMMbEr3MBxNdAHN/T2JhHZhhtZtQBYqKofVVL5ahzrHDfGxLpw06o/F7xdYh7HOUDMBA6NS8RHoVs+Ns5X3cUxxpgqF+kiTACoahouU+6X0S1Ozac+L9tKQS7EJVdvYYwxphqEm1Z9djTOqQskOHAYY0wMCrfGcZCIzC/juACNo1CeGk999dyTfAscxpjYFG7g6LX/U4iJzH/iDxxW4zDGxKhwO8fXVHZBaguNT3SP+TlINZfFGGOqQ9gzx42jCfUByM/eXc0lMcaY6mGBI0KFCQ0AKLDAYYyJURY4IqQJKQAUZFngMMbEpojncYhIf+Bob3Oqqs6LbpFquERX48jP3lXNBTHGmOoRUY1DRK4D3sQlOWwFvCEi10Z4j/EisllEFgbte1hElorIfBH5UESalHLtahFZICJzRWRmJK8bNfVc4CjM2VMtL2+MMdUt0qaqy4HBqnq3qt4NHA5cGeE9XgFOLLFvEtBHVfsBvwG3l3H9MFUdoKqpEb5uVIhX4yjMtsBhjIlNkQYOofh8jQJvX9hU9Qdge4l9X6sW5Sr/GWgfYbmqTFxCEgCFeTnVXBJjjKkekfZxvAz8IiIf4gLGGcD4KJfpMuCdUo4p8LWIKPCcqj4f6iQRuQq4CqBjx45RLZwvwc3jKLSZ48aYGBVR4FDVR0XkO+Ao3Jf4pao6J1qFEZG/Afm4fpRQjlLV9SLSCpgkIku9GkzJcj4PPA+Qmpqq0SofQEKCy1WlNnPcGBOjwl0B8MZSDg0TkWGq+mhFCyIiY4BTgRGqGvLLXlXXe4+bvVrPYcA+gaMyJcT7yFUfajUOY0yMCrfG0dB77AkcCnzibZ8G/FrRQojIicAtwLGqmlnKOSlAnKru9p6fANxf0deOVIIvjjziLXAYY2JWWJ3jqnqfqt6H67Q+RFVvUtWbgEFARJ0IIvIWMB3oKSJpInI58BQuOE3yhto+653bVkS+8C5tDUwTkXm4YPW5qn4VyWtHgz9wWB+HMaZGyMuCaY9BQdUtaR1p53hrIPgbM9fbFzZVHRVi90ulnLsBONl7/jvQP5LXqgz1vMBh2XGNMVUqOwMWfwwDLwYJGsw67TH4/l+Q1ARSL62SokQaOF4DfvX6FwDOBF6NbpFqtoR4IdcChzGmqk35O8x4ARq0hi3LYPCfID7RBRSAvJCt/JUi0lFV40TkSwIpR6I6qqo2SPDFkafx+KqwWmiMKafCQlg+EQ48sfiv9Gha8hk06Qht+lXO/Yt4Y4Y+uxF2pbntI6+DorFEVbfQQ3mSHK7C9VHMARqKyDHRLVLNFmiqssBharnNS2BGyFbiumPum/DWhTD7tejfOyMNvhkH71wEzx29//MrquEB7nFXmnvMyyp+fPFHlV8GT0Q1DhG5ArgO10k+F5dyZDowPPpFq5kSfHHsJp4ka6oytd0zR4IWwKGXV3dJKs/uje5xZyWsRTfhT7BmWvTvG+ynpyB3Lxx9EyycUPyYL8F74tU41v1SuWUJEmmN4zrccNw1qjoMGAjsjHqparAEn5CLDym0Goep5dTLHlRYGP1752XBvY1h/nvRv3ck4nzusTDEytabl7oypi+O/L6qkL5w/+eVZfrTsKzEwNDsDNi7LbD99d/guwdg+pOwuUQ54xJcORa8H9iXsxvysitWrjBEGjiyVTUbQEQSVXUpbm5HzEhM8JFHPJJvuapMHVEZP4J2b3KP3/4jvPN3rIZdGwLbOXtg++8VL0ec16hSmL/vsUXeGJ9F3i/5L2+FB8OYXVBYCC+fBNlh/GbevLT4F/vmJfB4X9izGSbeAW9dUPz8/x4BD3eFRR/Bul+L36ckXwJ8cg1kBaX++2d7eOm4/ZergiIdVZXmpTz/CDfnYgcQU+uR10/wkaWJxOVn7f9kY2qDglw3Oieain7ph1mbecIbaX+vN0Lo7VGw6ge4Z2fFOrWLAkeIGoe/iUe838+/POseZ7wIn98Ed24O/e8y53VYOz30662bAUmNXB/oAX3gv4Pd/h7Hw9ODwVcPdq6F3yYGrvn6LsjZBac9Eei/eG908fuGGjGVvgjmvLHv/k0L4OEecPp/oOdJoctZQWEHDhER4C+quhO4V0S+BRoDVT4JrzrFxQlZcfWJz0+v7qIYEx3+gR7ZGbB3KzTvVvF7amHxx0it8jIJ5eyCpMZln7tiCnQ5FnxBX2fbVsJPTwY6lDVE4Pj+X+5x9TR4PGhE1Oc3ucfsDPj4GjfstfuIwPGyakLBv/b/GNQnsfaXQH8LQEJy4PlP/3GPXYeWft8ln+y7b+tvpZ+/dzN8OBZuq5zf9WE3VXn5o74I2v5eVT9R1ZjrJc6JS6Fe/t7qLoaJdXu3Ruc+/oEeLx4PTx5S8fu9fAr88LB7Xt7A4a8pPNjJNSFtWxk07DTIqqnwxtmBIOC3cALMehl+9L6UC/Nh6wqY6SXzDr7Xmh9Dd55Pud8N5X3jbJgXlLA7p5Rlo9eVyL609ufA86ztxY+Fajp7b0zo+wZr2jnwPCOt7HMbRDQ3OyKR9nHMFpFDK6UktUhufAr1Ci1wmGq06CN4uBusKaXJJBL+GsfWZcX3Z6TB62cFJpiFa820QBOKFrgv6ZXfuF/1/iGkedmuvb80CSneE3VNSE8eAvc3D/zaV4U3z4cPrnDbvz4HPz4RaBrzB8O8vYHtl46Dz26AgnzILPFFHsqc1wPPP7wK3r7IXT+zlCHMLx1ffHvX+qDr/1T8WMntcB17a+D5nv20esTXK99rhCHSwDEYmC4iK71lXheIyPzKKFhNlhffgOTCzMoZjWJMOFZ7w0A3zqv4vUoOLff/Gv/+X+4Lf+EHgWMzX3YjkcJdOnlPOjzQDt441/2q37Ha7f/4/+C/h0P2rtD9D/VS9t2nBfCsN23s1xdcbWCP1wmfnQGT7oZlX7hZ1SW/3HP2QNYO9zx3d/Fmo3At/SxQYwnH3NJWh6iA+KTA81C1Fr+EFNfXMfFv0S8DkQeOkUA33LyN03Bp0E+LdqFquvx4t3xs0a8ZY6pLOB3H62bArjK+KLcsdR2tfv4aiL/TOLi5aeq/3WNm0JDR/cnbG+hjyN4F+TkuIIGboxDcxLPUaw2vVz/0vXJ3Q9ZO+PKvoY+/cxE8fdi+5Vs9NfA8Zw8sqIZhwm0GVOz6Bge4TvbOYUw2vNCr8flHjkVZpClHYmoEVWkK63mBI2c3JDYs+2RjqsKsV2H9LDeSpqSXjoPkZnDrqtDXvv2H4tsFOa6ZoyhwBPUH+IPKmh8BLd7mDvuvhY8/Adof6pqLAN69BNKCAsfboXKglvCvTvs/p6TgQPLpXwKBK1wJ9QMjm5p3h20rIi9D24GwcS4MuKj02sgZ/3W1qPaHwqslfpP/+Rf3fTPmM1frK+nYW2H7Kuh9RiBIDb8r8nKGoaILOQFuZcDoFKd2KKznBYvSOslMzZe5HR7qAhe8AQfVxkpzifxEn/7FPZYMHP4v/ZKds2XZuQ7qNwPxhtRqoevvaNQu0Kz10dXu8cpv3ZfgyY+4X7fvh5GdNW1G0PMKL+cT2jkvwe/fAuLmUQQPny8raJz0sBtV9sbZxfcnJAcCx7WzXM3pwQ5u+5ZV7r+lYEdeDz8+Dm0Pge7Hwe4NcOwt0PpgaDcoEDgu+Rg2zIXJ97jtghw3S9z/ufU5F84N0ady+pNwQD94/li3fdhVMOyO4ufcG2HfVARqxEJOtY0kWuCo9bZ4E6p+emr/gWPjfJj9qvtyrKxEedGiWryMwRNVSx4rzTND3OMBfd3jhjnw5S1unkHJHG0vHe/a2ofeDpPvrVDRI3bIJTDyATfpLdhhf4K+57o/v+CO7tK0GwSDrypew0pu6vpGEuoDQbWWpEZw7Wz371u/GVw+ufhQ3Hbe6DQthOFB/QyHXRkYDdWsmxuC23UoDB7rRoH1u9AdE4Hb1nqvW8p7BzjuPlcLqeK0MVW+kFNdIEn+wLGregtiKsD/BRrGkvSvn+Umhe3dEvnLbFsZmEUdTf4vt20rik8myy+RbiI3qB9u5ZTIXmPTAvc47y33uPrHfTvS/R20Sz/fN+leRZ0wbj/H/1G8qTipiXvsV2I29mn/gb+FGIHU4kCXtsOvu/fFLwJ3bIA7t8DV3qi1/iGa0Jp3g9a93fMOh7rag18rb3/HIfte16gdHP93GP1pYF9CEhx+dfG+naTGQfmoSnHU9dWSa6zKF3KqC3zJjQAoyN6Fr5rLYsopkppD0eSxEtc80A6OuRmOuqH0a/3zIkI1G+Rlu36ESIdNqgbShPz6nPvF67dtpZux7JcbNPppyzLX+dzrlP1PqgtlwbvFtzsMDiTW8zeVRUOno9yQ3q7Hln1eovv/sKj/oX5zlwYksUHx8+LiIC4pcE3qZe5zS2wIO9a4kVhJTYoHHP+orkZt4NY1UK8B/PBQ2eXpOhR6n+my1DbvDlf/BM177HueCBwZxX+vamALOZVDfLL7ny53bwbJ+znX1FDTHnOPoSaVleTv8E1fAOlAt+FuX+4e1zzTf1RghnIkxrWGFj3hml/dr/n4RGifWvY1BXkw6Z7iacKDZzI/eyRc/BF0GwZ7tsATQTOiJ3pt4KXNQ4hEq97Q8+TKycg66i3YNN81lV2/0A3pzd3r/m0eaOv6AEbcHTj/z7/A1uWuOWr7ykBAKenW1a6GERxYmnZyv/TLktwk8NzfRFSas1+AUx9zwaH1wWWfW4tFNBxXVccBlwI7vL9LVfWBSO4hIuNFZLOILAza10xEJonIcu+xaSnXjvbOWS4io0OdUxUS6nuBI9Oaqmqt38LMlPPb15Dj1RZeP8v9QfHO1jfPC33dzJdLv69/CKp/0t0rJ8OLI0Kfm7PbjT7avsrlO/r56eLH579TfPv1M91jeSeZlabdIBhyjXseFw+DxsDx9weOp17mRm+VdPkkOPAkV5MA6O5NlGvew/WbjPxn8fOTGkFn79wmHVzA6HqsqwXcvh6G3Vn8/CYdXUqQ05+CMV+4WkIoyU33rY1E4s4tcOoTZZ8TX694DbCOirTGAW4hp3ggCW8hJ1X9IYLrXwGewtVe/G4DpqjqgyJym7d9a/BFItIMuAdIxTVMzxKRT1R1RzneQ4XUS3G/aPIzK2/Ugqkq+6lxTH8y9P7g9vyS6a4B/lcimDzeD64PmisbPMs4eGhl9i43UW7NT5CxDo660Y3AWfyx+wvXQ90gMwopSVr3gePvcx25zbpA2kyY/pQLHMlN3Ap00x6D/Fw45VEXIP53ngsgWdvhsonQ4TD4w9uuljbjReh5ossQ22mICz6qLh9U1s79T8wr64s/sQF0PrLi77k0lTgTu7ap8oWcVPUHEelcYvcZwFDv+avAd5QIHLjJh5NUdbtXlknAicBbEbyFqKiflESW1iM/y2ocNcacN1zziX80S0X98pxb47ltiElbqsU7ygvz3ZdiXBkV+J1rXA6nE+53v9xL4x/i6bdhbvl+wQYHjbNfgAlXhj7v9Cfhk2v33X/iv9ykuRP+4QKGn39UVXCn7bWzXa1IBA48AS79ygWLjDTXFOQXF+dGLYHrdG7W1T0XcUN/U5q7P1Pj1ZSFnFqrqv+nxiZCd7i3A9YFbad5+/YhIleJyEwRmbllSzlGwuxHSmI8yZJLs9/e3f/JJrqydrpf6HP/V3z/x3+GF4ZFfr+0GYFUFH7bVrrhp7m7iw9n9Vs/26XLCPbqqft/rTXT4PWzIxu2unpq6JrGKY/C2S+GviZ4hnKno6DPOcWP97sArvwGjrsXBl7s5iGAG33k16oXXPhm8aABgcy5qUEjeeo3Kx4gOg1xadWD95XUurcbSWRqpRq3kJOXhTeMHssy7/G8qqaqamrLli2jVLKABomuopaQU+WtZMafxXT6f8t/j5LB4LsHA8/zsopniA01Qe3FEBXsNT+6x9U/wuc3l/7a2TsDHfPlFRfvhmAeeMK+x/pdABcHpZkYdof7Eg9O8d3+UFfrOeoG92u/fjM36mtQULdhw7ahX1r+0KEAACAASURBVLtBK3du/wtCHzcxIdLAUXIhp4+JzkJO6SLSBsB73BzinPVAcD2+vbevyjVOTuCDAq/zzlYCjEx+Lkx7vPR/t43zXbrwn56EWa+UcaOg3xbBSfIm3e2ypAL8/j3MeAleGll89FTJbK+/TQwscRpO1tTSfHCl6+Se8UJk19VvAUeEaC4KdtZzgefne5PZkhpDN69D/bKJruZw9vMuEPx1JQz7W2AeQeejXCqK816BQ6/Yf5n8zUjGhBBpripvSEmxhZy+jEI5PgFGAw96j6F6AScCDwSNuDoBuD0Krx2xNo2TWKjdOIdpLmFatFdPq065mW4SWWWNDJnxokuvIHGhx7I/dzQ0bBPoJG3WFbocEzjuz3EUHAiCJ735g0b6Ypj/dmD/3i2Q0hKeH7pv88uOVTDhCtcZ/c3fy/3W9pnnUJo+58LCoOVEM7fCcfe7YOk34m63HgS4iWl9z3OBdO106HVy4LzzX3Oz4EsO401p4VJc+MUnunP357xX3ZBWX3nGzZhYEWnn+N0hdg8A7g+xv7R7vIXrCG8hImm4kVIPAu+KyOW4Gsz53rmpwFhVvUJVt4vI3wF/opv7/R3lVS3eF0d8UiPIx7WD16UOvZeOh/SFlZfnxp8zKXcvfP+QGxdfcg5E8MiaV08rXpaijMTq+jvmvB6YMRwsOGgAzHvbdeBunOv+QplyX0RvJSytDobNi4rvO+Zml2LCn6LizGdcx/HYH908DHBzFY6+qfh1oz/dN5V2YoP9z/2IxMFnRu9eps6KtKlqb9BfAXAS0DmSG6jqKFVto6oJqtpeVV9S1W2qOkJVe6jqcf6AoKozVfWKoGvHq2p376+MQfKVr1iG3HDk7nVfdNGwZ7NbyCda0mbCa2e4MqYv3P/5kVjwPrzidRx/9OfAynDpC+Hbca5T2y+/lMUkM7e7BHq5mYFhsJsXuyypX98Jn1yz/3JMumv/M38jNfpTaOOtlX3ig27oakndQnTYJzd1KSoO/z+37c9PdEAfOPhsNwM6FF9C8SVHjakmkTZV/Tt4W0QewTUhxZx8/wpl4QaOpwe7ppBo/JJ/81y3gE+3dW6yVLiWfAqdjty3Gco/8WxHUHdVuAnxgqm6zt/koPmbH3ijb1Z+C3PfCOz3D+vcsgyWfeVmCZf2pejPPHroFS71Q2Vr0Lr46mp/nLBvttQRd7smtMsmurUs2g1yHdPpCwPpsJv3gKG3wRF/gX8fGLjWX0Ma+YCbQBc8jPe8av09ZExYKtqQWR/XSR1z9tZr4Z7sXAvtUl2bfVntwhnrSj8Wqe2r3WNZK4Bt/92lnOg42G3v3gTv/NF92QUnVwsW3FdQkOuagZp1C/xq3rwEXjkFrvrezegt6ZEDYe9muPAt9+s5uHyvl2gCWe793shYB295I3TGTiv9/YDrHylLfHLxGd3lMfZHaHWQSyfy+tku/1D3EfB/P7uA8vMzrpPZnw8qITnQVFS/GbQ8KHCva2e6x8SGcP0CF/T2pAeGoYrsP4mdMTVQpH0cCwgMZ/EBLYEK9CbWXtuTO7snX97i1iboebL7JXnSg+Vb3GnhB25lrwatwr8mOFPp7k3F+wr+M9A93rAIGrcPNPFsC8prBMWzpwbPg8jdC597bex/mQPz3nH9C5nbXH/BsUErsG2cB8u+dEEDwluMJ5Rnj4r8mjGfw4rJbojr6U+6ld1G3FX6vZp0ckN62x4CG2a7WkH/C9zILNVAQEhqDFcGZZNt5QWE4ftZijM5ZLYclxYDYiIdhan7Iq1xBM9yygfSVbWMn711V70EL/2Af2jn0s/cY8uekWe+zNoB71/mai5XRpD62h841v7iVlY756XiaxAAPHawq2H4yxnc/LRpYaAztqR3Lg48f+9S16F8qDf7+MfH3TyCDy6HHie4CW0l03lHS/8/wMhxrh/Gn8bjnp2uRpXUxA1M6HyUW/0sIRn6lUj1cerjrlY47VFo3RcunuAWKmrUFhZNcP0M0Vxjw0YjmRgQ6X/l55TcIUH/08XSSoDJ9XyM953PZQUlhmAGf4EWFrqhlmXVIjbMcUNEwTXbqLo28wP6uCR5zbq45pJQ/J3JG+a4x7U/7xs4oPgSlBnrXO3k23HFm1VKWhPUbOSvrfgXoMnd4zq1V34T+RKcwaulDb7ardLmX1Sp3wX7JuzzT1DzT3ZrO9Dt889g9ivZP+If0pvqrUg3/C4vtYUEPo8hf6ZStOjp1oY2po6KNHCkEnoFwOXRLFRtsHB9Bh/uPZPLkkoEjuCmn1njXXPPZUHjB7b/7uYm5GW55pFiX7ziZjF//yBc+iV8dr3bHdyhnrUz0HdQkFP80efVgsqauQzwb2+yf4Mwl1LxZ3BdPzOwb9kX4V176mPwmbdeRd/zXRD44wRo0cM13+xc55rphlzjfq2f9gSMO8CNLMrcBl2Dms/++nv42U3//EvxRIRl5ZGKtmtiblFME2MiDRz+FQB3A4jIvcDnqvrHaBesplu+eU/oAwsnwIh7XFbVSd60l/EjA8dfPQNuWOBGWe1cU3yFMIlzQQNc80pJqm4Iqp9/9rW/yernp92X/IrJ4b2J4JFD4SjPCnj+L/7LJkJHL79T96D04U06uFXM/BKSXed7825u5FVwn0Ak82WSGpdvsSJjzH5F+jPMVgD03HmKa+b5tX+J5S0z1sL9TQNBo6SMtS4Vhj/n0trpgWN5mYHnJddSyNy+79KfeZmwaqpbSc4v3KBRFv8Xrn/5y6TGbkgpQEqrQFK8kjof7Y75a0gHn+Wa2u7NCASNcLQd4AYYWEeyMTVSNFYAfCWqJaolzh3Unn98voSFLU/hsC4TYVUES5K8dnro/dmlTBC8t5Rfzl/dFlgXOlpuWORqOx+OdQvjNOngcinFxbnJbi17uS/0G5fCo70C1439sfiSpXdsAF8dSsVijCkS6QTAcSLyJXC0t+tSVZ0T/WLVfP4Mubuz8+GCN2D5JNeJ/fYfKmc5zVDCCRrJTeGYW2DmeBjyf4H+hmAXve/WSJY4N3S3cfviiw75BXe8N2oD182Dr++Cs54NrNHsV3LbGFNnRDx2UFVnA7MroSy1SrwvjpR6PrbsyXZNOf4v1Uu/dMNrU1q4ztl/93SdwKG+5O/a5jqEXz0tUGMRH2jBvueG67p5bq7CzPHw+Y2uI7rdIS5ogJs5/uppbkTR/PegcbvyjwBq2hkueL38ZTXG1EqiWqGlL2q81NRUnTlz5v5PLIcrXp3B/LQMfrljRLFhycWour/tK10w+elJ18ncdRj08dJY7NoAj3pDYxu1d0Fo41yXAjtnt1t/Ye6bLhPvxnluCCtAxyPckpuT73XLe0ocHHRa4HUzt7nXNMaYCInILFUNmUHTZitVwNCerZi8ZDNpO7Lo0Kx+6JP8cwda9HDbI0J0mjdq61JSPDHADUftcVzgmH8m8jFBM7Uzt7uV4Q48ya2DHCqjqYgFDWNMpbDAUQGdm7t2/A07ywgc4WrSEe4JM0t8/WYuX5IxxlSDiAOHiPQn0Dk+VVXnRbdItUezFDfhbvveUtKBG2NMHRTRPA4RuQ54E2jl/b0hIvtZ87LuatHABY6te2z5WGNM7Ih0AuDlwGBVvVtV7wYOB66MfrFqh6Yp9WiQGM/kJaGWSDfGmLop0sAhuJX//Aq8fTEpwRfH6QPaMmP1dgoK6/boNGOM8Ys0cLwM/CIi94rIfcAvwPhoFEREeorI3KC/XSJyfYlzhopIRtA5peT1qDpHdGtOZm4B78+K4kJNxhhTg0U6c/xREfkO8C/iMFpV50ajIKq6DBgAICI+YD3wYYhTp6rqqSH2V4tT+rbh3y1+44PZ67ng0I7VXRxjjKl0YQUOEZmmqkeJyG7cCoASdExVNYKFr8MyAlipqmv2e2Y1ExEObN2AiYvSeWTiMtbvzOKfZ/clKcFX3UUzxphKEVZTlaoe5T02VNVG3qP/L9pBA+BC4K1Sjg0RkXki8qWIHBzqBBG5SkRmisjMLVvKkQo8Qn861i0q9NS3K/hwznpmrt5R6a9pjDHVpQpXtwmPiNQDTgfeC3F4NtBJVfsDTwIfhbqHqj6vqqmqmtqyZcvKK6znkI5NGXtsYEW6vMLCSn9NY4ypLuE2Vd1Y1vEoLxl7EjBbVfdZZUhVdwU9/0JE/isiLVR1axRfv1xO7nsAz36/EoDVW/eS3bXAmquMMXVSuDWOht5fKnA10M77GwscEuUyjaKUZioROUC8bIIichiu/Nui/PrlcmDrhkXP7/t0MX944WcWrs8o4wpjjKmdIsqOKyI/AKcELR3bELd07DFRKYxICrAW6KqqGd6+sQCq+qyIXIMLXPlAFnCjqv5U1j0rMztuSbPWbOfxycuZujxQAZp/7wk0Skqoktc3xphoKSs7bqSBYxnQT1VzvO1EYL6q9oxKSStBVQYOgD05+fS5Z2LRdpvGSUy/fUQZVxhjTM1TVuCItHPcv3TsvSJyL24C4KsVLF+d0iAxnmf/OKhoe2NGNp1v+5ynv11RtC+voJCs3Aos1mSMMdUoosChquOAS4Ed3t+lqvpAZRSsNjuxzwH8dWTxStjDE5dx6pNTyckvYMzLv3LQ3V/xyMRlFFqqEmNMLRNpdlwBegONVfUJYJvXSW1K+POw7qx+8BRO6N26aN/C9bv4auEmflzh+vOf+nYF860D3RhTy+w3cIjIUV4KEID/AkNwI58AdgNPV1LZ6oQHzu5bbPu6t4tnaDnz6R95dNJvfLVwY1UWyxhjyi2cGkch8Iz3fLCq/hnIBlDVHUC9SipbndCiQSKrHzylzHP+M2U5Y9+YDcDbv67lxneikv7LGGMqxX4Dhzfc9b/eZp5X+1AAEWmJCywmTOcNal/qsW+WpnPbhAVMmLO+CktkjDGRCTdXlf8n8H9wGWtbicg4YBpgneNheO7iQdx9am9OOPgAAHoGTRj0u+yVwLDhd2asJZKh0sYYU1UimscBICK9cNlrBZiiqksqo2DRUtXzOPZHVZm+chuHd23Okf/6ho0Z2aWeO+aIzvxhcEfWbc9k1da9HN+7NZ2ap1RhaY0xsSpqEwBro5oWOIKt3rqXoY98B4AIhPNR/PPsvpw1sB2A5cIyxlSaaM4cTwX+BnTCJUgUQFW1XzQKWhlqcuAAyMjKY932TLbtzeVvHy4gbUdWRNd/df3R9DqgMjLbG2NiWTRnjr+JWz72HOA04FTv0ZRT4+QE+rRrzLEHtmTarcNZMe6komP/vWj/+SNPfHwq2XkF5BcUcvuEBfy4wuXJWp6+m5GP/cDOzNxKK7sxJjZFWuOY5l/Uqbao6TWOUNJ3ZZObX0j7pslc9OIvnDmgHbd8MD/s6x84qy9TlqQzZelmAJb940QS461ZyxgTvmg2VY3ATf6bAuT496vqhIoWsrLUxsARSp97JtKxWX0Wb9y1/5NL+OzaoygoVPq1b4yXlb6YnPwCDv3HZMad1ZfT+reNRnGNMbVcWYEjrIWcglwK9AISCMzfUKDGBo66Ys7dxyPA3pwCFqzP4P1Z6+jcIoWP5qxn9bbMMq8d9/kSpv++jfNT23PF0V2ZtDiduet28teRPUnwxZGUEMeu7Hzu/2wxp/Zrw8ote+neqkHVvDFjTK0TcVr1mpxCPZS6UuMoy2vTV3P3x4sivq5ZSj22781lyk3HMuLf3wNw1sB2fDhnPe+PHUJq52ZRLqkxpraIZuf4TyLSOwplMlF0Rv92xbY/veYoRh7cupSzA7bvdR3nj379W9G+D71Z679v2bvf67ftyWHd9rJrO8aYuifSGscSoBuwCtfHYcNxawhVZfnmPTROTqB1oyQAXp++mrvKURMJdnzv1sxcvZ1XLj2M/h2aFDvW796J7MrOZ949J7B2WyZ92zeu0GsZY2qOaHaOdwq1X1XXlLNslS5WAkdpcvIL2Lonl/g4YfADUyp8v3tP6824L5bw8Ln9ud5LxnhY52b8uno7z108iBN6tw7ZAW+MqV1qzcxxEVmNS9VeAOSXLLS3HsgTwMlAJjBGVWeXdc9YDxzBNuzMonFyAll5Bcxdu5N1O1wqk9emB+J+PV8cuQXlz1t5ziHteeS8fvzrq2XszMzl0M7NOMdL7Lh1Tw5PTF7OHw/vxJ6cPLbtyS3K3WWMqVlqW+BIVdWtpRw/GbgWFzgGA0+o6uCy7mmBY/+27snh5R9XcePxPdmbm089XxxTl2/lytfK9+/m73T3u+e03izasItP5m7YJyjNvut4UhJ9HPvQd9x3xsGMtEBiTI0Qzc7x6nYG8Jo6PwNNRKRNdReqtmvRIJG/juyFL05olJRAUoKP43u3pl2TZE7uewDf/3UoN59wYNH5c+46vszO9+CgAXDfp4t5f1ZayJrMpoxsbp+wgE27svnT67MsI7AxtUCk8zgQkf7A0d7mVFWdF8XyKPC1iCjwnKo+X+J4O2Bd0Haat6/Y8nkichVwFUDHjh2jWLzY8uNtw4ueXzO8BxcP6cyurDyaptTjuYtT2bAzi/97czZz1+3k25uHct+ni9iUkc3STbvDf40VW5kwO7D+yJQlmzmud2uy8wr4dN4Gvl22maf/cAgZWXms3Z5Jv/ZNyribMaYqRNo5fh1wJYEJf2cBz6vqk1EpjEg7VV0vIq2AScC1qvpD0PHPgAdVdZq3PQW4VVVLbVOxpqrKtXl3Nl8vSueiwYEAvXTTbn5dtR1V5d5PFwPw9zMO5suFm/hp5bYy73dqvzbkFyhfLdpUtK9+PR+ZuQUA/OnYrtw6she5BYXszcmneYPESnhXxphojqqaDwxR1b3edgowvTKG44rIvcAeVX0kaN9zwHeq+pa3vQwYqqqlLthtgaN6bd2TQ4IvjsbJCYBb5TB4wSq/f53Tl1s/WBDx/f90TFduHtmTDTuz+MfnS/j3+f1plJSwz3mTFqfTt11jDmicFPmbMCYGRTPliOBGPPkVePsqzAtCcaq623t+AnB/idM+Aa4RkbdxneMZZQUNU/1alKgRDO/Vmp9vH8HOrFyenLKCzxdsZPrtw2nTOLlcgeO5H37nuR9+L9p++psVrNyyl2uGd6dTs/rsys5j+L+/p6DQ/UA6b1B7/nVOP+LiQv9nu257Jh2a1efHFVtJSYxnQAdrGjOmpEhrHDcCo3HLxwKcCbyiqo9XuCAiXYPuGw/8T1XHichYAFV91huO+xRwIm447qVlNVOB1Thqsj05+azdlknvtm49ka8WbqR+vXi+/20Lp/Vvy0FtGrJ1Ty4bdmZx3rPTARjRq1VR1t/ymn77cPbmFHDco9/z8Ln9OC+1AwCTF6dzxWsz+ePhHXnj57UA/P3MPlx8eMjpS8bUaVFpqvK+tNsDLQF/avWpqjonKqWsJBY46oate3JIjI+jXnwccSJMXpxOu6bJ/PHFX9iVnR/x/To3r1+UHPLyo7rQJDmBf0/6LeS5qx88BVUtmti4cH0GPVo34Nulm1m3PYv1O7MYe2w3awYzdUo0+zgWqGrfqJWsCljgqNuy8wr424cLuXZ4d6b/vo3kBB/pu7L5enE6c9buoDAKo3vvPOUg/vH5Ep64cADtmiRz7rPT+evInjw8cVmx81Y/eEqxbX8erw7N6le8EMZUsWgGjleBp1R1RrQKV9kscMS2zrd9DsBR3Vtw8ZBO3PvJIjZmZJf7fkd2b86PK0KPDHtv7BA6N0/h/VlpPP/DSnZk5gGBgLIpIxtfnNCyoY0EMzVfNAPHUqA7sAbYiyU5NDXcL79vo1FyAge1CazLvikjm2+Wbubdmevo064Rfds1LuqYT07wkZVXUNrtyuV/VwzmiO4tioLYX0b0oHlKPc4c2I7E+DiSEnxFnfe+Ujrtjalq0ezjOBoXNIqxJIemNlNVutz+BR2aJXPnKb350+uzOLhtIxZtKL7a4u0n9eKfXy4t12u8cumhjHk5dEX92uHdefKbFYCrtfRo1YDZa3fwtw8Xclr/thzYuiHxccKZA136/KzcAjZmZNG1ZWCxrc27snnk62Xcf0YfkhJsmWBTcdbHYYHD7MfOzFxEhOQEH09M+Y3T+7ej5wENAcjIymPuup0ce2BLNmZkkV+g5OQX8PuWvVz1+iy6tEhh1db9r19SUU+OGsienHxun+BqRyvGnYQvTpi4KJ2xb8wC4IkLB3Bk9xbc9+lixp3Vp2hOy1cLN5KdV1gUfIL9uGIru7PzObGP5QkzAdbHYYHDVJL8gkLifS7l296cfJ774XeWbdrFxEXpIWst0dQ8pR4DOjQpNjz5kfP6syBtJ69OX0P7psl89OcjSUrw0eeeicC+Hfi/b9nDcG/1x5LH/FZv3YsvTqyTP8ZEcwLgYOCPXhbbWtHHYUxl8gcNgJTEeG483iWDVFVy8gu56b15HNujJYhLOb9oQwbfLt3CoE5NeWjiUjJzC1ixeU+5Xnvb3tx95rRMmJ1GstdUlbYji9R/TKZFg3pFx2et2cH4aat44Ky+LNyQwUUv/lJ0bG9OPimJ7ishMzefB75Yws0n9GToI98BpQcWE3tsISdjqtGu7DzGT1tF91YNSO3UjBem/s6Qrs2pFx/HpMXpvP6z+1/ryVEDeWzyb2Et6Ruunq0bsiy9eELKMUd05t7TDy5aPfLKo7vwwtRVANx6Yi+e+W4F1x93ID0PaMiR3VtUuAwFhUqhKgm+2paou+6rcFOViNyiqg95z89T1feCjj2gqndErbRRZoHD1GYZWXk0TIwnLk5I35XNs9+v5NN5G9i6x6Wuv/jwTnRv1YB7PqnYEsHB+rd3Ob0mLkrn6B4tmLo85PI4fPTnI3nj5zU0rZ/AFUd3LVqyGNwkyQ7N6hflKMvNL2T55t0c3Lb48sKXjP+VH37bYrWZGigagWO2qh5S8nmo7ZrGAoepiybMTuPGd+fxzlWHM7hrczKy8vhs/gae/X4ld596MHPX7WB4r9as35nFX97aN7lD/w5NmLduZ1TLdFiXZmzdncPNI3vyf2/O5uxD2jHuzL68MPV31u/I4p2Z6/jflYP5csEmrjuuBxt3ZnPaU9MAWPXPk/dZcvjkJ6ZyWv+2XD20W8jXm7w4nYysvKIVJgsLlay8gqLmNlMx0Qgcc1R1YMnnobZrGgscpq5as20vnZqnlHnO1j053PjuPDIyc5mXlgG4kVdnDGhH59s+p2n9BE7u24b5aRksWJ9RFcUOad49J/DwxKW88fNaDuvSjDaNk/h47gYAfrljRLHajJ9/Xoy/tvLE5OU8Nvk3zhrYjscuGBDW605bvpU12/dy0eBO5OQXkBAXV2oCzFgTjc5xLeV5qG1jTBXYX9AAl534tcsOIze/kN/Sd9OnXaCpaMbfjiMxIY5GSQmoKo98vYzl6XsY2rMVQ7o1p1lKPfrf9zUAz108iJvencdBbRoyY/WOkK81tGdLlqfvYf3OrIjfyzX/m13UJPbrqu3Fjg1+YAr3n3Ewgzo15dnvf6dt4yTOS21fdPzxyb/Ro1VDPpidBsCHc9Zzct82fD5/A6f0a8thnZuxJzefdk2SAUjflV0UiP74khsc8IfDOtLzzq84P7U9D53bn4JCJTuvgNXb9u7TvGbCr3EUEBhFlYzLTIu3naSq+y6AUENYjcOY8rv744Ws35HFS2MOLdq3Jyef39J3szx9N0f3aMkRD35D91YNmHzjsTz69TL+401mrGqNkuLLTHi56p8n89XCTVz95mze/dMQ8gsL+cMLLnB8fcMxnPCYWzNu9YOn8Je35vDJPFfjeevKwxnSrXnlv4EapsI1DlW1qajGxKD7z+izz74GifEc0rEph3RsCsAHVx9BKy//1rUjenBYl+aIwL2fLOLR8wfQvVUDlm/ezelP/bjPvXod0DCipYbLsr8syV1u/6Lo+S+/byMzKLXMam8CZ4JPyMkvKAoaALPX7tgncPz9s8Uc3rU5x/duHY2i1zoRDcetjazGYUzNsGhDBskJPto0TuaRr5cxe+0O3rlqCON/XEU9XxyXHdWF7LwC1m3PRASOe9TVAB44qy93fFh8ka92TZLL1SRWHm0aJ/HTbcNZsD6Djt5IMX8QWv3gKSxP3835z03n+UtSGdSxKXFxUtRf8v7sNE7v33afNDBLN+3ik7kb+OvInvsMCqgpojZzvDaywGFM7ZSb79aVb5pSj/Rd2Tw26TfenrEOcF/Ye3LyWZCWwfuz0rh55IH8uGIbN783D4Am9RPo2iKF2WvdyLEnRw3kWm90WZvGSRXKkNy6USLpu3IAOPuQdny1cBOZuYHay0Pn9OOWD+bTv31j5qVl0KJBIjPvPI512zM5+qFvufmEA3lvVhprtmXy423DGT9tFckJPoZ0a06DxHh2ZedxdI+WbN+bS6Ok+GKTTKuSBQ4LHMbUetl5BTz45VJO6nMAg7uG7nPIzM0nPs4t+JWZm8+wR75jeK/W/PPsvvy+ZQ8//76dRsnxXPO/ORxzYEvaNEpi8cZdZY4oS0qIIzuvsFLe012n9ubvny3eZ//C+0Yy5J9TaNckma+uP4bc/EI+nJPG4C7NGf7v7/jvRYcwtGerYjWZzbuzaZ6SGLUMyxY4LHAYY4Jk5xUU+9L9ZN4GGibGk5IYz5iXfy1Wg/ju5qHEiTBtxdZ9msyqwoNn92Xaiq18Nn/jPk10D53bj1ven88/z+7L7RMWMOaIzvzx8I5c8785jDurL4M6NS3369aKwCEiHYDXgNa4Ib7Pq+oTJc4ZCnwMrPJ2TVDV+8u6rwUOY0ykCguVLXty2JGZS68DGhU7tis7j9enryE7r4DLj+pCvfg4bnl/Pp/N31h0Tu82jRg1uCPdWqbw26bd3P/Z4qisRhmp3/5xEvXiy9fUVVsCRxugjarOFpGGwCzgTFVdHHTOUOBmVT013Pta4DDGVLaCQiVtRya/rNpOo6R4jjuo9T59E/kFhWRk5fF/b84mToThvVqxetteUjs35bf0PTzz3cqwXiuSgQHnDWrPw+f1j/j963Fo0AAACMlJREFUQHSz41YaVd0IbPSe7xaRJUA7YN8GQGOMqUF8cUKn5illTsqM98XRvEEi7/xpSMjjzerX4+C2jTiiewve/GUNzVMSKShU8gsL+Wz+Rm49sRf5hYV0a9mAHn/7Mqxy/ZYenaHOJdWYGkcwEekM/AD0UdVdQfuHAh8AacAGXO2jzOxuVuMwxtQ1e3LyyczN57BxUwCYesswOjSrz56cfCYu3ESbJkms2ZbJ6f3bljt3V61oqvITkQbA98A4VZ1Q4lgjoFBV94jIycATqtojxD2uAq4C6Nix46A1a2ps1ndjjCm316evpnH9epzev23U711rAoeIJACfARNV9dEwzl8NpKpq6LzPWI3DGGPKo6zAUWNWTxE3ffIlYElpQUNEDvDOQ0QOw5V/W9WV0hhjTI3pHAeOBC4GFojIXG/fHUBHAFV9FjgXuFpE8oEs4EKtSVUmY4yJATUmcKjqNFy23bLOeQp4qmpKZIwxJpQa01RljDGmdrDAYYwxJiIWOIwxxkTEAocxxpiIWOAwxhgTEQscxhhjImKBwxhjTEQscBhjjImIBQ5jjDERscBhjDEmIhY4jDHGRMQChzHGmIhY4DDGGBMRCxzGGGMiYoHDGGNMRCxwGGOMiYgFDmOMMRGxwGGMMSYiFjiMMcZEpEYFDhE5UUSWicgKEbktxPFEEXnHO/6LiHSu+lIaY0xsqzGBQ0R8wNPASUBvYJSI9C5x2uXADlXtDjwG/KtqS2mMMabGBA7gMGCFqv6uqrnA28AZJc45A3jVe/4+MEJEpArLaIwxMS++ugsQpB2wLmg7DRhc2jmqmi8iGUBzYGvwSSJyFXCVt7lHRJZVoFwtSt4/BsTae4619wv2nmNFRd5zp9IO1KTAETWq+jzwfDTuJSIzVTU1GveqLWLtPcfa+wV7z7Gist5zTWqqWg90CNpu7+0LeY6IxAONgW1VUjpjjDFAzQocM4AeItJFROoBFwKflDjnE2C09/xc4BtV1SosozHGxLwa01Tl9VlcA0wEfMB4VV0kIvcDM1X1E+Al4HURWQFsxwWXyhaVJq9aJtbec6y9X7D3HCsq5T2L/WA3xhgTiZrUVGWMMaYWsMBhjDEmIhY4SrG/9Ce1lYh0EJFvRWSxiCwSkeu8/c1EZJKILPcem3r7RUT+4/07zBeRQ6r3HZSfiPhEZI6IfOZtd/FS16zwUtnU8/bXidQ2ItJERN4XkaUiskREhtT1z1lEbvD+u14oIm+JSFJd+5xFZLyIbBaRhUH7Iv5cRWS0d/5yERkd6rVKY4EjhDDTn9RW+cBNqtobOBz4s/febgOmqGoPYIq3De7foIf3dxXwTNUXOWquA5YEbf8LeMxLYbMDl9IG6k5qmyeAr1S1F9Af997r7OcsIu2AvwCpqtoHN8jmQure5/wKcGKJfRF9riLSDLgHN8n6MOAef7AJi6raX4k/YAgwMWj7duD26i5XJb3Xj4HjgWVAG29fG2CZ9/w5YFTQ+UXn1aY/3LygKcBw4DNAcDNq40t+5riRfUO85/HeeVLd7yHC99sYWFWy3HX5cyaQWaKZ97l9Boysi58z0BlYWN7PFRgFPBe0v9h5+/uzGkdoodKftKumslQar2o+EPgFaK2qG71Dm4DW3vO68m/xOHALUOhtNwd2qmq+tx38voqltgH8qW1qky7AFuBlr3nuRRFJoQ5/zqq6HngEWAtsxH1us6jbn7NfpJ9rhT5vCxwxSkQaAB8A16vqruBj6n6C1Jlx2iJyKrBZVWdVd1mqUDxwCPCMqg4E9hJovgDq5OfcFJcItQvQFkhh3yadOq8qPlcLHKGFk/6k1hKRBFzQeFNVJ3i700WkjXe8DbDZ218X/i2OBE4XkdW4rMvDce3/TbzUNVD8fdWF1DZpQJqq/uJtv48LJHX5cz4OWKWqW1Q1D5iA++zr8ufsF+nnWqHP2wJHaOGkP6mVRERwM/CXqOqjQYeC07mMxvV9+Pdf4o3OOBzICKoS1wqqeruqtlfVzrjP8htVvQj4Fpe6BvZ9z7U6tY2qbgLWiUhPb9cIYDF1+HPGNVEdLiL1vf/O/e+5zn7OQSL9XCcCJ4hIU6+mdoK3LzzV3clTU/+Ak4HfgJXA36q7PFF8X0fhqrHzgbne38m4tt0pwHJgMtDMO19wI8xWAgtwI1aq/X1U4P0PBT7znncFfgVWAO8Bid7+JG97hXe8a3WXu5zvdQAw0/usPwKa1vXPGbgPWAosBF4HEuva5wy8hevDycPVLC8vz+cKXOa99xXApZGUwVKOGGOMiYg1VRljjImIBQ5jjDERscBhjDEmIhY4zP+3d8egUQRhFMffUxtBUDCQRvRACxu1MIVaiY1YWgWxioVgY22ZUiyjlY1YCFppmSaSIKgIiiIWQhBJE8GACCIECc9iJnCohy7u3qL8f7Dc3NxxzFSPndmbDwAaITgAAI0QHEBLbG+xPW97b99jAbrE47hAS2zvl7QnyVLfYwG6RHAALbC9ofIHq013k1ztazxAlwgOoAW2vyTZ0fc4gHFgjwPokO33tq/Zfm37me0DtX9g+2GtyrawuS9ie9L2fduv6nWi9j+w/bxWt7vY55wAggNox3bbL4eu6aHPPic5JOmGSl0QSbou6XaSw5LuSJqr/XOSlpIcUTnN9k3tv5DkqKQpSZdt/6t1I/AfYKkKaMGopap6lPupJO/qcfYfkuy2vaZSse1b7V9NMmH7o8oG+/oPvzMr6Wx9O5B0OsnTDqcEjLTt918B8Jcyov1HbJ9UqTVxPMlX24sqJ7sCvWCpCuje9NDrk9p+rFIbRJLOS3pU2wuSLkmS7a22d6oUGPpUQ+OgpGNjGTUwAktVQAt+8TjufJIrdanqnqQzktYlnUuybHufpFuSJlRqg88kWbE9KemmSg2JDZUQeaFST2Mg6a2kXZJmkyyOYWrATwgOoEM1OKaSrPU9FqAtLFUBABrhjgMA0Ah3HACARggOAEAjBAcAoBGCAwDQCMEBAGjkO4ptniOsHfvQAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1nE-T6rxa2XA"
      },
      "source": [
        "Este grafo mostra as pequenas melhoras, ou mesmo a diminuição do `validation error` após 100 épocas. Vamos atualizar o `model.fit` para que pare automaticamente o treinamento quando o `validation score` não aumentar mais. Usaremos o `EarlyStopping callback` que esta a condição do treinamento a cada `epoch`. Se um grupo de `epochs` decorre sem mostrar melhoras, o treinamento irá parar automaticamente."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xvzrXAtKaRIZ",
        "outputId": "a0baeaff-0156-49d8-a28d-4629abed9780",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model = build_model()\n",
        "\n",
        "# O parâmetro \"patience\" é a quantidade de épocas para checar as melhoras\n",
        "early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)\n",
        "\n",
        "history = model.fit(normed_train_data, train_labels, epochs=1000,\n",
        "                    validation_split=0.2, verbose=1, callbacks=early_stop)\n",
        "\n",
        "plot_history(history)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/1000\n",
            "8/8 [==============================] - 0s 19ms/step - loss: 568.0292 - mae: 22.5222 - mse: 568.0292 - val_loss: 550.2452 - val_mae: 22.1193 - val_mse: 550.2452\n",
            "Epoch 2/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 507.7133 - mae: 21.1846 - mse: 507.7133 - val_loss: 488.0003 - val_mae: 20.7163 - val_mse: 488.0003\n",
            "Epoch 3/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 451.0223 - mae: 19.8608 - mse: 451.0223 - val_loss: 423.4315 - val_mae: 19.1635 - val_mse: 423.4315\n",
            "Epoch 4/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 389.7686 - mae: 18.3561 - mse: 389.7686 - val_loss: 353.5698 - val_mae: 17.3567 - val_mse: 353.5698\n",
            "Epoch 5/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 325.1056 - mae: 16.6236 - mse: 325.1056 - val_loss: 283.6001 - val_mae: 15.3445 - val_mse: 283.6001\n",
            "Epoch 6/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 261.4028 - mae: 14.6922 - mse: 261.4028 - val_loss: 217.4426 - val_mae: 13.1425 - val_mse: 217.4426\n",
            "Epoch 7/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 201.5045 - mae: 12.6535 - mse: 201.5045 - val_loss: 158.7173 - val_mae: 10.9213 - val_mse: 158.7173\n",
            "Epoch 8/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 147.7877 - mae: 10.5708 - mse: 147.7877 - val_loss: 110.3917 - val_mae: 8.9154 - val_mse: 110.3917\n",
            "Epoch 9/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 104.0696 - mae: 8.5678 - mse: 104.0696 - val_loss: 76.0713 - val_mae: 7.3571 - val_mse: 76.0713\n",
            "Epoch 10/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 72.0069 - mae: 6.8186 - mse: 72.0069 - val_loss: 54.8844 - val_mae: 6.2051 - val_mse: 54.8844\n",
            "Epoch 11/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 51.2444 - mae: 5.6027 - mse: 51.2444 - val_loss: 44.5663 - val_mae: 5.6155 - val_mse: 44.5663\n",
            "Epoch 12/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 38.8789 - mae: 4.8693 - mse: 38.8789 - val_loss: 38.2756 - val_mae: 5.1432 - val_mse: 38.2756\n",
            "Epoch 13/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 30.7834 - mae: 4.3795 - mse: 30.7834 - val_loss: 32.9017 - val_mae: 4.7155 - val_mse: 32.9017\n",
            "Epoch 14/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 25.0443 - mae: 3.9444 - mse: 25.0443 - val_loss: 28.6624 - val_mae: 4.2810 - val_mse: 28.6624\n",
            "Epoch 15/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 20.5900 - mae: 3.5581 - mse: 20.5900 - val_loss: 25.0668 - val_mae: 3.9439 - val_mse: 25.0668\n",
            "Epoch 16/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 17.6584 - mae: 3.3002 - mse: 17.6584 - val_loss: 21.8062 - val_mae: 3.6634 - val_mse: 21.8062\n",
            "Epoch 17/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 15.5683 - mae: 3.0617 - mse: 15.5683 - val_loss: 19.5269 - val_mae: 3.4148 - val_mse: 19.5269\n",
            "Epoch 18/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 13.9523 - mae: 2.8258 - mse: 13.9523 - val_loss: 17.5839 - val_mae: 3.2235 - val_mse: 17.5839\n",
            "Epoch 19/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 12.6359 - mae: 2.7081 - mse: 12.6359 - val_loss: 16.1579 - val_mae: 3.0559 - val_mse: 16.1579\n",
            "Epoch 20/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 11.7157 - mae: 2.5872 - mse: 11.7157 - val_loss: 13.9155 - val_mae: 2.9510 - val_mse: 13.9155\n",
            "Epoch 21/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 10.7290 - mae: 2.4304 - mse: 10.7290 - val_loss: 12.9073 - val_mae: 2.7906 - val_mse: 12.9073\n",
            "Epoch 22/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 10.2273 - mae: 2.3735 - mse: 10.2273 - val_loss: 11.8696 - val_mae: 2.7901 - val_mse: 11.8696\n",
            "Epoch 23/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 9.8096 - mae: 2.2809 - mse: 9.8096 - val_loss: 11.3294 - val_mae: 2.6092 - val_mse: 11.3294\n",
            "Epoch 24/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 9.1702 - mae: 2.2031 - mse: 9.1702 - val_loss: 10.7766 - val_mae: 2.5929 - val_mse: 10.7766\n",
            "Epoch 25/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 8.9306 - mae: 2.1587 - mse: 8.9306 - val_loss: 10.2188 - val_mae: 2.5081 - val_mse: 10.2188\n",
            "Epoch 26/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 8.7165 - mae: 2.0829 - mse: 8.7165 - val_loss: 9.7918 - val_mae: 2.4508 - val_mse: 9.7918\n",
            "Epoch 27/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 8.4118 - mae: 2.0416 - mse: 8.4118 - val_loss: 9.7762 - val_mae: 2.4239 - val_mse: 9.7762\n",
            "Epoch 28/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 8.2598 - mae: 2.0360 - mse: 8.2598 - val_loss: 9.7311 - val_mae: 2.4017 - val_mse: 9.7311\n",
            "Epoch 29/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 7.9885 - mae: 1.9738 - mse: 7.9885 - val_loss: 10.1559 - val_mae: 2.3411 - val_mse: 10.1559\n",
            "Epoch 30/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 7.8507 - mae: 1.9997 - mse: 7.8507 - val_loss: 9.4414 - val_mae: 2.3213 - val_mse: 9.4414\n",
            "Epoch 31/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 7.7396 - mae: 1.9685 - mse: 7.7396 - val_loss: 9.2251 - val_mae: 2.3161 - val_mse: 9.2251\n",
            "Epoch 32/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 7.5605 - mae: 1.9407 - mse: 7.5605 - val_loss: 9.2037 - val_mae: 2.3553 - val_mse: 9.2037\n",
            "Epoch 33/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 7.5562 - mae: 1.9551 - mse: 7.5562 - val_loss: 8.9701 - val_mae: 2.3305 - val_mse: 8.9701\n",
            "Epoch 34/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 7.3651 - mae: 1.8901 - mse: 7.3651 - val_loss: 9.5249 - val_mae: 2.2609 - val_mse: 9.5249\n",
            "Epoch 35/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 7.4595 - mae: 1.9393 - mse: 7.4595 - val_loss: 9.1098 - val_mae: 2.2660 - val_mse: 9.1098\n",
            "Epoch 36/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 7.3159 - mae: 1.8959 - mse: 7.3159 - val_loss: 8.9947 - val_mae: 2.2689 - val_mse: 8.9947\n",
            "Epoch 37/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 7.2943 - mae: 1.9112 - mse: 7.2943 - val_loss: 9.1976 - val_mae: 2.2757 - val_mse: 9.1976\n",
            "Epoch 38/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 7.1663 - mae: 1.8805 - mse: 7.1663 - val_loss: 8.9966 - val_mae: 2.2803 - val_mse: 8.9966\n",
            "Epoch 39/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 7.0908 - mae: 1.8940 - mse: 7.0908 - val_loss: 9.3059 - val_mae: 2.3170 - val_mse: 9.3059\n",
            "Epoch 40/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 7.0809 - mae: 1.8784 - mse: 7.0809 - val_loss: 8.9500 - val_mae: 2.2800 - val_mse: 8.9500\n",
            "Epoch 41/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 7.2921 - mae: 1.9184 - mse: 7.2921 - val_loss: 9.1945 - val_mae: 2.2427 - val_mse: 9.1945\n",
            "Epoch 42/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 7.0993 - mae: 1.8732 - mse: 7.0993 - val_loss: 9.0758 - val_mae: 2.2689 - val_mse: 9.0758\n",
            "Epoch 43/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.9863 - mae: 1.8491 - mse: 6.9863 - val_loss: 9.1171 - val_mae: 2.2589 - val_mse: 9.1171\n",
            "Epoch 44/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 7.0946 - mae: 1.8816 - mse: 7.0946 - val_loss: 8.9840 - val_mae: 2.3404 - val_mse: 8.9840\n",
            "Epoch 45/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.8736 - mae: 1.8509 - mse: 6.8736 - val_loss: 9.5630 - val_mae: 2.2586 - val_mse: 9.5630\n",
            "Epoch 46/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.9419 - mae: 1.8584 - mse: 6.9419 - val_loss: 8.9140 - val_mae: 2.2830 - val_mse: 8.9140\n",
            "Epoch 47/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.8087 - mae: 1.8508 - mse: 6.8087 - val_loss: 9.0066 - val_mae: 2.2533 - val_mse: 9.0066\n",
            "Epoch 48/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.8993 - mae: 1.8669 - mse: 6.8993 - val_loss: 8.9909 - val_mae: 2.2492 - val_mse: 8.9909\n",
            "Epoch 49/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.6961 - mae: 1.8347 - mse: 6.6961 - val_loss: 9.0150 - val_mae: 2.2641 - val_mse: 9.0150\n",
            "Epoch 50/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.7252 - mae: 1.8397 - mse: 6.7252 - val_loss: 8.9919 - val_mae: 2.3358 - val_mse: 8.9919\n",
            "Epoch 51/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.4962 - mae: 1.8200 - mse: 6.4962 - val_loss: 10.0604 - val_mae: 2.2852 - val_mse: 10.0604\n",
            "Epoch 52/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.8704 - mae: 1.8654 - mse: 6.8704 - val_loss: 8.9984 - val_mae: 2.2888 - val_mse: 8.9984\n",
            "Epoch 53/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.5846 - mae: 1.8166 - mse: 6.5846 - val_loss: 9.2973 - val_mae: 2.2489 - val_mse: 9.2973\n",
            "Epoch 54/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.8593 - mae: 1.8825 - mse: 6.8593 - val_loss: 8.7067 - val_mae: 2.2659 - val_mse: 8.7067\n",
            "Epoch 55/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.5252 - mae: 1.8135 - mse: 6.5252 - val_loss: 8.8199 - val_mae: 2.2650 - val_mse: 8.8199\n",
            "Epoch 56/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.7752 - mae: 1.8339 - mse: 6.7752 - val_loss: 8.8923 - val_mae: 2.3010 - val_mse: 8.8923\n",
            "Epoch 57/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.5924 - mae: 1.8137 - mse: 6.5924 - val_loss: 9.0232 - val_mae: 2.2191 - val_mse: 9.0232\n",
            "Epoch 58/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.5640 - mae: 1.8168 - mse: 6.5640 - val_loss: 8.6985 - val_mae: 2.2531 - val_mse: 8.6985\n",
            "Epoch 59/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.3520 - mae: 1.8001 - mse: 6.3520 - val_loss: 8.8639 - val_mae: 2.2951 - val_mse: 8.8639\n",
            "Epoch 60/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.5416 - mae: 1.8124 - mse: 6.5416 - val_loss: 8.7826 - val_mae: 2.3166 - val_mse: 8.7826\n",
            "Epoch 61/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.5386 - mae: 1.8075 - mse: 6.5386 - val_loss: 8.8821 - val_mae: 2.3024 - val_mse: 8.8821\n",
            "Epoch 62/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.5219 - mae: 1.8183 - mse: 6.5219 - val_loss: 8.8309 - val_mae: 2.2183 - val_mse: 8.8309\n",
            "Epoch 63/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.3305 - mae: 1.7794 - mse: 6.3305 - val_loss: 8.7681 - val_mae: 2.2225 - val_mse: 8.7681\n",
            "Epoch 64/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.4578 - mae: 1.8025 - mse: 6.4578 - val_loss: 8.8096 - val_mae: 2.3147 - val_mse: 8.8096\n",
            "Epoch 65/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.3480 - mae: 1.7760 - mse: 6.3480 - val_loss: 8.9805 - val_mae: 2.2170 - val_mse: 8.9805\n",
            "Epoch 66/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.3157 - mae: 1.7926 - mse: 6.3157 - val_loss: 9.1808 - val_mae: 2.3609 - val_mse: 9.1808\n",
            "Epoch 67/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.4606 - mae: 1.7646 - mse: 6.4606 - val_loss: 8.9492 - val_mae: 2.2463 - val_mse: 8.9492\n",
            "Epoch 68/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.3398 - mae: 1.7822 - mse: 6.3398 - val_loss: 8.6390 - val_mae: 2.2273 - val_mse: 8.6390\n",
            "Epoch 69/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.2976 - mae: 1.7662 - mse: 6.2976 - val_loss: 8.6702 - val_mae: 2.2614 - val_mse: 8.6702\n",
            "Epoch 70/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.3186 - mae: 1.7710 - mse: 6.3186 - val_loss: 8.9478 - val_mae: 2.2272 - val_mse: 8.9478\n",
            "Epoch 71/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.3149 - mae: 1.7830 - mse: 6.3149 - val_loss: 8.7177 - val_mae: 2.2117 - val_mse: 8.7177\n",
            "Epoch 72/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.3732 - mae: 1.7788 - mse: 6.3732 - val_loss: 8.7884 - val_mae: 2.1964 - val_mse: 8.7884\n",
            "Epoch 73/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.2009 - mae: 1.7650 - mse: 6.2009 - val_loss: 8.6369 - val_mae: 2.2317 - val_mse: 8.6369\n",
            "Epoch 74/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.2827 - mae: 1.7702 - mse: 6.2827 - val_loss: 8.7008 - val_mae: 2.2699 - val_mse: 8.7008\n",
            "Epoch 75/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.2582 - mae: 1.7446 - mse: 6.2582 - val_loss: 8.6275 - val_mae: 2.2535 - val_mse: 8.6275\n",
            "Epoch 76/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.0635 - mae: 1.7541 - mse: 6.0635 - val_loss: 9.0058 - val_mae: 2.3774 - val_mse: 9.0058\n",
            "Epoch 77/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.2623 - mae: 1.7451 - mse: 6.2623 - val_loss: 8.8063 - val_mae: 2.1978 - val_mse: 8.8063\n",
            "Epoch 78/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.3244 - mae: 1.7885 - mse: 6.3244 - val_loss: 8.7136 - val_mae: 2.2628 - val_mse: 8.7136\n",
            "Epoch 79/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.2333 - mae: 1.7479 - mse: 6.2333 - val_loss: 8.5964 - val_mae: 2.2117 - val_mse: 8.5964\n",
            "Epoch 80/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.2945 - mae: 1.7743 - mse: 6.2945 - val_loss: 8.7022 - val_mae: 2.2503 - val_mse: 8.7022\n",
            "Epoch 81/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.1122 - mae: 1.7258 - mse: 6.1122 - val_loss: 8.6180 - val_mae: 2.2615 - val_mse: 8.6180\n",
            "Epoch 82/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.2348 - mae: 1.7555 - mse: 6.2348 - val_loss: 8.7288 - val_mae: 2.2265 - val_mse: 8.7288\n",
            "Epoch 83/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.0887 - mae: 1.7450 - mse: 6.0887 - val_loss: 8.9988 - val_mae: 2.2161 - val_mse: 8.9988\n",
            "Epoch 84/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.1971 - mae: 1.7169 - mse: 6.1971 - val_loss: 9.1033 - val_mae: 2.1886 - val_mse: 9.1033\n",
            "Epoch 85/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.9972 - mae: 1.7344 - mse: 5.9972 - val_loss: 8.5455 - val_mae: 2.2249 - val_mse: 8.5455\n",
            "Epoch 86/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.1793 - mae: 1.7485 - mse: 6.1793 - val_loss: 8.5821 - val_mae: 2.2640 - val_mse: 8.5821\n",
            "Epoch 87/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.9023 - mae: 1.6962 - mse: 5.9023 - val_loss: 9.0590 - val_mae: 2.1896 - val_mse: 9.0590\n",
            "Epoch 88/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.1452 - mae: 1.7591 - mse: 6.1452 - val_loss: 8.6668 - val_mae: 2.3000 - val_mse: 8.6668\n",
            "Epoch 89/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.2133 - mae: 1.7410 - mse: 6.2133 - val_loss: 8.7821 - val_mae: 2.2033 - val_mse: 8.7821\n",
            "Epoch 90/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.9115 - mae: 1.7307 - mse: 5.9115 - val_loss: 8.6481 - val_mae: 2.2095 - val_mse: 8.6481\n",
            "Epoch 91/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.0625 - mae: 1.7159 - mse: 6.0625 - val_loss: 8.7076 - val_mae: 2.2588 - val_mse: 8.7076\n",
            "Epoch 92/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.8940 - mae: 1.6970 - mse: 5.8940 - val_loss: 8.7537 - val_mae: 2.1795 - val_mse: 8.7537\n",
            "Epoch 93/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.9823 - mae: 1.7242 - mse: 5.9823 - val_loss: 8.7773 - val_mae: 2.1918 - val_mse: 8.7773\n",
            "Epoch 94/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 6.0147 - mae: 1.7270 - mse: 6.0147 - val_loss: 8.4526 - val_mae: 2.2385 - val_mse: 8.4526\n",
            "Epoch 95/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.8355 - mae: 1.6844 - mse: 5.8355 - val_loss: 8.3961 - val_mae: 2.1884 - val_mse: 8.3961\n",
            "Epoch 96/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.0263 - mae: 1.7162 - mse: 6.0263 - val_loss: 8.7015 - val_mae: 2.3139 - val_mse: 8.7015\n",
            "Epoch 97/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.8974 - mae: 1.6946 - mse: 5.8974 - val_loss: 8.7354 - val_mae: 2.1739 - val_mse: 8.7354\n",
            "Epoch 98/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.9308 - mae: 1.6880 - mse: 5.9308 - val_loss: 8.7280 - val_mae: 2.1971 - val_mse: 8.7280\n",
            "Epoch 99/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 6.0088 - mae: 1.7060 - mse: 6.0088 - val_loss: 8.6680 - val_mae: 2.2105 - val_mse: 8.6680\n",
            "Epoch 100/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.8358 - mae: 1.6896 - mse: 5.8358 - val_loss: 8.5552 - val_mae: 2.2565 - val_mse: 8.5552\n",
            "Epoch 101/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.8111 - mae: 1.6851 - mse: 5.8111 - val_loss: 8.4960 - val_mae: 2.2063 - val_mse: 8.4960\n",
            "Epoch 102/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.7987 - mae: 1.6902 - mse: 5.7987 - val_loss: 8.6357 - val_mae: 2.2200 - val_mse: 8.6357\n",
            "Epoch 103/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.8175 - mae: 1.6827 - mse: 5.8175 - val_loss: 8.6939 - val_mae: 2.1667 - val_mse: 8.6939\n",
            "Epoch 104/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.8416 - mae: 1.6915 - mse: 5.8416 - val_loss: 8.5974 - val_mae: 2.1659 - val_mse: 8.5974\n",
            "Epoch 105/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.8140 - mae: 1.7124 - mse: 5.8140 - val_loss: 8.3859 - val_mae: 2.2430 - val_mse: 8.3859\n",
            "Epoch 106/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.8044 - mae: 1.6775 - mse: 5.8044 - val_loss: 8.6741 - val_mae: 2.2510 - val_mse: 8.6741\n",
            "Epoch 107/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.8660 - mae: 1.6795 - mse: 5.8660 - val_loss: 8.4028 - val_mae: 2.1912 - val_mse: 8.4028\n",
            "Epoch 108/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.8452 - mae: 1.6776 - mse: 5.8452 - val_loss: 8.4683 - val_mae: 2.2482 - val_mse: 8.4683\n",
            "Epoch 109/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.9320 - mae: 1.6834 - mse: 5.9320 - val_loss: 8.2820 - val_mae: 2.2294 - val_mse: 8.2820\n",
            "Epoch 110/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.9456 - mae: 1.7015 - mse: 5.9456 - val_loss: 8.4653 - val_mae: 2.2578 - val_mse: 8.4653\n",
            "Epoch 111/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.7014 - mae: 1.6775 - mse: 5.7014 - val_loss: 8.5628 - val_mae: 2.2207 - val_mse: 8.5628\n",
            "Epoch 112/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.7450 - mae: 1.6720 - mse: 5.7450 - val_loss: 8.3384 - val_mae: 2.1900 - val_mse: 8.3384\n",
            "Epoch 113/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.9927 - mae: 1.7490 - mse: 5.9927 - val_loss: 8.5015 - val_mae: 2.2826 - val_mse: 8.5015\n",
            "Epoch 114/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.8086 - mae: 1.6718 - mse: 5.8086 - val_loss: 8.4170 - val_mae: 2.1668 - val_mse: 8.4170\n",
            "Epoch 115/1000\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 5.7859 - mae: 1.6568 - mse: 5.7859 - val_loss: 8.3511 - val_mae: 2.1722 - val_mse: 8.3511\n",
            "Epoch 116/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.6729 - mae: 1.6472 - mse: 5.6729 - val_loss: 8.3506 - val_mae: 2.1980 - val_mse: 8.3506\n",
            "Epoch 117/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.7312 - mae: 1.6776 - mse: 5.7312 - val_loss: 8.4120 - val_mae: 2.2071 - val_mse: 8.4120\n",
            "Epoch 118/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.6057 - mae: 1.6410 - mse: 5.6057 - val_loss: 8.6012 - val_mae: 2.1740 - val_mse: 8.6012\n",
            "Epoch 119/1000\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 5.5593 - mae: 1.6417 - mse: 5.5593 - val_loss: 9.2500 - val_mae: 2.3898 - val_mse: 9.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7U5M41u3aW3dHQjoUbwdbu8AYE2R+femr6gcikgDMEJHFQE1p4bvAbwJ5Q1WdKSJZgZzjGQGsUdV1ACIyFbgQWN6Ia7UbPVM7IOKrxNENqsphfz7EpYUmOGNMu+T3CoCq+jLQG9covhfIAy5W1ReCFMtPRGSpV5WV7GN/d2Bzrde53rZDiMgkEZkvIvPb+6IvMZHhdEuMZf0hJQ4by2GMaR7+juOYICK7cF/WFwB/U9U/q+qSIMXxCC4pDQa2AX9tysVU9XFVzVHVnE6dOgUjvlYtOy2O9fV7Vtm0I8aYZuJvieMO4EygH7AR+EMwg1DVHapaparVwL9x1VL1bQF61Hqd4W076tUkjjod32oGARbZr8gYE1z+Jo4iVV2kqjtV9Q58f7E3mtfIXuNiDrah1DYP6CMi2SISBVwJvBXMONqqrLQ4ikorKdhXfnBjxy5uJUDrkmuMCTJ/e0Sle+twrARWEGAX3NpE5AVgNJAmIrnA74DR3nofCmwAfuwd2w14QlXPVdVKEfkJ8AFuffOnVPXrxsbRnvTyelat37WP1I7RbmN4BMR1ti65xpig8zdx/A4YAIz3fnYUkXeBJcDSQBrIVXWcj81PNnDsVuDcWq/fBQ7pqnu0y66VOHKyUg7uSEi3EocxJuj8TRxrgddVNQ9ARDJwCWQgcB4QrJ5VphEykmOJCJNDG8jju8FuW0LWGBNc/iaOacBOEanGtT8sA5Z62x9optiMnyLCw8hM6cCG/Po9q9Jh4+ehCcoY02752zj+U2Ar8CBuFPlK3MSHf8X1sjIhlp0Wx7q8+iWOdCjdAxUlvk8yxphG8CtxqOrDwCm4xuu/AxXAjao6RlW7NmN8xk9ZaXFsyN9HdXWtLrk2CNAY0wwCGTleoqp/AsYAxwBzRWRks0VmApKdFkdpRTXbi2qtBmiDAI0xzcCvNg4ROQ03+K8f0B/oDBQDqc0XmglE704dAVizc++BtchJyHA/d2+ErFNDFJkxpr3xt8TxCTAZ2A5cp6rDVHW0r1lsTWj06xoPwMrtRQc3pvRyqwFunhOiqIwx7ZG/vaquA07Adb29RUTycT2rlgFfqeobzRSf8VNyXBRdE2JYua344MawMOhxImz6MnSBGWPaHX+nVX+s9ut64zi+C1jiaAX6pcezYntx3Y2ZJ8LqD2BfPsRZzaIxpukCXYQJAFXNxc2U+15wwzFN0a9rAp+vWUd5ZTVREV4tZOZJ7ufmL6HfeaELzhjTbvg7rfrCYBxjmlf/9HgqqrTuaoDdh0J4NGyaHbrAjDHtir8ljv4isvQw+wVIDEI8pgn6pycAsHJbMf26uudERLvkYe0cxpgg8Tdx9DvyIVQ1JRDTdNlpcUSFh7FiexEX1V4cMfNE+OKfUL4fojqELkBjTLvgb+O4TSvSBkSGh3FM5451e1aBa+f47O+wZQFkjwpNcMaYdsPvkeOmbeiXHl93LAdAjxGAWHWVMSYoLHG0M/27JrCjqKzuaoCxydD5OGsgN8YEhSWOdqZfuo8R5ODaOTbPhWprijLGNE3AiUNEBonIT7zHoOYIyjReTW+qQ9s5ToTyYti5PARRGWPak4ASh4jcCDyPm+SwM/AfEflpgNd4SkR2ishXtbbdLyIrRWSpiLwuIkkNnLtBRJaJyGIRmR/I+x4tOsVHk9YxuoF2DmzeKmNMkwVa4rgGGKmqd6rqncCJwI8CvMbTwNn1tk0DTlDVgcA3wO2HOX+Mqg5W1ZwA3/eo0T89nqW5hXU3JvWEjl1hkyUOY0zTBJo4hLrjNaq8bX5T1ZlAQb1tH6pqpffySyAjwLhMLSf2SmXl9uK6DeQirtSx2XpWGWOaJtDEMQWYIyJ3icjduC/5p4Ic09U0PAeWAh+KyAIRmdTQBURkkojMF5H5eXl5QQ6v9Tupt5vM8Mt1+XV3ZJ4IezZBkS3sZIxpvIASh6r+DfghrsSwC/ihqv49WMGIyG+ASlw7ii+nqupQ4BzgBm+BKV9xPq6qOaqa06lTp2CF12YM7J5Ix+gIvli7q+6OHt6CjdbOYYxpAn9XAPx5A7vGiMgYL6E0iYhMBM4Hxqqq+jpGVbd4P3eKyOvACGBmU9+7vYkID2NEdgpfrK1X4ug6ECJiXLfc4y8KTXDGmDbP3xJHvPfIwS3q1N17TAaGNjUIETkbuBW4QFX3N3BMnIjE1zwHzgK+8nWsgZN7p7Iubx/bC2utQR4RBd2GWjuHMaZJ/Eocqnq3qt6Na7Qeqqq3qOotwDAgM5A3FJEXgNlAXxHJFZFrgH/iEtM0r6vto96x3USkZnnaLsBnIrIEmAu8o6rvB/LeR5Oado7Z6+pVV2WOhG1L3ISHxhjTCIEu5NQFqNVVh3Jvm99UdZyPzU82cOxW4Fzv+TrABhz6qX/XBJI6RPLFmnwuHlKrk1qPkVD9d9i6CLJOCV2Axpg2K9DE8Sww12tfALgIeCa4IZlgCAsTTuqVyhdr81FVRLxe0zUN5OtnWuIwxjRKoL2q7sP1qtrtPX6oqn9ojsBM053cO5Ute0rYXFBycGOHFMg+HRY+C1UVoQvOGNNmNWaSw/W4NopFQHxDXWJN6J3UOw3g0G65J90AxVth+ZshiMoY09YFOlfVtbjurx8Ad3s/7wp+WCYYeneKI61jFHM3FNTdccyZkHoMzH4YfPd8NsaYBgVa4rgRGA5sVNUxwBBgT9CjMkEhIuT0TGFe/cQRFgYjJ8PWhZA7LzTBGWParEATR6mqlgKISLSqrgT6Bj8sEywjslPYXFDC1j0ldXcMGgcxia7UYYwxAQg0ceR6U56/gRtz8SZg65G3YiOyUwAOLXVEd4ShE2DFW7BncwgiM8a0VX4nDnH9OX+mqntU9S7gDtz4C5u7ohXrn55Ax+gI5q4vOHTnCG+eyPk+h9EYY4xPficOb/6od2u9/lRV31LV8sOcZkIsPEwY1jPZd+JI6gH9zoMFT0NFyaH7jTHGh0CrqhaKyPBmicQ0mxHZKazeubfu+hw1Rk6Gkt2w7OWWD8wY0yYFmjhGArNFZK23zOsyEVnaHIGZ4GmwnQOg5ynQ5QSY85h1zTXG+CXQxPFtoDdwBvAd3DTo3wl2UCa4BmYkEhURxjxf1VUiMPLHsOMr2Ph5ywdnjGlzAp1yZKOvR3MFZ4IjOiKcwT2SmOMrcQAMuAxik2HOoy0bmDGmTWrqQk7AgZUBTSs2um8n/vz+KlZuL6Jf14S6OyNjXdfcLx50XXOTeoQmSGNMm9AqFnIyzW/8iJ7ERYXzyCdrfR8w/Br307rmGmOOoMUXcjKhkdghkvEn9uR/S7ayMX/foQckZULfc2HBM1BReuh+Y4zxBNo43uSFnEzoXHNqNhFhYTw2c53vA0ZMgpIC+OrVlg3MGNOmBJo4ahZyuktE7gLmYAs5tRldEmL47rAMXpmfy84iH6WK7NOgU3+Ya11zjTENa/GFnETkKRHZKSJf1dqWIiLTRGS19zO5gXMneMesFpEJgbyvcSaf3ovK6mr+5autQwRG/MitSb5h1pEvtnUx7Nt15OOMMe1KKBZyeho4u96224DpqtoHmO69rkNEUoDf4QYhjgB+11CCMQ3rmRrHFcMz+c+XG1mbt/fQAwZ/DxIyYNqdUF3d8IUK1sET34L3b2++YFuD3AWH/z0YcxRq8YWcVHUmUH9AwYUcrPJ6Bt8TJ34bmKaqBaq6G5jGoQnI+OHnZx5LTGQ4//fuikN3RsbC2Dtg6yL46pWGLzLtd1BdAavea7+N6VsWwBNnuBmEjTEHtJaFnLqo6jbv+XZ8N7h3B2rP/53rbTuEiEwSkfkiMj8vLy8I4bUvneKjuWHMMXy0YiefrfZR1TTgckgfBNPv8T354cbZ7ss0axSUF8Pa6c0fdChs/KLuT2MM0AoXcvJm4W1Sy6yqPq6qOaqa06lTpyBF1r788JQsMpJj+f07y6mqrvfrDguDs34PhZvh/dtg+Vuw4TMo3uGqbT78DcSnw5XPuxHnX78Rmptobpvnup+5c0MbhzGtjF8jx2upv5DTboKzkNMOEUlX1W0ikg7s9HHMFmB0rdcZwCdBeO+jUkxkOL84qy83vbiYL9buYlSfegk2+zQ44btuyvUFTx/cHpviuuxe+C+3gmC/8+DrN111VWRMS95C81I9uKzu9mWu5BUZG9qYjGklAu1VdbGPhZwuDEIcbwE1vaQmAG/6OOYD4CwRSfYaxc/ytplGOvuEriTERPDqglzfB1zyBNy4FH48C37wBpz9Rzj2bBh6FQy60h1z3MVeddXHLRd4SyjMheJt0PsMqK50bT7GGCDAEoeI3Olj82DgngCu8QKu5JAmIrm4nlJ/BF4SkWtwJZjLvWNzgMmqeq2qFojIvYD3ZyD3qGoDs/YZf8REhnP+oG68tjCX4tIK4mMi6x4QFgbJPQ++7j3m0Iv0Oh1ikmD5m9Dv3CO/6eIX3DkJ3ZoWvL8+vd/1ALv4kcDOqyltnHiDS4qb50DPk4MfnzFtUKBtHPtqPaqAc4CsQC6gquNUNV1VI1U1Q1WfVNV8VR2rqn1U9Vs1CUFV56vqtbXOfUpVj/EeUwKM3fjw3aEZlFZU896y7Y27QHgk9DsfVr0LpUWHPzZ3PrwxGd68oWUGGJYWwWd/hyX/hYL1gZ2bOw8iYl2SS+kFm+cd+RxjjhKBVlX9tdbjPlzJoVezRGZaxNDMJLLT4nhlYQPVVf4YNgEq9sMz58NeX81Tnppp29d+DKs/9P/6je3uu/RFqPDm5Vr6UmDnbp4L3Ya4xNhjpGsgr0l2NqreHOUaMwCwtg64RmrTRokI3x3anbnrC9hcsL9xF+kxAsZNhbxv4Klvu+6rhblQWWtas+LtrvfV8Gsh9Rj44NdQVXHka6/7FP6UBfOeCCwmVZj/lOtWnDUKlrzg/xd+RakbPd/DWyU5Yzjsy4PdG1y11z8GwZKpgcVjTEub9wQsO8xYrCYIdADgMm/J2KUi8jWwCvhHs0RmWszFQzMQgf/O3dT4i/Q5Eya8BfsLYMo58Pfj4f+6w8Jn3f75U1wj84nXw7f/APlrYO6/615j0fPw4BA3qBCgaCu8eg1UlsCHd7gvbYCibfDEmfDaj2HXmoPnV1UcTA6b58DO5ZBzDQwaB7vXH2y3OJJtS9zgxowR7nUP7+e6T2Dq92HPRpj119CWPJa+DK9fB6WFoYvBtF6V5fDxfbDynWa5fKDdcc+v9bwS2KGqlUGMx4RA96RYzhuQzqOfriU7LY7Lcxq5kFOPEXDDHDfiel8efPUavPVT15V1wRSXXFJ7uzaD3mfAx7+HsmI3P9bsf7ov46iO8MI4OPMe125Svh9+8Dq8NBHe/AlcOgWevQAKt7hussteciWK4u0uGaX0grPude8dnQgDLgWthnducaWOmiRwODUJJsMrcXQ+zsX1/m1QWeYS0ZIXXMkq65SGr6PqHmFNLdjXUl0NM37vflcAu1bB919142lau/0F7t/B0pfd53vsWYceU1EK6z+FY84M7u/taLPmI9dtvqb3Y5AFmji+W3+DiBx4bisBtl1/uWwQhSUV/OrVpYSJcOmwRtZAxnd1YzvAfcG++AN471b3euSP3U8RuOAhePeX8MkfYOb97i/8oRPcl/6bN8C0O9yxlzzhkszZf3Db/zXSfXl//1VI7eNWLVw9zVV/9T3H/YX1gvefZcSPISrOPe9/vksm/S+Axf+FrQshOt6NRSnf7xJP6R7okOqSWVImxHsTGISFQ/dh7gtt7O9g5GRY+a4b3+Ircai6OKbf7b7QJ7wNEVGN+33WVlrkfgcr3nK/q2O+5Upkz1wAV70JHVJ8n7dnEyT2cL/3QH3yR9ex4Ly/uN9XY33xT/eHQmWJ63Tw6Z98J46Z98Osv8B5f3XVmqZxlrwAcZ3c/51mIBpAcVtE/oubcqRm8p7vAHOB1eAWfAp2gE2Vk5Oj8+fPD3UYbUJpRRXXPjOfz9fu4vcXncD4kT2PfNKRVJbB65NdtdMP3zv0r8jty2D2w64tYuRk9+VWXQ2fPwASBqfe5I5Thf9e7to8xr8EvUb7fr+qClcttuwluORxVwIB9xfYf7y/e6ITIXsUVJZCyR6XXOLTXRLZnw97d0D/7xxMdADffOAazM/4rYvx3V+6Ra9uWc67VPQAACAASURBVHnwC3tfPqz+wFXPbZrtks+eTXDST+Db97lj9he4L+Jug11CUnXX3fQFdD4eMnJ8J4DN81ySKNwMZ94LJ93g4lg9DaaOd4ltwv8gvN7fgvOehHd+DoO/Dxc86N7TXxtnwxRvOrjOx8P3prp7CtSi5+HN6+HYc2DsnbB+Jrz/K/jRxy7uGvsL4IGBUL4XYhLgpwshLi3w9zvaleyGvxzrEu/Z/9foy4jIAlXN8bkvwMQxEzhPVYu91/HAO6oa6Ay5LcYSR2BKyqu44b8L+XjlTm76Vh9uHNunTqmy0VQb9xdvbZVl7ou9MWNAqqvcX8+pvV2pI6pD02LZ8TU8cjKcdR90Od4luvUzXbVYQgacdgsMucp9Qc57Asa/4pLaWz+F/bsgrrMrIW1dBNuX1r12Wl/XDThjOBR5VXJfvwGJ3V0JLHNk3eOXvgSv/QhOudFVAdVY8xE8f7lbQ373BhhwGVz0KOSvdlPIZI2Czv18319lOTw2Csr3uYGfb1zvSk2jb3PXiUn07/e0ZrpL+FmnwvdedtcoLYK/9XfJ+eJHDx778e9dieOyp+HVa2HglXDRw64dZ+3HLl5fiaSq0rU7FW9zVaSZJx8sLR6N5j8Fb98Mkz51f6A0UjATxypgoKqWea+jgaWqGtT5qoLJEkfgKqqquf21ZbyyIJeJJ2dx1wXHhzqk1umJM117jla5EsvQq9zyu+mDDibJilJ4Yizkr3XVNF0HuCq01R+6R0ov18bT73zY9Y0rfWz83LWfVHi93BK6wzFj3fxhDX1hv32z+8K48gVXPbF1oUsayVlw9Xsw93E3aWVM4sEG9bBIOPVmGPVz2LnCrcESk+RmB1j0rPsiH/ci9D0b8la5L/PtS11V04Dvwqk/d4lY1SWiTV+6OLsNcdV9C56GT//sBpH+8D1Xiqjxzi9g4TNw83Lo2OlgaeOYM+DyZ920/p//A4b90FUxlhVCdAKc9gs3FU7N72nrIpfEK2t12U7OcrMd1H4/cIn7swfcOQVr3VoyETEQEQ2d+rrE1HsMdO7f8GdeVQnbl7hrdB/m7rU+VdeRI7X3Yf7xNKMnz3LJ+frZTfpjLZiJ4ze4Ud2ve5suAl5U1caXh5qZJY7GUVXufXsFT32+nj9eMoArR9jS8odYPQ1m/MGNYxk0zn0B+ZK3Cp6/DE64BEb/+mB7R3WVq47z9Z+7sgx2rYbEDIhNOnIsFaWuK/T2ZS6RAXTs6qqDEr1JpBc84/5y7zXadRL4/B9urEtYhOvxdoC4uPqf777Ea6i6hLTgGVfKqSqD4y5yX8Lblhw8LqW3K1WVFro5zy5+7NBSYt438PBwV/U3/EcuwXz5MFz3hSvBle2Fh0dCUa4rmQwe76ogV9eaZSiqo/viTh/kOjAkdIOyInj5h+53fcm/6/5u3/+1e49O/d2Xelwnl0zK97r4d3uDRLNPh1G3uNhrzt+3Cz6+F5a96qbYqZE+2PUUHHj5wWM/+I3r7HHOn+tWdx5JdXXTOwTsWg3/zIFv3eX+KGiCoCUO72JDgVHey5mq2qon8bHE0XhV1crEKXOZs76AVyefzIAMP6snTGgU5sKXj7hSRXKW++KL73r4c9Z8BN986NpWeo12AzhXvetKFuf+peHzi3fA7IdcG0piD9fmcuy3XVvQ16+7ZHfyT+u2YdT37EWuw4F6C2Udf7GrpqqxZ5NLoGl9Dm5b94krYfQ40SWM+m064KaZmfF7NxHnkPFu21evwSs/dKW9c//sO549m+Hr11yb294drjSYNcolpC//5artBl7pSiXdhrgkPO9JyFvhkt85f3Lje968Hjp2cb/LK58/2FnEl4pSWP6Gd51V7vhs7+u1ogTWz4LuQ/1r69mywHVGKdkNP13Q5Gl9gpo42hpLHE1TsK+c7zz0GQCv33AynePb0Qy4pukOV2o6km1LXfVachakHeuquRoqtQUa07MXuilujr8Y0gfC9HtdSWbiO0fu4VZRCkunwqr3XZVhWaFLquf82VVp1XmvavjoTvjiIVdS2TTbzWl2+XPw3EWwYzmcdD1smuNKgydOhtNvcyWLDZ+7Dg/F21wpTcQl/8ufc9V3r/3YdbeWcPe7Of5i6D320Pab4u0uWU/7ndt3xfPunpvIEocljiZZsnkPlz82mw5R4dxx/nFcPKR7cBrMjWkuxdvhvV+5dpv9+a5a6sczA/8rvLrKfbEndD98cvzyEbeMcko2XDvd9YzbmwdPnumqwLoOcKWQNR+5MSo9T3ID9FKy4dz7IXu0Kyn85xLY8ZW7ZlxnOPNuV8Ja9oqrtgPXw62m593eHa5tDFxyu3RKw92yA2SJwxJHk63eUcyvXl3Kwk17OKNfZ/41figxkQF07TQmFFRdvX90x+afkTl3gWtPql29V1roeqh17HRwGpz3fuXGLfW/AC58uG4jfmmhK2l0SHFduGsGdlZXu4Sy5iPXEaFmZc6YBOh5iquWrN0pIwgscVjiCIrqamXKFxu49+3lnDcgnYfGDSEszEoexgRk6yKXzAZcFtQv+mA7XOIIdOQ4IjKIg43js1R1yeGON+1HWJhwzanZVFcr9727gu7Jsfz63MN0XTTGHKrbEN/deNuQQCc5vBF4HujsPf4jIj9tjsBM63XtqGwmnNSTx2eu4753lrOvzKYrM+ZoEmiJ4xpgpKruAxCRPwGzgYeCHZhpvUSEO79zPOVV1fx71nreXLyVX53dj4uGdCfcqq6MafcCHW0iuJX/alR528xRJjxM+L9LBvLqdSfTNTGGW15ewti/fsJ/52yitKLqyBcwxrRZgSaOKcAcEblLRO4G5gBPBSMQEekrIotrPYpE5KZ6x4wWkcJax/haA920oGE9k3nj+lN4ZPxQEmIj+fXryzj3wVms3H6EZWSNMW1WY0eO18wlPUtVFwc9KJFwYAuuWmxjre2jgV+o6vkNnVuf9apqOarKJ6vyuPXVpRSXVvC77xzPyb1TiQgPIzUuyrrvGtOGNLlXlYh8pqqnikgxoNSqnhIRVdWEhs9ulLHA2tpJw7R+IsKYfp1592ejuHHqIm5/bdmBfWkdo3nk+0MZnhWcwUnGmNBpleM4ROQpYKGq/rPe9tHAq0AusBVX+vjax/mTgEkAmZmZwzZutPzT0qqqlRkrd1JYUkF5VTWPz1zH5oL9/Pa8/kRFhDN13iY27NrHz8b2YcLJWUSG22pvxrQmbWoAoIhE4ZLC8aq6o96+BKBaVfeKyLnAP1S1j6/r1LCqqtahsKSCm6YuYsaqPAD6dY0nrWM0n63ZRd8u8VyWk0Gn+GgykmMZmpl8xClNtu4pIcWqv4xpNk1OHCLy88PtD+aSsSJyIXCDqvpYV/KQYzcAOaq6q6FjLHG0HlXVyvtfbSc9KYYhPdxU4dOW7+Cet5eTu7vkwHGDeiRx29n9OKl3qs/rvLYwl1tfWUqfLvE8/oNh9Ehp4qJMxphDBCNx/M572hcfS8eq6veDEaj3XlOBD1R1io99XYEdqqoiMgJ4Beiph7kJSxytn6pSWFJBXnEZCzbu5h/TV7OtsJThWcmcc0I6Zx7XhW5JsYQJ/OuTtdz/wSqGZCaxZudeIsPDePh7Qw9JMmWVVURHWGnEmMZqM0vHikgcsAnopaqF3rbJAKr6qIj8BLgOqARKgJ+r6heHu6YljrantKKK52Zv5JUFuazacXDRnKiIMMorq7lwcDfuv3QQubv386Nn57Nu1z7OOaEr148+hr1llTw+cx2ffpPHb87tz9WnZofwToxpu2zpWEscbdaGXfv49Js8du8vp6S8iozkWMaP7HlgcsXi0goe+WQtz83eSLE39UlKXBTZaXEs2LibG8f24aZv9SF3dwkLN+1mRHYK6YmxobwlY9qE5l469iVV/UOTo2wmljiODkWlFbw8P5cOUeFcPKQ7EWHCbd666d0SY9ha6NakjokMY9JpvTlvQDrvLN3Ku19t58ReKdx5/vFERTTcs2v22nyWbyvi6lOyDjTc5+7ez+y1+Vw6LMPWJzHtTtBmx1XV+0TkPQ7OjvvD1r50rDk6JMREck29aqk/f3cgPZI7sGjzbq4+NZuBGUk8O3sDD05fzYPTVyMCAzOS+M+Xm1i7cx+Pfn8YiR0iqaiqJiJMDiSD/L1lXP/8Anbvr0BVuXZULwr2lfP9J+awIX8/pZXV/ODEngDsKCrludkbueqknnROsNUSTfsUaIlDgPG4Noh7RCQT6Kqqc5srwKayEoepb8HG3Xy1pfBAo/urC3K57bWlJMZGER4GO4vLGJSRxJSJw0mOi+LmFxfz9tKt5PRMYc76fP41fhj/nrWOZVsK6d81npXbi3nnZ6eS1CGKKx6bzdq8faR1jOahcUMa7BlmTGvXpKoqETkVmK2qVSLyCFANnKGq/UUkGfhQVYcHPeogscRh/DFnXT7/nrWOpA5RpMRF8fQXG+iVFsePT+/FzS8u4adnHMN1o3tz6SOzWb7NzcP18PeGMjw7mbMfmEWXhBgEWLdrL/dccAKPzlzLhl37uHRYBr07daRnagfG9Otcp6dXeWU1JeVVhIVBZHiYjUkxrUpTE8fJwERVnSQiC1V1qIgsUtUh3v4lqjoo+GEHhyUO0xifr9nFtc/Mp6Siil5pcbx74yhiIsPZuqeEq5+ex2U5PQ5UjX20fAfXPjufyHDhiQnDOf3YTuwtq+TON75i2oodFJe6RvuR2Sk8flUOibGRfPpNHjdNXcTu/RUH3jMmMoyk2Ci+dVxnfnvecQcSSVllFVXVSoco3zXLRaUVRFniMUEWjHEcg1V1sYjMAU4G5nkJpBOuxNFql7OyxGEaa+76Au5662vuufB4co4wx9ZL8zbTPTmWU45JO2RfUWkFH369g9tfW0p2WhxnH9+Vf85Yw7Fd4rl0WAaqUF5VTWFJBVt2l/DOsm0M7pHEP783hBmr8nhw+mqKSiq4LCeDSaN6k5nqBjyWVlTxrxlrePTTdSTERnL96N58b2TmERNIZVU163ftIzO1g411MQ0KZq+q8cAVwFDgGeBS4Leq+nIwAm0OljhMa/H5ml38+LkF7C2r5LyB6dx/6UCfpYj3v9rGz19aQklFFaowPCuZ7LQ43li0lYrqajJTOpCZ0oEN+fvYXFDCdwZ1Y1dxGbPX5ZMaF8WQzCT6do0nLjqCXcXl7N5fjuDWUNlRXMaCDQXsK6+ib5d4HvreEI7tEg/A2ry97C+roktiNKlx0YcsyrW3rJK56/P5fE0+RSUVXDC4G6f0dolyxfYi8orLOK1PJ1uHvp0I6lxVItIPN3utANNVdUXTQ2w+ljhMa7J6RzHLtxVxwaBuh+3Cu2p7MQ/PWMNFQ7oxpm9nRIQdRaW8NG8z3+zcy6b8fYSFCb/8dl9O9r68v1i7i6lzN7NyexFr8/ZRVa3ERYWT1CGKsDCoqlISYiMZnpVCr05xPDxjDcWllVwxvAdz1hXUGWwZFxXOvRedwCVDMwB4Z+k2fvnKEvaXVxEVEUZ0RBjFpZWkJ8ZQUlHFHq/K7Yx+nfn75YNJiI3goxU7eW/ZNi4e2p1RfToBbtqZeRsKGNA9kbjog0mzpLyK6IiwQ5JOVbWyePNuFm8u5NKhGSR2iAzOB2GOqE1NchhsljjM0aissorqaoiNargqKq+4jFteXsLMb/LI6ZnMdwZ1o2tiDDuLSvnf0m3MXV/ApNN60TE6gr9N+4ahmUncclZfhvVMBtw8Y28t2UpSbCQnH5NKwb4K/vjeCrokxJDaMZolm/cQFR5GeVU1FwzqxrCeyUz5fD0b8vfTr2s8T04cTvekWKYt38HPX1rM8KwUHv/BMCLCw1BV/j7tG579cuOBpHTasZ2YMnG4LU/cQoJZVZUD/AboiRsDIoCq6sBgBNocLHEY0zBVpaSi6pAqs4qqau59eznPznZLElw8pDv/d8mAI7afLN68hxueXwjAjWP7cN7AdB6fuY5HPllLeVU1gzISOWdAOg9/vIboyHDOPK4LL8zdRI+UWDYXlHDVST25+4Lj+f07K3jys/WcdVwXLhjcjR1FZdz79nJ+esYx3HJWXxZsLOBP769i1DFpXDe6NxHetPyqSv6+cjbm72dvWSWneAuJ1dzT64u2cFKvVJ8TYxaVVrC9sPRA1V1rUFpRFbJOD8GecuSXwDJct1wAWvOCS5Y4jGm81xflsresiu+PzPR7dHx5ZTVhwoEvbHCj7Hfvq+CE7gmICKt3FHPNM/PZVLCf8SMzueP84/jbtG94fOY6RmSnMHd9ARNPzuJ33zkOEUFVue3VZbw4fzNnHdeFaSt20DE6guLSSnJ6JnPLWX2ZtTqPNxZtOTBLAMCJvVJ48MohhIUJ1z+/kLnrC4gMF35wYhbfG9mDnUVlrM3by4xVecxanUdFlXLJkO7cc9EJdIwOaHy0X6qrlS/X57NkcyHFpRWUVFRx2bAeHNft0LXwnvliA/e+vZzJp/fm5jOPbfGSVjATx2eqemrQImsBljiMaZ0K91ewJq+YYT1dj7XqauX65xfy/tfbGT8yk99fdEKdZFVaUcXlj81m2ZZCJp6cxS/O6su05Tu4442vKC6rJDxMOK1PGqf26URWaocDpZS46AiiwoX8feXccf5xLMst5OUFm6mu9dWXkRzLuQPSCQ8THvt0LZkpHfjZ2D50T4qlS0IM8TERB9pkdu8vJ39vOXl7y9hRWMr+8irSE2PolhRLUodIYqPCKa+sZt6GAr5Yk8+ekgrSOkYRFR7GRyt2smWPW0IgIkwIEyEmMoz//uhETuieeCCel+Zt5tZXlx4oiY3qk8b9lw6iS0J0i01vE8zEMRYYB0wHymq2q+prTQ2yuVjiMKbtKK2oYt6GAk7pneazd1ZxaQW79paTnRZ3YFvu7v3MWVfAqGPT6Bxfd5qXb3YUc/3zCykpr+KxHww78OW8ekcxCzftpkdyB3qmxdEtMebAF/Kcdfnc9OJittUquTRWUodIusTHkL+vjKKSSk7qncolQ7tzRr/OdIyOYMueEq547Ev2l1fy3DUjiYoI49NVefzhvRWM6tOJf181jNcXbuHOt76mvLKaqIgwuiREc/7Ablx9Sjad4qOpqKpm9Y69ZKTEkhATvM4DwUwc/wH6AV9zsKpKVfXqJkfZTCxxGHN0q6pWqqr1sJNY1ldWWcWm/P1sLyplZ1EZe8sq2evNvpzcIYqUuEg6xcfQJSGaDlERbCssYeueUopKKrxu1MqQzGSOS084Yvfkjfn7uPyx2ewoOvC3OCf1SuWpicMPdG74ZkcxM7/JI6/YVa1NX7mTqPAwBvVI4qsthewvryLRG8sz4eSsoLSLBLWNozVPoe6LJQ5jTGu3MX8fbyzaSmZqLP3TE+jTOf6wbRrr8vby2Kfr+HpbIUN6JDOoRxLvLN3KjFV5pMRFMTQzmRO6JzA8K8XnoFR/BDNxTAHuV9XljYokBCxxGGOOFnPW5fPC3E18tbWItXl7GduvC09M8Pndf0RBm1YdOBFYLCLrcW0crb47rjHGHC1G9kplZC83I/O+skqKSiuOcEbjBJo4zm6WKIwxxgRVXHREndH5wRToQk7NOl5DRDYAxUAVUFm/mOStB/IP4FxgP27W3oXNGZMxxpi6micdNc0YVd3VwL5zgD7eYyTwiPfTGGNMC/G/f1rrcCHwrDpfAkkikh7qoIwx5mgScIlDRAZxcM3xWaq6JIjxKPChiCjwmKo+Xm9/d2Bzrde53rZt9WKcBEwCyMzMDGJ4xhhjAipxiMiNwPNAZ+/xHxH5aRDjOVVVh+KqpG4QkdMacxFVfVxVc1Q1p1OnTkEMzxhjTKAljmuAkaq6D0BE/gTMBh4KRjCqusX7uVNEXgdGADNrHbIF6FHrdYa3zRhjTAsJtI1DcD2ealR525pMROJEJL7mOXAW8FW9w94CrhLnRKBQVbdhjDGmxQRa4pgCzPFKAwAXAU8GKZYuwOveRGMRwH9V9X0RmQygqo8C7+K64q7Bdcf9YZDe2xhjjJ/8nnLEG0ORAXQCaqZWn6Wqi5optqCwKUeMMSZwQZlyRFVVRN5V1QGADbozxpijVKBtHAtFZHizRGKMMaZNCLSNYyQwXkQ2AvuwSQ6NMeao43fi8No4JgGtdn1xY4wxzS/QNo6HvTYOY4wxRylr4zDGGBOQxrRxfN+b/tzaOIwx5igUaOL4drNEYYwxps3wq6pKRG6FAws5jVDVjTUP4MfNGaAxxpjWxd82jitrPb+93j5bTtYYY44i/iYOaeC5r9fGGGPaMX8Thzbw3NdrY4wx7Zi/jeODRKQIV7qI9Z7jvY5plsiMMca0Sn4lDlUNb+5AjDHGtA2BDgA0xhhzlLPEYYwxJiCWOIwxxgTEEocxxpiAtJrEISI9RGSGiCwXka9F5EYfx4wWkUIRWew97gxFrMYYczQLdK6q5lQJ3KKqC0UkHlggItNUdXm942ap6vkhiM8YYwytqMShqttUdaH3vBhYAXQPbVTGGGPqazWJozYRyQKGAHN87D5JRJaIyHsicnyLBmaMMaZVVVUBICIdgVeBm1S1qN7uhUBPVd0rIucCbwB9fFxjEm6ZWzIzM5s5YmOMObq0qhKHiETiksbzqvpa/f2qWqSqe73n7wKRIpLm47jHVTVHVXM6derU7HEbY8zRpNUkDhER4Elghar+rYFjunrHISIjcPHnt1yUxhhjWlNV1SnAD4BlIrLY2/ZrIBNAVR8FLgWuE5FKoAS4UlVtdl5jjGlBrSZxqOpnHGFtD1X9J/DPlonIGGOML62mqsoYY0zbYInDGGNMQCxxGGOMCYglDmOMMQGxxGGMMSYgljiMMcYExBKHMcaYgFjiMMYYExBLHMYYYwJiicMYY0xALHEYY4wJiCUOY4wxAbHEYYwxJiCWOIwxxgTEEocxxpiAWOIwxhgTEEscxhhjAmKJwxhjTEAscRhjjAlIq0ocInK2iKwSkTUicpuP/dEi8qK3f46IZLV8lMYYc3RrNYlDRMKBh4FzgOOAcSJyXL3DrgF2q+oxwN+BP7VslMYYY1pN4gBGAGtUdZ2qlgNTgQvrHXMh8Iz3/BVgrIhIC8ZojDFHvYhQB1BLd2Bzrde5wMiGjlHVShEpBFKBXbUPEpFJwCTv5V4RWdWEuNLqX78Na0/3Au3rftrTvYDdT2vm7730bGhHa0ocQaOqjwOPB+NaIjJfVXOCca1Qa0/3Au3rftrTvYDdT2sWjHtpTVVVW4AetV5neNt8HiMiEUAikN8i0RljjAFaV+KYB/QRkWwRiQKuBN6qd8xbwATv+aXAx6qqLRijMcYc9VpNVZXXZvET4AMgHHhKVb8WkXuA+ar6FvAk8JyIrAEKcMmluQWlyquVaE/3Au3rftrTvYDdT2vW5HsR+4PdGGNMIFpTVZUxxpg2wBKHMcaYgFjiaMCRpj9p7USkh4jMEJHlIvK1iNzobU8RkWkistr7mRzqWP0lIuEiskhE3vZeZ3tTz6zxpqKJCnWM/hKRJBF5RURWisgKETmprX42InKz92/sKxF5QURi2tJnIyJPichOEfmq1jafn4U4D3r3tVREhoYuct8auJ/7vX9rS0XkdRFJqrXvdu9+VonIt/15D0scPvg5/UlrVwncoqrHAScCN3j3cBswXVX7ANO9123FjcCKWq//BPzdm4JmN25KmrbiH8D7qtoPGIS7rzb32YhId+BnQI6qnoDr2HIlbeuzeRo4u962hj6Lc4A+3mMS8EgLxRiIpzn0fqYBJ6jqQOAb4HYA7zvhSuB475x/ed9/h2WJwzd/pj9p1VR1m6ou9J4X476YulN32pZngItCE2FgRCQDOA94wnstwBm4qWegbd1LInAarpcgqlquqntoo58NrndmrDe2qgOwjTb02ajqTFwvzdoa+iwuBJ5V50sgSUTSWyZS//i6H1X9UFUrvZdf4sbJgbufqapapqrrgTW477/DssThm6/pT7qHKJYm82YRHgLMAbqo6jZv13agS4jCCtQDwK1Atfc6FdhT6z9DW/qMsoE8YIpX9faEiMTRBj8bVd0C/AXYhEsYhcAC2u5nU6Ohz6I9fDdcDbznPW/U/VjiaOdEpCPwKnCTqhbV3ucNnmz1/bFF5Hxgp6ouCHUsQRIBDAUeUdUhwD7qVUu1oc8mGfdXazbQDYjj0GqSNq2tfBb+EJHf4Kqxn2/KdSxx+ObP9CetnohE4pLG86r6mrd5R03R2vu5M1TxBeAU4AIR2YCrNjwD10aQ5FWPQNv6jHKBXFWd471+BZdI2uJn8y1gvarmqWoF8Bru82qrn02Nhj6LNvvdICITgfOB8bVm3GjU/Vji8M2f6U9aNa8N4Elghar+rdau2tO2TADebOnYAqWqt6tqhqpm4T6Lj1V1PDADN/UMtJF7AVDV7cBmEenrbRoLLKcNfja4KqoTRaSD92+u5l7a5GdTS0OfxVvAVV7vqhOBwlpVWq2WiJyNq+q9QFX319r1FnCluEXysnGN/nOPeEFVtYePB3AurvfBWuA3oY6nEfGfiiteLwUWe49zcW0D04HVwEdASqhjDfC+RgNve897ef/I1wAvA9Ghji+A+xgMzPc+nzeA5Lb62QB3AyuBr4DngOi29NkAL+DaZypwpcFrGvosAMH1uFwLLMP1Jgv5PfhxP2twbRk13wWP1jr+N979rALO8ec9bMoRY4wxAbGqKmOMMQGxxGGMMSYgljiMMcYExBKHMcaYgFjiMMYYExBLHMYEiYiEicj7IpIZ6liMaU7WHdeYIBGR3kCGqn4a6liMaU6WOIwJAhGpwg0IqzFVVf8YqniMaU6WOIwJAhHZq6odQx2HMS3B2jiMaUYiskFE/iwiy0Rkrogc423PEpGPvRXZpte0i4hIF2+FtiXe42Rv+xsissBbaW9SKO/JGEscxgRHrIgsrvW4ota+QlUdAPwTt64IwEPAM+pWZHseeNDb/iDwqaoOws2Y+7W3/WpV+d629gAAAR5JREFUHQbkAD8TkdTmviFjGmJVVcYEQUNVVd5U8Geo6jpvmvvtqpoqIruAdFWt8LZvU9U0EcnDNbCX1bvOXcDF3sss4NvqVqAzpsVFHPkQY0wTaQPP/SIio3HrXpykqvtF5BMgJjihGRM4q6oypvldUevnbO/5F7i1RQDGA7O859OB6wBEJNxbnzwR2O0ljX7AiS0StTENsKoqY4LAR3fc91X1Nq+q6kXgHKAMGKeqa0SkJzAFSMOtP/5DVd0kIl2Ax3HrWVThkshC3JodWbg1E5KAu1T1kxa4NWMOYYnDmGbkJY4cVd0V6liMCRarqjLGGBMQK3EYY4wJiJU4jDHGBMQShzHGmIBY4jDGGBMQSxzGGGMCYonDGGNMQP4f6QxaWoitql4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "td2IK57kcTGO"
      },
      "source": [
        "O gráfico mostra que no conjunto de validação, a média de erro é proximo de 2MPG. Isso é bom?\n",
        "Deixaremos essa decisão a você.\n",
        "\n",
        "Vamos ver quão bem o modelo generaliza usando o conjunto de **teste**, que não usamos para treinar o modelo. Isso diz quão bem podemos esperar que o modelo se saia quando usarmos na vida real."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ak7hA9vUcOd3",
        "outputId": "47760020-b7c4-4389-f696-0ce5a146d8df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=2)\n",
        "\n",
        "print(f'Erro absoluto médio do conjunto de teste: {mae:5.2f}')"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3/3 - 0s - loss: 6.0927 - mae: 1.7962 - mse: 6.0927\n",
            "Erro absoluto médio do conjunto de teste:  1.80\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LV4-QmBFdOFn"
      },
      "source": [
        "### Facça predições\n",
        "\n",
        "Finalmente, preveremos os valores MPG usando o conjunto de teste"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WXq1d3xTdByU",
        "outputId": "2c827555-f1f9-42a2-f1af-94db1fa78c8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "test_predictions = model.predict(normed_test_data).flatten()\n",
        "\n",
        "plt.scatter(test_labels, test_predictions)\n",
        "plt.xlabel('Valores reais [MPG]')\n",
        "plt.ylabel('Predições [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])"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+oWj4Cm/LDQ4bXVJkx5y4zjpb+gd4+LN/X2bpXaUk1fTXEawq+x3ge8C7wv3vC/edUd2iubEqPJWPu12Wvz/u9D+qq+2uoeHImW+9z3xjS2rIOzB8fo2ZfcfMBsPHEiB5BUFXd1GzzpZzTG78eilrvfkMtY0tKfRbw+cXJV0kKRs+3kd4z941rqiaOc4+4ztjj+kfGGLxXU+MdLwpFFV7+wy1jS3p9P788Pl/E1zTf5mgPeZXwAeqXC43RmnHnHdmxWffdkTiMS9s3UFXR4YuiV1DwyP7k2pvn6G2cSXNkTcYPv8RSFyL3jWeuOvqSd2dsS3occcI+O68E3iur78t1nprdWlmzlkBvM/M+sLtfYClBLfxOs3speoW0ZUjbk76RecdERvUqGMAPjLn0JHbcnHHVnIhS1ddaTrn9OQCD2BmmyUdC3wWeCvppsp2NRY3Jz0EQ2Gjwpl7XnzXE7ywdQciCPzlf5/cAFfJhSxd9aUJ/bCkabn78pIOAjaa2cclHVDd4rmxKLyuThPOkw/Zj/F7ZRnflY3uaReh3IUsXX2kCf2ngV9K+gXB5d2bCLrmYmb/EHeQpAOB/wKmEnTKvNbMviZpX+BmgvXunwHebWabx/Df4FIqFs64rrW1XC7aVV/RATdm9mNgNnALQVjfaGYrU3z2IHC5mR0OnAh8WNLhwELgbjN7HXB3uO1qICmcS3/1DCcvvoenN21nfFd25L1J9+5zylnI0tVPbOglzQyfjwUOIhhs8xxwULgvkZk9b2YPhX+/TDAyrwd4O0FDIOHz3LH8B7j04kL4qnEdoxaieHHbrpFgJ50d5Jw2M7qvVtx+V19Jp/eXAx8kuD9fyIDUE5dJmg7MAlYDU83s+fClFwhO/12V5J+aTxrfSWdGo/rfj+vIsG3nYOxCFGlO3Vc9sSnyPXH7XX0l3af/YPh82li+QNIE4DaCyTS3Km/VEjMzSZETqkiaD8wHOOigg8ZShLZ15bJHuPHXfxoJ9OZXBujMikndnWzpH2DqxHEMm7Hx5Z2Rx+eu4Yv1o/dr+uaSdHr/jqRHmg+X1EkQ+BvN7PZw918k7R++vj+wMepYM7vWzGab2ewpU/w0sVTL1vWOCnzOwJCx914drL7idMbvlWXbzkEmT+iK/Ixco12xfvR+Td9ckk7v3xY+vwY4Gbgn3D6NoCvu7VEH5Sio0q8HHjezr+S9tJxg9N7i8PmO0ovtcpJGxcXNSdfb1z+qlT5pIYr8+/29ff1kpVHX9HNn9cR2BPIBNo0p6fT+AwCSfgIcnrsOD2vnJSk++xTgH4FH8lbEuYIg7LeEK+b8EXh32aVvc0n33YudWj+9aTsZYPn6Xr4w90ggfiGK3HOxe/zeI685pFns4nEze33edgbYkL+v2nze+2hxi0z0hKfVaYbTAozvzNA/MJwY1qTv8sUoGs+Y5r0H7pa0UtL7Jb0fuBP4WSUL6MqT1IAWt9Z7lFcGhouu/+6Nda0jTeecjwD/CRwdPq41s49Wu2Au2rJ1vZyy+B5mLLyTTMT67RA0oM2d1cM7j+uh1Dlp49Z/98a61pGmGy7AQ8DLZvYzSeMlvSrscONqqPAaPmrtNxHU2LP++SdseWWg5AUmIH42HG+saw1plrX6IPAD4Fvhrh5gWTUL5aLFzYaTDWt8wah78sN7vDOdV3d37rHPZ8NpHWlq+g8DxxP0psPMfifpNVUtlYsUd/08bEZPTCeacsRcNfhsOC0iTUPeTjPblduQ1EHpy5K5Cki6ri6lQa27M8tXLzgm9nq/75XSFpl0zSVN6H8u6QqgW9IZwK3Af1e3WC5KXIv89p2DTBq/5yl5lIwYOS33xrn2lCb0nwQ2AY8AFwM/Aq6sZqFctNx19T4FAe/rH2DbjkE6ivy/2ZkVX3n3MSOn6D5VdXtKvKaXlCXoiDMT+HZtiuSi5He3jbpVNzBsZLS7MS8rMWQ28twT0fHGe9K1p8TQm9mQpCclHWRmf6pVodxoaW7VAQwbfHTOoVx33x9GvbewH30+b5xrP2la7/cBNkh6ENie22lmPi12jaRduGLyhC5uf6jX56tzidKE/v9WvRRtrtw56PJ1ZTNcee7hXHbzw5Gve3dZl5M0nn6cpEsJFq6cCdxvZj/PPWpWwhY3ljnociZP6OJL5x/lLfIulaT23qUEE2I+ApxN9LRZbozSzEEX1couoKsjwy0Xn8SaK8/wFnmXWtLp/eFmdiSApOuBB2tTpPaSZvRa4UQWHRmRyYjvzjthj3npvUXeFZMU+pFuWWY2qLi+mW5M0q7lPndWDycfsl/kvPSFvEXeJUk6vT9a0tbw8TJwVO5vSVsTjnMlSHs6HrcQhXOlig29mWXNbGL4eJWZdeT9PbGWhWxluXHvuZFyWYl3Hje6pvbAu0pKO57eVcmydb3ctrZ3pMPNkBm3re1l9mv3HbXU1LOb+5k4roMLvvUAB0zq5rSZU1j1xCa/bnclKzpHXiNo5Tny4uaey3Wf7cgEZwAZiV1D8SPkuzuzPr7djRjrHHmuiuJa73M1/+CwMThsiYGH+GmunCvkoa+zSnaa8V53Lg0PfZ0tOPMwOjOVuR3qve5cGh76BlDuXHb5vNedS8tb72uscHDN5u07GRou3pjamYHXTOweOc5b7125PPQ1FLUMVVoDw/hKMq4iPPRjUGxIbKG04+KdqyYPfZmSFo+MC/5YWtcL58VzrlzekFemNENiC5Xbut6ZFZ992xFlHetcIQ99mcpZ0LGURSWBkZVkrj7/aG+kcxXjp/dlihsSm5GYsfDOyGv8ubN62NI/wOdXPMbgsDF5QheDQ0Zf/56LS/gS0K5avKYvU1ytPWQWO+3Vxq07WPrAM6NmvFl03hE+042rKQ99mQoXdMxGTDKSf40fNzzWF4Z0teaj7CpkxsI7Ixf4E7D6itN9PLyrKR9lVwNxLfNTJ47zwLuG4qGvkKhr/HEdGYbNPPCuoVQt9JJukLRR0qN5+/aV9FNJvwuf96nW99da4bX530wcx8TuTrbtHPTAu4ZSzZp+CXBWwb6FwN1m9jrg7nC7Zcyd1cP9C+ew+orTGb9X1gPvGlLVQm9mvwBeKtj9doJFNAif51br++vFJ7F0ja7W1/RTzez58O8XgKlxb5Q0X9IaSWs2bdpUm9KNkQfeNYO6NeRZcK8w9n6hmV1rZrPNbPaUKVNqWLLyeOBds6h16P8iaX+A8Hljjb+/KjzwrpnUOvTLgYvCvy8C7qjx91ecB941m2resrsJeAA4TNKzkuYBi4EzJP0O+F/hdtPywLtmVLVRdmb2npiXTq/Wd9aSB941K++RVwYPvGtmHvoSeeBds/PQl8AD71qBhz4lD7xrFR76FDzwrpV46IvwwLtW46FP4IF3rchDH8MD71qVhz6CB961Mg99AQ+8a3Ue+jweeNcOPPQhD7xrFx56PPCuvbR96D3wrt20deg98K4dtW3oPfCuXbVl6D3wrp21Xeg98K7dtVXoPfDOtVHoPfDOBdoi9B5453Zr+dB74J0braVD74F3bk8tG3oPvHPRWjL0Hnjn4rVc6D3wziVrqdB74J0rrmVC74F3Lp2WCL0H3rn0mj70HnjnStPUoffAO1e6pg29B9658jRl6D3wzpWv6ULvgXdubJoq9B5458auaULvgXeuMuoSeklnSXpS0lOSFhZ7/+CQeeCdq5Cah15SFvgP4GzgcOA9kg5POubpF7d54J2rkHrU9McDT5nZ02a2C/g+8PakAwaGzAPvXIV01OE7e4A/520/C5xQ+CZJ84H54ebOEw7e79EalK1SJgMv1rsQJfIyV18ty/vauBfqEfpUzOxa4FoASWvMbHadi5Ras5UXvMy10CjlrcfpfS9wYN72tHCfc64G6hH63wCvkzRDUhdwIbC8DuVwri3V/PTezAYlfQRYCWSBG8xsQ5HDrq1+ySqq2coLXuZaaIjyyszqXQbnXA01TY8851xleOidazMNHfpSu+vWg6QbJG2U9Gjevn0l/VTS78LnfepZxnySDpS0StJjkjZIuiTc38hlHifpQUnrwzJ/Ltw/Q9Lq8N/HzWHDcMOQlJW0TtKKcLshytuwoS+nu26dLAHOKti3ELjbzF4H3B1uN4pB4HIzOxw4Efhw+L9rI5d5JzDHzI4GjgHOknQicBVwjZkdCmwG5tWxjFEuAR7P226I8jZs6Cmju249mNkvgJcKdr8dWBr+vRSYW9NCJTCz583sofDvlwn+UfbQ2GU2M9sWbnaGDwPmAD8I9zdUmSVNA84Frgu3RYOUt5FDH9Vdt6dOZSnVVDN7Pvz7BWBqPQsTR9J0YBawmgYvc3iq/DCwEfgp8Hugz8wGw7c02r+PrwKfAIbD7f1okPI2cuhbggX3RBvuvqikCcBtwKVmtjX/tUYss5kNmdkxBD04jwdm1rlIsSS9FdhoZmvrXZYoDdv3nuburvsXSfub2fOS9ieonRqGpE6CwN9oZreHuxu6zDlm1idpFXASMElSR1h7NtK/j1OA8ySdA4wDJgJfo0HK28g1fTN3110OXBT+fRFwRx3LMkp4bXk98LiZfSXvpUYu8xRJk8K/u4EzCNoiVgHnh29rmDKb2afMbJqZTSf4d3uPmb2XRimvmTXsAzgH+B+C67dP17s8MWW8CXgeGCC4TptHcP12N/A74GfAvvUuZ15530Rw6v5b4OHwcU6Dl/koYF1Y5keBz4T7DwYeBJ4CbgX2qndZI8r+FmBFI5XXu+E612Ya+fTeOVcFHnrn2oyH3rk246F3rs146J1rMx5659qMh76OwiGuZxbsu1TSNxOOuVdS3WdUTUPSeWmHREt6i6Qtkn4Ubk+XZJK+kPeeyZIGJP17uL1IUq+khyU9Kum8vPe+T9Jvw6G46yVdl9fB50ZJL0k6v7Ac7cBDX183EfTYyndhuL8iwiHKlfickrtsm9lyM1tcwiH3mdk5edt/IBiplvMuoHA+xWss6JP/LuAGSRlJZwGXAWeb2RHAscCvCAcRWdA7rll6d1ach76+fgCcm5tMIRz1dgBwn6RvSlqTP2lEIUnvkfRIWMtdlbd/m6QvS1oPnBTWeg+GNeK3whFrWUlLwmMfkXRZxOcvkfSfklYDX5J0iKQfS1or6T5JM8P3vS2cHGKdpJ9Jmhruf39erfyu8LvWS/pFyv99XgEezzuzuQC4JeqNZvY4wVwBk4FPAx83s97wtSEzu8HMnkz5vS3NQ19HZvYSQbfMs8NdFwK3WNBN8tMWLIxwFPBmSUflHyvpAIJJGeYQTCzxRkm58dl7A6stmHTirwRhOSWsEYeA94bH9JjZG8zsSOA7McWcBpxsZh8jmM31o2Z2HPBx4Bvhe34JnGhmswjmPfhExOd8BjgzLNN5Ea/H+T5woaQDw7I/F/UmSScQDGPdBBwBPFTCd7QVD3395Z/i55/av1vSQwR9zo8gmD0o3xuBe81skwWjtm4ETg1fGyIYRQdwOnAc8JtwPPrpBH3AnwYOlvT18HR41PDaPLea2VA4FPdk4Nbwc74F7B++ZxqwUtIjwIKwvIXuB5ZI+iDB1Odp/ZhggM2FwM0Rr18WludfgQusoF+5pCPDM5zfS7qghO9tWR76+rsDOF3SscB4M1sraQZBTXq6mR0F3EkwRDOtHWY2FP4tYKmZHRM+DjOzRWa2GTgauBf4EOEMLxG2h88Zgkkgjsl7vD587evAv4dnDBdHldXMPgRcSTBceq2k/dL8h1gwa9Ja4HJ2zzqT75qwLH9nZveF+zYQXMdjZo+EZzh3Ad1pvrPVeejrzIJpoFYBN7C7lp9IELYt4fXx2RGHPkhw2j85bKx7D/DziPfdDZwv6TUwMgHmayVNBjJmdhtBGI8tUs6twB8kvSv8HEk6Onz51eweG35R1PGSDjGz1Wb2GYJT8AOj3hfjy8Anw8uhNL4I/KuCKatyPPChRp5Eo53cBPyQ8DTfzNZLWgc8QTBl2P2FB1gw2cVCgh8MAXea2R7js83sMUlXAj+RlCEYAvxhoB/4TrgP4FMpyvle4Jvh53USXG+vBxYRnPZvBu4BZkQce7Wk14VlvTs8LhULVkAqtgpS/vt/JGkKcFf4g9hHMCR3ZdrPaGU+tNY1BElvIWhxf2uNvm8JwTj3qPjJ3FQAAAA5SURBVEuGluan965R7ALekOucU02SbgTeDOyo9nc1Iq/pnWszXtM712Y89M61GQ+9c23GQ+9cm/n/wfLlIvGYarYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dc4tEYQ4eJoF"
      },
      "source": [
        "Parec que o nosso modelo prediz razoavelmente bem. Vamos dar uma olhada na distribuição dos erros."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pIV6v7ZeeFr9",
        "outputId": "0ea354a5-618f-4b36-a5a8-082103f6a81e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "error = test_predictions - test_labels\n",
        "plt.hist(error, bins=25)\n",
        "plt.xlabel('Predição errada [MPG]')\n",
        "_ = plt.ylabel('Contagem')"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UWXFSy0FejdU"
      },
      "source": [
        "Não é tão gaussiana, porém podemos esperar que por conta do número de exemplos ser bem pequeno."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TAFGYOEce5Ve"
      },
      "source": [
        "## Conclusão\n",
        "\n",
        "Este notebook introduz algumas técnicas para trabalhar com problemas de regressão.\n",
        "\n",
        "* Mean Squared Error (MSE), é uma função comum da perda (loss) usada para problemas de regressão (diferentes funções de *loss* são usadas para problemas de classificação)\n",
        "\n",
        "* Similarmente, as métricas de evolução usadas na regressão são diferentes da classificação. Uma métrica comum de regressão é a Mean Absolute Error (MAE).\n",
        "\n",
        "* Quando o dado de entrada (*features*) tem diferentes intervalos, cada *feature* deve ser escalada para o mesmo intervalo. (processos de normalização).\n",
        "\n",
        "* Se não possuir muitos dados de treinamento, uma técnica é preferir uma pequena rede com poucas camada para evitar *overfitting*.\n",
        "\n",
        "* *Early stopping* é uma boa técnica para evitar *overfitting*."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MMMFJFBmf6L7"
      },
      "source": [
        "#\n",
        "# Copyright (c) 2017 François Chollet\n",
        "#\n",
        "# Permission is hereby granted, free of charge, to any person obtaining a\n",
        "# copy of this software and associated documentation files (the \"Software\"),\n",
        "# to deal in the Software without restriction, including without limitation\n",
        "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
        "# and/or sell copies of the Software, and to permit persons to whom the\n",
        "# Software is furnished to do so, subject to the following conditions:\n",
        "#\n",
        "# The above copyright notice and this permission notice shall be included in\n",
        "# all copies or substantial portions of the Software.\n",
        "#\n",
        "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
        "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
        "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
        "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
        "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
        "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
        "# DEALINGS IN THE SOFTWARE."
      ],
      "execution_count": 58,
      "outputs": []
    }
  ]
}