linear_regression.ipynb

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  "metadata": {
    "colab": {
      "name": "linear_regression.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOXVSUvlcS/hm7P85/tfgjd",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sarthakpant772/MLnow_2.0/blob/main/linear_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ifG_U12xb3F1",
        "outputId": "abd61382-db71-4b36-d0ed-981a2ec1d2cf"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ecnWLyG7eOCU",
        "outputId": "39f15807-b76f-4432-d5ac-9f79cbf0867f"
      },
      "source": [
        "cd /content/drive/MyDrive/mashine learning classroom/linear regressing model/dataset\n"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/mashine learning classroom/linear regressing model/dataset\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TdKh2SGceddh",
        "outputId": "fdd4ebe8-1052-43aa-9a7b-97ed0c0b45b3"
      },
      "source": [
        "ls"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cleanedMathData.csv  student-merge.R  univarit_MathData_2.csv\n",
            "student-mat.csv      student-por.csv\n",
            "student-mat.gsheet   student.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BFpK189tsNXu"
      },
      "source": [
        "import os\n"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "36D-mfP9sRNk"
      },
      "source": [
        "import pandas as pd #working with csv or excel files\n",
        "import numpy as np #working with numbers/ arrays/ tensors\n",
        "import tensorflow as tf #framework\n",
        "from tensorflow import keras #API / library \n",
        "import os #using os commands between the python language"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EratEDz2sTy2"
      },
      "source": [
        "mathData = pd.read_csv('student-mat.csv', sep=';') #load the csv file as dataframe"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "eKdGD0USsmHe",
        "outputId": "15387def-428b-42eb-86c3-4ae68a8d133e"
      },
      "source": [
        "\n",
        "\n",
        "mathData.head(10) #print the first ten rows of the dataframe\n",
        "\n"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>school</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>address</th>\n",
              "      <th>famsize</th>\n",
              "      <th>Pstatus</th>\n",
              "      <th>Medu</th>\n",
              "      <th>Fedu</th>\n",
              "      <th>Mjob</th>\n",
              "      <th>Fjob</th>\n",
              "      <th>reason</th>\n",
              "      <th>guardian</th>\n",
              "      <th>traveltime</th>\n",
              "      <th>studytime</th>\n",
              "      <th>failures</th>\n",
              "      <th>schoolsup</th>\n",
              "      <th>famsup</th>\n",
              "      <th>paid</th>\n",
              "      <th>activities</th>\n",
              "      <th>nursery</th>\n",
              "      <th>higher</th>\n",
              "      <th>internet</th>\n",
              "      <th>romantic</th>\n",
              "      <th>famrel</th>\n",
              "      <th>freetime</th>\n",
              "      <th>goout</th>\n",
              "      <th>Dalc</th>\n",
              "      <th>Walc</th>\n",
              "      <th>health</th>\n",
              "      <th>absences</th>\n",
              "      <th>G1</th>\n",
              "      <th>G2</th>\n",
              "      <th>G3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>18</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>A</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>at_home</td>\n",
              "      <td>teacher</td>\n",
              "      <td>course</td>\n",
              "      <td>mother</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>17</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>at_home</td>\n",
              "      <td>other</td>\n",
              "      <td>course</td>\n",
              "      <td>father</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>15</td>\n",
              "      <td>U</td>\n",
              "      <td>LE3</td>\n",
              "      <td>T</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>at_home</td>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
              "      <td>mother</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>15</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>health</td>\n",
              "      <td>services</td>\n",
              "      <td>home</td>\n",
              "      <td>mother</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>16</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
              "      <td>home</td>\n",
              "      <td>father</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>GP</td>\n",
              "      <td>M</td>\n",
              "      <td>16</td>\n",
              "      <td>U</td>\n",
              "      <td>LE3</td>\n",
              "      <td>T</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>services</td>\n",
              "      <td>other</td>\n",
              "      <td>reputation</td>\n",
              "      <td>mother</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
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              "      <td>10</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>GP</td>\n",
              "      <td>M</td>\n",
              "      <td>16</td>\n",
              "      <td>U</td>\n",
              "      <td>LE3</td>\n",
              "      <td>T</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
              "      <td>home</td>\n",
              "      <td>mother</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>17</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>A</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>other</td>\n",
              "      <td>teacher</td>\n",
              "      <td>home</td>\n",
              "      <td>mother</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>GP</td>\n",
              "      <td>M</td>\n",
              "      <td>15</td>\n",
              "      <td>U</td>\n",
              "      <td>LE3</td>\n",
              "      <td>A</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>other</td>\n",
              "      <td>home</td>\n",
              "      <td>mother</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>18</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>GP</td>\n",
              "      <td>M</td>\n",
              "      <td>15</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
              "      <td>home</td>\n",
              "      <td>mother</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  school sex  age address famsize Pstatus  ...  Walc  health absences  G1  G2  G3\n",
              "0     GP   F   18       U     GT3       A  ...     1       3        6   5   6   6\n",
              "1     GP   F   17       U     GT3       T  ...     1       3        4   5   5   6\n",
              "2     GP   F   15       U     LE3       T  ...     3       3       10   7   8  10\n",
              "3     GP   F   15       U     GT3       T  ...     1       5        2  15  14  15\n",
              "4     GP   F   16       U     GT3       T  ...     2       5        4   6  10  10\n",
              "5     GP   M   16       U     LE3       T  ...     2       5       10  15  15  15\n",
              "6     GP   M   16       U     LE3       T  ...     1       3        0  12  12  11\n",
              "7     GP   F   17       U     GT3       A  ...     1       1        6   6   5   6\n",
              "8     GP   M   15       U     LE3       A  ...     1       1        0  16  18  19\n",
              "9     GP   M   15       U     GT3       T  ...     1       5        0  14  15  15\n",
              "\n",
              "[10 rows x 33 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kozR3hfestHt"
      },
      "source": [
        "we are going to consider only `G1` and `g3`\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "dcQLh_hwsmto",
        "outputId": "b6723236-b028-4fef-97c1-27435d799cc8"
      },
      "source": [
        "uniMathData = mathData[['G1', 'G3']]\n",
        "uniMathData.head(5)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .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>G1</th>\n",
              "      <th>G3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   G1  G3\n",
              "0   5   6\n",
              "1   5   6\n",
              "2   7  10\n",
              "3  15  15\n",
              "4   6  10"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AzFiUU3c9d86"
      },
      "source": [
        "uniMathData.to_csv('univarit_MathData_2.csv')"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mvv0R_oO9pMo",
        "outputId": "0e44b3a3-fdb7-4ec7-edd1-ec4d0c2c8953"
      },
      "source": [
        "ls"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cleanedMathData.csv  student-merge.R  univarit_MathData_2.csv\n",
            "student-mat.csv      student-por.csv\n",
            "student-mat.gsheet   student.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3tgRbtZL9UEY"
      },
      "source": [
        "## data visualization\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "id": "gc8jD2YT-Zl3",
        "outputId": "a6ea3417-d0d4-48e8-848f-c2051249d4f2"
      },
      "source": [
        "import seaborn as sns\n",
        "sns.palplot(sns.color_palette('PuOr')) #Purple to Orange colors\n",
        "pal = sns.color_palette('PuOr', 6) #print 6 color shades from Purple to Orange\n",
        "pal.as_hex() #set hex code values for colors\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use(['seaborn'])\n",
        "sns_colors = ['#c6690c', '#664697'] #orange Purple hex codes\n",
        "sns.set_palette(sns_colors) #set the palette as sns_colors \n",
        "sns.palplot(sns.color_palette(sns_colors)) #plot the color codes"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 144x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bbEM-Czl_CGS",
        "outputId": "220ad378-e563-4477-99d0-4551f19b8650"
      },
      "source": [
        "uniMathData.columns"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['G1', 'G3'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "wjtMFGng_Lqu",
        "outputId": "6d569843-fb7c-4a6f-e940-1c8bdaa067f1"
      },
      "source": [
        "sns.pairplot(uniMathData,\n",
        "             x_vars=['G1','G3'],\n",
        "             y_vars=['G1','G3'],\n",
        "             diag_kind='kde'\n",
        "             )"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7fd9fd9833d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w9GEgp9K_9rn"
      },
      "source": [
        "##Data split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wy34z7pj_tg9",
        "outputId": "d2490204-99ad-409d-9fce-8cb77ca725d2"
      },
      "source": [
        "trainDataset = uniMathData.sample(frac=0.8, random_state=0) \n",
        "testDataset = uniMathData.drop(trainDataset.index)\n",
        "\n",
        "print(trainDataset.head())\n",
        "print(testDataset.head())"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     G1  G3\n",
            "329  14  14\n",
            "318  11  10\n",
            "317   9   9\n",
            "65   16  15\n",
            "59   15  16\n",
            "    G1  G3\n",
            "9   14  15\n",
            "25   6   8\n",
            "28  11  11\n",
            "31  17  17\n",
            "32  17  16\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8dWJuc99FI-E",
        "outputId": "bfe36e97-5d13-46fa-fe78-27e8a91c6e5b"
      },
      "source": [
        "print(trainDataset.shape)\n",
        "print(testDataset.shape)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(316, 2)\n",
            "(79, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OnTMuIysASm5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "01f1af62-eae9-4d77-82d7-a6d03ce38ad2"
      },
      "source": [
        "trainFeatures = trainDataset.copy()\n",
        "testFeatures = testDataset.copy()\n",
        "\n",
        "print(trainFeatures.head())\n",
        "print(testFeatures.head())"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     G1  G3\n",
            "329  14  14\n",
            "318  11  10\n",
            "317   9   9\n",
            "65   16  15\n",
            "59   15  16\n",
            "    G1  G3\n",
            "9   14  15\n",
            "25   6   8\n",
            "28  11  11\n",
            "31  17  17\n",
            "32  17  16\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M7RZoSrjBF6H",
        "outputId": "322f00a3-ba98-481e-a544-b7a11717b618"
      },
      "source": [
        "trainLabels = trainFeatures.pop('G3')\n",
        "testLabels = testFeatures.pop('G3')\n",
        "\n",
        "print(trainLabels.head())\n",
        "print(testLabels.head())"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "329    14\n",
            "318    10\n",
            "317     9\n",
            "65     15\n",
            "59     16\n",
            "Name: G3, dtype: int64\n",
            "9     15\n",
            "25     8\n",
            "28    11\n",
            "31    17\n",
            "32    16\n",
            "Name: G3, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kkIpX4A2KpBc",
        "outputId": "c100bc39-f7f5-4fa0-8842-4e6d38ce66cd"
      },
      "source": [
        "print(trainFeatures.head())\n",
        "print(testFeatures.head())"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     G1\n",
            "329  14\n",
            "318  11\n",
            "317   9\n",
            "65   16\n",
            "59   15\n",
            "    G1\n",
            "9   14\n",
            "25   6\n",
            "28  11\n",
            "31  17\n",
            "32  17\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zVe1D48vBsTq"
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "                             tf.keras.layers.Dense(1)\n",
        "\n",
        "])"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4sKyGlLNCfp9"
      },
      "source": [
        "model.compile(\n",
        "    loss = 'mean_absolute_error', #minimizing the MAE loss\n",
        "    optimizer = tf.keras.optimizers.Adam(0.001), #learning rate specified as 0.001\n",
        "    # optimizer = 'adam', #takes the default learning rate\n",
        "    metrics = ['mae', 'mse'] #meanSquare and meanAbsolute error metrics\n",
        ")"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9HgPbL8XC7DF",
        "outputId": "880183f7-07cd-4327-e5c0-cf62f4b26ddf"
      },
      "source": [
        "numEpochs = 600\n",
        "history = model.fit(x = trainFeatures, y = trainLabels, validation_data = (testFeatures, testLabels), epochs = numEpochs)"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/600\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 1.8929 - mae: 1.8929 - mse: 8.7629 - val_loss: 1.6348 - val_mae: 1.6348 - val_mse: 6.7224\n",
            "Epoch 2/600\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 1.8928 - mae: 1.8928 - mse: 8.7663 - val_loss: 1.6368 - val_mae: 1.6368 - val_mse: 6.7300\n",
            "Epoch 3/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8928 - mae: 1.8928 - mse: 8.7697 - val_loss: 1.6357 - val_mae: 1.6357 - val_mse: 6.7250\n",
            "Epoch 4/600\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 1.8925 - mae: 1.8925 - mse: 8.7648 - val_loss: 1.6348 - val_mae: 1.6348 - val_mse: 6.7208\n",
            "Epoch 5/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8926 - mae: 1.8926 - mse: 8.7634 - val_loss: 1.6349 - val_mae: 1.6349 - val_mse: 6.7209\n",
            "Epoch 6/600\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 1.8926 - mae: 1.8926 - mse: 8.7601 - val_loss: 1.6345 - val_mae: 1.6345 - val_mse: 6.7188\n",
            "Epoch 7/600\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 1.8925 - mae: 1.8925 - mse: 8.7619 - val_loss: 1.6354 - val_mae: 1.6354 - val_mse: 6.7218\n",
            "Epoch 8/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8925 - mae: 1.8925 - mse: 8.7636 - val_loss: 1.6346 - val_mae: 1.6346 - val_mse: 6.7180\n",
            "Epoch 9/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8923 - mae: 1.8923 - mse: 8.7606 - val_loss: 1.6354 - val_mae: 1.6354 - val_mse: 6.7207\n",
            "Epoch 10/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8925 - mae: 1.8925 - mse: 8.7620 - val_loss: 1.6344 - val_mae: 1.6344 - val_mse: 6.7163\n",
            "Epoch 11/600\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 1.8923 - mae: 1.8923 - mse: 8.7604 - val_loss: 1.6352 - val_mae: 1.6352 - val_mse: 6.7190\n",
            "Epoch 12/600\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 1.8921 - mae: 1.8921 - mse: 8.7596 - val_loss: 1.6347 - val_mae: 1.6347 - val_mse: 6.7166\n",
            "Epoch 13/600\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 1.8921 - mae: 1.8921 - mse: 8.7575 - val_loss: 1.6347 - val_mae: 1.6347 - val_mse: 6.7160\n",
            "Epoch 14/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8922 - mae: 1.8922 - mse: 8.7604 - val_loss: 1.6352 - val_mae: 1.6352 - val_mse: 6.7175\n",
            "Epoch 15/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8923 - mae: 1.8923 - mse: 8.7563 - val_loss: 1.6344 - val_mae: 1.6344 - val_mse: 6.7137\n",
            "Epoch 16/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8920 - mae: 1.8920 - mse: 8.7583 - val_loss: 1.6356 - val_mae: 1.6356 - val_mse: 6.7180\n",
            "Epoch 17/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8920 - mae: 1.8920 - mse: 8.7608 - val_loss: 1.6352 - val_mae: 1.6352 - val_mse: 6.7158\n",
            "Epoch 18/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8922 - mae: 1.8922 - mse: 8.7533 - val_loss: 1.6328 - val_mae: 1.6328 - val_mse: 6.7052\n",
            "Epoch 19/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8918 - mae: 1.8918 - mse: 8.7501 - val_loss: 1.6346 - val_mae: 1.6346 - val_mse: 6.7124\n",
            "Epoch 20/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8917 - mae: 1.8917 - mse: 8.7556 - val_loss: 1.6347 - val_mae: 1.6347 - val_mse: 6.7125\n",
            "Epoch 21/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8916 - mae: 1.8916 - mse: 8.7535 - val_loss: 1.6347 - val_mae: 1.6347 - val_mse: 6.7120\n",
            "Epoch 22/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8916 - mae: 1.8916 - mse: 8.7548 - val_loss: 1.6344 - val_mae: 1.6344 - val_mse: 6.7099\n",
            "Epoch 23/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8918 - mae: 1.8918 - mse: 8.7496 - val_loss: 1.6332 - val_mae: 1.6332 - val_mse: 6.7044\n",
            "Epoch 24/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8917 - mae: 1.8917 - mse: 8.7541 - val_loss: 1.6350 - val_mae: 1.6350 - val_mse: 6.7114\n",
            "Epoch 25/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8914 - mae: 1.8914 - mse: 8.7514 - val_loss: 1.6342 - val_mae: 1.6342 - val_mse: 6.7077\n",
            "Epoch 26/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8918 - mae: 1.8918 - mse: 8.7548 - val_loss: 1.6351 - val_mae: 1.6351 - val_mse: 6.7108\n",
            "Epoch 27/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8917 - mae: 1.8917 - mse: 8.7492 - val_loss: 1.6322 - val_mae: 1.6322 - val_mse: 6.6978\n",
            "Epoch 28/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8914 - mae: 1.8914 - mse: 8.7428 - val_loss: 1.6332 - val_mae: 1.6332 - val_mse: 6.7016\n",
            "Epoch 29/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8912 - mae: 1.8912 - mse: 8.7478 - val_loss: 1.6347 - val_mae: 1.6347 - val_mse: 6.7077\n",
            "Epoch 30/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8911 - mae: 1.8911 - mse: 8.7506 - val_loss: 1.6346 - val_mae: 1.6346 - val_mse: 6.7068\n",
            "Epoch 31/600\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 1.8909 - mae: 1.8909 - mse: 8.7476 - val_loss: 1.6339 - val_mae: 1.6339 - val_mse: 6.7030\n",
            "Epoch 32/600\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 1.8909 - mae: 1.8909 - mse: 8.7465 - val_loss: 1.6343 - val_mae: 1.6343 - val_mse: 6.7043\n",
            "Epoch 33/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8910 - mae: 1.8910 - mse: 8.7464 - val_loss: 1.6341 - val_mae: 1.6341 - val_mse: 6.7028\n",
            "Epoch 34/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8910 - mae: 1.8910 - mse: 8.7497 - val_loss: 1.6349 - val_mae: 1.6349 - val_mse: 6.7053\n",
            "Epoch 35/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8907 - mae: 1.8907 - mse: 8.7471 - val_loss: 1.6339 - val_mae: 1.6339 - val_mse: 6.7010\n",
            "Epoch 36/600\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 1.8908 - mae: 1.8908 - mse: 8.7413 - val_loss: 1.6325 - val_mae: 1.6325 - val_mse: 6.6946\n",
            "Epoch 37/600\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 1.8909 - mae: 1.8909 - mse: 8.7374 - val_loss: 1.6328 - val_mae: 1.6328 - val_mse: 6.6951\n",
            "Epoch 38/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8906 - mae: 1.8906 - mse: 8.7426 - val_loss: 1.6348 - val_mae: 1.6348 - val_mse: 6.7030\n",
            "Epoch 39/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8906 - mae: 1.8906 - mse: 8.7460 - val_loss: 1.6340 - val_mae: 1.6340 - val_mse: 6.6993\n",
            "Epoch 40/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8904 - mae: 1.8904 - mse: 8.7430 - val_loss: 1.6336 - val_mae: 1.6336 - val_mse: 6.6969\n",
            "Epoch 41/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8904 - mae: 1.8904 - mse: 8.7401 - val_loss: 1.6336 - val_mae: 1.6336 - val_mse: 6.6962\n",
            "Epoch 42/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8905 - mae: 1.8905 - mse: 8.7433 - val_loss: 1.6347 - val_mae: 1.6347 - val_mse: 6.7001\n",
            "Epoch 43/600\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 1.8905 - mae: 1.8905 - mse: 8.7413 - val_loss: 1.6326 - val_mae: 1.6326 - val_mse: 6.6908\n",
            "Epoch 44/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8903 - mae: 1.8903 - mse: 8.7352 - val_loss: 1.6328 - val_mae: 1.6328 - val_mse: 6.6912\n",
            "Epoch 45/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8905 - mae: 1.8905 - mse: 8.7432 - val_loss: 1.6345 - val_mae: 1.6345 - val_mse: 6.6976\n",
            "Epoch 46/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8902 - mae: 1.8902 - mse: 8.7371 - val_loss: 1.6333 - val_mae: 1.6333 - val_mse: 6.6921\n",
            "Epoch 47/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8899 - mae: 1.8899 - mse: 8.7375 - val_loss: 1.6336 - val_mae: 1.6336 - val_mse: 6.6929\n",
            "Epoch 48/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8903 - mae: 1.8903 - mse: 8.7413 - val_loss: 1.6343 - val_mae: 1.6343 - val_mse: 6.6951\n",
            "Epoch 49/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8899 - mae: 1.8899 - mse: 8.7340 - val_loss: 1.6319 - val_mae: 1.6319 - val_mse: 6.6847\n",
            "Epoch 50/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8902 - mae: 1.8902 - mse: 8.7283 - val_loss: 1.6319 - val_mae: 1.6319 - val_mse: 6.6838\n",
            "Epoch 51/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8899 - mae: 1.8899 - mse: 8.7283 - val_loss: 1.6320 - val_mae: 1.6320 - val_mse: 6.6838\n",
            "Epoch 52/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8907 - mae: 1.8907 - mse: 8.7392 - val_loss: 1.6353 - val_mae: 1.6353 - val_mse: 6.6966\n",
            "Epoch 53/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8899 - mae: 1.8899 - mse: 8.7361 - val_loss: 1.6322 - val_mae: 1.6322 - val_mse: 6.6837\n",
            "Epoch 54/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8896 - mae: 1.8896 - mse: 8.7299 - val_loss: 1.6324 - val_mae: 1.6324 - val_mse: 6.6837\n",
            "Epoch 55/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8895 - mae: 1.8895 - mse: 8.7310 - val_loss: 1.6335 - val_mae: 1.6335 - val_mse: 6.6878\n",
            "Epoch 56/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8894 - mae: 1.8894 - mse: 8.7318 - val_loss: 1.6332 - val_mae: 1.6332 - val_mse: 6.6862\n",
            "Epoch 57/600\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 1.8893 - mae: 1.8893 - mse: 8.7311 - val_loss: 1.6333 - val_mae: 1.6333 - val_mse: 6.6858\n",
            "Epoch 58/600\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 1.8893 - mae: 1.8893 - mse: 8.7306 - val_loss: 1.6329 - val_mae: 1.6329 - val_mse: 6.6840\n",
            "Epoch 59/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8892 - mae: 1.8892 - mse: 8.7301 - val_loss: 1.6325 - val_mae: 1.6325 - val_mse: 6.6813\n",
            "Epoch 60/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8893 - mae: 1.8893 - mse: 8.7303 - val_loss: 1.6333 - val_mae: 1.6333 - val_mse: 6.6839\n",
            "Epoch 61/600\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 1.8894 - mae: 1.8894 - mse: 8.7328 - val_loss: 1.6329 - val_mae: 1.6329 - val_mse: 6.6821\n",
            "Epoch 62/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8891 - mae: 1.8891 - mse: 8.7254 - val_loss: 1.6317 - val_mae: 1.6317 - val_mse: 6.6761\n",
            "Epoch 63/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8890 - mae: 1.8890 - mse: 8.7231 - val_loss: 1.6323 - val_mae: 1.6323 - val_mse: 6.6783\n",
            "Epoch 64/600\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 1.8889 - mae: 1.8889 - mse: 8.7251 - val_loss: 1.6335 - val_mae: 1.6335 - val_mse: 6.6825\n",
            "Epoch 65/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8890 - mae: 1.8890 - mse: 8.7308 - val_loss: 1.6341 - val_mae: 1.6341 - val_mse: 6.6842\n",
            "Epoch 66/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8888 - mae: 1.8888 - mse: 8.7246 - val_loss: 1.6320 - val_mae: 1.6320 - val_mse: 6.6751\n",
            "Epoch 67/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8887 - mae: 1.8887 - mse: 8.7237 - val_loss: 1.6323 - val_mae: 1.6323 - val_mse: 6.6759\n",
            "Epoch 68/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8888 - mae: 1.8888 - mse: 8.7264 - val_loss: 1.6331 - val_mae: 1.6331 - val_mse: 6.6786\n",
            "Epoch 69/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8887 - mae: 1.8887 - mse: 8.7213 - val_loss: 1.6319 - val_mae: 1.6319 - val_mse: 6.6729\n",
            "Epoch 70/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8887 - mae: 1.8887 - mse: 8.7182 - val_loss: 1.6313 - val_mae: 1.6313 - val_mse: 6.6696\n",
            "Epoch 71/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8886 - mae: 1.8886 - mse: 8.7226 - val_loss: 1.6335 - val_mae: 1.6335 - val_mse: 6.6782\n",
            "Epoch 72/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8885 - mae: 1.8885 - mse: 8.7230 - val_loss: 1.6329 - val_mae: 1.6329 - val_mse: 6.6754\n",
            "Epoch 73/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8883 - mae: 1.8883 - mse: 8.7212 - val_loss: 1.6321 - val_mae: 1.6321 - val_mse: 6.6716\n",
            "Epoch 74/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8882 - mae: 1.8882 - mse: 8.7192 - val_loss: 1.6319 - val_mae: 1.6319 - val_mse: 6.6698\n",
            "Epoch 75/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8882 - mae: 1.8882 - mse: 8.7179 - val_loss: 1.6326 - val_mae: 1.6326 - val_mse: 6.6723\n",
            "Epoch 76/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8881 - mae: 1.8881 - mse: 8.7185 - val_loss: 1.6326 - val_mae: 1.6326 - val_mse: 6.6715\n",
            "Epoch 77/600\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 1.8879 - mae: 1.8879 - mse: 8.7174 - val_loss: 1.6314 - val_mae: 1.6314 - val_mse: 6.6660\n",
            "Epoch 78/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8881 - mae: 1.8881 - mse: 8.7142 - val_loss: 1.6312 - val_mae: 1.6312 - val_mse: 6.6645\n",
            "Epoch 79/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8880 - mae: 1.8880 - mse: 8.7157 - val_loss: 1.6325 - val_mae: 1.6325 - val_mse: 6.6696\n",
            "Epoch 80/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8876 - mae: 1.8876 - mse: 8.7143 - val_loss: 1.6309 - val_mae: 1.6309 - val_mse: 6.6618\n",
            "Epoch 81/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8878 - mae: 1.8878 - mse: 8.7119 - val_loss: 1.6315 - val_mae: 1.6315 - val_mse: 6.6639\n",
            "Epoch 82/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8876 - mae: 1.8876 - mse: 8.7116 - val_loss: 1.6317 - val_mae: 1.6317 - val_mse: 6.6642\n",
            "Epoch 83/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8877 - mae: 1.8877 - mse: 8.7156 - val_loss: 1.6323 - val_mae: 1.6323 - val_mse: 6.6661\n",
            "Epoch 84/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8876 - mae: 1.8876 - mse: 8.7106 - val_loss: 1.6309 - val_mae: 1.6309 - val_mse: 6.6594\n",
            "Epoch 85/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8876 - mae: 1.8876 - mse: 8.7132 - val_loss: 1.6320 - val_mae: 1.6320 - val_mse: 6.6636\n",
            "Epoch 86/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8875 - mae: 1.8875 - mse: 8.7089 - val_loss: 1.6311 - val_mae: 1.6311 - val_mse: 6.6590\n",
            "Epoch 87/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8873 - mae: 1.8873 - mse: 8.7117 - val_loss: 1.6330 - val_mae: 1.6330 - val_mse: 6.6662\n",
            "Epoch 88/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8874 - mae: 1.8874 - mse: 8.7139 - val_loss: 1.6318 - val_mae: 1.6318 - val_mse: 6.6609\n",
            "Epoch 89/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8873 - mae: 1.8873 - mse: 8.7098 - val_loss: 1.6314 - val_mae: 1.6314 - val_mse: 6.6588\n",
            "Epoch 90/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8872 - mae: 1.8872 - mse: 8.7051 - val_loss: 1.6310 - val_mae: 1.6310 - val_mse: 6.6560\n",
            "Epoch 91/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8870 - mae: 1.8870 - mse: 8.7062 - val_loss: 1.6319 - val_mae: 1.6319 - val_mse: 6.6595\n",
            "Epoch 92/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8869 - mae: 1.8869 - mse: 8.7061 - val_loss: 1.6303 - val_mae: 1.6303 - val_mse: 6.6517\n",
            "Epoch 93/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8869 - mae: 1.8869 - mse: 8.7056 - val_loss: 1.6313 - val_mae: 1.6313 - val_mse: 6.6554\n",
            "Epoch 94/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8868 - mae: 1.8868 - mse: 8.7014 - val_loss: 1.6304 - val_mae: 1.6304 - val_mse: 6.6511\n",
            "Epoch 95/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8867 - mae: 1.8867 - mse: 8.7011 - val_loss: 1.6314 - val_mae: 1.6314 - val_mse: 6.6549\n",
            "Epoch 96/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8865 - mae: 1.8865 - mse: 8.7025 - val_loss: 1.6306 - val_mae: 1.6306 - val_mse: 6.6506\n",
            "Epoch 97/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8865 - mae: 1.8865 - mse: 8.7004 - val_loss: 1.6309 - val_mae: 1.6309 - val_mse: 6.6513\n",
            "Epoch 98/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8867 - mae: 1.8867 - mse: 8.7063 - val_loss: 1.6326 - val_mae: 1.6326 - val_mse: 6.6575\n",
            "Epoch 99/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8863 - mae: 1.8863 - mse: 8.7020 - val_loss: 1.6308 - val_mae: 1.6308 - val_mse: 6.6495\n",
            "Epoch 100/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8863 - mae: 1.8863 - mse: 8.6974 - val_loss: 1.6303 - val_mae: 1.6303 - val_mse: 6.6469\n",
            "Epoch 101/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8862 - mae: 1.8862 - mse: 8.6989 - val_loss: 1.6315 - val_mae: 1.6315 - val_mse: 6.6513\n",
            "Epoch 102/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8861 - mae: 1.8861 - mse: 8.6997 - val_loss: 1.6312 - val_mae: 1.6312 - val_mse: 6.6494\n",
            "Epoch 103/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8860 - mae: 1.8860 - mse: 8.6977 - val_loss: 1.6306 - val_mae: 1.6306 - val_mse: 6.6460\n",
            "Epoch 104/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8859 - mae: 1.8859 - mse: 8.6975 - val_loss: 1.6307 - val_mae: 1.6307 - val_mse: 6.6458\n",
            "Epoch 105/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8858 - mae: 1.8858 - mse: 8.6969 - val_loss: 1.6307 - val_mae: 1.6307 - val_mse: 6.6454\n",
            "Epoch 106/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8858 - mae: 1.8858 - mse: 8.6952 - val_loss: 1.6312 - val_mae: 1.6312 - val_mse: 6.6466\n",
            "Epoch 107/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8857 - mae: 1.8857 - mse: 8.6960 - val_loss: 1.6309 - val_mae: 1.6309 - val_mse: 6.6448\n",
            "Epoch 108/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8857 - mae: 1.8857 - mse: 8.6947 - val_loss: 1.6307 - val_mae: 1.6307 - val_mse: 6.6435\n",
            "Epoch 109/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8855 - mae: 1.8855 - mse: 8.6930 - val_loss: 1.6304 - val_mae: 1.6304 - val_mse: 6.6414\n",
            "Epoch 110/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8854 - mae: 1.8854 - mse: 8.6930 - val_loss: 1.6306 - val_mae: 1.6306 - val_mse: 6.6415\n",
            "Epoch 111/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8857 - mae: 1.8857 - mse: 8.6943 - val_loss: 1.6295 - val_mae: 1.6295 - val_mse: 6.6362\n",
            "Epoch 112/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8855 - mae: 1.8855 - mse: 8.6852 - val_loss: 1.6283 - val_mae: 1.6283 - val_mse: 6.6300\n",
            "Epoch 113/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8855 - mae: 1.8855 - mse: 8.6886 - val_loss: 1.6305 - val_mae: 1.6305 - val_mse: 6.6389\n",
            "Epoch 114/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8852 - mae: 1.8852 - mse: 8.6898 - val_loss: 1.6306 - val_mae: 1.6306 - val_mse: 6.6388\n",
            "Epoch 115/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8852 - mae: 1.8852 - mse: 8.6906 - val_loss: 1.6298 - val_mae: 1.6298 - val_mse: 6.6349\n",
            "Epoch 116/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8852 - mae: 1.8852 - mse: 8.6859 - val_loss: 1.6298 - val_mae: 1.6298 - val_mse: 6.6343\n",
            "Epoch 117/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8851 - mae: 1.8851 - mse: 8.6897 - val_loss: 1.6309 - val_mae: 1.6309 - val_mse: 6.6380\n",
            "Epoch 118/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8848 - mae: 1.8848 - mse: 8.6864 - val_loss: 1.6296 - val_mae: 1.6296 - val_mse: 6.6318\n",
            "Epoch 119/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8852 - mae: 1.8852 - mse: 8.6837 - val_loss: 1.6290 - val_mae: 1.6290 - val_mse: 6.6286\n",
            "Epoch 120/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8845 - mae: 1.8845 - mse: 8.6851 - val_loss: 1.6306 - val_mae: 1.6306 - val_mse: 6.6347\n",
            "Epoch 121/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8846 - mae: 1.8846 - mse: 8.6857 - val_loss: 1.6300 - val_mae: 1.6300 - val_mse: 6.6314\n",
            "Epoch 122/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8850 - mae: 1.8850 - mse: 8.6785 - val_loss: 1.6274 - val_mae: 1.6274 - val_mse: 6.6197\n",
            "Epoch 123/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8846 - mae: 1.8846 - mse: 8.6793 - val_loss: 1.6300 - val_mae: 1.6300 - val_mse: 6.6303\n",
            "Epoch 124/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8845 - mae: 1.8845 - mse: 8.6824 - val_loss: 1.6293 - val_mae: 1.6293 - val_mse: 6.6265\n",
            "Epoch 125/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8843 - mae: 1.8843 - mse: 8.6819 - val_loss: 1.6304 - val_mae: 1.6304 - val_mse: 6.6305\n",
            "Epoch 126/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8843 - mae: 1.8843 - mse: 8.6813 - val_loss: 1.6296 - val_mae: 1.6296 - val_mse: 6.6267\n",
            "Epoch 127/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8841 - mae: 1.8841 - mse: 8.6792 - val_loss: 1.6298 - val_mae: 1.6298 - val_mse: 6.6267\n",
            "Epoch 128/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8842 - mae: 1.8842 - mse: 8.6790 - val_loss: 1.6288 - val_mae: 1.6288 - val_mse: 6.6216\n",
            "Epoch 129/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8842 - mae: 1.8842 - mse: 8.6731 - val_loss: 1.6280 - val_mae: 1.6280 - val_mse: 6.6175\n",
            "Epoch 130/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8839 - mae: 1.8839 - mse: 8.6769 - val_loss: 1.6298 - val_mae: 1.6298 - val_mse: 6.6245\n",
            "Epoch 131/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8844 - mae: 1.8844 - mse: 8.6768 - val_loss: 1.6279 - val_mae: 1.6279 - val_mse: 6.6155\n",
            "Epoch 132/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8844 - mae: 1.8844 - mse: 8.6803 - val_loss: 1.6315 - val_mae: 1.6315 - val_mse: 6.6299\n",
            "Epoch 133/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8837 - mae: 1.8837 - mse: 8.6793 - val_loss: 1.6294 - val_mae: 1.6294 - val_mse: 6.6208\n",
            "Epoch 134/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8837 - mae: 1.8837 - mse: 8.6697 - val_loss: 1.6267 - val_mae: 1.6267 - val_mse: 6.6085\n",
            "Epoch 135/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8837 - mae: 1.8837 - mse: 8.6659 - val_loss: 1.6282 - val_mae: 1.6282 - val_mse: 6.6143\n",
            "Epoch 136/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8837 - mae: 1.8837 - mse: 8.6678 - val_loss: 1.6278 - val_mae: 1.6278 - val_mse: 6.6117\n",
            "Epoch 137/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8834 - mae: 1.8834 - mse: 8.6732 - val_loss: 1.6314 - val_mae: 1.6314 - val_mse: 6.6258\n",
            "Epoch 138/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8836 - mae: 1.8836 - mse: 8.6800 - val_loss: 1.6314 - val_mae: 1.6314 - val_mse: 6.6251\n",
            "Epoch 139/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8841 - mae: 1.8841 - mse: 8.6744 - val_loss: 1.6275 - val_mae: 1.6275 - val_mse: 6.6084\n",
            "Epoch 140/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8833 - mae: 1.8833 - mse: 8.6719 - val_loss: 1.6309 - val_mae: 1.6309 - val_mse: 6.6216\n",
            "Epoch 141/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8830 - mae: 1.8830 - mse: 8.6728 - val_loss: 1.6293 - val_mae: 1.6293 - val_mse: 6.6147\n",
            "Epoch 142/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8831 - mae: 1.8831 - mse: 8.6648 - val_loss: 1.6265 - val_mae: 1.6265 - val_mse: 6.6017\n",
            "Epoch 143/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8829 - mae: 1.8829 - mse: 8.6599 - val_loss: 1.6281 - val_mae: 1.6281 - val_mse: 6.6081\n",
            "Epoch 144/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8829 - mae: 1.8829 - mse: 8.6630 - val_loss: 1.6290 - val_mae: 1.6290 - val_mse: 6.6112\n",
            "Epoch 145/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8829 - mae: 1.8829 - mse: 8.6700 - val_loss: 1.6293 - val_mae: 1.6293 - val_mse: 6.6119\n",
            "Epoch 146/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8825 - mae: 1.8825 - mse: 8.6658 - val_loss: 1.6280 - val_mae: 1.6280 - val_mse: 6.6057\n",
            "Epoch 147/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8826 - mae: 1.8826 - mse: 8.6585 - val_loss: 1.6268 - val_mae: 1.6268 - val_mse: 6.5996\n",
            "Epoch 148/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8826 - mae: 1.8826 - mse: 8.6610 - val_loss: 1.6294 - val_mae: 1.6294 - val_mse: 6.6100\n",
            "Epoch 149/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8824 - mae: 1.8824 - mse: 8.6665 - val_loss: 1.6289 - val_mae: 1.6289 - val_mse: 6.6074\n",
            "Epoch 150/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8821 - mae: 1.8821 - mse: 8.6608 - val_loss: 1.6277 - val_mae: 1.6277 - val_mse: 6.6015\n",
            "Epoch 151/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8823 - mae: 1.8823 - mse: 8.6601 - val_loss: 1.6288 - val_mae: 1.6288 - val_mse: 6.6056\n",
            "Epoch 152/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8819 - mae: 1.8819 - mse: 8.6582 - val_loss: 1.6273 - val_mae: 1.6273 - val_mse: 6.5982\n",
            "Epoch 153/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8823 - mae: 1.8823 - mse: 8.6615 - val_loss: 1.6285 - val_mae: 1.6285 - val_mse: 6.6028\n",
            "Epoch 154/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8820 - mae: 1.8820 - mse: 8.6541 - val_loss: 1.6263 - val_mae: 1.6263 - val_mse: 6.5927\n",
            "Epoch 155/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8819 - mae: 1.8819 - mse: 8.6545 - val_loss: 1.6281 - val_mae: 1.6281 - val_mse: 6.5997\n",
            "Epoch 156/600\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 1.8817 - mae: 1.8817 - mse: 8.6573 - val_loss: 1.6276 - val_mae: 1.6276 - val_mse: 6.5968\n",
            "Epoch 157/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8821 - mae: 1.8821 - mse: 8.6506 - val_loss: 1.6257 - val_mae: 1.6257 - val_mse: 6.5876\n",
            "Epoch 158/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8816 - mae: 1.8816 - mse: 8.6519 - val_loss: 1.6281 - val_mae: 1.6281 - val_mse: 6.5974\n",
            "Epoch 159/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8813 - mae: 1.8813 - mse: 8.6531 - val_loss: 1.6276 - val_mae: 1.6276 - val_mse: 6.5947\n",
            "Epoch 160/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8812 - mae: 1.8812 - mse: 8.6524 - val_loss: 1.6279 - val_mae: 1.6279 - val_mse: 6.5954\n",
            "Epoch 161/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8812 - mae: 1.8812 - mse: 8.6499 - val_loss: 1.6273 - val_mae: 1.6273 - val_mse: 6.5921\n",
            "Epoch 162/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8811 - mae: 1.8811 - mse: 8.6483 - val_loss: 1.6278 - val_mae: 1.6278 - val_mse: 6.5934\n",
            "Epoch 163/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8810 - mae: 1.8810 - mse: 8.6518 - val_loss: 1.6282 - val_mae: 1.6282 - val_mse: 6.5943\n",
            "Epoch 164/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8809 - mae: 1.8809 - mse: 8.6495 - val_loss: 1.6272 - val_mae: 1.6272 - val_mse: 6.5896\n",
            "Epoch 165/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8809 - mae: 1.8809 - mse: 8.6453 - val_loss: 1.6264 - val_mae: 1.6264 - val_mse: 6.5854\n",
            "Epoch 166/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8812 - mae: 1.8812 - mse: 8.6519 - val_loss: 1.6288 - val_mae: 1.6288 - val_mse: 6.5943\n",
            "Epoch 167/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8808 - mae: 1.8808 - mse: 8.6466 - val_loss: 1.6267 - val_mae: 1.6267 - val_mse: 6.5849\n",
            "Epoch 168/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8806 - mae: 1.8806 - mse: 8.6460 - val_loss: 1.6273 - val_mae: 1.6273 - val_mse: 6.5870\n",
            "Epoch 169/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8807 - mae: 1.8807 - mse: 8.6467 - val_loss: 1.6264 - val_mae: 1.6264 - val_mse: 6.5821\n",
            "Epoch 170/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8803 - mae: 1.8803 - mse: 8.6427 - val_loss: 1.6272 - val_mae: 1.6272 - val_mse: 6.5848\n",
            "Epoch 171/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8802 - mae: 1.8802 - mse: 8.6431 - val_loss: 1.6263 - val_mae: 1.6263 - val_mse: 6.5803\n",
            "Epoch 172/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8804 - mae: 1.8804 - mse: 8.6364 - val_loss: 1.6249 - val_mae: 1.6249 - val_mse: 6.5736\n",
            "Epoch 173/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8802 - mae: 1.8802 - mse: 8.6374 - val_loss: 1.6276 - val_mae: 1.6276 - val_mse: 6.5842\n",
            "Epoch 174/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8800 - mae: 1.8800 - mse: 8.6421 - val_loss: 1.6266 - val_mae: 1.6266 - val_mse: 6.5793\n",
            "Epoch 175/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8800 - mae: 1.8800 - mse: 8.6380 - val_loss: 1.6263 - val_mae: 1.6263 - val_mse: 6.5776\n",
            "Epoch 176/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8799 - mae: 1.8799 - mse: 8.6416 - val_loss: 1.6279 - val_mae: 1.6279 - val_mse: 6.5833\n",
            "Epoch 177/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8799 - mae: 1.8799 - mse: 8.6400 - val_loss: 1.6270 - val_mae: 1.6270 - val_mse: 6.5789\n",
            "Epoch 178/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8795 - mae: 1.8795 - mse: 8.6354 - val_loss: 1.6260 - val_mae: 1.6260 - val_mse: 6.5741\n",
            "Epoch 179/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8796 - mae: 1.8796 - mse: 8.6361 - val_loss: 1.6267 - val_mae: 1.6267 - val_mse: 6.5760\n",
            "Epoch 180/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8797 - mae: 1.8797 - mse: 8.6308 - val_loss: 1.6248 - val_mae: 1.6248 - val_mse: 6.5673\n",
            "Epoch 181/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8796 - mae: 1.8796 - mse: 8.6339 - val_loss: 1.6260 - val_mae: 1.6260 - val_mse: 6.5718\n",
            "Epoch 182/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8798 - mae: 1.8798 - mse: 8.6290 - val_loss: 1.6255 - val_mae: 1.6255 - val_mse: 6.5689\n",
            "Epoch 183/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8794 - mae: 1.8794 - mse: 8.6383 - val_loss: 1.6287 - val_mae: 1.6287 - val_mse: 6.5812\n",
            "Epoch 184/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8793 - mae: 1.8793 - mse: 8.6392 - val_loss: 1.6279 - val_mae: 1.6279 - val_mse: 6.5772\n",
            "Epoch 185/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8789 - mae: 1.8789 - mse: 8.6308 - val_loss: 1.6245 - val_mae: 1.6245 - val_mse: 6.5620\n",
            "Epoch 186/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8790 - mae: 1.8790 - mse: 8.6227 - val_loss: 1.6242 - val_mae: 1.6242 - val_mse: 6.5599\n",
            "Epoch 187/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8790 - mae: 1.8790 - mse: 8.6280 - val_loss: 1.6263 - val_mae: 1.6263 - val_mse: 6.5683\n",
            "Epoch 188/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8789 - mae: 1.8789 - mse: 8.6274 - val_loss: 1.6236 - val_mae: 1.6236 - val_mse: 6.5559\n",
            "Epoch 189/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8789 - mae: 1.8789 - mse: 8.6201 - val_loss: 1.6247 - val_mae: 1.6247 - val_mse: 6.5599\n",
            "Epoch 190/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8787 - mae: 1.8787 - mse: 8.6263 - val_loss: 1.6261 - val_mae: 1.6261 - val_mse: 6.5655\n",
            "Epoch 191/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8783 - mae: 1.8783 - mse: 8.6223 - val_loss: 1.6243 - val_mae: 1.6243 - val_mse: 6.5568\n",
            "Epoch 192/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8784 - mae: 1.8784 - mse: 8.6223 - val_loss: 1.6256 - val_mae: 1.6256 - val_mse: 6.5617\n",
            "Epoch 193/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8784 - mae: 1.8784 - mse: 8.6271 - val_loss: 1.6262 - val_mae: 1.6262 - val_mse: 6.5636\n",
            "Epoch 194/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8781 - mae: 1.8781 - mse: 8.6216 - val_loss: 1.6252 - val_mae: 1.6252 - val_mse: 6.5585\n",
            "Epoch 195/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8779 - mae: 1.8779 - mse: 8.6214 - val_loss: 1.6256 - val_mae: 1.6256 - val_mse: 6.5594\n",
            "Epoch 196/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8779 - mae: 1.8779 - mse: 8.6226 - val_loss: 1.6252 - val_mae: 1.6252 - val_mse: 6.5572\n",
            "Epoch 197/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8782 - mae: 1.8782 - mse: 8.6129 - val_loss: 1.6236 - val_mae: 1.6236 - val_mse: 6.5493\n",
            "Epoch 198/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8778 - mae: 1.8778 - mse: 8.6141 - val_loss: 1.6249 - val_mae: 1.6249 - val_mse: 6.5543\n",
            "Epoch 199/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8777 - mae: 1.8777 - mse: 8.6199 - val_loss: 1.6253 - val_mae: 1.6253 - val_mse: 6.5553\n",
            "Epoch 200/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8775 - mae: 1.8775 - mse: 8.6174 - val_loss: 1.6256 - val_mae: 1.6256 - val_mse: 6.5559\n",
            "Epoch 201/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8778 - mae: 1.8778 - mse: 8.6141 - val_loss: 1.6236 - val_mae: 1.6236 - val_mse: 6.5462\n",
            "Epoch 202/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8774 - mae: 1.8774 - mse: 8.6138 - val_loss: 1.6252 - val_mae: 1.6252 - val_mse: 6.5527\n",
            "Epoch 203/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8773 - mae: 1.8773 - mse: 8.6176 - val_loss: 1.6254 - val_mae: 1.6254 - val_mse: 6.5524\n",
            "Epoch 204/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8770 - mae: 1.8770 - mse: 8.6129 - val_loss: 1.6236 - val_mae: 1.6236 - val_mse: 6.5440\n",
            "Epoch 205/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8773 - mae: 1.8773 - mse: 8.6107 - val_loss: 1.6242 - val_mae: 1.6242 - val_mse: 6.5458\n",
            "Epoch 206/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8769 - mae: 1.8769 - mse: 8.6097 - val_loss: 1.6242 - val_mae: 1.6242 - val_mse: 6.5450\n",
            "Epoch 207/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8774 - mae: 1.8774 - mse: 8.6147 - val_loss: 1.6255 - val_mae: 1.6255 - val_mse: 6.5499\n",
            "Epoch 208/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8767 - mae: 1.8767 - mse: 8.6088 - val_loss: 1.6228 - val_mae: 1.6228 - val_mse: 6.5372\n",
            "Epoch 209/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8769 - mae: 1.8769 - mse: 8.6074 - val_loss: 1.6246 - val_mae: 1.6246 - val_mse: 6.5447\n",
            "Epoch 210/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8765 - mae: 1.8765 - mse: 8.6071 - val_loss: 1.6246 - val_mae: 1.6246 - val_mse: 6.5438\n",
            "Epoch 211/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8766 - mae: 1.8766 - mse: 8.6099 - val_loss: 1.6254 - val_mae: 1.6254 - val_mse: 6.5464\n",
            "Epoch 212/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8765 - mae: 1.8765 - mse: 8.6051 - val_loss: 1.6226 - val_mae: 1.6226 - val_mse: 6.5337\n",
            "Epoch 213/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8768 - mae: 1.8768 - mse: 8.6067 - val_loss: 1.6253 - val_mae: 1.6253 - val_mse: 6.5443\n",
            "Epoch 214/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8763 - mae: 1.8763 - mse: 8.6076 - val_loss: 1.6231 - val_mae: 1.6231 - val_mse: 6.5344\n",
            "Epoch 215/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8763 - mae: 1.8763 - mse: 8.5972 - val_loss: 1.6222 - val_mae: 1.6222 - val_mse: 6.5295\n",
            "Epoch 216/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8760 - mae: 1.8760 - mse: 8.5974 - val_loss: 1.6248 - val_mae: 1.6248 - val_mse: 6.5402\n",
            "Epoch 217/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8765 - mae: 1.8765 - mse: 8.6127 - val_loss: 1.6272 - val_mae: 1.6272 - val_mse: 6.5488\n",
            "Epoch 218/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8770 - mae: 1.8770 - mse: 8.6057 - val_loss: 1.6220 - val_mae: 1.6220 - val_mse: 6.5262\n",
            "Epoch 219/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8757 - mae: 1.8757 - mse: 8.5969 - val_loss: 1.6250 - val_mae: 1.6250 - val_mse: 6.5385\n",
            "Epoch 220/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8757 - mae: 1.8757 - mse: 8.6059 - val_loss: 1.6259 - val_mae: 1.6259 - val_mse: 6.5416\n",
            "Epoch 221/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8761 - mae: 1.8761 - mse: 8.5996 - val_loss: 1.6213 - val_mae: 1.6213 - val_mse: 6.5208\n",
            "Epoch 222/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8756 - mae: 1.8756 - mse: 8.5870 - val_loss: 1.6210 - val_mae: 1.6210 - val_mse: 6.5188\n",
            "Epoch 223/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8755 - mae: 1.8755 - mse: 8.5859 - val_loss: 1.6221 - val_mae: 1.6221 - val_mse: 6.5231\n",
            "Epoch 224/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8758 - mae: 1.8758 - mse: 8.6015 - val_loss: 1.6260 - val_mae: 1.6260 - val_mse: 6.5385\n",
            "Epoch 225/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8753 - mae: 1.8753 - mse: 8.5998 - val_loss: 1.6234 - val_mae: 1.6234 - val_mse: 6.5271\n",
            "Epoch 226/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8748 - mae: 1.8748 - mse: 8.5929 - val_loss: 1.6237 - val_mae: 1.6237 - val_mse: 6.5277\n",
            "Epoch 227/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8751 - mae: 1.8751 - mse: 8.5975 - val_loss: 1.6237 - val_mae: 1.6237 - val_mse: 6.5270\n",
            "Epoch 228/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8749 - mae: 1.8749 - mse: 8.5924 - val_loss: 1.6235 - val_mae: 1.6235 - val_mse: 6.5253\n",
            "Epoch 229/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8746 - mae: 1.8746 - mse: 8.5890 - val_loss: 1.6228 - val_mae: 1.6228 - val_mse: 6.5218\n",
            "Epoch 230/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8747 - mae: 1.8747 - mse: 8.5901 - val_loss: 1.6220 - val_mae: 1.6220 - val_mse: 6.5175\n",
            "Epoch 231/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8749 - mae: 1.8749 - mse: 8.5906 - val_loss: 1.6236 - val_mae: 1.6236 - val_mse: 6.5233\n",
            "Epoch 232/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8744 - mae: 1.8744 - mse: 8.5907 - val_loss: 1.6226 - val_mae: 1.6226 - val_mse: 6.5186\n",
            "Epoch 233/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8744 - mae: 1.8744 - mse: 8.5848 - val_loss: 1.6220 - val_mae: 1.6220 - val_mse: 6.5151\n",
            "Epoch 234/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8743 - mae: 1.8743 - mse: 8.5807 - val_loss: 1.6217 - val_mae: 1.6217 - val_mse: 6.5129\n",
            "Epoch 235/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8743 - mae: 1.8743 - mse: 8.5880 - val_loss: 1.6232 - val_mae: 1.6232 - val_mse: 6.5190\n",
            "Epoch 236/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8746 - mae: 1.8746 - mse: 8.5808 - val_loss: 1.6205 - val_mae: 1.6205 - val_mse: 6.5062\n",
            "Epoch 237/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8740 - mae: 1.8740 - mse: 8.5803 - val_loss: 1.6234 - val_mae: 1.6234 - val_mse: 6.5179\n",
            "Epoch 238/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8739 - mae: 1.8739 - mse: 8.5884 - val_loss: 1.6238 - val_mae: 1.6238 - val_mse: 6.5188\n",
            "Epoch 239/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8737 - mae: 1.8737 - mse: 8.5834 - val_loss: 1.6214 - val_mae: 1.6214 - val_mse: 6.5078\n",
            "Epoch 240/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8738 - mae: 1.8738 - mse: 8.5753 - val_loss: 1.6214 - val_mae: 1.6214 - val_mse: 6.5073\n",
            "Epoch 241/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8733 - mae: 1.8733 - mse: 8.5791 - val_loss: 1.6230 - val_mae: 1.6230 - val_mse: 6.5134\n",
            "Epoch 242/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8734 - mae: 1.8734 - mse: 8.5827 - val_loss: 1.6227 - val_mae: 1.6227 - val_mse: 6.5113\n",
            "Epoch 243/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8733 - mae: 1.8733 - mse: 8.5743 - val_loss: 1.6201 - val_mae: 1.6201 - val_mse: 6.4992\n",
            "Epoch 244/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8733 - mae: 1.8733 - mse: 8.5730 - val_loss: 1.6218 - val_mae: 1.6218 - val_mse: 6.5061\n",
            "Epoch 245/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8735 - mae: 1.8735 - mse: 8.5831 - val_loss: 1.6239 - val_mae: 1.6239 - val_mse: 6.5139\n",
            "Epoch 246/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8731 - mae: 1.8731 - mse: 8.5796 - val_loss: 1.6217 - val_mae: 1.6217 - val_mse: 6.5042\n",
            "Epoch 247/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8742 - mae: 1.8742 - mse: 8.5687 - val_loss: 1.6181 - val_mae: 1.6181 - val_mse: 6.4875\n",
            "Epoch 248/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8731 - mae: 1.8731 - mse: 8.5656 - val_loss: 1.6200 - val_mae: 1.6200 - val_mse: 6.4952\n",
            "Epoch 249/600\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 1.8730 - mae: 1.8730 - mse: 8.5749 - val_loss: 1.6220 - val_mae: 1.6220 - val_mse: 6.5032\n",
            "Epoch 250/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8725 - mae: 1.8725 - mse: 8.5716 - val_loss: 1.6209 - val_mae: 1.6209 - val_mse: 6.4976\n",
            "Epoch 251/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8725 - mae: 1.8725 - mse: 8.5692 - val_loss: 1.6212 - val_mae: 1.6212 - val_mse: 6.4979\n",
            "Epoch 252/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8729 - mae: 1.8729 - mse: 8.5647 - val_loss: 1.6204 - val_mae: 1.6204 - val_mse: 6.4937\n",
            "Epoch 253/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8721 - mae: 1.8721 - mse: 8.5706 - val_loss: 1.6224 - val_mae: 1.6224 - val_mse: 6.5014\n",
            "Epoch 254/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8723 - mae: 1.8723 - mse: 8.5727 - val_loss: 1.6221 - val_mae: 1.6221 - val_mse: 6.4994\n",
            "Epoch 255/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8719 - mae: 1.8719 - mse: 8.5658 - val_loss: 1.6199 - val_mae: 1.6199 - val_mse: 6.4892\n",
            "Epoch 256/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8727 - mae: 1.8727 - mse: 8.5696 - val_loss: 1.6217 - val_mae: 1.6217 - val_mse: 6.4965\n",
            "Epoch 257/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8717 - mae: 1.8717 - mse: 8.5628 - val_loss: 1.6199 - val_mae: 1.6199 - val_mse: 6.4877\n",
            "Epoch 258/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8718 - mae: 1.8718 - mse: 8.5645 - val_loss: 1.6221 - val_mae: 1.6221 - val_mse: 6.4963\n",
            "Epoch 259/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8716 - mae: 1.8716 - mse: 8.5649 - val_loss: 1.6212 - val_mae: 1.6212 - val_mse: 6.4923\n",
            "Epoch 260/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8715 - mae: 1.8715 - mse: 8.5646 - val_loss: 1.6205 - val_mae: 1.6205 - val_mse: 6.4883\n",
            "Epoch 261/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8715 - mae: 1.8715 - mse: 8.5636 - val_loss: 1.6207 - val_mae: 1.6207 - val_mse: 6.4883\n",
            "Epoch 262/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8713 - mae: 1.8713 - mse: 8.5559 - val_loss: 1.6192 - val_mae: 1.6192 - val_mse: 6.4810\n",
            "Epoch 263/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8712 - mae: 1.8712 - mse: 8.5581 - val_loss: 1.6210 - val_mae: 1.6210 - val_mse: 6.4881\n",
            "Epoch 264/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8716 - mae: 1.8716 - mse: 8.5565 - val_loss: 1.6188 - val_mae: 1.6188 - val_mse: 6.4776\n",
            "Epoch 265/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8720 - mae: 1.8720 - mse: 8.5650 - val_loss: 1.6222 - val_mae: 1.6222 - val_mse: 6.4914\n",
            "Epoch 266/600\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 1.8709 - mae: 1.8709 - mse: 8.5619 - val_loss: 1.6203 - val_mae: 1.6203 - val_mse: 6.4829\n",
            "Epoch 267/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8711 - mae: 1.8711 - mse: 8.5557 - val_loss: 1.6203 - val_mae: 1.6203 - val_mse: 6.4820\n",
            "Epoch 268/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8709 - mae: 1.8709 - mse: 8.5514 - val_loss: 1.6194 - val_mae: 1.6194 - val_mse: 6.4772\n",
            "Epoch 269/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8705 - mae: 1.8705 - mse: 8.5537 - val_loss: 1.6206 - val_mae: 1.6206 - val_mse: 6.4821\n",
            "Epoch 270/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8705 - mae: 1.8705 - mse: 8.5550 - val_loss: 1.6201 - val_mae: 1.6201 - val_mse: 6.4790\n",
            "Epoch 271/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8703 - mae: 1.8703 - mse: 8.5535 - val_loss: 1.6203 - val_mae: 1.6203 - val_mse: 6.4793\n",
            "Epoch 272/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8703 - mae: 1.8703 - mse: 8.5541 - val_loss: 1.6201 - val_mae: 1.6201 - val_mse: 6.4776\n",
            "Epoch 273/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8701 - mae: 1.8701 - mse: 8.5515 - val_loss: 1.6205 - val_mae: 1.6205 - val_mse: 6.4785\n",
            "Epoch 274/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8706 - mae: 1.8706 - mse: 8.5475 - val_loss: 1.6182 - val_mae: 1.6182 - val_mse: 6.4677\n",
            "Epoch 275/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8699 - mae: 1.8699 - mse: 8.5480 - val_loss: 1.6207 - val_mae: 1.6207 - val_mse: 6.4775\n",
            "Epoch 276/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8702 - mae: 1.8702 - mse: 8.5529 - val_loss: 1.6196 - val_mae: 1.6196 - val_mse: 6.4726\n",
            "Epoch 277/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8696 - mae: 1.8696 - mse: 8.5451 - val_loss: 1.6191 - val_mae: 1.6191 - val_mse: 6.4695\n",
            "Epoch 278/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8697 - mae: 1.8697 - mse: 8.5463 - val_loss: 1.6207 - val_mae: 1.6207 - val_mse: 6.4754\n",
            "Epoch 279/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8698 - mae: 1.8698 - mse: 8.5474 - val_loss: 1.6187 - val_mae: 1.6187 - val_mse: 6.4663\n",
            "Epoch 280/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8693 - mae: 1.8693 - mse: 8.5427 - val_loss: 1.6204 - val_mae: 1.6204 - val_mse: 6.4726\n",
            "Epoch 281/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8693 - mae: 1.8693 - mse: 8.5472 - val_loss: 1.6203 - val_mae: 1.6203 - val_mse: 6.4715\n",
            "Epoch 282/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8697 - mae: 1.8697 - mse: 8.5367 - val_loss: 1.6174 - val_mae: 1.6174 - val_mse: 6.4581\n",
            "Epoch 283/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8691 - mae: 1.8691 - mse: 8.5406 - val_loss: 1.6203 - val_mae: 1.6203 - val_mse: 6.4698\n",
            "Epoch 284/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8691 - mae: 1.8691 - mse: 8.5409 - val_loss: 1.6190 - val_mae: 1.6190 - val_mse: 6.4636\n",
            "Epoch 285/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8688 - mae: 1.8688 - mse: 8.5426 - val_loss: 1.6198 - val_mae: 1.6198 - val_mse: 6.4664\n",
            "Epoch 286/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8688 - mae: 1.8688 - mse: 8.5395 - val_loss: 1.6186 - val_mae: 1.6186 - val_mse: 6.4602\n",
            "Epoch 287/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8688 - mae: 1.8688 - mse: 8.5418 - val_loss: 1.6207 - val_mae: 1.6207 - val_mse: 6.4683\n",
            "Epoch 288/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8690 - mae: 1.8690 - mse: 8.5369 - val_loss: 1.6182 - val_mae: 1.6182 - val_mse: 6.4569\n",
            "Epoch 289/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8683 - mae: 1.8683 - mse: 8.5367 - val_loss: 1.6197 - val_mae: 1.6197 - val_mse: 6.4628\n",
            "Epoch 290/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8686 - mae: 1.8686 - mse: 8.5348 - val_loss: 1.6183 - val_mae: 1.6183 - val_mse: 6.4561\n",
            "Epoch 291/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8683 - mae: 1.8683 - mse: 8.5379 - val_loss: 1.6206 - val_mae: 1.6206 - val_mse: 6.4650\n",
            "Epoch 292/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8683 - mae: 1.8683 - mse: 8.5411 - val_loss: 1.6194 - val_mae: 1.6194 - val_mse: 6.4592\n",
            "Epoch 293/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8683 - mae: 1.8683 - mse: 8.5296 - val_loss: 1.6167 - val_mae: 1.6167 - val_mse: 6.4465\n",
            "Epoch 294/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8681 - mae: 1.8681 - mse: 8.5287 - val_loss: 1.6193 - val_mae: 1.6193 - val_mse: 6.4571\n",
            "Epoch 295/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8677 - mae: 1.8677 - mse: 8.5321 - val_loss: 1.6178 - val_mae: 1.6178 - val_mse: 6.4503\n",
            "Epoch 296/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8682 - mae: 1.8682 - mse: 8.5236 - val_loss: 1.6171 - val_mae: 1.6171 - val_mse: 6.4463\n",
            "Epoch 297/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8677 - mae: 1.8677 - mse: 8.5285 - val_loss: 1.6194 - val_mae: 1.6194 - val_mse: 6.4553\n",
            "Epoch 298/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8675 - mae: 1.8675 - mse: 8.5281 - val_loss: 1.6171 - val_mae: 1.6171 - val_mse: 6.4448\n",
            "Epoch 299/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8677 - mae: 1.8677 - mse: 8.5199 - val_loss: 1.6160 - val_mae: 1.6160 - val_mse: 6.4391\n",
            "Epoch 300/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8672 - mae: 1.8672 - mse: 8.5247 - val_loss: 1.6191 - val_mae: 1.6191 - val_mse: 6.4518\n",
            "Epoch 301/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8671 - mae: 1.8671 - mse: 8.5283 - val_loss: 1.6182 - val_mae: 1.6182 - val_mse: 6.4473\n",
            "Epoch 302/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8671 - mae: 1.8671 - mse: 8.5254 - val_loss: 1.6180 - val_mae: 1.6180 - val_mse: 6.4457\n",
            "Epoch 303/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8672 - mae: 1.8672 - mse: 8.5186 - val_loss: 1.6158 - val_mae: 1.6158 - val_mse: 6.4353\n",
            "Epoch 304/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8672 - mae: 1.8672 - mse: 8.5234 - val_loss: 1.6188 - val_mae: 1.6188 - val_mse: 6.4476\n",
            "Epoch 305/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8672 - mae: 1.8672 - mse: 8.5192 - val_loss: 1.6151 - val_mae: 1.6151 - val_mse: 6.4306\n",
            "Epoch 306/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8666 - mae: 1.8666 - mse: 8.5131 - val_loss: 1.6171 - val_mae: 1.6171 - val_mse: 6.4387\n",
            "Epoch 307/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8668 - mae: 1.8668 - mse: 8.5253 - val_loss: 1.6203 - val_mae: 1.6203 - val_mse: 6.4513\n",
            "Epoch 308/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8668 - mae: 1.8668 - mse: 8.5273 - val_loss: 1.6169 - val_mae: 1.6169 - val_mse: 6.4365\n",
            "Epoch 309/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8664 - mae: 1.8664 - mse: 8.5182 - val_loss: 1.6178 - val_mae: 1.6178 - val_mse: 6.4397\n",
            "Epoch 310/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8661 - mae: 1.8661 - mse: 8.5143 - val_loss: 1.6165 - val_mae: 1.6165 - val_mse: 6.4333\n",
            "Epoch 311/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8663 - mae: 1.8663 - mse: 8.5153 - val_loss: 1.6183 - val_mae: 1.6183 - val_mse: 6.4400\n",
            "Epoch 312/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8659 - mae: 1.8659 - mse: 8.5161 - val_loss: 1.6174 - val_mae: 1.6174 - val_mse: 6.4356\n",
            "Epoch 313/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8658 - mae: 1.8658 - mse: 8.5160 - val_loss: 1.6184 - val_mae: 1.6184 - val_mse: 6.4391\n",
            "Epoch 314/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8659 - mae: 1.8659 - mse: 8.5182 - val_loss: 1.6167 - val_mae: 1.6167 - val_mse: 6.4311\n",
            "Epoch 315/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8658 - mae: 1.8658 - mse: 8.5098 - val_loss: 1.6165 - val_mae: 1.6165 - val_mse: 6.4296\n",
            "Epoch 316/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8661 - mae: 1.8661 - mse: 8.5178 - val_loss: 1.6191 - val_mae: 1.6191 - val_mse: 6.4395\n",
            "Epoch 317/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8663 - mae: 1.8663 - mse: 8.5078 - val_loss: 1.6149 - val_mae: 1.6149 - val_mse: 6.4205\n",
            "Epoch 318/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8651 - mae: 1.8651 - mse: 8.5049 - val_loss: 1.6172 - val_mae: 1.6172 - val_mse: 6.4303\n",
            "Epoch 319/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8651 - mae: 1.8651 - mse: 8.5126 - val_loss: 1.6179 - val_mae: 1.6179 - val_mse: 6.4322\n",
            "Epoch 320/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8651 - mae: 1.8651 - mse: 8.5090 - val_loss: 1.6174 - val_mae: 1.6174 - val_mse: 6.4297\n",
            "Epoch 321/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8650 - mae: 1.8650 - mse: 8.5084 - val_loss: 1.6178 - val_mae: 1.6178 - val_mse: 6.4305\n",
            "Epoch 322/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8650 - mae: 1.8650 - mse: 8.5125 - val_loss: 1.6177 - val_mae: 1.6177 - val_mse: 6.4292\n",
            "Epoch 323/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8649 - mae: 1.8649 - mse: 8.5029 - val_loss: 1.6153 - val_mae: 1.6153 - val_mse: 6.4182\n",
            "Epoch 324/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8647 - mae: 1.8647 - mse: 8.5050 - val_loss: 1.6170 - val_mae: 1.6170 - val_mse: 6.4249\n",
            "Epoch 325/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8650 - mae: 1.8650 - mse: 8.5117 - val_loss: 1.6176 - val_mae: 1.6176 - val_mse: 6.4267\n",
            "Epoch 326/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8653 - mae: 1.8653 - mse: 8.4975 - val_loss: 1.6115 - val_mae: 1.6115 - val_mse: 6.3992\n",
            "Epoch 327/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8651 - mae: 1.8651 - mse: 8.4938 - val_loss: 1.6149 - val_mae: 1.6149 - val_mse: 6.4135\n",
            "Epoch 328/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8646 - mae: 1.8646 - mse: 8.5022 - val_loss: 1.6167 - val_mae: 1.6167 - val_mse: 6.4206\n",
            "Epoch 329/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8640 - mae: 1.8640 - mse: 8.5002 - val_loss: 1.6160 - val_mae: 1.6160 - val_mse: 6.4169\n",
            "Epoch 330/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8639 - mae: 1.8639 - mse: 8.5002 - val_loss: 1.6149 - val_mae: 1.6149 - val_mse: 6.4111\n",
            "Epoch 331/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8640 - mae: 1.8640 - mse: 8.4956 - val_loss: 1.6162 - val_mae: 1.6162 - val_mse: 6.4162\n",
            "Epoch 332/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8640 - mae: 1.8640 - mse: 8.4952 - val_loss: 1.6149 - val_mae: 1.6149 - val_mse: 6.4098\n",
            "Epoch 333/600\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 1.8636 - mae: 1.8636 - mse: 8.4963 - val_loss: 1.6164 - val_mae: 1.6164 - val_mse: 6.4158\n",
            "Epoch 334/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8640 - mae: 1.8640 - mse: 8.4958 - val_loss: 1.6154 - val_mae: 1.6154 - val_mse: 6.4105\n",
            "Epoch 335/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8633 - mae: 1.8633 - mse: 8.4957 - val_loss: 1.6165 - val_mae: 1.6165 - val_mse: 6.4146\n",
            "Epoch 336/600\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 1.8632 - mae: 1.8632 - mse: 8.4962 - val_loss: 1.6153 - val_mae: 1.6153 - val_mse: 6.4086\n",
            "Epoch 337/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8632 - mae: 1.8632 - mse: 8.4890 - val_loss: 1.6141 - val_mae: 1.6141 - val_mse: 6.4027\n",
            "Epoch 338/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8632 - mae: 1.8632 - mse: 8.4929 - val_loss: 1.6154 - val_mae: 1.6154 - val_mse: 6.4080\n",
            "Epoch 339/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8633 - mae: 1.8633 - mse: 8.4962 - val_loss: 1.6162 - val_mae: 1.6162 - val_mse: 6.4104\n",
            "Epoch 340/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8627 - mae: 1.8627 - mse: 8.4902 - val_loss: 1.6140 - val_mae: 1.6140 - val_mse: 6.4002\n",
            "Epoch 341/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8631 - mae: 1.8631 - mse: 8.4815 - val_loss: 1.6122 - val_mae: 1.6122 - val_mse: 6.3912\n",
            "Epoch 342/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8628 - mae: 1.8628 - mse: 8.4856 - val_loss: 1.6152 - val_mae: 1.6152 - val_mse: 6.4041\n",
            "Epoch 343/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8630 - mae: 1.8630 - mse: 8.4962 - val_loss: 1.6178 - val_mae: 1.6178 - val_mse: 6.4138\n",
            "Epoch 344/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8630 - mae: 1.8630 - mse: 8.4900 - val_loss: 1.6132 - val_mae: 1.6132 - val_mse: 6.3933\n",
            "Epoch 345/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8623 - mae: 1.8623 - mse: 8.4818 - val_loss: 1.6150 - val_mae: 1.6150 - val_mse: 6.4008\n",
            "Epoch 346/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8623 - mae: 1.8623 - mse: 8.4833 - val_loss: 1.6147 - val_mae: 1.6147 - val_mse: 6.3988\n",
            "Epoch 347/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8623 - mae: 1.8623 - mse: 8.4865 - val_loss: 1.6146 - val_mae: 1.6146 - val_mse: 6.3979\n",
            "Epoch 348/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8622 - mae: 1.8622 - mse: 8.4868 - val_loss: 1.6164 - val_mae: 1.6164 - val_mse: 6.4044\n",
            "Epoch 349/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8625 - mae: 1.8625 - mse: 8.4849 - val_loss: 1.6131 - val_mae: 1.6131 - val_mse: 6.3894\n",
            "Epoch 350/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8616 - mae: 1.8616 - mse: 8.4814 - val_loss: 1.6159 - val_mae: 1.6159 - val_mse: 6.4009\n",
            "Epoch 351/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8619 - mae: 1.8619 - mse: 8.4854 - val_loss: 1.6144 - val_mae: 1.6144 - val_mse: 6.3940\n",
            "Epoch 352/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8614 - mae: 1.8614 - mse: 8.4802 - val_loss: 1.6152 - val_mae: 1.6152 - val_mse: 6.3968\n",
            "Epoch 353/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8615 - mae: 1.8615 - mse: 8.4835 - val_loss: 1.6147 - val_mae: 1.6147 - val_mse: 6.3940\n",
            "Epoch 354/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8612 - mae: 1.8612 - mse: 8.4767 - val_loss: 1.6133 - val_mae: 1.6133 - val_mse: 6.3868\n",
            "Epoch 355/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8616 - mae: 1.8616 - mse: 8.4749 - val_loss: 1.6145 - val_mae: 1.6145 - val_mse: 6.3914\n",
            "Epoch 356/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8610 - mae: 1.8610 - mse: 8.4779 - val_loss: 1.6135 - val_mae: 1.6135 - val_mse: 6.3866\n",
            "Epoch 357/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8611 - mae: 1.8611 - mse: 8.4703 - val_loss: 1.6126 - val_mae: 1.6126 - val_mse: 6.3817\n",
            "Epoch 358/600\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 1.8606 - mae: 1.8606 - mse: 8.4715 - val_loss: 1.6150 - val_mae: 1.6150 - val_mse: 6.3911\n",
            "Epoch 359/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8610 - mae: 1.8610 - mse: 8.4803 - val_loss: 1.6161 - val_mae: 1.6161 - val_mse: 6.3949\n",
            "Epoch 360/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8609 - mae: 1.8609 - mse: 8.4755 - val_loss: 1.6136 - val_mae: 1.6136 - val_mse: 6.3837\n",
            "Epoch 361/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8606 - mae: 1.8606 - mse: 8.4708 - val_loss: 1.6138 - val_mae: 1.6138 - val_mse: 6.3841\n",
            "Epoch 362/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8603 - mae: 1.8603 - mse: 8.4713 - val_loss: 1.6129 - val_mae: 1.6129 - val_mse: 6.3795\n",
            "Epoch 363/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8603 - mae: 1.8603 - mse: 8.4694 - val_loss: 1.6140 - val_mae: 1.6140 - val_mse: 6.3832\n",
            "Epoch 364/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8602 - mae: 1.8602 - mse: 8.4697 - val_loss: 1.6122 - val_mae: 1.6122 - val_mse: 6.3748\n",
            "Epoch 365/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8601 - mae: 1.8601 - mse: 8.4613 - val_loss: 1.6115 - val_mae: 1.6115 - val_mse: 6.3711\n",
            "Epoch 366/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8602 - mae: 1.8602 - mse: 8.4682 - val_loss: 1.6154 - val_mae: 1.6154 - val_mse: 6.3870\n",
            "Epoch 367/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8603 - mae: 1.8603 - mse: 8.4766 - val_loss: 1.6156 - val_mae: 1.6156 - val_mse: 6.3871\n",
            "Epoch 368/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8597 - mae: 1.8597 - mse: 8.4712 - val_loss: 1.6119 - val_mae: 1.6119 - val_mse: 6.3707\n",
            "Epoch 369/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8600 - mae: 1.8600 - mse: 8.4560 - val_loss: 1.6092 - val_mae: 1.6092 - val_mse: 6.3580\n",
            "Epoch 370/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8607 - mae: 1.8607 - mse: 8.4569 - val_loss: 1.6136 - val_mae: 1.6136 - val_mse: 6.3766\n",
            "Epoch 371/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8594 - mae: 1.8594 - mse: 8.4650 - val_loss: 1.6117 - val_mae: 1.6117 - val_mse: 6.3674\n",
            "Epoch 372/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8597 - mae: 1.8597 - mse: 8.4544 - val_loss: 1.6103 - val_mae: 1.6103 - val_mse: 6.3606\n",
            "Epoch 373/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8596 - mae: 1.8596 - mse: 8.4520 - val_loss: 1.6118 - val_mae: 1.6118 - val_mse: 6.3666\n",
            "Epoch 374/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8597 - mae: 1.8597 - mse: 8.4676 - val_loss: 1.6175 - val_mae: 1.6175 - val_mse: 6.3900\n",
            "Epoch 375/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8594 - mae: 1.8594 - mse: 8.4735 - val_loss: 1.6144 - val_mae: 1.6144 - val_mse: 6.3763\n",
            "Epoch 376/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8587 - mae: 1.8587 - mse: 8.4607 - val_loss: 1.6114 - val_mae: 1.6114 - val_mse: 6.3630\n",
            "Epoch 377/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8588 - mae: 1.8588 - mse: 8.4528 - val_loss: 1.6115 - val_mae: 1.6115 - val_mse: 6.3624\n",
            "Epoch 378/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8589 - mae: 1.8589 - mse: 8.4590 - val_loss: 1.6128 - val_mae: 1.6128 - val_mse: 6.3674\n",
            "Epoch 379/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8587 - mae: 1.8587 - mse: 8.4517 - val_loss: 1.6097 - val_mae: 1.6097 - val_mse: 6.3528\n",
            "Epoch 380/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8587 - mae: 1.8587 - mse: 8.4511 - val_loss: 1.6124 - val_mae: 1.6124 - val_mse: 6.3644\n",
            "Epoch 381/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8583 - mae: 1.8583 - mse: 8.4544 - val_loss: 1.6132 - val_mae: 1.6132 - val_mse: 6.3673\n",
            "Epoch 382/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8582 - mae: 1.8582 - mse: 8.4566 - val_loss: 1.6116 - val_mae: 1.6116 - val_mse: 6.3595\n",
            "Epoch 383/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8581 - mae: 1.8581 - mse: 8.4499 - val_loss: 1.6108 - val_mae: 1.6108 - val_mse: 6.3553\n",
            "Epoch 384/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8585 - mae: 1.8585 - mse: 8.4546 - val_loss: 1.6146 - val_mae: 1.6146 - val_mse: 6.3708\n",
            "Epoch 385/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8581 - mae: 1.8581 - mse: 8.4612 - val_loss: 1.6123 - val_mae: 1.6123 - val_mse: 6.3607\n",
            "Epoch 386/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8585 - mae: 1.8585 - mse: 8.4421 - val_loss: 1.6065 - val_mae: 1.6065 - val_mse: 6.3340\n",
            "Epoch 387/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8581 - mae: 1.8581 - mse: 8.4309 - val_loss: 1.6075 - val_mae: 1.6075 - val_mse: 6.3378\n",
            "Epoch 388/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8580 - mae: 1.8580 - mse: 8.4391 - val_loss: 1.6120 - val_mae: 1.6120 - val_mse: 6.3572\n",
            "Epoch 389/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8575 - mae: 1.8575 - mse: 8.4473 - val_loss: 1.6111 - val_mae: 1.6111 - val_mse: 6.3522\n",
            "Epoch 390/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8575 - mae: 1.8575 - mse: 8.4510 - val_loss: 1.6130 - val_mae: 1.6130 - val_mse: 6.3598\n",
            "Epoch 391/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8575 - mae: 1.8575 - mse: 8.4504 - val_loss: 1.6103 - val_mae: 1.6103 - val_mse: 6.3475\n",
            "Epoch 392/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8573 - mae: 1.8573 - mse: 8.4380 - val_loss: 1.6099 - val_mae: 1.6099 - val_mse: 6.3448\n",
            "Epoch 393/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8571 - mae: 1.8571 - mse: 8.4416 - val_loss: 1.6121 - val_mae: 1.6121 - val_mse: 6.3539\n",
            "Epoch 394/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8569 - mae: 1.8569 - mse: 8.4439 - val_loss: 1.6117 - val_mae: 1.6117 - val_mse: 6.3517\n",
            "Epoch 395/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8568 - mae: 1.8568 - mse: 8.4461 - val_loss: 1.6112 - val_mae: 1.6112 - val_mse: 6.3490\n",
            "Epoch 396/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8565 - mae: 1.8565 - mse: 8.4391 - val_loss: 1.6100 - val_mae: 1.6100 - val_mse: 6.3427\n",
            "Epoch 397/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8566 - mae: 1.8566 - mse: 8.4390 - val_loss: 1.6114 - val_mae: 1.6114 - val_mse: 6.3482\n",
            "Epoch 398/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8568 - mae: 1.8568 - mse: 8.4401 - val_loss: 1.6120 - val_mae: 1.6120 - val_mse: 6.3501\n",
            "Epoch 399/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8568 - mae: 1.8568 - mse: 8.4508 - val_loss: 1.6144 - val_mae: 1.6144 - val_mse: 6.3597\n",
            "Epoch 400/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8566 - mae: 1.8566 - mse: 8.4403 - val_loss: 1.6094 - val_mae: 1.6094 - val_mse: 6.3375\n",
            "Epoch 401/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8561 - mae: 1.8561 - mse: 8.4321 - val_loss: 1.6102 - val_mae: 1.6102 - val_mse: 6.3405\n",
            "Epoch 402/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8558 - mae: 1.8558 - mse: 8.4360 - val_loss: 1.6107 - val_mae: 1.6107 - val_mse: 6.3421\n",
            "Epoch 403/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8557 - mae: 1.8557 - mse: 8.4370 - val_loss: 1.6103 - val_mae: 1.6103 - val_mse: 6.3396\n",
            "Epoch 404/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8556 - mae: 1.8556 - mse: 8.4345 - val_loss: 1.6105 - val_mae: 1.6105 - val_mse: 6.3399\n",
            "Epoch 405/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8556 - mae: 1.8556 - mse: 8.4340 - val_loss: 1.6108 - val_mae: 1.6108 - val_mse: 6.3401\n",
            "Epoch 406/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8554 - mae: 1.8554 - mse: 8.4350 - val_loss: 1.6096 - val_mae: 1.6096 - val_mse: 6.3340\n",
            "Epoch 407/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8555 - mae: 1.8555 - mse: 8.4261 - val_loss: 1.6075 - val_mae: 1.6075 - val_mse: 6.3240\n",
            "Epoch 408/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8561 - mae: 1.8561 - mse: 8.4358 - val_loss: 1.6112 - val_mae: 1.6112 - val_mse: 6.3396\n",
            "Epoch 409/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8549 - mae: 1.8549 - mse: 8.4308 - val_loss: 1.6092 - val_mae: 1.6092 - val_mse: 6.3301\n",
            "Epoch 410/600\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 1.8565 - mae: 1.8565 - mse: 8.4181 - val_loss: 1.6061 - val_mae: 1.6061 - val_mse: 6.3158\n",
            "Epoch 411/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8559 - mae: 1.8559 - mse: 8.4298 - val_loss: 1.6120 - val_mae: 1.6120 - val_mse: 6.3410\n",
            "Epoch 412/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8555 - mae: 1.8555 - mse: 8.4303 - val_loss: 1.6081 - val_mae: 1.6081 - val_mse: 6.3232\n",
            "Epoch 413/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8547 - mae: 1.8547 - mse: 8.4270 - val_loss: 1.6110 - val_mae: 1.6110 - val_mse: 6.3352\n",
            "Epoch 414/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8549 - mae: 1.8549 - mse: 8.4337 - val_loss: 1.6104 - val_mae: 1.6104 - val_mse: 6.3323\n",
            "Epoch 415/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8549 - mae: 1.8549 - mse: 8.4181 - val_loss: 1.6063 - val_mae: 1.6063 - val_mse: 6.3131\n",
            "Epoch 416/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8546 - mae: 1.8546 - mse: 8.4169 - val_loss: 1.6081 - val_mae: 1.6081 - val_mse: 6.3205\n",
            "Epoch 417/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8546 - mae: 1.8546 - mse: 8.4257 - val_loss: 1.6116 - val_mae: 1.6116 - val_mse: 6.3350\n",
            "Epoch 418/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8543 - mae: 1.8543 - mse: 8.4234 - val_loss: 1.6077 - val_mae: 1.6077 - val_mse: 6.3173\n",
            "Epoch 419/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8543 - mae: 1.8543 - mse: 8.4216 - val_loss: 1.6087 - val_mae: 1.6087 - val_mse: 6.3215\n",
            "Epoch 420/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8539 - mae: 1.8539 - mse: 8.4220 - val_loss: 1.6089 - val_mae: 1.6089 - val_mse: 6.3217\n",
            "Epoch 421/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8538 - mae: 1.8538 - mse: 8.4243 - val_loss: 1.6097 - val_mae: 1.6097 - val_mse: 6.3244\n",
            "Epoch 422/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8537 - mae: 1.8537 - mse: 8.4175 - val_loss: 1.6082 - val_mae: 1.6082 - val_mse: 6.3171\n",
            "Epoch 423/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8543 - mae: 1.8543 - mse: 8.4145 - val_loss: 1.6079 - val_mae: 1.6079 - val_mse: 6.3150\n",
            "Epoch 424/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8537 - mae: 1.8537 - mse: 8.4209 - val_loss: 1.6113 - val_mae: 1.6113 - val_mse: 6.3290\n",
            "Epoch 425/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8534 - mae: 1.8534 - mse: 8.4216 - val_loss: 1.6087 - val_mae: 1.6087 - val_mse: 6.3176\n",
            "Epoch 426/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8534 - mae: 1.8534 - mse: 8.4180 - val_loss: 1.6090 - val_mae: 1.6090 - val_mse: 6.3181\n",
            "Epoch 427/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8532 - mae: 1.8532 - mse: 8.4096 - val_loss: 1.6067 - val_mae: 1.6067 - val_mse: 6.3072\n",
            "Epoch 428/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8532 - mae: 1.8532 - mse: 8.4083 - val_loss: 1.6078 - val_mae: 1.6078 - val_mse: 6.3115\n",
            "Epoch 429/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8527 - mae: 1.8527 - mse: 8.4138 - val_loss: 1.6101 - val_mae: 1.6101 - val_mse: 6.3207\n",
            "Epoch 430/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8530 - mae: 1.8530 - mse: 8.4156 - val_loss: 1.6073 - val_mae: 1.6073 - val_mse: 6.3079\n",
            "Epoch 431/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8527 - mae: 1.8527 - mse: 8.4107 - val_loss: 1.6101 - val_mae: 1.6101 - val_mse: 6.3193\n",
            "Epoch 432/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8526 - mae: 1.8526 - mse: 8.4150 - val_loss: 1.6084 - val_mae: 1.6084 - val_mse: 6.3116\n",
            "Epoch 433/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8527 - mae: 1.8527 - mse: 8.4141 - val_loss: 1.6087 - val_mae: 1.6087 - val_mse: 6.3122\n",
            "Epoch 434/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8529 - mae: 1.8529 - mse: 8.4033 - val_loss: 1.6037 - val_mae: 1.6037 - val_mse: 6.2890\n",
            "Epoch 435/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8531 - mae: 1.8531 - mse: 8.3907 - val_loss: 1.6028 - val_mae: 1.6028 - val_mse: 6.2846\n",
            "Epoch 436/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8526 - mae: 1.8526 - mse: 8.3964 - val_loss: 1.6085 - val_mae: 1.6085 - val_mse: 6.3095\n",
            "Epoch 437/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8522 - mae: 1.8522 - mse: 8.4129 - val_loss: 1.6085 - val_mae: 1.6085 - val_mse: 6.3085\n",
            "Epoch 438/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8523 - mae: 1.8523 - mse: 8.4140 - val_loss: 1.6088 - val_mae: 1.6088 - val_mse: 6.3091\n",
            "Epoch 439/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8521 - mae: 1.8521 - mse: 8.3996 - val_loss: 1.6051 - val_mae: 1.6051 - val_mse: 6.2918\n",
            "Epoch 440/600\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 1.8520 - mae: 1.8520 - mse: 8.3930 - val_loss: 1.6046 - val_mae: 1.6046 - val_mse: 6.2894\n",
            "Epoch 441/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8518 - mae: 1.8518 - mse: 8.4021 - val_loss: 1.6095 - val_mae: 1.6095 - val_mse: 6.3103\n",
            "Epoch 442/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8514 - mae: 1.8514 - mse: 8.4086 - val_loss: 1.6082 - val_mae: 1.6082 - val_mse: 6.3041\n",
            "Epoch 443/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8516 - mae: 1.8516 - mse: 8.4020 - val_loss: 1.6047 - val_mae: 1.6047 - val_mse: 6.2878\n",
            "Epoch 444/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8513 - mae: 1.8513 - mse: 8.3959 - val_loss: 1.6069 - val_mae: 1.6069 - val_mse: 6.2971\n",
            "Epoch 445/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8515 - mae: 1.8515 - mse: 8.4075 - val_loss: 1.6088 - val_mae: 1.6088 - val_mse: 6.3049\n",
            "Epoch 446/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8509 - mae: 1.8509 - mse: 8.3999 - val_loss: 1.6058 - val_mae: 1.6058 - val_mse: 6.2908\n",
            "Epoch 447/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8508 - mae: 1.8508 - mse: 8.3950 - val_loss: 1.6067 - val_mae: 1.6067 - val_mse: 6.2943\n",
            "Epoch 448/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8514 - mae: 1.8514 - mse: 8.4054 - val_loss: 1.6097 - val_mae: 1.6097 - val_mse: 6.3064\n",
            "Epoch 449/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8515 - mae: 1.8515 - mse: 8.4112 - val_loss: 1.6094 - val_mae: 1.6094 - val_mse: 6.3045\n",
            "Epoch 450/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8509 - mae: 1.8509 - mse: 8.3964 - val_loss: 1.6033 - val_mae: 1.6033 - val_mse: 6.2768\n",
            "Epoch 451/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8508 - mae: 1.8508 - mse: 8.3889 - val_loss: 1.6059 - val_mae: 1.6059 - val_mse: 6.2879\n",
            "Epoch 452/600\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 1.8504 - mae: 1.8504 - mse: 8.3963 - val_loss: 1.6074 - val_mae: 1.6074 - val_mse: 6.2942\n",
            "Epoch 453/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8502 - mae: 1.8502 - mse: 8.3976 - val_loss: 1.6061 - val_mae: 1.6061 - val_mse: 6.2875\n",
            "Epoch 454/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8501 - mae: 1.8501 - mse: 8.3874 - val_loss: 1.6049 - val_mae: 1.6049 - val_mse: 6.2817\n",
            "Epoch 455/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8503 - mae: 1.8503 - mse: 8.3839 - val_loss: 1.6040 - val_mae: 1.6040 - val_mse: 6.2767\n",
            "Epoch 456/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8505 - mae: 1.8505 - mse: 8.3939 - val_loss: 1.6091 - val_mae: 1.6091 - val_mse: 6.2987\n",
            "Epoch 457/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8499 - mae: 1.8499 - mse: 8.3986 - val_loss: 1.6074 - val_mae: 1.6074 - val_mse: 6.2909\n",
            "Epoch 458/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8499 - mae: 1.8499 - mse: 8.3867 - val_loss: 1.6035 - val_mae: 1.6035 - val_mse: 6.2727\n",
            "Epoch 459/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8496 - mae: 1.8496 - mse: 8.3837 - val_loss: 1.6061 - val_mae: 1.6061 - val_mse: 6.2839\n",
            "Epoch 460/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8499 - mae: 1.8499 - mse: 8.3911 - val_loss: 1.6051 - val_mae: 1.6051 - val_mse: 6.2787\n",
            "Epoch 461/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8491 - mae: 1.8491 - mse: 8.3886 - val_loss: 1.6078 - val_mae: 1.6078 - val_mse: 6.2898\n",
            "Epoch 462/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8494 - mae: 1.8494 - mse: 8.3909 - val_loss: 1.6062 - val_mae: 1.6062 - val_mse: 6.2826\n",
            "Epoch 463/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8490 - mae: 1.8490 - mse: 8.3831 - val_loss: 1.6051 - val_mae: 1.6051 - val_mse: 6.2768\n",
            "Epoch 464/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8496 - mae: 1.8496 - mse: 8.3909 - val_loss: 1.6081 - val_mae: 1.6081 - val_mse: 6.2893\n",
            "Epoch 465/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8489 - mae: 1.8489 - mse: 8.3901 - val_loss: 1.6034 - val_mae: 1.6034 - val_mse: 6.2675\n",
            "Epoch 466/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8490 - mae: 1.8490 - mse: 8.3713 - val_loss: 1.6020 - val_mae: 1.6020 - val_mse: 6.2609\n",
            "Epoch 467/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8491 - mae: 1.8491 - mse: 8.3776 - val_loss: 1.6042 - val_mae: 1.6042 - val_mse: 6.2704\n",
            "Epoch 468/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8487 - mae: 1.8487 - mse: 8.3848 - val_loss: 1.6081 - val_mae: 1.6081 - val_mse: 6.2868\n",
            "Epoch 469/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8483 - mae: 1.8483 - mse: 8.3872 - val_loss: 1.6050 - val_mae: 1.6050 - val_mse: 6.2726\n",
            "Epoch 470/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8482 - mae: 1.8482 - mse: 8.3790 - val_loss: 1.6056 - val_mae: 1.6056 - val_mse: 6.2750\n",
            "Epoch 471/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8480 - mae: 1.8480 - mse: 8.3793 - val_loss: 1.6049 - val_mae: 1.6049 - val_mse: 6.2709\n",
            "Epoch 472/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8481 - mae: 1.8481 - mse: 8.3830 - val_loss: 1.6060 - val_mae: 1.6060 - val_mse: 6.2752\n",
            "Epoch 473/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8476 - mae: 1.8476 - mse: 8.3779 - val_loss: 1.6041 - val_mae: 1.6041 - val_mse: 6.2661\n",
            "Epoch 474/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8479 - mae: 1.8479 - mse: 8.3727 - val_loss: 1.6037 - val_mae: 1.6037 - val_mse: 6.2639\n",
            "Epoch 475/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8476 - mae: 1.8476 - mse: 8.3766 - val_loss: 1.6049 - val_mae: 1.6049 - val_mse: 6.2688\n",
            "Epoch 476/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8476 - mae: 1.8476 - mse: 8.3770 - val_loss: 1.6044 - val_mae: 1.6044 - val_mse: 6.2656\n",
            "Epoch 477/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8474 - mae: 1.8474 - mse: 8.3741 - val_loss: 1.6055 - val_mae: 1.6055 - val_mse: 6.2700\n",
            "Epoch 478/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8472 - mae: 1.8472 - mse: 8.3767 - val_loss: 1.6043 - val_mae: 1.6043 - val_mse: 6.2638\n",
            "Epoch 479/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8473 - mae: 1.8473 - mse: 8.3741 - val_loss: 1.6034 - val_mae: 1.6034 - val_mse: 6.2594\n",
            "Epoch 480/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8472 - mae: 1.8472 - mse: 8.3679 - val_loss: 1.6036 - val_mae: 1.6036 - val_mse: 6.2595\n",
            "Epoch 481/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8474 - mae: 1.8474 - mse: 8.3672 - val_loss: 1.6029 - val_mae: 1.6029 - val_mse: 6.2556\n",
            "Epoch 482/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8469 - mae: 1.8469 - mse: 8.3737 - val_loss: 1.6082 - val_mae: 1.6082 - val_mse: 6.2784\n",
            "Epoch 483/600\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 1.8474 - mae: 1.8474 - mse: 8.3757 - val_loss: 1.6046 - val_mae: 1.6046 - val_mse: 6.2623\n",
            "Epoch 484/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8469 - mae: 1.8469 - mse: 8.3747 - val_loss: 1.6039 - val_mae: 1.6039 - val_mse: 6.2585\n",
            "Epoch 485/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8472 - mae: 1.8472 - mse: 8.3618 - val_loss: 1.6002 - val_mae: 1.6002 - val_mse: 6.2409\n",
            "Epoch 486/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8467 - mae: 1.8467 - mse: 8.3567 - val_loss: 1.6044 - val_mae: 1.6044 - val_mse: 6.2598\n",
            "Epoch 487/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8463 - mae: 1.8463 - mse: 8.3665 - val_loss: 1.6034 - val_mae: 1.6034 - val_mse: 6.2544\n",
            "Epoch 488/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8461 - mae: 1.8461 - mse: 8.3677 - val_loss: 1.6042 - val_mae: 1.6042 - val_mse: 6.2575\n",
            "Epoch 489/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8463 - mae: 1.8463 - mse: 8.3625 - val_loss: 1.6032 - val_mae: 1.6032 - val_mse: 6.2524\n",
            "Epoch 490/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8460 - mae: 1.8460 - mse: 8.3643 - val_loss: 1.6060 - val_mae: 1.6060 - val_mse: 6.2637\n",
            "Epoch 491/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8460 - mae: 1.8460 - mse: 8.3714 - val_loss: 1.6048 - val_mae: 1.6048 - val_mse: 6.2580\n",
            "Epoch 492/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8459 - mae: 1.8459 - mse: 8.3619 - val_loss: 1.6031 - val_mae: 1.6031 - val_mse: 6.2503\n",
            "Epoch 493/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8455 - mae: 1.8455 - mse: 8.3616 - val_loss: 1.6034 - val_mae: 1.6034 - val_mse: 6.2508\n",
            "Epoch 494/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8456 - mae: 1.8456 - mse: 8.3616 - val_loss: 1.6045 - val_mae: 1.6045 - val_mse: 6.2552\n",
            "Epoch 495/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8457 - mae: 1.8457 - mse: 8.3600 - val_loss: 1.6012 - val_mae: 1.6012 - val_mse: 6.2393\n",
            "Epoch 496/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8454 - mae: 1.8454 - mse: 8.3595 - val_loss: 1.6034 - val_mae: 1.6034 - val_mse: 6.2491\n",
            "Epoch 497/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8460 - mae: 1.8460 - mse: 8.3712 - val_loss: 1.6057 - val_mae: 1.6057 - val_mse: 6.2581\n",
            "Epoch 498/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8459 - mae: 1.8459 - mse: 8.3600 - val_loss: 1.6012 - val_mae: 1.6012 - val_mse: 6.2378\n",
            "Epoch 499/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8451 - mae: 1.8451 - mse: 8.3523 - val_loss: 1.6027 - val_mae: 1.6027 - val_mse: 6.2440\n",
            "Epoch 500/600\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 1.8446 - mae: 1.8446 - mse: 8.3580 - val_loss: 1.6033 - val_mae: 1.6033 - val_mse: 6.2462\n",
            "Epoch 501/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8446 - mae: 1.8446 - mse: 8.3586 - val_loss: 1.6025 - val_mae: 1.6025 - val_mse: 6.2420\n",
            "Epoch 502/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8450 - mae: 1.8450 - mse: 8.3472 - val_loss: 1.5998 - val_mae: 1.5998 - val_mse: 6.2288\n",
            "Epoch 503/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8450 - mae: 1.8450 - mse: 8.3432 - val_loss: 1.5997 - val_mae: 1.5997 - val_mse: 6.2279\n",
            "Epoch 504/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8445 - mae: 1.8445 - mse: 8.3527 - val_loss: 1.6044 - val_mae: 1.6044 - val_mse: 6.2483\n",
            "Epoch 505/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8444 - mae: 1.8444 - mse: 8.3578 - val_loss: 1.6034 - val_mae: 1.6034 - val_mse: 6.2436\n",
            "Epoch 506/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8445 - mae: 1.8445 - mse: 8.3501 - val_loss: 1.6007 - val_mae: 1.6007 - val_mse: 6.2307\n",
            "Epoch 507/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8440 - mae: 1.8440 - mse: 8.3502 - val_loss: 1.6037 - val_mae: 1.6037 - val_mse: 6.2437\n",
            "Epoch 508/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8438 - mae: 1.8438 - mse: 8.3553 - val_loss: 1.6024 - val_mae: 1.6024 - val_mse: 6.2378\n",
            "Epoch 509/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8441 - mae: 1.8441 - mse: 8.3467 - val_loss: 1.6006 - val_mae: 1.6006 - val_mse: 6.2286\n",
            "Epoch 510/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8438 - mae: 1.8438 - mse: 8.3420 - val_loss: 1.6009 - val_mae: 1.6009 - val_mse: 6.2294\n",
            "Epoch 511/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8441 - mae: 1.8441 - mse: 8.3546 - val_loss: 1.6041 - val_mae: 1.6041 - val_mse: 6.2431\n",
            "Epoch 512/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8439 - mae: 1.8439 - mse: 8.3489 - val_loss: 1.6002 - val_mae: 1.6002 - val_mse: 6.2252\n",
            "Epoch 513/600\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 1.8433 - mae: 1.8433 - mse: 8.3472 - val_loss: 1.6026 - val_mae: 1.6026 - val_mse: 6.2357\n",
            "Epoch 514/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8435 - mae: 1.8435 - mse: 8.3443 - val_loss: 1.6012 - val_mae: 1.6012 - val_mse: 6.2283\n",
            "Epoch 515/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8430 - mae: 1.8430 - mse: 8.3462 - val_loss: 1.6021 - val_mae: 1.6021 - val_mse: 6.2322\n",
            "Epoch 516/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8428 - mae: 1.8428 - mse: 8.3458 - val_loss: 1.6015 - val_mae: 1.6015 - val_mse: 6.2287\n",
            "Epoch 517/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8430 - mae: 1.8430 - mse: 8.3411 - val_loss: 1.6015 - val_mae: 1.6015 - val_mse: 6.2284\n",
            "Epoch 518/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8426 - mae: 1.8426 - mse: 8.3463 - val_loss: 1.6025 - val_mae: 1.6025 - val_mse: 6.2320\n",
            "Epoch 519/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8426 - mae: 1.8426 - mse: 8.3471 - val_loss: 1.6010 - val_mae: 1.6010 - val_mse: 6.2248\n",
            "Epoch 520/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8431 - mae: 1.8431 - mse: 8.3459 - val_loss: 1.6026 - val_mae: 1.6026 - val_mse: 6.2314\n",
            "Epoch 521/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8428 - mae: 1.8428 - mse: 8.3402 - val_loss: 1.5993 - val_mae: 1.5993 - val_mse: 6.2157\n",
            "Epoch 522/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8427 - mae: 1.8427 - mse: 8.3439 - val_loss: 1.6025 - val_mae: 1.6025 - val_mse: 6.2297\n",
            "Epoch 523/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8420 - mae: 1.8420 - mse: 8.3392 - val_loss: 1.6010 - val_mae: 1.6010 - val_mse: 6.2228\n",
            "Epoch 524/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8427 - mae: 1.8427 - mse: 8.3331 - val_loss: 1.5992 - val_mae: 1.5992 - val_mse: 6.2136\n",
            "Epoch 525/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8447 - mae: 1.8447 - mse: 8.3524 - val_loss: 1.6064 - val_mae: 1.6064 - val_mse: 6.2441\n",
            "Epoch 526/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8421 - mae: 1.8421 - mse: 8.3410 - val_loss: 1.5999 - val_mae: 1.5999 - val_mse: 6.2161\n",
            "Epoch 527/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8423 - mae: 1.8423 - mse: 8.3303 - val_loss: 1.5992 - val_mae: 1.5992 - val_mse: 6.2119\n",
            "Epoch 528/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8414 - mae: 1.8414 - mse: 8.3339 - val_loss: 1.6021 - val_mae: 1.6021 - val_mse: 6.2248\n",
            "Epoch 529/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8419 - mae: 1.8419 - mse: 8.3455 - val_loss: 1.6034 - val_mae: 1.6034 - val_mse: 6.2298\n",
            "Epoch 530/600\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 1.8419 - mae: 1.8419 - mse: 8.3332 - val_loss: 1.5994 - val_mae: 1.5994 - val_mse: 6.2113\n",
            "Epoch 531/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8419 - mae: 1.8419 - mse: 8.3404 - val_loss: 1.6037 - val_mae: 1.6037 - val_mse: 6.2301\n",
            "Epoch 532/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8415 - mae: 1.8415 - mse: 8.3401 - val_loss: 1.5996 - val_mae: 1.5996 - val_mse: 6.2113\n",
            "Epoch 533/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8415 - mae: 1.8415 - mse: 8.3271 - val_loss: 1.5981 - val_mae: 1.5981 - val_mse: 6.2037\n",
            "Epoch 534/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8420 - mae: 1.8420 - mse: 8.3384 - val_loss: 1.6038 - val_mae: 1.6038 - val_mse: 6.2285\n",
            "Epoch 535/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8415 - mae: 1.8415 - mse: 8.3323 - val_loss: 1.5990 - val_mae: 1.5990 - val_mse: 6.2069\n",
            "Epoch 536/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8406 - mae: 1.8406 - mse: 8.3256 - val_loss: 1.5997 - val_mae: 1.5997 - val_mse: 6.2099\n",
            "Epoch 537/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8404 - mae: 1.8404 - mse: 8.3305 - val_loss: 1.6011 - val_mae: 1.6011 - val_mse: 6.2153\n",
            "Epoch 538/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8405 - mae: 1.8405 - mse: 8.3316 - val_loss: 1.6011 - val_mae: 1.6011 - val_mse: 6.2150\n",
            "Epoch 539/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8404 - mae: 1.8404 - mse: 8.3294 - val_loss: 1.5992 - val_mae: 1.5992 - val_mse: 6.2059\n",
            "Epoch 540/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8403 - mae: 1.8403 - mse: 8.3288 - val_loss: 1.6003 - val_mae: 1.6003 - val_mse: 6.2105\n",
            "Epoch 541/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8400 - mae: 1.8400 - mse: 8.3272 - val_loss: 1.5997 - val_mae: 1.5997 - val_mse: 6.2073\n",
            "Epoch 542/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8399 - mae: 1.8399 - mse: 8.3260 - val_loss: 1.5993 - val_mae: 1.5993 - val_mse: 6.2049\n",
            "Epoch 543/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8400 - mae: 1.8400 - mse: 8.3267 - val_loss: 1.6001 - val_mae: 1.6001 - val_mse: 6.2080\n",
            "Epoch 544/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8398 - mae: 1.8398 - mse: 8.3281 - val_loss: 1.5993 - val_mae: 1.5993 - val_mse: 6.2038\n",
            "Epoch 545/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8396 - mae: 1.8396 - mse: 8.3222 - val_loss: 1.5995 - val_mae: 1.5995 - val_mse: 6.2042\n",
            "Epoch 546/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8395 - mae: 1.8395 - mse: 8.3235 - val_loss: 1.6007 - val_mae: 1.6007 - val_mse: 6.2090\n",
            "Epoch 547/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8397 - mae: 1.8397 - mse: 8.3264 - val_loss: 1.5992 - val_mae: 1.5992 - val_mse: 6.2018\n",
            "Epoch 548/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8397 - mae: 1.8397 - mse: 8.3271 - val_loss: 1.6006 - val_mae: 1.6006 - val_mse: 6.2073\n",
            "Epoch 549/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8394 - mae: 1.8394 - mse: 8.3211 - val_loss: 1.5974 - val_mae: 1.5974 - val_mse: 6.1922\n",
            "Epoch 550/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8393 - mae: 1.8393 - mse: 8.3199 - val_loss: 1.5999 - val_mae: 1.5999 - val_mse: 6.2033\n",
            "Epoch 551/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8390 - mae: 1.8390 - mse: 8.3241 - val_loss: 1.5999 - val_mae: 1.5999 - val_mse: 6.2026\n",
            "Epoch 552/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8387 - mae: 1.8387 - mse: 8.3174 - val_loss: 1.5981 - val_mae: 1.5981 - val_mse: 6.1939\n",
            "Epoch 553/600\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 1.8390 - mae: 1.8390 - mse: 8.3122 - val_loss: 1.5979 - val_mae: 1.5979 - val_mse: 6.1925\n",
            "Epoch 554/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8384 - mae: 1.8384 - mse: 8.3173 - val_loss: 1.5999 - val_mae: 1.5999 - val_mse: 6.2009\n",
            "Epoch 555/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8386 - mae: 1.8386 - mse: 8.3184 - val_loss: 1.5990 - val_mae: 1.5990 - val_mse: 6.1968\n",
            "Epoch 556/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8385 - mae: 1.8385 - mse: 8.3189 - val_loss: 1.5981 - val_mae: 1.5981 - val_mse: 6.1918\n",
            "Epoch 557/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8388 - mae: 1.8388 - mse: 8.3100 - val_loss: 1.5961 - val_mae: 1.5961 - val_mse: 6.1800\n",
            "Epoch 558/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8383 - mae: 1.8383 - mse: 8.3142 - val_loss: 1.5995 - val_mae: 1.5995 - val_mse: 6.1973\n",
            "Epoch 559/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8380 - mae: 1.8380 - mse: 8.3176 - val_loss: 1.5988 - val_mae: 1.5988 - val_mse: 6.1936\n",
            "Epoch 560/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8381 - mae: 1.8381 - mse: 8.3113 - val_loss: 1.5975 - val_mae: 1.5975 - val_mse: 6.1870\n",
            "Epoch 561/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8381 - mae: 1.8381 - mse: 8.3091 - val_loss: 1.5983 - val_mae: 1.5983 - val_mse: 6.1902\n",
            "Epoch 562/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8378 - mae: 1.8378 - mse: 8.3175 - val_loss: 1.5996 - val_mae: 1.5996 - val_mse: 6.1955\n",
            "Epoch 563/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8376 - mae: 1.8376 - mse: 8.3147 - val_loss: 1.5980 - val_mae: 1.5980 - val_mse: 6.1879\n",
            "Epoch 564/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8375 - mae: 1.8375 - mse: 8.3084 - val_loss: 1.5976 - val_mae: 1.5976 - val_mse: 6.1853\n",
            "Epoch 565/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8377 - mae: 1.8377 - mse: 8.3139 - val_loss: 1.5986 - val_mae: 1.5986 - val_mse: 6.1896\n",
            "Epoch 566/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8372 - mae: 1.8372 - mse: 8.3099 - val_loss: 1.5980 - val_mae: 1.5980 - val_mse: 6.1865\n",
            "Epoch 567/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8373 - mae: 1.8373 - mse: 8.3121 - val_loss: 1.5988 - val_mae: 1.5988 - val_mse: 6.1895\n",
            "Epoch 568/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8373 - mae: 1.8373 - mse: 8.3049 - val_loss: 1.5964 - val_mae: 1.5964 - val_mse: 6.1778\n",
            "Epoch 569/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8366 - mae: 1.8366 - mse: 8.3052 - val_loss: 1.5992 - val_mae: 1.5992 - val_mse: 6.1902\n",
            "Epoch 570/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8369 - mae: 1.8369 - mse: 8.3121 - val_loss: 1.5982 - val_mae: 1.5982 - val_mse: 6.1852\n",
            "Epoch 571/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8371 - mae: 1.8371 - mse: 8.3015 - val_loss: 1.5960 - val_mae: 1.5960 - val_mse: 6.1735\n",
            "Epoch 572/600\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 1.8365 - mae: 1.8365 - mse: 8.3045 - val_loss: 1.5986 - val_mae: 1.5986 - val_mse: 6.1859\n",
            "Epoch 573/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8367 - mae: 1.8367 - mse: 8.3105 - val_loss: 1.5982 - val_mae: 1.5982 - val_mse: 6.1835\n",
            "Epoch 574/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8369 - mae: 1.8369 - mse: 8.2953 - val_loss: 1.5958 - val_mae: 1.5958 - val_mse: 6.1708\n",
            "Epoch 575/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8365 - mae: 1.8365 - mse: 8.3043 - val_loss: 1.5999 - val_mae: 1.5999 - val_mse: 6.1896\n",
            "Epoch 576/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8366 - mae: 1.8366 - mse: 8.3053 - val_loss: 1.5959 - val_mae: 1.5959 - val_mse: 6.1711\n",
            "Epoch 577/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8361 - mae: 1.8361 - mse: 8.2977 - val_loss: 1.5983 - val_mae: 1.5983 - val_mse: 6.1824\n",
            "Epoch 578/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8364 - mae: 1.8364 - mse: 8.3101 - val_loss: 1.5974 - val_mae: 1.5974 - val_mse: 6.1778\n",
            "Epoch 579/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8361 - mae: 1.8361 - mse: 8.2952 - val_loss: 1.5959 - val_mae: 1.5959 - val_mse: 6.1702\n",
            "Epoch 580/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8359 - mae: 1.8359 - mse: 8.3023 - val_loss: 1.5973 - val_mae: 1.5973 - val_mse: 6.1766\n",
            "Epoch 581/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8357 - mae: 1.8357 - mse: 8.2977 - val_loss: 1.5964 - val_mae: 1.5964 - val_mse: 6.1716\n",
            "Epoch 582/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8355 - mae: 1.8355 - mse: 8.2972 - val_loss: 1.5969 - val_mae: 1.5969 - val_mse: 6.1737\n",
            "Epoch 583/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8356 - mae: 1.8356 - mse: 8.3022 - val_loss: 1.5961 - val_mae: 1.5961 - val_mse: 6.1695\n",
            "Epoch 584/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8354 - mae: 1.8354 - mse: 8.2946 - val_loss: 1.5970 - val_mae: 1.5970 - val_mse: 6.1731\n",
            "Epoch 585/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8355 - mae: 1.8355 - mse: 8.2989 - val_loss: 1.5957 - val_mae: 1.5957 - val_mse: 6.1661\n",
            "Epoch 586/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8352 - mae: 1.8352 - mse: 8.2973 - val_loss: 1.5965 - val_mae: 1.5965 - val_mse: 6.1698\n",
            "Epoch 587/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8349 - mae: 1.8349 - mse: 8.2971 - val_loss: 1.5961 - val_mae: 1.5961 - val_mse: 6.1676\n",
            "Epoch 588/600\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 1.8348 - mae: 1.8348 - mse: 8.2932 - val_loss: 1.5960 - val_mae: 1.5960 - val_mse: 6.1665\n",
            "Epoch 589/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8347 - mae: 1.8347 - mse: 8.2946 - val_loss: 1.5962 - val_mae: 1.5962 - val_mse: 6.1669\n",
            "Epoch 590/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8346 - mae: 1.8346 - mse: 8.2935 - val_loss: 1.5961 - val_mae: 1.5961 - val_mse: 6.1662\n",
            "Epoch 591/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8348 - mae: 1.8348 - mse: 8.2979 - val_loss: 1.5955 - val_mae: 1.5955 - val_mse: 6.1629\n",
            "Epoch 592/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8346 - mae: 1.8346 - mse: 8.2946 - val_loss: 1.5957 - val_mae: 1.5957 - val_mse: 6.1632\n",
            "Epoch 593/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8345 - mae: 1.8345 - mse: 8.2891 - val_loss: 1.5960 - val_mae: 1.5960 - val_mse: 6.1643\n",
            "Epoch 594/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8343 - mae: 1.8343 - mse: 8.2934 - val_loss: 1.5953 - val_mae: 1.5953 - val_mse: 6.1603\n",
            "Epoch 595/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8342 - mae: 1.8342 - mse: 8.2870 - val_loss: 1.5958 - val_mae: 1.5958 - val_mse: 6.1626\n",
            "Epoch 596/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8340 - mae: 1.8340 - mse: 8.2923 - val_loss: 1.5966 - val_mae: 1.5966 - val_mse: 6.1654\n",
            "Epoch 597/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8338 - mae: 1.8338 - mse: 8.2889 - val_loss: 1.5950 - val_mae: 1.5950 - val_mse: 6.1568\n",
            "Epoch 598/600\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 1.8338 - mae: 1.8338 - mse: 8.2901 - val_loss: 1.5965 - val_mae: 1.5965 - val_mse: 6.1640\n",
            "Epoch 599/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8343 - mae: 1.8343 - mse: 8.2907 - val_loss: 1.5972 - val_mae: 1.5972 - val_mse: 6.1659\n",
            "Epoch 600/600\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 1.8335 - mae: 1.8335 - mse: 8.2867 - val_loss: 1.5951 - val_mae: 1.5951 - val_mse: 6.1569\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "75iFQllnHvbt",
        "outputId": "38469aba-6bc2-4041-d305-3ac5b53e5cc1"
      },
      "source": [
        "print(history)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<tensorflow.python.keras.callbacks.History object at 0x7fd9fbb1b1d0>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kvjTJr16DBcu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2c1d89b-470b-4af2-b013-c3ce7c265aab"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_1 (Dense)              (None, 1)                 2         \n",
            "=================================================================\n",
            "Total params: 2\n",
            "Trainable params: 2\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "PzTKakL7GM_8",
        "outputId": "79e9e081-26ef-465e-a59c-1cd17e622982"
      },
      "source": [
        "def curvePlots(tempString):\n",
        "  plt.plot(history.history[tempString])\n",
        "  plt.plot(history.history[f'val_{tempString}'])\n",
        "  plt.xlabel('NumEpochs')\n",
        "  plt.ylabel(tempString)\n",
        "  plt.legend([tempString, f'val_{tempString}'])\n",
        "  plt.show()\n",
        "\n",
        "\n",
        "curvePlots('mse')\n",
        "curvePlots('mae')\n",
        "curvePlots('loss')"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3s00veusHgVV",
        "outputId": "4211d5a0-b25d-4ea0-9c0d-88fdf95314da"
      },
      "source": [
        "model.predict([1])"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.0203218]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eO2JfxFhPYd-",
        "outputId": "a0ce945b-98a9-4de7-8bcb-bae4eaa0c406"
      },
      "source": [
        "print(f'Prediction for input value 1: {model.predict([1])}')\n",
        "\n",
        "tempListforPreds=[3,5,67,5]\n",
        "print(f'''\n",
        "\n",
        "input List = {tempListforPreds}\n",
        "\n",
        "\n",
        "List of Predictions:\n",
        "{model.predict(tempListforPreds)}\n",
        "\n",
        "List of Predictions (flattened out):\n",
        "{model.predict(tempListforPreds).flatten()}\n",
        "''')"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Prediction for input value 1: [[1.0203218]]\n",
            "\n",
            "\n",
            "input List = [3, 5, 67, 5]\n",
            "\n",
            "\n",
            "List of Predictions:\n",
            "[[ 3.0160568]\n",
            " [ 5.011791 ]\n",
            " [66.87957  ]\n",
            " [ 5.011791 ]]\n",
            "\n",
            "List of Predictions (flattened out):\n",
            "[ 3.0160568  5.011791  66.87957    5.011791 ]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BT5TlIh7JSox",
        "outputId": "6b327811-bdf7-4511-aa1a-bf2586ef3d4d"
      },
      "source": [
        "print(testFeatures)"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     G1\n",
            "9    14\n",
            "25    6\n",
            "28   11\n",
            "31   17\n",
            "32   17\n",
            "..   ..\n",
            "365  10\n",
            "367   7\n",
            "379  10\n",
            "381   7\n",
            "393  11\n",
            "\n",
            "[79 rows x 1 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "99LVn-8tQfLY"
      },
      "source": [
        "testPreds = model.predict(testFeatures).flatten()"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "W0HU3Qv-RUBc",
        "outputId": "7ba0ec2e-b63d-4e1b-bbe5-afa5496943c5"
      },
      "source": [
        "def predPlot(labels, predictions):\n",
        "  plt.scatter(labels, predictions)\n",
        "  plt.ylabel('Predictions')\n",
        "  plt.xlabel('True Value or Labels')\n",
        "  plt.axis('equal')\n",
        "  plt.axis('square')\n",
        "  plt.xlim([0, plt.xlim()[1]])\n",
        "  plt.ylim([0, plt.ylim()[1]])\n",
        "  plt.show()\n",
        "\n",
        "predPlot(testLabels, testPreds)"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "tQncb2InRUj1",
        "outputId": "fa292218-e1a4-4ea3-9421-fbc0dc477c8b"
      },
      "source": [
        "def errorPlot(preds, labels, counts):\n",
        "  errors = preds - labels\n",
        "  plt.hist(errors, counts)\n",
        "  plt.xlabel('Error')\n",
        "  plt.ylabel('Counts')\n",
        "  plt.show()\n",
        "\n",
        "errorPlot(testPreds, testLabels, numEpochs)"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}