Chronic_Kidney_Disease_classification (1).ipynb

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      "name": "Chronic_Kidney_Disease_classification.ipynb",
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      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "xe7rsdS5B0l2"
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      "source": [
        "#Import Libraries\n",
        "import glob\n",
        "from keras.models import Sequential, load_model\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import keras as k\n",
        "from keras.layers import Dense\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, EarlyStopping\n",
        "from tensorflow.keras.optimizers import Adam"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "wVzuBb4jCDg6",
        "outputId": "e89a8b2d-aa94-4a1f-fb8a-174ad8d8ff70"
      },
      "source": [
        "  #load the data \n",
        "df = pd.read_csv(\"kidney_disease.csv\")\n",
        "df.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>age</th>\n",
              "      <th>bp</th>\n",
              "      <th>sg</th>\n",
              "      <th>al</th>\n",
              "      <th>su</th>\n",
              "      <th>rbc</th>\n",
              "      <th>pc</th>\n",
              "      <th>pcc</th>\n",
              "      <th>ba</th>\n",
              "      <th>bgr</th>\n",
              "      <th>bu</th>\n",
              "      <th>sc</th>\n",
              "      <th>sod</th>\n",
              "      <th>pot</th>\n",
              "      <th>hemo</th>\n",
              "      <th>pcv</th>\n",
              "      <th>wc</th>\n",
              "      <th>rc</th>\n",
              "      <th>htn</th>\n",
              "      <th>dm</th>\n",
              "      <th>cad</th>\n",
              "      <th>appet</th>\n",
              "      <th>pe</th>\n",
              "      <th>ane</th>\n",
              "      <th>classification</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>121.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15.4</td>\n",
              "      <td>44</td>\n",
              "      <td>7800</td>\n",
              "      <td>5.2</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>7.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.3</td>\n",
              "      <td>38</td>\n",
              "      <td>6000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>62.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>423.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.6</td>\n",
              "      <td>31</td>\n",
              "      <td>7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>48.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.005</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>present</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>117.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>3.8</td>\n",
              "      <td>111.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>11.2</td>\n",
              "      <td>32</td>\n",
              "      <td>6700</td>\n",
              "      <td>3.9</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>51.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>106.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.6</td>\n",
              "      <td>35</td>\n",
              "      <td>7300</td>\n",
              "      <td>4.6</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id   age    bp     sg   al   su  ...   dm cad appet   pe  ane  classification\n",
              "0   0  48.0  80.0  1.020  1.0  0.0  ...  yes  no  good   no   no             ckd\n",
              "1   1   7.0  50.0  1.020  4.0  0.0  ...   no  no  good   no   no             ckd\n",
              "2   2  62.0  80.0  1.010  2.0  3.0  ...  yes  no  poor   no  yes             ckd\n",
              "3   3  48.0  70.0  1.005  4.0  0.0  ...   no  no  poor  yes  yes             ckd\n",
              "4   4  51.0  80.0  1.010  2.0  0.0  ...   no  no  good   no   no             ckd\n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
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        },
        "id": "J4yxlas9CSQs",
        "outputId": "9fb924b4-cc97-4070-a25a-092fc399a052"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 400 entries, 0 to 399\n",
            "Data columns (total 26 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   id              400 non-null    int64  \n",
            " 1   age             391 non-null    float64\n",
            " 2   bp              388 non-null    float64\n",
            " 3   sg              353 non-null    float64\n",
            " 4   al              354 non-null    float64\n",
            " 5   su              351 non-null    float64\n",
            " 6   rbc             248 non-null    object \n",
            " 7   pc              335 non-null    object \n",
            " 8   pcc             396 non-null    object \n",
            " 9   ba              396 non-null    object \n",
            " 10  bgr             356 non-null    float64\n",
            " 11  bu              381 non-null    float64\n",
            " 12  sc              383 non-null    float64\n",
            " 13  sod             313 non-null    float64\n",
            " 14  pot             312 non-null    float64\n",
            " 15  hemo            348 non-null    float64\n",
            " 16  pcv             330 non-null    object \n",
            " 17  wc              295 non-null    object \n",
            " 18  rc              270 non-null    object \n",
            " 19  htn             398 non-null    object \n",
            " 20  dm              398 non-null    object \n",
            " 21  cad             398 non-null    object \n",
            " 22  appet           399 non-null    object \n",
            " 23  pe              399 non-null    object \n",
            " 24  ane             399 non-null    object \n",
            " 25  classification  400 non-null    object \n",
            "dtypes: float64(11), int64(1), object(14)\n",
            "memory usage: 81.4+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lTwFN_1fCUMj",
        "outputId": "a2768364-ba1f-47f5-efe6-f4abdad4a702"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400, 26)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Htjebi3ZCcWl"
      },
      "source": [
        "#Create a list of columns to retain\n",
        "retain = [\"sg\", \"age\",\"bp\",\"al\", \"su\",\"sc\", \"hemo\",\"pcv\", \"wbcc\", \"rbcc\", \"htn\", \"classification\"]\n",
        "\n",
        "#retain = df.columns, Drop the columns that are not in retain\n",
        "df = df.drop([col for col in df.columns if not col in retain], axis=1)\n",
        "    \n",
        "# Drop the rows with na or missing values\n",
        "df = df.dropna(axis=0)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "wuwQn6a9Vlvo",
        "outputId": "27d6885f-25ca-413a-92ea-3213a6d6ceeb"
      },
      "source": [
        "sns.countplot(df['classification'],label=\"Count\")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0fff91b160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uvxck+XL3c/uVJIfs1v9Lk1yVZEX3lfarktyc5F3j+RM/dhkKEyTJMcB/BI6vqucAb+zbdi4wBbwWeDzwIeBlwPOBnxp9tVqC1gF/XFXHAN8B/jVwEfDWqno2cDNwTlVdDswAp1fVzwMPAx8B3tj93L4YeGiu0yS/CpwNnFRV3wTeD5xXVT8H7BjZn26JMBQmy/HAR7t/OFTV/V377wFPqarXV+87ys8Evl5Vd3Tr/2M85WqJ+XpV3dAtXwscBSyvqs93bZuAX5rnc88AdlTVVwGq6rtVtavbdjzwVuClVfXtru2FwCXd8o+NhrV3hoKgdyf585McNu5CtKT9sG/5YWD5AvT5v4FDgJ/drd0bsPaRoTBZ/gZ4VZKnAvSFwGeA9wCf6s7V3g6sSXJUt/20kVeqSfAA8O0k/6Jbfw0wN2r4Hr1f9gBfA45I8gKAJIckmZuN4S6601Dd6VGAL9GbIgfg9CHWvyQtqmkuNFxVdUuSdwOfT/IwcH3fto92gbAZOIne9OSfSvJ94Iv86B+otJDWA3+a5GDgTnrXtAAu7NofAn4B+DXgg0meSO96wovnOqiq25OcDnw0ycvoXSv7iyRvBa4Y2Z9kiXCaC0lS4+kjSVJjKEiSGkNBktQYCpKkxlCQJDWGgiZKkncmefMC9vflvuVzu3l9zk3y+iRn7EN/y5P8Zt/605JcvlD1SnvjV1I1UZK8E3iwqt47hL4fAA6rqof3o481wCer6lkLVZf0aDhS0JK2l1lhfyPJV7ttH+tuoCLJq5Js7dq/0LUd083OeUPX37qu/cHufTPwJODaJL/WPyJJ8jNJ/rrr77okRyV5UpIt3frNSU7uynoPcFR3nHO72UW3dv0clOTD3f7XJ/mXXfuZST6e5DNJ7kjyX4f/N6slq6p8+VqSL+AY4O+BFd36YcA7gTd360/t2/ddwG93yzcDK7vl5d37B+nN2glwIPDEbvnBvj76l/uPcw3wq93yQcDB9GYTeHLXtgLYBgRYA2zt66etA28CLuiWnwnc3fV3Jr27gZ/Srd8FrB7337+vx+bLkYKWsj3NCjvnWUm+mORmenPk9M+dc2GS3wAO6NquAt7eTZ3w9Kp6iAF0U4esrKpPdDX8oKq+Ty8A/iDJTcBfAyuBw/fS3S/SzVhbVbfT++U/NxHclqp6oKp+ANwKPH2Q+qTdGQqaZBcCv1W9eff/E73/ZVNVr6f33InV9E4HPbWq/gJ4Ob15dz6d5Pj9PPbp9J5f8fzqPTPgG3PH30e7z0DqvGbaJ4aClrI9zQo75xBgR5LH0zebZpKjquqaqnoHMAusTvLTwJ1V9QF6k6w9e5ACqup7wPYkp3R9P6G7dvEUYGdV/d/u2sDc/+z7Zwfd3Rfn6kzys8CR9GYQlRaMoaAlq6puAeZmhb0R+MPddvk9euf7v0RvuvA553YXc7cCXwZuBF4NbE1yA/Asek8MG9RrgN/pThV9md6T7C4GprtTV2fMHb+qvgV8qbvQfe5u/fwJ8LjuMx8BzqyqHyItIL+SKklqHClIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJav4/Nl+191pnRwsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "Ha2oHsK_VYCd",
        "outputId": "0dbb3c3a-4eb5-4d44-8044-d157b3a8f581"
      },
      "source": [
        "plt.figure(figsize=(20,10))  #This is used to change the size of the figure/ heatmap\n",
        "sns.heatmap(df.corr(), annot=True, fmt='.0%')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0ffe63a400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dvnCRxG5C0Xj",
        "outputId": "cfd5f8cc-89f2-42a5-ce4b-04ce2e66313c"
      },
      "source": [
        "print(retain)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['sg', 'age', 'bp', 'al', 'su', 'sc', 'hemo', 'pcv', 'wbcc', 'rbcc', 'htn', 'classification']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GnuX0F7BC4T2"
      },
      "source": [
        "#Transform non-numeric columns into numerical columns\n",
        "for column in df.columns:\n",
        "        if df[column].dtype == np.number:\n",
        "            continue\n",
        "        df[column] = LabelEncoder().fit_transform(df[column])"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "94aSiW5EDHij",
        "outputId": "065015f8-91af-454b-86f0-ed6e52200400"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>bp</th>\n",
              "      <th>sg</th>\n",
              "      <th>al</th>\n",
              "      <th>su</th>\n",
              "      <th>sc</th>\n",
              "      <th>hemo</th>\n",
              "      <th>pcv</th>\n",
              "      <th>htn</th>\n",
              "      <th>classification</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>48.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>15.4</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>11.3</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>62.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>9.6</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.005</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.8</td>\n",
              "      <td>11.2</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>51.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>11.6</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    age    bp     sg   al   su   sc  hemo  pcv  htn  classification\n",
              "0  48.0  80.0  1.020  1.0  0.0  1.2  15.4   27    1               0\n",
              "1   7.0  50.0  1.020  4.0  0.0  0.8  11.3   21    0               0\n",
              "2  62.0  80.0  1.010  2.0  3.0  1.8   9.6   14    0               0\n",
              "3  48.0  70.0  1.005  4.0  0.0  3.8  11.2   15    1               0\n",
              "4  51.0  80.0  1.010  2.0  0.0  1.4  11.6   18    0               0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GM1xgxnaDKy5",
        "outputId": "7509e74a-c26a-48e6-8d48-9c463c81dfe0"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 276 entries, 0 to 399\n",
            "Data columns (total 10 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   age             276 non-null    float64\n",
            " 1   bp              276 non-null    float64\n",
            " 2   sg              276 non-null    float64\n",
            " 3   al              276 non-null    float64\n",
            " 4   su              276 non-null    float64\n",
            " 5   sc              276 non-null    float64\n",
            " 6   hemo            276 non-null    float64\n",
            " 7   pcv             276 non-null    int64  \n",
            " 8   htn             276 non-null    int64  \n",
            " 9   classification  276 non-null    int64  \n",
            "dtypes: float64(7), int64(3)\n",
            "memory usage: 33.7 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tdE7iL5AKJ5o"
      },
      "source": [
        "#Split the data\n",
        "x= df[['sg', 'al', 'sc', 'pcv', 'htn']]\n",
        "y = df['classification']"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "FwbwLM3PKeO-",
        "outputId": "6489081e-663f-4c89-827d-6c90ffd6a96f"
      },
      "source": [
        "x"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sg</th>\n",
              "      <th>al</th>\n",
              "      <th>sc</th>\n",
              "      <th>pcv</th>\n",
              "      <th>htn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.020</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.020</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.005</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.8</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>1.020</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>1.025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>37</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>1.020</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>398</th>\n",
              "      <td>1.025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>1.025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>36</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>276 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        sg   al   sc  pcv  htn\n",
              "0    1.020  1.0  1.2   27    1\n",
              "1    1.020  4.0  0.8   21    0\n",
              "2    1.010  2.0  1.8   14    0\n",
              "3    1.005  4.0  3.8   15    1\n",
              "4    1.010  2.0  1.4   18    0\n",
              "..     ...  ...  ...  ...  ...\n",
              "395  1.020  0.0  0.5   30    0\n",
              "396  1.025  0.0  1.2   37    0\n",
              "397  1.020  0.0  0.6   32    0\n",
              "398  1.025  0.0  1.0   34    0\n",
              "399  1.025  0.0  1.1   36    0\n",
              "\n",
              "[276 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MrclbHYiFi02",
        "outputId": "52f480b0-3947-41ba-a9bd-51a5df1b3202"
      },
      "source": [
        "y"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      0\n",
              "1      0\n",
              "2      0\n",
              "3      0\n",
              "4      0\n",
              "      ..\n",
              "395    1\n",
              "396    1\n",
              "397    1\n",
              "398    1\n",
              "399    1\n",
              "Name: classification, Length: 276, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iPswz7GRGhjH"
      },
      "source": [
        "#Split the data into 80% training and 20% testing \n",
        "x_train,  x_test, y_train, y_test = train_test_split(x, y, test_size= 0.2,random_state=42)\n"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_dw9aYvFK8LL",
        "outputId": "68425b93-9937-4e08-86c4-e3d3b41d1c88"
      },
      "source": [
        "x_train.shape"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(220, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5AXNJYMzGF0c",
        "outputId": "82ef3710-8c21-4ee6-e93e-528caa0364c2"
      },
      "source": [
        "\n",
        "\n",
        "x_scaler = StandardScaler()\n",
        "x_scaler.fit(x)\n",
        "column_names = x.columns\n",
        "x[column_names] = x_scaler.transform(x)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:1734: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  isetter(loc, value[:, i].tolist())\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "85BrHBDtJZDj",
        "outputId": "556902db-cb23-4f57-e816-07faa8c2087f"
      },
      "source": [
        "x.shape[1]"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SW4gQ7UTLeLU"
      },
      "source": [
        "#Build The model\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Dense(128, input_dim=len(x.columns),kernel_initializer=k.initializers.random_normal(seed=13), activation=\"relu\"))\n",
        "model.add(Dense(1, activation=\"hard_sigmoid\"))\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FZ-wxFnWLpPA",
        "outputId": "2c27f969-35a7-4c4f-fc55-d04f14674c4d"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 128)               768       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 129       \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 2         \n",
            "=================================================================\n",
            "Total params: 899\n",
            "Trainable params: 899\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rzPfjvowLuhJ"
      },
      "source": [
        "model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J43YPYHsLx6Y"
      },
      "source": [
        "lrd = ReduceLROnPlateau(monitor = 'val_loss',\n",
        "                         patience = 200,\n",
        "                         verbose = 1,\n",
        "                         factor = 0.75,\n",
        "                         min_lr = 1e-10)\n",
        "\n",
        "mcp = ModelCheckpoint('model.h5')\n",
        "\n",
        "es = EarlyStopping(verbose=1, patience=200)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JNf1K4nVL7Wy",
        "outputId": "1ade0214-6465-45d2-b04a-77a5b3963bf3"
      },
      "source": [
        "history = model.fit(x=x_train, y=y_train, epochs=1000, callbacks=[lrd, mcp, es],batch_size=256, validation_split=0.1)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/1000\n",
            "1/1 [==============================] - 0s 166ms/step - loss: 0.6925 - accuracy: 0.4798 - val_loss: 0.6920 - val_accuracy: 0.5000\n",
            "Epoch 2/1000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6921 - accuracy: 0.4798 - val_loss: 0.6917 - val_accuracy: 0.5000\n",
            "Epoch 3/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6917 - accuracy: 0.4798 - val_loss: 0.6913 - val_accuracy: 0.5000\n",
            "Epoch 4/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6912 - accuracy: 0.4798 - val_loss: 0.6910 - val_accuracy: 0.5000\n",
            "Epoch 5/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6908 - accuracy: 0.4798 - val_loss: 0.6906 - val_accuracy: 0.5000\n",
            "Epoch 6/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6904 - accuracy: 0.4798 - val_loss: 0.6903 - val_accuracy: 0.5000\n",
            "Epoch 7/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6899 - accuracy: 0.4798 - val_loss: 0.6900 - val_accuracy: 0.5000\n",
            "Epoch 8/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6895 - accuracy: 0.4848 - val_loss: 0.6898 - val_accuracy: 0.5000\n",
            "Epoch 9/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6892 - accuracy: 0.4949 - val_loss: 0.6896 - val_accuracy: 0.5000\n",
            "Epoch 10/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6890 - accuracy: 0.5000 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
            "Epoch 11/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6888 - accuracy: 0.5051 - val_loss: 0.6893 - val_accuracy: 0.5000\n",
            "Epoch 12/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6886 - accuracy: 0.5152 - val_loss: 0.6892 - val_accuracy: 0.5000\n",
            "Epoch 13/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6883 - accuracy: 0.5202 - val_loss: 0.6889 - val_accuracy: 0.5000\n",
            "Epoch 14/1000\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.6880 - accuracy: 0.5455 - val_loss: 0.6886 - val_accuracy: 0.5000\n",
            "Epoch 15/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6876 - accuracy: 0.5505 - val_loss: 0.6883 - val_accuracy: 0.5000\n",
            "Epoch 16/1000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6873 - accuracy: 0.5556 - val_loss: 0.6880 - val_accuracy: 0.5000\n",
            "Epoch 17/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6869 - accuracy: 0.5657 - val_loss: 0.6878 - val_accuracy: 0.5000\n",
            "Epoch 18/1000\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.6866 - accuracy: 0.5758 - val_loss: 0.6875 - val_accuracy: 0.5000\n",
            "Epoch 19/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6862 - accuracy: 0.5960 - val_loss: 0.6872 - val_accuracy: 0.5000\n",
            "Epoch 20/1000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6859 - accuracy: 0.6162 - val_loss: 0.6869 - val_accuracy: 0.5455\n",
            "Epoch 21/1000\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.6856 - accuracy: 0.6364 - val_loss: 0.6866 - val_accuracy: 0.5909\n",
            "Epoch 22/1000\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.6853 - accuracy: 0.6465 - val_loss: 0.6863 - val_accuracy: 0.5909\n",
            "Epoch 23/1000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6850 - accuracy: 0.6566 - val_loss: 0.6860 - val_accuracy: 0.5909\n",
            "Epoch 24/1000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6847 - accuracy: 0.6616 - val_loss: 0.6856 - val_accuracy: 0.5909\n",
            "Epoch 25/1000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6843 - accuracy: 0.6768 - val_loss: 0.6853 - val_accuracy: 0.5909\n",
            "Epoch 26/1000\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.6840 - accuracy: 0.6818 - val_loss: 0.6850 - val_accuracy: 0.6364\n",
            "Epoch 27/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6836 - accuracy: 0.6970 - val_loss: 0.6847 - val_accuracy: 0.6364\n",
            "Epoch 28/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6832 - accuracy: 0.7020 - val_loss: 0.6843 - val_accuracy: 0.6364\n",
            "Epoch 29/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6829 - accuracy: 0.7121 - val_loss: 0.6840 - val_accuracy: 0.6818\n",
            "Epoch 30/1000\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.6825 - accuracy: 0.7172 - val_loss: 0.6836 - val_accuracy: 0.6818\n",
            "Epoch 31/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6822 - accuracy: 0.7222 - val_loss: 0.6832 - val_accuracy: 0.7273\n",
            "Epoch 32/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6818 - accuracy: 0.7222 - val_loss: 0.6828 - val_accuracy: 0.7727\n",
            "Epoch 33/1000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6814 - accuracy: 0.7374 - val_loss: 0.6824 - val_accuracy: 0.7727\n",
            "Epoch 34/1000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6810 - accuracy: 0.7626 - val_loss: 0.6819 - val_accuracy: 0.7727\n",
            "Epoch 35/1000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6806 - accuracy: 0.7929 - val_loss: 0.6815 - val_accuracy: 0.7727\n",
            "Epoch 36/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.6802 - accuracy: 0.8182 - val_loss: 0.6810 - val_accuracy: 0.8182\n",
            "Epoch 37/1000\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.6799 - accuracy: 0.8333 - val_loss: 0.6806 - val_accuracy: 0.8182\n",
            "Epoch 38/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.6795 - accuracy: 0.8384 - val_loss: 0.6802 - val_accuracy: 0.8182\n",
            "Epoch 39/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6791 - accuracy: 0.8434 - val_loss: 0.6798 - val_accuracy: 0.8182\n",
            "Epoch 40/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6788 - accuracy: 0.8586 - val_loss: 0.6793 - val_accuracy: 0.8636\n",
            "Epoch 41/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6784 - accuracy: 0.8687 - val_loss: 0.6789 - val_accuracy: 0.8636\n",
            "Epoch 42/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6780 - accuracy: 0.8687 - val_loss: 0.6785 - val_accuracy: 0.8636\n",
            "Epoch 43/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6777 - accuracy: 0.8687 - val_loss: 0.6781 - val_accuracy: 0.8636\n",
            "Epoch 44/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6773 - accuracy: 0.8687 - val_loss: 0.6777 - val_accuracy: 0.9091\n",
            "Epoch 45/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6769 - accuracy: 0.8788 - val_loss: 0.6772 - val_accuracy: 0.9091\n",
            "Epoch 46/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6765 - accuracy: 0.8788 - val_loss: 0.6768 - val_accuracy: 0.9091\n",
            "Epoch 47/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6761 - accuracy: 0.8838 - val_loss: 0.6764 - val_accuracy: 0.9091\n",
            "Epoch 48/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6758 - accuracy: 0.8838 - val_loss: 0.6760 - val_accuracy: 0.9091\n",
            "Epoch 49/1000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6754 - accuracy: 0.8838 - val_loss: 0.6756 - val_accuracy: 0.9091\n",
            "Epoch 50/1000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6750 - accuracy: 0.8939 - val_loss: 0.6752 - val_accuracy: 0.9091\n",
            "Epoch 51/1000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6746 - accuracy: 0.8990 - val_loss: 0.6749 - val_accuracy: 0.9091\n",
            "Epoch 52/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6743 - accuracy: 0.8990 - val_loss: 0.6745 - val_accuracy: 0.9091\n",
            "Epoch 53/1000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6739 - accuracy: 0.9040 - val_loss: 0.6742 - val_accuracy: 0.9091\n",
            "Epoch 54/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6735 - accuracy: 0.9091 - val_loss: 0.6738 - val_accuracy: 0.9091\n",
            "Epoch 55/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6731 - accuracy: 0.9141 - val_loss: 0.6735 - val_accuracy: 0.9091\n",
            "Epoch 56/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6728 - accuracy: 0.9192 - val_loss: 0.6731 - val_accuracy: 0.9091\n",
            "Epoch 57/1000\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.6724 - accuracy: 0.9192 - val_loss: 0.6727 - val_accuracy: 0.9091\n",
            "Epoch 58/1000\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.6720 - accuracy: 0.9192 - val_loss: 0.6724 - val_accuracy: 0.9091\n",
            "Epoch 59/1000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6716 - accuracy: 0.9242 - val_loss: 0.6720 - val_accuracy: 0.9091\n",
            "Epoch 60/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6713 - accuracy: 0.9242 - val_loss: 0.6716 - val_accuracy: 0.9091\n",
            "Epoch 61/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6709 - accuracy: 0.9242 - val_loss: 0.6712 - val_accuracy: 0.9091\n",
            "Epoch 62/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.6705 - accuracy: 0.9242 - val_loss: 0.6709 - val_accuracy: 0.9091\n",
            "Epoch 63/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6701 - accuracy: 0.9242 - val_loss: 0.6705 - val_accuracy: 0.9091\n",
            "Epoch 64/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6698 - accuracy: 0.9242 - val_loss: 0.6702 - val_accuracy: 0.9091\n",
            "Epoch 65/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6694 - accuracy: 0.9242 - val_loss: 0.6698 - val_accuracy: 0.9091\n",
            "Epoch 66/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6691 - accuracy: 0.9242 - val_loss: 0.6695 - val_accuracy: 0.9091\n",
            "Epoch 67/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6687 - accuracy: 0.9242 - val_loss: 0.6691 - val_accuracy: 0.9091\n",
            "Epoch 68/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.6683 - accuracy: 0.9242 - val_loss: 0.6688 - val_accuracy: 0.9091\n",
            "Epoch 69/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6680 - accuracy: 0.9242 - val_loss: 0.6684 - val_accuracy: 0.9091\n",
            "Epoch 70/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6677 - accuracy: 0.9242 - val_loss: 0.6681 - val_accuracy: 0.9545\n",
            "Epoch 71/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6673 - accuracy: 0.9242 - val_loss: 0.6678 - val_accuracy: 0.9545\n",
            "Epoch 72/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6670 - accuracy: 0.9242 - val_loss: 0.6674 - val_accuracy: 0.9545\n",
            "Epoch 73/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6666 - accuracy: 0.9293 - val_loss: 0.6670 - val_accuracy: 0.9545\n",
            "Epoch 74/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6663 - accuracy: 0.9293 - val_loss: 0.6667 - val_accuracy: 0.9545\n",
            "Epoch 75/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6660 - accuracy: 0.9293 - val_loss: 0.6663 - val_accuracy: 0.9545\n",
            "Epoch 76/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6656 - accuracy: 0.9293 - val_loss: 0.6659 - val_accuracy: 0.9545\n",
            "Epoch 77/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6653 - accuracy: 0.9293 - val_loss: 0.6656 - val_accuracy: 0.9545\n",
            "Epoch 78/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6650 - accuracy: 0.9293 - val_loss: 0.6652 - val_accuracy: 0.9545\n",
            "Epoch 79/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6647 - accuracy: 0.9293 - val_loss: 0.6649 - val_accuracy: 0.9545\n",
            "Epoch 80/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6643 - accuracy: 0.9293 - val_loss: 0.6645 - val_accuracy: 0.9545\n",
            "Epoch 81/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6640 - accuracy: 0.9293 - val_loss: 0.6642 - val_accuracy: 0.9545\n",
            "Epoch 82/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6637 - accuracy: 0.9293 - val_loss: 0.6638 - val_accuracy: 0.9545\n",
            "Epoch 83/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.6634 - accuracy: 0.9293 - val_loss: 0.6635 - val_accuracy: 0.9545\n",
            "Epoch 84/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6631 - accuracy: 0.9394 - val_loss: 0.6632 - val_accuracy: 0.9545\n",
            "Epoch 85/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6628 - accuracy: 0.9394 - val_loss: 0.6629 - val_accuracy: 0.9545\n",
            "Epoch 86/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.6625 - accuracy: 0.9394 - val_loss: 0.6626 - val_accuracy: 0.9545\n",
            "Epoch 87/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.6621 - accuracy: 0.9394 - val_loss: 0.6622 - val_accuracy: 0.9545\n",
            "Epoch 88/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6618 - accuracy: 0.9394 - val_loss: 0.6619 - val_accuracy: 0.9545\n",
            "Epoch 89/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6615 - accuracy: 0.9394 - val_loss: 0.6616 - val_accuracy: 0.9545\n",
            "Epoch 90/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.6612 - accuracy: 0.9394 - val_loss: 0.6612 - val_accuracy: 0.9545\n",
            "Epoch 91/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6609 - accuracy: 0.9394 - val_loss: 0.6609 - val_accuracy: 0.9545\n",
            "Epoch 92/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6606 - accuracy: 0.9394 - val_loss: 0.6606 - val_accuracy: 0.9545\n",
            "Epoch 93/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6603 - accuracy: 0.9394 - val_loss: 0.6602 - val_accuracy: 0.9545\n",
            "Epoch 94/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6599 - accuracy: 0.9394 - val_loss: 0.6599 - val_accuracy: 0.9545\n",
            "Epoch 95/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6596 - accuracy: 0.9444 - val_loss: 0.6596 - val_accuracy: 0.9545\n",
            "Epoch 96/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6593 - accuracy: 0.9444 - val_loss: 0.6592 - val_accuracy: 0.9545\n",
            "Epoch 97/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6590 - accuracy: 0.9444 - val_loss: 0.6589 - val_accuracy: 0.9545\n",
            "Epoch 98/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6587 - accuracy: 0.9444 - val_loss: 0.6586 - val_accuracy: 0.9545\n",
            "Epoch 99/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6584 - accuracy: 0.9444 - val_loss: 0.6582 - val_accuracy: 0.9545\n",
            "Epoch 100/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6581 - accuracy: 0.9444 - val_loss: 0.6579 - val_accuracy: 0.9545\n",
            "Epoch 101/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6578 - accuracy: 0.9444 - val_loss: 0.6576 - val_accuracy: 0.9545\n",
            "Epoch 102/1000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6575 - accuracy: 0.9444 - val_loss: 0.6573 - val_accuracy: 0.9545\n",
            "Epoch 103/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.6572 - accuracy: 0.9495 - val_loss: 0.6570 - val_accuracy: 0.9545\n",
            "Epoch 104/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6569 - accuracy: 0.9495 - val_loss: 0.6567 - val_accuracy: 0.9545\n",
            "Epoch 105/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6566 - accuracy: 0.9495 - val_loss: 0.6564 - val_accuracy: 0.9545\n",
            "Epoch 106/1000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6563 - accuracy: 0.9495 - val_loss: 0.6560 - val_accuracy: 0.9545\n",
            "Epoch 107/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6560 - accuracy: 0.9495 - val_loss: 0.6557 - val_accuracy: 0.9545\n",
            "Epoch 108/1000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6557 - accuracy: 0.9495 - val_loss: 0.6554 - val_accuracy: 0.9545\n",
            "Epoch 109/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6554 - accuracy: 0.9495 - val_loss: 0.6551 - val_accuracy: 0.9545\n",
            "Epoch 110/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6551 - accuracy: 0.9495 - val_loss: 0.6548 - val_accuracy: 0.9545\n",
            "Epoch 111/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.6548 - accuracy: 0.9495 - val_loss: 0.6545 - val_accuracy: 0.9545\n",
            "Epoch 112/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.6546 - accuracy: 0.9495 - val_loss: 0.6542 - val_accuracy: 0.9545\n",
            "Epoch 113/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6543 - accuracy: 0.9495 - val_loss: 0.6539 - val_accuracy: 0.9545\n",
            "Epoch 114/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6540 - accuracy: 0.9495 - val_loss: 0.6536 - val_accuracy: 0.9545\n",
            "Epoch 115/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6537 - accuracy: 0.9495 - val_loss: 0.6533 - val_accuracy: 0.9545\n",
            "Epoch 116/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6534 - accuracy: 0.9495 - val_loss: 0.6530 - val_accuracy: 0.9545\n",
            "Epoch 117/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6531 - accuracy: 0.9495 - val_loss: 0.6527 - val_accuracy: 0.9545\n",
            "Epoch 118/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6529 - accuracy: 0.9495 - val_loss: 0.6525 - val_accuracy: 0.9545\n",
            "Epoch 119/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6526 - accuracy: 0.9495 - val_loss: 0.6522 - val_accuracy: 0.9545\n",
            "Epoch 120/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6523 - accuracy: 0.9495 - val_loss: 0.6519 - val_accuracy: 0.9545\n",
            "Epoch 121/1000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6520 - accuracy: 0.9495 - val_loss: 0.6516 - val_accuracy: 0.9545\n",
            "Epoch 122/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6517 - accuracy: 0.9495 - val_loss: 0.6513 - val_accuracy: 0.9545\n",
            "Epoch 123/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6515 - accuracy: 0.9495 - val_loss: 0.6511 - val_accuracy: 0.9545\n",
            "Epoch 124/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6512 - accuracy: 0.9545 - val_loss: 0.6508 - val_accuracy: 0.9545\n",
            "Epoch 125/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6509 - accuracy: 0.9545 - val_loss: 0.6505 - val_accuracy: 0.9545\n",
            "Epoch 126/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6506 - accuracy: 0.9495 - val_loss: 0.6502 - val_accuracy: 0.9545\n",
            "Epoch 127/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6504 - accuracy: 0.9495 - val_loss: 0.6500 - val_accuracy: 0.9545\n",
            "Epoch 128/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6501 - accuracy: 0.9495 - val_loss: 0.6497 - val_accuracy: 0.9545\n",
            "Epoch 129/1000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6498 - accuracy: 0.9495 - val_loss: 0.6494 - val_accuracy: 0.9545\n",
            "Epoch 130/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6495 - accuracy: 0.9495 - val_loss: 0.6491 - val_accuracy: 0.9545\n",
            "Epoch 131/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6493 - accuracy: 0.9545 - val_loss: 0.6488 - val_accuracy: 0.9545\n",
            "Epoch 132/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6490 - accuracy: 0.9545 - val_loss: 0.6486 - val_accuracy: 0.9545\n",
            "Epoch 133/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6487 - accuracy: 0.9545 - val_loss: 0.6483 - val_accuracy: 0.9545\n",
            "Epoch 134/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6484 - accuracy: 0.9545 - val_loss: 0.6480 - val_accuracy: 0.9545\n",
            "Epoch 135/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6482 - accuracy: 0.9545 - val_loss: 0.6477 - val_accuracy: 0.9545\n",
            "Epoch 136/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6479 - accuracy: 0.9545 - val_loss: 0.6474 - val_accuracy: 0.9545\n",
            "Epoch 137/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.6476 - accuracy: 0.9545 - val_loss: 0.6472 - val_accuracy: 0.9545\n",
            "Epoch 138/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6474 - accuracy: 0.9545 - val_loss: 0.6469 - val_accuracy: 0.9545\n",
            "Epoch 139/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6471 - accuracy: 0.9545 - val_loss: 0.6466 - val_accuracy: 0.9545\n",
            "Epoch 140/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.6468 - accuracy: 0.9545 - val_loss: 0.6463 - val_accuracy: 0.9545\n",
            "Epoch 141/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6465 - accuracy: 0.9545 - val_loss: 0.6461 - val_accuracy: 0.9545\n",
            "Epoch 142/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.6463 - accuracy: 0.9545 - val_loss: 0.6458 - val_accuracy: 0.9545\n",
            "Epoch 143/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6460 - accuracy: 0.9545 - val_loss: 0.6455 - val_accuracy: 0.9545\n",
            "Epoch 144/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6457 - accuracy: 0.9545 - val_loss: 0.6452 - val_accuracy: 0.9545\n",
            "Epoch 145/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6455 - accuracy: 0.9545 - val_loss: 0.6449 - val_accuracy: 0.9545\n",
            "Epoch 146/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.6452 - accuracy: 0.9545 - val_loss: 0.6447 - val_accuracy: 0.9545\n",
            "Epoch 147/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6449 - accuracy: 0.9545 - val_loss: 0.6444 - val_accuracy: 0.9545\n",
            "Epoch 148/1000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6447 - accuracy: 0.9545 - val_loss: 0.6441 - val_accuracy: 0.9545\n",
            "Epoch 149/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6444 - accuracy: 0.9545 - val_loss: 0.6438 - val_accuracy: 0.9545\n",
            "Epoch 150/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6441 - accuracy: 0.9545 - val_loss: 0.6435 - val_accuracy: 0.9545\n",
            "Epoch 151/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6439 - accuracy: 0.9545 - val_loss: 0.6433 - val_accuracy: 0.9545\n",
            "Epoch 152/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6436 - accuracy: 0.9545 - val_loss: 0.6430 - val_accuracy: 0.9545\n",
            "Epoch 153/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6433 - accuracy: 0.9545 - val_loss: 0.6427 - val_accuracy: 0.9545\n",
            "Epoch 154/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6431 - accuracy: 0.9545 - val_loss: 0.6424 - val_accuracy: 0.9545\n",
            "Epoch 155/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6428 - accuracy: 0.9545 - val_loss: 0.6421 - val_accuracy: 0.9545\n",
            "Epoch 156/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6425 - accuracy: 0.9545 - val_loss: 0.6419 - val_accuracy: 0.9545\n",
            "Epoch 157/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6422 - accuracy: 0.9545 - val_loss: 0.6416 - val_accuracy: 0.9545\n",
            "Epoch 158/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6420 - accuracy: 0.9545 - val_loss: 0.6413 - val_accuracy: 0.9545\n",
            "Epoch 159/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6417 - accuracy: 0.9545 - val_loss: 0.6410 - val_accuracy: 0.9545\n",
            "Epoch 160/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.6414 - accuracy: 0.9545 - val_loss: 0.6407 - val_accuracy: 0.9545\n",
            "Epoch 161/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.6412 - accuracy: 0.9545 - val_loss: 0.6404 - val_accuracy: 0.9545\n",
            "Epoch 162/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6409 - accuracy: 0.9545 - val_loss: 0.6402 - val_accuracy: 0.9545\n",
            "Epoch 163/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6406 - accuracy: 0.9545 - val_loss: 0.6399 - val_accuracy: 0.9545\n",
            "Epoch 164/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6403 - accuracy: 0.9545 - val_loss: 0.6396 - val_accuracy: 0.9545\n",
            "Epoch 165/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6401 - accuracy: 0.9545 - val_loss: 0.6393 - val_accuracy: 0.9545\n",
            "Epoch 166/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6398 - accuracy: 0.9545 - val_loss: 0.6390 - val_accuracy: 0.9545\n",
            "Epoch 167/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.6395 - accuracy: 0.9545 - val_loss: 0.6387 - val_accuracy: 0.9545\n",
            "Epoch 168/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6393 - accuracy: 0.9545 - val_loss: 0.6384 - val_accuracy: 0.9545\n",
            "Epoch 169/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6390 - accuracy: 0.9545 - val_loss: 0.6382 - val_accuracy: 0.9545\n",
            "Epoch 170/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6387 - accuracy: 0.9545 - val_loss: 0.6379 - val_accuracy: 0.9545\n",
            "Epoch 171/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6385 - accuracy: 0.9596 - val_loss: 0.6376 - val_accuracy: 0.9545\n",
            "Epoch 172/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6382 - accuracy: 0.9596 - val_loss: 0.6373 - val_accuracy: 0.9545\n",
            "Epoch 173/1000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6379 - accuracy: 0.9596 - val_loss: 0.6370 - val_accuracy: 0.9545\n",
            "Epoch 174/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6376 - accuracy: 0.9596 - val_loss: 0.6367 - val_accuracy: 0.9545\n",
            "Epoch 175/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.6374 - accuracy: 0.9596 - val_loss: 0.6364 - val_accuracy: 0.9545\n",
            "Epoch 176/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6371 - accuracy: 0.9596 - val_loss: 0.6361 - val_accuracy: 0.9545\n",
            "Epoch 177/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6368 - accuracy: 0.9596 - val_loss: 0.6358 - val_accuracy: 0.9545\n",
            "Epoch 178/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.6366 - accuracy: 0.9596 - val_loss: 0.6355 - val_accuracy: 0.9545\n",
            "Epoch 179/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.6363 - accuracy: 0.9596 - val_loss: 0.6353 - val_accuracy: 0.9545\n",
            "Epoch 180/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6360 - accuracy: 0.9596 - val_loss: 0.6350 - val_accuracy: 0.9545\n",
            "Epoch 181/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6357 - accuracy: 0.9596 - val_loss: 0.6347 - val_accuracy: 0.9545\n",
            "Epoch 182/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.6355 - accuracy: 0.9596 - val_loss: 0.6344 - val_accuracy: 0.9545\n",
            "Epoch 183/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6352 - accuracy: 0.9596 - val_loss: 0.6341 - val_accuracy: 0.9545\n",
            "Epoch 184/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.6349 - accuracy: 0.9596 - val_loss: 0.6338 - val_accuracy: 0.9545\n",
            "Epoch 185/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6347 - accuracy: 0.9596 - val_loss: 0.6336 - val_accuracy: 0.9545\n",
            "Epoch 186/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6344 - accuracy: 0.9646 - val_loss: 0.6333 - val_accuracy: 0.9545\n",
            "Epoch 187/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.6341 - accuracy: 0.9646 - val_loss: 0.6330 - val_accuracy: 0.9545\n",
            "Epoch 188/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.6339 - accuracy: 0.9596 - val_loss: 0.6328 - val_accuracy: 0.9545\n",
            "Epoch 189/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6336 - accuracy: 0.9596 - val_loss: 0.6325 - val_accuracy: 0.9545\n",
            "Epoch 190/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6333 - accuracy: 0.9596 - val_loss: 0.6323 - val_accuracy: 0.9545\n",
            "Epoch 191/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6331 - accuracy: 0.9596 - val_loss: 0.6320 - val_accuracy: 0.9545\n",
            "Epoch 192/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6328 - accuracy: 0.9596 - val_loss: 0.6317 - val_accuracy: 0.9545\n",
            "Epoch 193/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6325 - accuracy: 0.9596 - val_loss: 0.6315 - val_accuracy: 0.9545\n",
            "Epoch 194/1000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6323 - accuracy: 0.9646 - val_loss: 0.6312 - val_accuracy: 0.9545\n",
            "Epoch 195/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6320 - accuracy: 0.9646 - val_loss: 0.6310 - val_accuracy: 0.9545\n",
            "Epoch 196/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.6317 - accuracy: 0.9697 - val_loss: 0.6307 - val_accuracy: 0.9545\n",
            "Epoch 197/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6315 - accuracy: 0.9697 - val_loss: 0.6304 - val_accuracy: 0.9545\n",
            "Epoch 198/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.6312 - accuracy: 0.9646 - val_loss: 0.6302 - val_accuracy: 0.9545\n",
            "Epoch 199/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6309 - accuracy: 0.9596 - val_loss: 0.6299 - val_accuracy: 0.9545\n",
            "Epoch 200/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6307 - accuracy: 0.9596 - val_loss: 0.6297 - val_accuracy: 0.9545\n",
            "Epoch 201/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6304 - accuracy: 0.9596 - val_loss: 0.6294 - val_accuracy: 0.9545\n",
            "Epoch 202/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6302 - accuracy: 0.9596 - val_loss: 0.6292 - val_accuracy: 0.9545\n",
            "Epoch 203/1000\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.6299 - accuracy: 0.9596 - val_loss: 0.6289 - val_accuracy: 0.9545\n",
            "Epoch 204/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6296 - accuracy: 0.9646 - val_loss: 0.6286 - val_accuracy: 0.9545\n",
            "Epoch 205/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6294 - accuracy: 0.9646 - val_loss: 0.6284 - val_accuracy: 0.9545\n",
            "Epoch 206/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6291 - accuracy: 0.9646 - val_loss: 0.6281 - val_accuracy: 0.9545\n",
            "Epoch 207/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6288 - accuracy: 0.9646 - val_loss: 0.6279 - val_accuracy: 0.9545\n",
            "Epoch 208/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6286 - accuracy: 0.9646 - val_loss: 0.6276 - val_accuracy: 0.9545\n",
            "Epoch 209/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6283 - accuracy: 0.9646 - val_loss: 0.6274 - val_accuracy: 0.9545\n",
            "Epoch 210/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.6281 - accuracy: 0.9646 - val_loss: 0.6271 - val_accuracy: 0.9545\n",
            "Epoch 211/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6278 - accuracy: 0.9646 - val_loss: 0.6268 - val_accuracy: 0.9545\n",
            "Epoch 212/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6275 - accuracy: 0.9646 - val_loss: 0.6266 - val_accuracy: 0.9545\n",
            "Epoch 213/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6273 - accuracy: 0.9646 - val_loss: 0.6263 - val_accuracy: 0.9545\n",
            "Epoch 214/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6270 - accuracy: 0.9646 - val_loss: 0.6261 - val_accuracy: 0.9545\n",
            "Epoch 215/1000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6268 - accuracy: 0.9646 - val_loss: 0.6258 - val_accuracy: 0.9545\n",
            "Epoch 216/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6265 - accuracy: 0.9646 - val_loss: 0.6255 - val_accuracy: 0.9545\n",
            "Epoch 217/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6263 - accuracy: 0.9646 - val_loss: 0.6253 - val_accuracy: 0.9545\n",
            "Epoch 218/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6260 - accuracy: 0.9646 - val_loss: 0.6251 - val_accuracy: 0.9545\n",
            "Epoch 219/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6257 - accuracy: 0.9646 - val_loss: 0.6248 - val_accuracy: 0.9545\n",
            "Epoch 220/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6255 - accuracy: 0.9646 - val_loss: 0.6245 - val_accuracy: 0.9545\n",
            "Epoch 221/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6253 - accuracy: 0.9697 - val_loss: 0.6243 - val_accuracy: 0.9545\n",
            "Epoch 222/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6250 - accuracy: 0.9697 - val_loss: 0.6240 - val_accuracy: 0.9545\n",
            "Epoch 223/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6247 - accuracy: 0.9697 - val_loss: 0.6237 - val_accuracy: 0.9545\n",
            "Epoch 224/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6245 - accuracy: 0.9697 - val_loss: 0.6235 - val_accuracy: 0.9545\n",
            "Epoch 225/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6242 - accuracy: 0.9697 - val_loss: 0.6234 - val_accuracy: 0.9545\n",
            "Epoch 226/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6240 - accuracy: 0.9646 - val_loss: 0.6232 - val_accuracy: 0.9545\n",
            "Epoch 227/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6238 - accuracy: 0.9646 - val_loss: 0.6229 - val_accuracy: 0.9545\n",
            "Epoch 228/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6235 - accuracy: 0.9646 - val_loss: 0.6225 - val_accuracy: 0.9545\n",
            "Epoch 229/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6233 - accuracy: 0.9697 - val_loss: 0.6222 - val_accuracy: 0.9545\n",
            "Epoch 230/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.6230 - accuracy: 0.9697 - val_loss: 0.6220 - val_accuracy: 0.9545\n",
            "Epoch 231/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6228 - accuracy: 0.9697 - val_loss: 0.6218 - val_accuracy: 0.9545\n",
            "Epoch 232/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.6225 - accuracy: 0.9697 - val_loss: 0.6217 - val_accuracy: 0.9545\n",
            "Epoch 233/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.6223 - accuracy: 0.9646 - val_loss: 0.6215 - val_accuracy: 0.9545\n",
            "Epoch 234/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6220 - accuracy: 0.9646 - val_loss: 0.6212 - val_accuracy: 0.9545\n",
            "Epoch 235/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6218 - accuracy: 0.9646 - val_loss: 0.6208 - val_accuracy: 0.9545\n",
            "Epoch 236/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6215 - accuracy: 0.9697 - val_loss: 0.6205 - val_accuracy: 0.9545\n",
            "Epoch 237/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6213 - accuracy: 0.9697 - val_loss: 0.6202 - val_accuracy: 0.9545\n",
            "Epoch 238/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6210 - accuracy: 0.9697 - val_loss: 0.6201 - val_accuracy: 0.9545\n",
            "Epoch 239/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6208 - accuracy: 0.9697 - val_loss: 0.6200 - val_accuracy: 0.9545\n",
            "Epoch 240/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6206 - accuracy: 0.9697 - val_loss: 0.6197 - val_accuracy: 0.9545\n",
            "Epoch 241/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6203 - accuracy: 0.9697 - val_loss: 0.6194 - val_accuracy: 0.9545\n",
            "Epoch 242/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6201 - accuracy: 0.9697 - val_loss: 0.6191 - val_accuracy: 0.9545\n",
            "Epoch 243/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6198 - accuracy: 0.9697 - val_loss: 0.6187 - val_accuracy: 0.9545\n",
            "Epoch 244/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6196 - accuracy: 0.9697 - val_loss: 0.6184 - val_accuracy: 0.9545\n",
            "Epoch 245/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6193 - accuracy: 0.9697 - val_loss: 0.6182 - val_accuracy: 0.9545\n",
            "Epoch 246/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6191 - accuracy: 0.9697 - val_loss: 0.6180 - val_accuracy: 0.9545\n",
            "Epoch 247/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6189 - accuracy: 0.9697 - val_loss: 0.6178 - val_accuracy: 0.9545\n",
            "Epoch 248/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6186 - accuracy: 0.9697 - val_loss: 0.6176 - val_accuracy: 0.9545\n",
            "Epoch 249/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6184 - accuracy: 0.9697 - val_loss: 0.6173 - val_accuracy: 0.9545\n",
            "Epoch 250/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6181 - accuracy: 0.9697 - val_loss: 0.6170 - val_accuracy: 0.9545\n",
            "Epoch 251/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6179 - accuracy: 0.9697 - val_loss: 0.6168 - val_accuracy: 0.9545\n",
            "Epoch 252/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6176 - accuracy: 0.9697 - val_loss: 0.6165 - val_accuracy: 0.9545\n",
            "Epoch 253/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6174 - accuracy: 0.9697 - val_loss: 0.6162 - val_accuracy: 0.9545\n",
            "Epoch 254/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6172 - accuracy: 0.9697 - val_loss: 0.6160 - val_accuracy: 0.9545\n",
            "Epoch 255/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6169 - accuracy: 0.9697 - val_loss: 0.6159 - val_accuracy: 0.9545\n",
            "Epoch 256/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.6167 - accuracy: 0.9697 - val_loss: 0.6157 - val_accuracy: 0.9545\n",
            "Epoch 257/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6164 - accuracy: 0.9697 - val_loss: 0.6155 - val_accuracy: 0.9545\n",
            "Epoch 258/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6162 - accuracy: 0.9697 - val_loss: 0.6152 - val_accuracy: 0.9545\n",
            "Epoch 259/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6159 - accuracy: 0.9697 - val_loss: 0.6149 - val_accuracy: 0.9545\n",
            "Epoch 260/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6157 - accuracy: 0.9697 - val_loss: 0.6146 - val_accuracy: 0.9545\n",
            "Epoch 261/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6154 - accuracy: 0.9697 - val_loss: 0.6142 - val_accuracy: 0.9545\n",
            "Epoch 262/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6152 - accuracy: 0.9697 - val_loss: 0.6140 - val_accuracy: 0.9545\n",
            "Epoch 263/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.6150 - accuracy: 0.9697 - val_loss: 0.6138 - val_accuracy: 0.9545\n",
            "Epoch 264/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.6147 - accuracy: 0.9697 - val_loss: 0.6137 - val_accuracy: 0.9545\n",
            "Epoch 265/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6145 - accuracy: 0.9697 - val_loss: 0.6136 - val_accuracy: 0.9545\n",
            "Epoch 266/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6142 - accuracy: 0.9697 - val_loss: 0.6132 - val_accuracy: 0.9545\n",
            "Epoch 267/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6140 - accuracy: 0.9697 - val_loss: 0.6128 - val_accuracy: 0.9545\n",
            "Epoch 268/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6137 - accuracy: 0.9697 - val_loss: 0.6125 - val_accuracy: 0.9545\n",
            "Epoch 269/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6135 - accuracy: 0.9697 - val_loss: 0.6122 - val_accuracy: 0.9545\n",
            "Epoch 270/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6133 - accuracy: 0.9697 - val_loss: 0.6121 - val_accuracy: 0.9545\n",
            "Epoch 271/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6130 - accuracy: 0.9697 - val_loss: 0.6119 - val_accuracy: 0.9545\n",
            "Epoch 272/1000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6128 - accuracy: 0.9697 - val_loss: 0.6117 - val_accuracy: 0.9545\n",
            "Epoch 273/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6125 - accuracy: 0.9697 - val_loss: 0.6115 - val_accuracy: 0.9545\n",
            "Epoch 274/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6123 - accuracy: 0.9697 - val_loss: 0.6113 - val_accuracy: 0.9545\n",
            "Epoch 275/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6120 - accuracy: 0.9697 - val_loss: 0.6110 - val_accuracy: 0.9545\n",
            "Epoch 276/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6118 - accuracy: 0.9697 - val_loss: 0.6106 - val_accuracy: 0.9545\n",
            "Epoch 277/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6116 - accuracy: 0.9697 - val_loss: 0.6103 - val_accuracy: 0.9545\n",
            "Epoch 278/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6113 - accuracy: 0.9697 - val_loss: 0.6100 - val_accuracy: 0.9545\n",
            "Epoch 279/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6111 - accuracy: 0.9747 - val_loss: 0.6098 - val_accuracy: 0.9545\n",
            "Epoch 280/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6108 - accuracy: 0.9697 - val_loss: 0.6096 - val_accuracy: 0.9545\n",
            "Epoch 281/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6106 - accuracy: 0.9697 - val_loss: 0.6095 - val_accuracy: 0.9545\n",
            "Epoch 282/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6103 - accuracy: 0.9697 - val_loss: 0.6093 - val_accuracy: 0.9545\n",
            "Epoch 283/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6101 - accuracy: 0.9697 - val_loss: 0.6091 - val_accuracy: 0.9545\n",
            "Epoch 284/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6099 - accuracy: 0.9697 - val_loss: 0.6088 - val_accuracy: 0.9545\n",
            "Epoch 285/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6096 - accuracy: 0.9697 - val_loss: 0.6085 - val_accuracy: 0.9545\n",
            "Epoch 286/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6094 - accuracy: 0.9697 - val_loss: 0.6081 - val_accuracy: 0.9545\n",
            "Epoch 287/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6091 - accuracy: 0.9697 - val_loss: 0.6078 - val_accuracy: 0.9545\n",
            "Epoch 288/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6089 - accuracy: 0.9747 - val_loss: 0.6074 - val_accuracy: 1.0000\n",
            "Epoch 289/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6086 - accuracy: 0.9747 - val_loss: 0.6073 - val_accuracy: 0.9545\n",
            "Epoch 290/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6084 - accuracy: 0.9747 - val_loss: 0.6072 - val_accuracy: 0.9545\n",
            "Epoch 291/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6082 - accuracy: 0.9697 - val_loss: 0.6070 - val_accuracy: 0.9545\n",
            "Epoch 292/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6079 - accuracy: 0.9697 - val_loss: 0.6069 - val_accuracy: 0.9545\n",
            "Epoch 293/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6077 - accuracy: 0.9697 - val_loss: 0.6066 - val_accuracy: 0.9545\n",
            "Epoch 294/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6074 - accuracy: 0.9697 - val_loss: 0.6062 - val_accuracy: 0.9545\n",
            "Epoch 295/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6072 - accuracy: 0.9697 - val_loss: 0.6059 - val_accuracy: 0.9545\n",
            "Epoch 296/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6069 - accuracy: 0.9747 - val_loss: 0.6057 - val_accuracy: 0.9545\n",
            "Epoch 297/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6067 - accuracy: 0.9747 - val_loss: 0.6056 - val_accuracy: 0.9545\n",
            "Epoch 298/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6065 - accuracy: 0.9697 - val_loss: 0.6054 - val_accuracy: 0.9545\n",
            "Epoch 299/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6062 - accuracy: 0.9697 - val_loss: 0.6050 - val_accuracy: 0.9545\n",
            "Epoch 300/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.6060 - accuracy: 0.9747 - val_loss: 0.6047 - val_accuracy: 0.9545\n",
            "Epoch 301/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6057 - accuracy: 0.9747 - val_loss: 0.6046 - val_accuracy: 0.9545\n",
            "Epoch 302/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.6055 - accuracy: 0.9747 - val_loss: 0.6045 - val_accuracy: 0.9545\n",
            "Epoch 303/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.6053 - accuracy: 0.9697 - val_loss: 0.6042 - val_accuracy: 0.9545\n",
            "Epoch 304/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.6050 - accuracy: 0.9697 - val_loss: 0.6038 - val_accuracy: 0.9545\n",
            "Epoch 305/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6048 - accuracy: 0.9747 - val_loss: 0.6036 - val_accuracy: 1.0000\n",
            "Epoch 306/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.6045 - accuracy: 0.9747 - val_loss: 0.6035 - val_accuracy: 0.9545\n",
            "Epoch 307/1000\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.6043 - accuracy: 0.9747 - val_loss: 0.6032 - val_accuracy: 0.9545\n",
            "Epoch 308/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.6041 - accuracy: 0.9747 - val_loss: 0.6029 - val_accuracy: 1.0000\n",
            "Epoch 309/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.6038 - accuracy: 0.9747 - val_loss: 0.6027 - val_accuracy: 0.9545\n",
            "Epoch 310/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.6036 - accuracy: 0.9747 - val_loss: 0.6025 - val_accuracy: 0.9545\n",
            "Epoch 311/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.6033 - accuracy: 0.9747 - val_loss: 0.6022 - val_accuracy: 1.0000\n",
            "Epoch 312/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.6031 - accuracy: 0.9747 - val_loss: 0.6019 - val_accuracy: 1.0000\n",
            "Epoch 313/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.6029 - accuracy: 0.9747 - val_loss: 0.6018 - val_accuracy: 0.9545\n",
            "Epoch 314/1000\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.6026 - accuracy: 0.9747 - val_loss: 0.6014 - val_accuracy: 1.0000\n",
            "Epoch 315/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.6024 - accuracy: 0.9747 - val_loss: 0.6011 - val_accuracy: 1.0000\n",
            "Epoch 316/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.6022 - accuracy: 0.9747 - val_loss: 0.6010 - val_accuracy: 1.0000\n",
            "Epoch 317/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.6019 - accuracy: 0.9747 - val_loss: 0.6009 - val_accuracy: 0.9545\n",
            "Epoch 318/1000\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.6017 - accuracy: 0.9747 - val_loss: 0.6006 - val_accuracy: 1.0000\n",
            "Epoch 319/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.6014 - accuracy: 0.9747 - val_loss: 0.6001 - val_accuracy: 1.0000\n",
            "Epoch 320/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.6012 - accuracy: 0.9747 - val_loss: 0.5998 - val_accuracy: 1.0000\n",
            "Epoch 321/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.6010 - accuracy: 0.9798 - val_loss: 0.5996 - val_accuracy: 1.0000\n",
            "Epoch 322/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.6008 - accuracy: 0.9747 - val_loss: 0.5995 - val_accuracy: 1.0000\n",
            "Epoch 323/1000\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.6005 - accuracy: 0.9747 - val_loss: 0.5995 - val_accuracy: 0.9545\n",
            "Epoch 324/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.6003 - accuracy: 0.9747 - val_loss: 0.5992 - val_accuracy: 1.0000\n",
            "Epoch 325/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.6000 - accuracy: 0.9747 - val_loss: 0.5987 - val_accuracy: 1.0000\n",
            "Epoch 326/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5998 - accuracy: 0.9747 - val_loss: 0.5983 - val_accuracy: 1.0000\n",
            "Epoch 327/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5996 - accuracy: 0.9798 - val_loss: 0.5980 - val_accuracy: 1.0000\n",
            "Epoch 328/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5993 - accuracy: 0.9798 - val_loss: 0.5979 - val_accuracy: 1.0000\n",
            "Epoch 329/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5991 - accuracy: 0.9798 - val_loss: 0.5979 - val_accuracy: 1.0000\n",
            "Epoch 330/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5988 - accuracy: 0.9747 - val_loss: 0.5980 - val_accuracy: 0.9545\n",
            "Epoch 331/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5987 - accuracy: 0.9747 - val_loss: 0.5978 - val_accuracy: 0.9545\n",
            "Epoch 332/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.5984 - accuracy: 0.9747 - val_loss: 0.5973 - val_accuracy: 1.0000\n",
            "Epoch 333/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5981 - accuracy: 0.9747 - val_loss: 0.5966 - val_accuracy: 1.0000\n",
            "Epoch 334/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5979 - accuracy: 0.9798 - val_loss: 0.5962 - val_accuracy: 1.0000\n",
            "Epoch 335/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5977 - accuracy: 0.9798 - val_loss: 0.5963 - val_accuracy: 1.0000\n",
            "Epoch 336/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5974 - accuracy: 0.9798 - val_loss: 0.5965 - val_accuracy: 1.0000\n",
            "Epoch 337/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5972 - accuracy: 0.9747 - val_loss: 0.5964 - val_accuracy: 1.0000\n",
            "Epoch 338/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5970 - accuracy: 0.9747 - val_loss: 0.5959 - val_accuracy: 1.0000\n",
            "Epoch 339/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5967 - accuracy: 0.9747 - val_loss: 0.5952 - val_accuracy: 1.0000\n",
            "Epoch 340/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5965 - accuracy: 0.9798 - val_loss: 0.5948 - val_accuracy: 1.0000\n",
            "Epoch 341/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5963 - accuracy: 0.9798 - val_loss: 0.5947 - val_accuracy: 1.0000\n",
            "Epoch 342/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5960 - accuracy: 0.9798 - val_loss: 0.5949 - val_accuracy: 1.0000\n",
            "Epoch 343/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5958 - accuracy: 0.9747 - val_loss: 0.5948 - val_accuracy: 1.0000\n",
            "Epoch 344/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5955 - accuracy: 0.9747 - val_loss: 0.5943 - val_accuracy: 1.0000\n",
            "Epoch 345/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5953 - accuracy: 0.9798 - val_loss: 0.5941 - val_accuracy: 1.0000\n",
            "Epoch 346/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5951 - accuracy: 0.9798 - val_loss: 0.5941 - val_accuracy: 1.0000\n",
            "Epoch 347/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5948 - accuracy: 0.9747 - val_loss: 0.5937 - val_accuracy: 1.0000\n",
            "Epoch 348/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.5946 - accuracy: 0.9798 - val_loss: 0.5934 - val_accuracy: 1.0000\n",
            "Epoch 349/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5944 - accuracy: 0.9798 - val_loss: 0.5933 - val_accuracy: 1.0000\n",
            "Epoch 350/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5941 - accuracy: 0.9747 - val_loss: 0.5931 - val_accuracy: 1.0000\n",
            "Epoch 351/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.5939 - accuracy: 0.9798 - val_loss: 0.5927 - val_accuracy: 1.0000\n",
            "Epoch 352/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5937 - accuracy: 0.9798 - val_loss: 0.5926 - val_accuracy: 1.0000\n",
            "Epoch 353/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5934 - accuracy: 0.9798 - val_loss: 0.5923 - val_accuracy: 1.0000\n",
            "Epoch 354/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5932 - accuracy: 0.9798 - val_loss: 0.5919 - val_accuracy: 1.0000\n",
            "Epoch 355/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5930 - accuracy: 0.9798 - val_loss: 0.5920 - val_accuracy: 1.0000\n",
            "Epoch 356/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5928 - accuracy: 0.9747 - val_loss: 0.5919 - val_accuracy: 1.0000\n",
            "Epoch 357/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5926 - accuracy: 0.9747 - val_loss: 0.5916 - val_accuracy: 1.0000\n",
            "Epoch 358/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5923 - accuracy: 0.9747 - val_loss: 0.5913 - val_accuracy: 1.0000\n",
            "Epoch 359/1000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.5921 - accuracy: 0.9747 - val_loss: 0.5909 - val_accuracy: 1.0000\n",
            "Epoch 360/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5919 - accuracy: 0.9798 - val_loss: 0.5904 - val_accuracy: 1.0000\n",
            "Epoch 361/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5917 - accuracy: 0.9798 - val_loss: 0.5902 - val_accuracy: 1.0000\n",
            "Epoch 362/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5914 - accuracy: 0.9798 - val_loss: 0.5905 - val_accuracy: 1.0000\n",
            "Epoch 363/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5912 - accuracy: 0.9747 - val_loss: 0.5906 - val_accuracy: 1.0000\n",
            "Epoch 364/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5910 - accuracy: 0.9747 - val_loss: 0.5905 - val_accuracy: 0.9545\n",
            "Epoch 365/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5908 - accuracy: 0.9747 - val_loss: 0.5896 - val_accuracy: 1.0000\n",
            "Epoch 366/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5905 - accuracy: 0.9747 - val_loss: 0.5888 - val_accuracy: 1.0000\n",
            "Epoch 367/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5904 - accuracy: 0.9798 - val_loss: 0.5883 - val_accuracy: 1.0000\n",
            "Epoch 368/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5902 - accuracy: 0.9798 - val_loss: 0.5882 - val_accuracy: 1.0000\n",
            "Epoch 369/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5899 - accuracy: 0.9798 - val_loss: 0.5885 - val_accuracy: 1.0000\n",
            "Epoch 370/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5896 - accuracy: 0.9798 - val_loss: 0.5886 - val_accuracy: 1.0000\n",
            "Epoch 371/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5894 - accuracy: 0.9747 - val_loss: 0.5886 - val_accuracy: 1.0000\n",
            "Epoch 372/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5892 - accuracy: 0.9747 - val_loss: 0.5885 - val_accuracy: 1.0000\n",
            "Epoch 373/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5890 - accuracy: 0.9747 - val_loss: 0.5882 - val_accuracy: 1.0000\n",
            "Epoch 374/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5888 - accuracy: 0.9747 - val_loss: 0.5878 - val_accuracy: 1.0000\n",
            "Epoch 375/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5885 - accuracy: 0.9747 - val_loss: 0.5873 - val_accuracy: 1.0000\n",
            "Epoch 376/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5883 - accuracy: 0.9747 - val_loss: 0.5868 - val_accuracy: 1.0000\n",
            "Epoch 377/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5880 - accuracy: 0.9798 - val_loss: 0.5862 - val_accuracy: 1.0000\n",
            "Epoch 378/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5879 - accuracy: 0.9798 - val_loss: 0.5860 - val_accuracy: 1.0000\n",
            "Epoch 379/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5876 - accuracy: 0.9798 - val_loss: 0.5861 - val_accuracy: 1.0000\n",
            "Epoch 380/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5874 - accuracy: 0.9798 - val_loss: 0.5861 - val_accuracy: 1.0000\n",
            "Epoch 381/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5872 - accuracy: 0.9747 - val_loss: 0.5860 - val_accuracy: 1.0000\n",
            "Epoch 382/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5870 - accuracy: 0.9747 - val_loss: 0.5858 - val_accuracy: 1.0000\n",
            "Epoch 383/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5867 - accuracy: 0.9747 - val_loss: 0.5855 - val_accuracy: 1.0000\n",
            "Epoch 384/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5865 - accuracy: 0.9747 - val_loss: 0.5850 - val_accuracy: 1.0000\n",
            "Epoch 385/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5863 - accuracy: 0.9798 - val_loss: 0.5845 - val_accuracy: 1.0000\n",
            "Epoch 386/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5860 - accuracy: 0.9798 - val_loss: 0.5839 - val_accuracy: 1.0000\n",
            "Epoch 387/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5859 - accuracy: 0.9798 - val_loss: 0.5838 - val_accuracy: 1.0000\n",
            "Epoch 388/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5856 - accuracy: 0.9798 - val_loss: 0.5840 - val_accuracy: 1.0000\n",
            "Epoch 389/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5854 - accuracy: 0.9798 - val_loss: 0.5841 - val_accuracy: 1.0000\n",
            "Epoch 390/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5852 - accuracy: 0.9747 - val_loss: 0.5840 - val_accuracy: 1.0000\n",
            "Epoch 391/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5850 - accuracy: 0.9747 - val_loss: 0.5838 - val_accuracy: 1.0000\n",
            "Epoch 392/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5847 - accuracy: 0.9747 - val_loss: 0.5835 - val_accuracy: 1.0000\n",
            "Epoch 393/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5845 - accuracy: 0.9747 - val_loss: 0.5830 - val_accuracy: 1.0000\n",
            "Epoch 394/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5843 - accuracy: 0.9747 - val_loss: 0.5825 - val_accuracy: 1.0000\n",
            "Epoch 395/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.5840 - accuracy: 0.9798 - val_loss: 0.5819 - val_accuracy: 1.0000\n",
            "Epoch 396/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5838 - accuracy: 0.9798 - val_loss: 0.5819 - val_accuracy: 1.0000\n",
            "Epoch 397/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5836 - accuracy: 0.9798 - val_loss: 0.5818 - val_accuracy: 1.0000\n",
            "Epoch 398/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5834 - accuracy: 0.9798 - val_loss: 0.5816 - val_accuracy: 1.0000\n",
            "Epoch 399/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5831 - accuracy: 0.9798 - val_loss: 0.5813 - val_accuracy: 1.0000\n",
            "Epoch 400/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5829 - accuracy: 0.9798 - val_loss: 0.5808 - val_accuracy: 1.0000\n",
            "Epoch 401/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5827 - accuracy: 0.9798 - val_loss: 0.5805 - val_accuracy: 1.0000\n",
            "Epoch 402/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5825 - accuracy: 0.9798 - val_loss: 0.5803 - val_accuracy: 1.0000\n",
            "Epoch 403/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5823 - accuracy: 0.9798 - val_loss: 0.5802 - val_accuracy: 1.0000\n",
            "Epoch 404/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5820 - accuracy: 0.9798 - val_loss: 0.5801 - val_accuracy: 1.0000\n",
            "Epoch 405/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5818 - accuracy: 0.9798 - val_loss: 0.5799 - val_accuracy: 1.0000\n",
            "Epoch 406/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5816 - accuracy: 0.9798 - val_loss: 0.5796 - val_accuracy: 1.0000\n",
            "Epoch 407/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5814 - accuracy: 0.9798 - val_loss: 0.5794 - val_accuracy: 1.0000\n",
            "Epoch 408/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5812 - accuracy: 0.9798 - val_loss: 0.5793 - val_accuracy: 1.0000\n",
            "Epoch 409/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5809 - accuracy: 0.9798 - val_loss: 0.5791 - val_accuracy: 1.0000\n",
            "Epoch 410/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5807 - accuracy: 0.9798 - val_loss: 0.5787 - val_accuracy: 1.0000\n",
            "Epoch 411/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5805 - accuracy: 0.9798 - val_loss: 0.5784 - val_accuracy: 1.0000\n",
            "Epoch 412/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5803 - accuracy: 0.9798 - val_loss: 0.5783 - val_accuracy: 1.0000\n",
            "Epoch 413/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5801 - accuracy: 0.9798 - val_loss: 0.5782 - val_accuracy: 1.0000\n",
            "Epoch 414/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5798 - accuracy: 0.9798 - val_loss: 0.5782 - val_accuracy: 1.0000\n",
            "Epoch 415/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5796 - accuracy: 0.9798 - val_loss: 0.5780 - val_accuracy: 1.0000\n",
            "Epoch 416/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5794 - accuracy: 0.9798 - val_loss: 0.5777 - val_accuracy: 1.0000\n",
            "Epoch 417/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5792 - accuracy: 0.9798 - val_loss: 0.5773 - val_accuracy: 1.0000\n",
            "Epoch 418/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5790 - accuracy: 0.9798 - val_loss: 0.5770 - val_accuracy: 1.0000\n",
            "Epoch 419/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5788 - accuracy: 0.9798 - val_loss: 0.5768 - val_accuracy: 1.0000\n",
            "Epoch 420/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5785 - accuracy: 0.9798 - val_loss: 0.5766 - val_accuracy: 1.0000\n",
            "Epoch 421/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5783 - accuracy: 0.9798 - val_loss: 0.5766 - val_accuracy: 1.0000\n",
            "Epoch 422/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5781 - accuracy: 0.9798 - val_loss: 0.5764 - val_accuracy: 1.0000\n",
            "Epoch 423/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5779 - accuracy: 0.9798 - val_loss: 0.5761 - val_accuracy: 1.0000\n",
            "Epoch 424/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5777 - accuracy: 0.9798 - val_loss: 0.5758 - val_accuracy: 1.0000\n",
            "Epoch 425/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5775 - accuracy: 0.9798 - val_loss: 0.5757 - val_accuracy: 1.0000\n",
            "Epoch 426/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5772 - accuracy: 0.9798 - val_loss: 0.5756 - val_accuracy: 1.0000\n",
            "Epoch 427/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.5770 - accuracy: 0.9798 - val_loss: 0.5754 - val_accuracy: 1.0000\n",
            "Epoch 428/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5768 - accuracy: 0.9798 - val_loss: 0.5750 - val_accuracy: 1.0000\n",
            "Epoch 429/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5766 - accuracy: 0.9798 - val_loss: 0.5748 - val_accuracy: 1.0000\n",
            "Epoch 430/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5764 - accuracy: 0.9798 - val_loss: 0.5746 - val_accuracy: 1.0000\n",
            "Epoch 431/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5762 - accuracy: 0.9798 - val_loss: 0.5745 - val_accuracy: 1.0000\n",
            "Epoch 432/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5759 - accuracy: 0.9798 - val_loss: 0.5743 - val_accuracy: 1.0000\n",
            "Epoch 433/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5757 - accuracy: 0.9798 - val_loss: 0.5741 - val_accuracy: 1.0000\n",
            "Epoch 434/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5755 - accuracy: 0.9798 - val_loss: 0.5734 - val_accuracy: 1.0000\n",
            "Epoch 435/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5753 - accuracy: 0.9798 - val_loss: 0.5728 - val_accuracy: 1.0000\n",
            "Epoch 436/1000\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.5751 - accuracy: 0.9798 - val_loss: 0.5724 - val_accuracy: 1.0000\n",
            "Epoch 437/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5749 - accuracy: 0.9798 - val_loss: 0.5725 - val_accuracy: 1.0000\n",
            "Epoch 438/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5747 - accuracy: 0.9798 - val_loss: 0.5727 - val_accuracy: 1.0000\n",
            "Epoch 439/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5744 - accuracy: 0.9798 - val_loss: 0.5729 - val_accuracy: 1.0000\n",
            "Epoch 440/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5743 - accuracy: 0.9747 - val_loss: 0.5723 - val_accuracy: 1.0000\n",
            "Epoch 441/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5740 - accuracy: 0.9798 - val_loss: 0.5719 - val_accuracy: 1.0000\n",
            "Epoch 442/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5738 - accuracy: 0.9798 - val_loss: 0.5715 - val_accuracy: 1.0000\n",
            "Epoch 443/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5736 - accuracy: 0.9798 - val_loss: 0.5713 - val_accuracy: 1.0000\n",
            "Epoch 444/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5734 - accuracy: 0.9798 - val_loss: 0.5712 - val_accuracy: 1.0000\n",
            "Epoch 445/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5732 - accuracy: 0.9798 - val_loss: 0.5712 - val_accuracy: 1.0000\n",
            "Epoch 446/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5729 - accuracy: 0.9798 - val_loss: 0.5712 - val_accuracy: 1.0000\n",
            "Epoch 447/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5727 - accuracy: 0.9798 - val_loss: 0.5709 - val_accuracy: 1.0000\n",
            "Epoch 448/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5725 - accuracy: 0.9798 - val_loss: 0.5707 - val_accuracy: 1.0000\n",
            "Epoch 449/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5723 - accuracy: 0.9798 - val_loss: 0.5706 - val_accuracy: 1.0000\n",
            "Epoch 450/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5721 - accuracy: 0.9798 - val_loss: 0.5701 - val_accuracy: 1.0000\n",
            "Epoch 451/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.5719 - accuracy: 0.9798 - val_loss: 0.5697 - val_accuracy: 1.0000\n",
            "Epoch 452/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5717 - accuracy: 0.9798 - val_loss: 0.5695 - val_accuracy: 1.0000\n",
            "Epoch 453/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5714 - accuracy: 0.9798 - val_loss: 0.5693 - val_accuracy: 1.0000\n",
            "Epoch 454/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5712 - accuracy: 0.9798 - val_loss: 0.5693 - val_accuracy: 1.0000\n",
            "Epoch 455/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5710 - accuracy: 0.9798 - val_loss: 0.5694 - val_accuracy: 1.0000\n",
            "Epoch 456/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5708 - accuracy: 0.9798 - val_loss: 0.5690 - val_accuracy: 1.0000\n",
            "Epoch 457/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5706 - accuracy: 0.9798 - val_loss: 0.5688 - val_accuracy: 1.0000\n",
            "Epoch 458/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5704 - accuracy: 0.9798 - val_loss: 0.5687 - val_accuracy: 1.0000\n",
            "Epoch 459/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5701 - accuracy: 0.9798 - val_loss: 0.5682 - val_accuracy: 1.0000\n",
            "Epoch 460/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5699 - accuracy: 0.9798 - val_loss: 0.5678 - val_accuracy: 1.0000\n",
            "Epoch 461/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5697 - accuracy: 0.9798 - val_loss: 0.5676 - val_accuracy: 1.0000\n",
            "Epoch 462/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5695 - accuracy: 0.9798 - val_loss: 0.5675 - val_accuracy: 1.0000\n",
            "Epoch 463/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5693 - accuracy: 0.9798 - val_loss: 0.5674 - val_accuracy: 1.0000\n",
            "Epoch 464/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.5691 - accuracy: 0.9798 - val_loss: 0.5675 - val_accuracy: 1.0000\n",
            "Epoch 465/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5689 - accuracy: 0.9798 - val_loss: 0.5672 - val_accuracy: 1.0000\n",
            "Epoch 466/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5687 - accuracy: 0.9798 - val_loss: 0.5670 - val_accuracy: 1.0000\n",
            "Epoch 467/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5684 - accuracy: 0.9798 - val_loss: 0.5669 - val_accuracy: 1.0000\n",
            "Epoch 468/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5682 - accuracy: 0.9798 - val_loss: 0.5664 - val_accuracy: 1.0000\n",
            "Epoch 469/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5680 - accuracy: 0.9798 - val_loss: 0.5660 - val_accuracy: 1.0000\n",
            "Epoch 470/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5678 - accuracy: 0.9798 - val_loss: 0.5658 - val_accuracy: 1.0000\n",
            "Epoch 471/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5676 - accuracy: 0.9798 - val_loss: 0.5657 - val_accuracy: 1.0000\n",
            "Epoch 472/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5674 - accuracy: 0.9798 - val_loss: 0.5657 - val_accuracy: 1.0000\n",
            "Epoch 473/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5672 - accuracy: 0.9798 - val_loss: 0.5658 - val_accuracy: 1.0000\n",
            "Epoch 474/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5670 - accuracy: 0.9798 - val_loss: 0.5652 - val_accuracy: 1.0000\n",
            "Epoch 475/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5667 - accuracy: 0.9798 - val_loss: 0.5648 - val_accuracy: 1.0000\n",
            "Epoch 476/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5665 - accuracy: 0.9798 - val_loss: 0.5645 - val_accuracy: 1.0000\n",
            "Epoch 477/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5663 - accuracy: 0.9798 - val_loss: 0.5644 - val_accuracy: 1.0000\n",
            "Epoch 478/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5661 - accuracy: 0.9798 - val_loss: 0.5644 - val_accuracy: 1.0000\n",
            "Epoch 479/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5659 - accuracy: 0.9798 - val_loss: 0.5644 - val_accuracy: 1.0000\n",
            "Epoch 480/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5657 - accuracy: 0.9798 - val_loss: 0.5641 - val_accuracy: 1.0000\n",
            "Epoch 481/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5655 - accuracy: 0.9798 - val_loss: 0.5639 - val_accuracy: 1.0000\n",
            "Epoch 482/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5653 - accuracy: 0.9798 - val_loss: 0.5638 - val_accuracy: 1.0000\n",
            "Epoch 483/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5650 - accuracy: 0.9798 - val_loss: 0.5633 - val_accuracy: 1.0000\n",
            "Epoch 484/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5648 - accuracy: 0.9798 - val_loss: 0.5629 - val_accuracy: 1.0000\n",
            "Epoch 485/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5646 - accuracy: 0.9798 - val_loss: 0.5627 - val_accuracy: 1.0000\n",
            "Epoch 486/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5644 - accuracy: 0.9798 - val_loss: 0.5627 - val_accuracy: 1.0000\n",
            "Epoch 487/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5642 - accuracy: 0.9798 - val_loss: 0.5627 - val_accuracy: 1.0000\n",
            "Epoch 488/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5640 - accuracy: 0.9798 - val_loss: 0.5629 - val_accuracy: 1.0000\n",
            "Epoch 489/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5638 - accuracy: 0.9798 - val_loss: 0.5623 - val_accuracy: 1.0000\n",
            "Epoch 490/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5636 - accuracy: 0.9798 - val_loss: 0.5619 - val_accuracy: 1.0000\n",
            "Epoch 491/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5634 - accuracy: 0.9798 - val_loss: 0.5616 - val_accuracy: 1.0000\n",
            "Epoch 492/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5631 - accuracy: 0.9798 - val_loss: 0.5615 - val_accuracy: 1.0000\n",
            "Epoch 493/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5629 - accuracy: 0.9798 - val_loss: 0.5615 - val_accuracy: 1.0000\n",
            "Epoch 494/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5627 - accuracy: 0.9798 - val_loss: 0.5617 - val_accuracy: 1.0000\n",
            "Epoch 495/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5626 - accuracy: 0.9798 - val_loss: 0.5611 - val_accuracy: 1.0000\n",
            "Epoch 496/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5623 - accuracy: 0.9798 - val_loss: 0.5606 - val_accuracy: 1.0000\n",
            "Epoch 497/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5621 - accuracy: 0.9798 - val_loss: 0.5604 - val_accuracy: 1.0000\n",
            "Epoch 498/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5619 - accuracy: 0.9798 - val_loss: 0.5602 - val_accuracy: 1.0000\n",
            "Epoch 499/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5617 - accuracy: 0.9798 - val_loss: 0.5602 - val_accuracy: 1.0000\n",
            "Epoch 500/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5614 - accuracy: 0.9798 - val_loss: 0.5604 - val_accuracy: 1.0000\n",
            "Epoch 501/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5613 - accuracy: 0.9798 - val_loss: 0.5598 - val_accuracy: 1.0000\n",
            "Epoch 502/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5610 - accuracy: 0.9798 - val_loss: 0.5594 - val_accuracy: 1.0000\n",
            "Epoch 503/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.5608 - accuracy: 0.9798 - val_loss: 0.5591 - val_accuracy: 1.0000\n",
            "Epoch 504/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5606 - accuracy: 0.9798 - val_loss: 0.5590 - val_accuracy: 1.0000\n",
            "Epoch 505/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5604 - accuracy: 0.9798 - val_loss: 0.5591 - val_accuracy: 1.0000\n",
            "Epoch 506/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5602 - accuracy: 0.9798 - val_loss: 0.5592 - val_accuracy: 1.0000\n",
            "Epoch 507/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5600 - accuracy: 0.9798 - val_loss: 0.5586 - val_accuracy: 1.0000\n",
            "Epoch 508/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5598 - accuracy: 0.9798 - val_loss: 0.5582 - val_accuracy: 1.0000\n",
            "Epoch 509/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5596 - accuracy: 0.9798 - val_loss: 0.5580 - val_accuracy: 1.0000\n",
            "Epoch 510/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5594 - accuracy: 0.9798 - val_loss: 0.5579 - val_accuracy: 1.0000\n",
            "Epoch 511/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5591 - accuracy: 0.9798 - val_loss: 0.5580 - val_accuracy: 1.0000\n",
            "Epoch 512/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5589 - accuracy: 0.9798 - val_loss: 0.5576 - val_accuracy: 1.0000\n",
            "Epoch 513/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5587 - accuracy: 0.9798 - val_loss: 0.5574 - val_accuracy: 1.0000\n",
            "Epoch 514/1000\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.5585 - accuracy: 0.9798 - val_loss: 0.5573 - val_accuracy: 1.0000\n",
            "Epoch 515/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.5583 - accuracy: 0.9798 - val_loss: 0.5574 - val_accuracy: 1.0000\n",
            "Epoch 516/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5581 - accuracy: 0.9798 - val_loss: 0.5567 - val_accuracy: 1.0000\n",
            "Epoch 517/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5579 - accuracy: 0.9798 - val_loss: 0.5562 - val_accuracy: 1.0000\n",
            "Epoch 518/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5577 - accuracy: 0.9798 - val_loss: 0.5560 - val_accuracy: 1.0000\n",
            "Epoch 519/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.5575 - accuracy: 0.9798 - val_loss: 0.5559 - val_accuracy: 1.0000\n",
            "Epoch 520/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5573 - accuracy: 0.9798 - val_loss: 0.5559 - val_accuracy: 1.0000\n",
            "Epoch 521/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5570 - accuracy: 0.9798 - val_loss: 0.5562 - val_accuracy: 1.0000\n",
            "Epoch 522/1000\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.5568 - accuracy: 0.9798 - val_loss: 0.5556 - val_accuracy: 1.0000\n",
            "Epoch 523/1000\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.5566 - accuracy: 0.9798 - val_loss: 0.5552 - val_accuracy: 1.0000\n",
            "Epoch 524/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5564 - accuracy: 0.9798 - val_loss: 0.5550 - val_accuracy: 1.0000\n",
            "Epoch 525/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5562 - accuracy: 0.9798 - val_loss: 0.5550 - val_accuracy: 1.0000\n",
            "Epoch 526/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5560 - accuracy: 0.9798 - val_loss: 0.5552 - val_accuracy: 1.0000\n",
            "Epoch 527/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5558 - accuracy: 0.9798 - val_loss: 0.5545 - val_accuracy: 1.0000\n",
            "Epoch 528/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5556 - accuracy: 0.9798 - val_loss: 0.5541 - val_accuracy: 1.0000\n",
            "Epoch 529/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5554 - accuracy: 0.9798 - val_loss: 0.5539 - val_accuracy: 1.0000\n",
            "Epoch 530/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5552 - accuracy: 0.9798 - val_loss: 0.5539 - val_accuracy: 1.0000\n",
            "Epoch 531/1000\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.5550 - accuracy: 0.9798 - val_loss: 0.5540 - val_accuracy: 1.0000\n",
            "Epoch 532/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5547 - accuracy: 0.9798 - val_loss: 0.5543 - val_accuracy: 1.0000\n",
            "Epoch 533/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5546 - accuracy: 0.9798 - val_loss: 0.5537 - val_accuracy: 1.0000\n",
            "Epoch 534/1000\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.5543 - accuracy: 0.9798 - val_loss: 0.5528 - val_accuracy: 1.0000\n",
            "Epoch 535/1000\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.5541 - accuracy: 0.9798 - val_loss: 0.5521 - val_accuracy: 1.0000\n",
            "Epoch 536/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5540 - accuracy: 0.9798 - val_loss: 0.5520 - val_accuracy: 1.0000\n",
            "Epoch 537/1000\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.5537 - accuracy: 0.9798 - val_loss: 0.5521 - val_accuracy: 1.0000\n",
            "Epoch 538/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5535 - accuracy: 0.9798 - val_loss: 0.5524 - val_accuracy: 1.0000\n",
            "Epoch 539/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5533 - accuracy: 0.9798 - val_loss: 0.5528 - val_accuracy: 1.0000\n",
            "Epoch 540/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5531 - accuracy: 0.9798 - val_loss: 0.5524 - val_accuracy: 1.0000\n",
            "Epoch 541/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.5529 - accuracy: 0.9798 - val_loss: 0.5516 - val_accuracy: 1.0000\n",
            "Epoch 542/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5527 - accuracy: 0.9798 - val_loss: 0.5510 - val_accuracy: 1.0000\n",
            "Epoch 543/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.5525 - accuracy: 0.9798 - val_loss: 0.5507 - val_accuracy: 1.0000\n",
            "Epoch 544/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5523 - accuracy: 0.9798 - val_loss: 0.5506 - val_accuracy: 1.0000\n",
            "Epoch 545/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5521 - accuracy: 0.9798 - val_loss: 0.5507 - val_accuracy: 1.0000\n",
            "Epoch 546/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5518 - accuracy: 0.9798 - val_loss: 0.5510 - val_accuracy: 1.0000\n",
            "Epoch 547/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5516 - accuracy: 0.9798 - val_loss: 0.5515 - val_accuracy: 1.0000\n",
            "Epoch 548/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5515 - accuracy: 0.9798 - val_loss: 0.5511 - val_accuracy: 1.0000\n",
            "Epoch 549/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5512 - accuracy: 0.9798 - val_loss: 0.5500 - val_accuracy: 1.0000\n",
            "Epoch 550/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5510 - accuracy: 0.9798 - val_loss: 0.5491 - val_accuracy: 1.0000\n",
            "Epoch 551/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5509 - accuracy: 0.9798 - val_loss: 0.5491 - val_accuracy: 1.0000\n",
            "Epoch 552/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5506 - accuracy: 0.9798 - val_loss: 0.5496 - val_accuracy: 1.0000\n",
            "Epoch 553/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5504 - accuracy: 0.9798 - val_loss: 0.5501 - val_accuracy: 1.0000\n",
            "Epoch 554/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5502 - accuracy: 0.9798 - val_loss: 0.5499 - val_accuracy: 1.0000\n",
            "Epoch 555/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5500 - accuracy: 0.9798 - val_loss: 0.5489 - val_accuracy: 1.0000\n",
            "Epoch 556/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5498 - accuracy: 0.9798 - val_loss: 0.5481 - val_accuracy: 1.0000\n",
            "Epoch 557/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5496 - accuracy: 0.9798 - val_loss: 0.5479 - val_accuracy: 1.0000\n",
            "Epoch 558/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5494 - accuracy: 0.9798 - val_loss: 0.5482 - val_accuracy: 1.0000\n",
            "Epoch 559/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5492 - accuracy: 0.9798 - val_loss: 0.5488 - val_accuracy: 1.0000\n",
            "Epoch 560/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5489 - accuracy: 0.9798 - val_loss: 0.5490 - val_accuracy: 1.0000\n",
            "Epoch 561/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5488 - accuracy: 0.9798 - val_loss: 0.5483 - val_accuracy: 1.0000\n",
            "Epoch 562/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5485 - accuracy: 0.9798 - val_loss: 0.5479 - val_accuracy: 1.0000\n",
            "Epoch 563/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5483 - accuracy: 0.9798 - val_loss: 0.5477 - val_accuracy: 1.0000\n",
            "Epoch 564/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.5481 - accuracy: 0.9798 - val_loss: 0.5477 - val_accuracy: 1.0000\n",
            "Epoch 565/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.5479 - accuracy: 0.9798 - val_loss: 0.5478 - val_accuracy: 1.0000\n",
            "Epoch 566/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5477 - accuracy: 0.9798 - val_loss: 0.5472 - val_accuracy: 1.0000\n",
            "Epoch 567/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5475 - accuracy: 0.9798 - val_loss: 0.5468 - val_accuracy: 1.0000\n",
            "Epoch 568/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.5473 - accuracy: 0.9798 - val_loss: 0.5469 - val_accuracy: 1.0000\n",
            "Epoch 569/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5471 - accuracy: 0.9798 - val_loss: 0.5472 - val_accuracy: 1.0000\n",
            "Epoch 570/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5469 - accuracy: 0.9798 - val_loss: 0.5466 - val_accuracy: 1.0000\n",
            "Epoch 571/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5466 - accuracy: 0.9798 - val_loss: 0.5463 - val_accuracy: 1.0000\n",
            "Epoch 572/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.5465 - accuracy: 0.9798 - val_loss: 0.5464 - val_accuracy: 1.0000\n",
            "Epoch 573/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5463 - accuracy: 0.9798 - val_loss: 0.5460 - val_accuracy: 1.0000\n",
            "Epoch 574/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5460 - accuracy: 0.9798 - val_loss: 0.5461 - val_accuracy: 1.0000\n",
            "Epoch 575/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5459 - accuracy: 0.9798 - val_loss: 0.5454 - val_accuracy: 1.0000\n",
            "Epoch 576/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5457 - accuracy: 0.9798 - val_loss: 0.5452 - val_accuracy: 1.0000\n",
            "Epoch 577/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5455 - accuracy: 0.9798 - val_loss: 0.5455 - val_accuracy: 1.0000\n",
            "Epoch 578/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5452 - accuracy: 0.9798 - val_loss: 0.5455 - val_accuracy: 1.0000\n",
            "Epoch 579/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5451 - accuracy: 0.9798 - val_loss: 0.5446 - val_accuracy: 1.0000\n",
            "Epoch 580/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5449 - accuracy: 0.9798 - val_loss: 0.5443 - val_accuracy: 1.0000\n",
            "Epoch 581/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5447 - accuracy: 0.9798 - val_loss: 0.5445 - val_accuracy: 1.0000\n",
            "Epoch 582/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.5444 - accuracy: 0.9798 - val_loss: 0.5451 - val_accuracy: 0.9545\n",
            "Epoch 583/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5443 - accuracy: 0.9798 - val_loss: 0.5445 - val_accuracy: 1.0000\n",
            "Epoch 584/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5440 - accuracy: 0.9798 - val_loss: 0.5433 - val_accuracy: 1.0000\n",
            "Epoch 585/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5439 - accuracy: 0.9798 - val_loss: 0.5428 - val_accuracy: 1.0000\n",
            "Epoch 586/1000\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.5438 - accuracy: 0.9798 - val_loss: 0.5430 - val_accuracy: 1.0000\n",
            "Epoch 587/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.5435 - accuracy: 0.9798 - val_loss: 0.5436 - val_accuracy: 1.0000\n",
            "Epoch 588/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5432 - accuracy: 0.9798 - val_loss: 0.5437 - val_accuracy: 1.0000\n",
            "Epoch 589/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5431 - accuracy: 0.9798 - val_loss: 0.5431 - val_accuracy: 1.0000\n",
            "Epoch 590/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5428 - accuracy: 0.9798 - val_loss: 0.5424 - val_accuracy: 1.0000\n",
            "Epoch 591/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5427 - accuracy: 0.9798 - val_loss: 0.5422 - val_accuracy: 1.0000\n",
            "Epoch 592/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5425 - accuracy: 0.9798 - val_loss: 0.5425 - val_accuracy: 1.0000\n",
            "Epoch 593/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5422 - accuracy: 0.9798 - val_loss: 0.5432 - val_accuracy: 0.9545\n",
            "Epoch 594/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5421 - accuracy: 0.9798 - val_loss: 0.5432 - val_accuracy: 0.9545\n",
            "Epoch 595/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5420 - accuracy: 0.9798 - val_loss: 0.5421 - val_accuracy: 1.0000\n",
            "Epoch 596/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5416 - accuracy: 0.9798 - val_loss: 0.5409 - val_accuracy: 1.0000\n",
            "Epoch 597/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5416 - accuracy: 0.9798 - val_loss: 0.5405 - val_accuracy: 1.0000\n",
            "Epoch 598/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5414 - accuracy: 0.9798 - val_loss: 0.5409 - val_accuracy: 1.0000\n",
            "Epoch 599/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5411 - accuracy: 0.9798 - val_loss: 0.5419 - val_accuracy: 0.9545\n",
            "Epoch 600/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5409 - accuracy: 0.9798 - val_loss: 0.5423 - val_accuracy: 0.9545\n",
            "Epoch 601/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5408 - accuracy: 0.9798 - val_loss: 0.5418 - val_accuracy: 0.9545\n",
            "Epoch 602/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5406 - accuracy: 0.9798 - val_loss: 0.5409 - val_accuracy: 1.0000\n",
            "Epoch 603/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5403 - accuracy: 0.9798 - val_loss: 0.5394 - val_accuracy: 1.0000\n",
            "Epoch 604/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5402 - accuracy: 0.9798 - val_loss: 0.5389 - val_accuracy: 1.0000\n",
            "Epoch 605/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5401 - accuracy: 0.9798 - val_loss: 0.5394 - val_accuracy: 1.0000\n",
            "Epoch 606/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5398 - accuracy: 0.9798 - val_loss: 0.5406 - val_accuracy: 0.9545\n",
            "Epoch 607/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5395 - accuracy: 0.9798 - val_loss: 0.5411 - val_accuracy: 0.9545\n",
            "Epoch 608/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5394 - accuracy: 0.9798 - val_loss: 0.5410 - val_accuracy: 0.9545\n",
            "Epoch 609/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.5393 - accuracy: 0.9798 - val_loss: 0.5400 - val_accuracy: 0.9545\n",
            "Epoch 610/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5390 - accuracy: 0.9798 - val_loss: 0.5386 - val_accuracy: 1.0000\n",
            "Epoch 611/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5388 - accuracy: 0.9798 - val_loss: 0.5380 - val_accuracy: 1.0000\n",
            "Epoch 612/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5387 - accuracy: 0.9798 - val_loss: 0.5382 - val_accuracy: 1.0000\n",
            "Epoch 613/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5384 - accuracy: 0.9798 - val_loss: 0.5390 - val_accuracy: 1.0000\n",
            "Epoch 614/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5381 - accuracy: 0.9798 - val_loss: 0.5392 - val_accuracy: 0.9545\n",
            "Epoch 615/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5380 - accuracy: 0.9798 - val_loss: 0.5389 - val_accuracy: 0.9545\n",
            "Epoch 616/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.5378 - accuracy: 0.9798 - val_loss: 0.5380 - val_accuracy: 1.0000\n",
            "Epoch 617/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5375 - accuracy: 0.9798 - val_loss: 0.5371 - val_accuracy: 1.0000\n",
            "Epoch 618/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5375 - accuracy: 0.9798 - val_loss: 0.5369 - val_accuracy: 1.0000\n",
            "Epoch 619/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5373 - accuracy: 0.9798 - val_loss: 0.5374 - val_accuracy: 1.0000\n",
            "Epoch 620/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5369 - accuracy: 0.9798 - val_loss: 0.5380 - val_accuracy: 0.9545\n",
            "Epoch 621/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5368 - accuracy: 0.9798 - val_loss: 0.5379 - val_accuracy: 0.9545\n",
            "Epoch 622/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5366 - accuracy: 0.9798 - val_loss: 0.5373 - val_accuracy: 1.0000\n",
            "Epoch 623/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5364 - accuracy: 0.9798 - val_loss: 0.5362 - val_accuracy: 1.0000\n",
            "Epoch 624/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5362 - accuracy: 0.9798 - val_loss: 0.5360 - val_accuracy: 1.0000\n",
            "Epoch 625/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5361 - accuracy: 0.9798 - val_loss: 0.5364 - val_accuracy: 1.0000\n",
            "Epoch 626/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5358 - accuracy: 0.9798 - val_loss: 0.5369 - val_accuracy: 0.9545\n",
            "Epoch 627/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5356 - accuracy: 0.9798 - val_loss: 0.5367 - val_accuracy: 0.9545\n",
            "Epoch 628/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.5355 - accuracy: 0.9798 - val_loss: 0.5361 - val_accuracy: 1.0000\n",
            "Epoch 629/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5352 - accuracy: 0.9798 - val_loss: 0.5352 - val_accuracy: 1.0000\n",
            "Epoch 630/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5351 - accuracy: 0.9798 - val_loss: 0.5351 - val_accuracy: 1.0000\n",
            "Epoch 631/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5349 - accuracy: 0.9798 - val_loss: 0.5357 - val_accuracy: 0.9545\n",
            "Epoch 632/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5346 - accuracy: 0.9798 - val_loss: 0.5357 - val_accuracy: 0.9545\n",
            "Epoch 633/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5345 - accuracy: 0.9798 - val_loss: 0.5352 - val_accuracy: 1.0000\n",
            "Epoch 634/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5342 - accuracy: 0.9798 - val_loss: 0.5344 - val_accuracy: 1.0000\n",
            "Epoch 635/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5341 - accuracy: 0.9798 - val_loss: 0.5344 - val_accuracy: 1.0000\n",
            "Epoch 636/1000\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.5339 - accuracy: 0.9798 - val_loss: 0.5351 - val_accuracy: 0.9545\n",
            "Epoch 637/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5337 - accuracy: 0.9798 - val_loss: 0.5352 - val_accuracy: 0.9545\n",
            "Epoch 638/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5336 - accuracy: 0.9798 - val_loss: 0.5348 - val_accuracy: 0.9545\n",
            "Epoch 639/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5333 - accuracy: 0.9798 - val_loss: 0.5338 - val_accuracy: 1.0000\n",
            "Epoch 640/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5331 - accuracy: 0.9798 - val_loss: 0.5329 - val_accuracy: 1.0000\n",
            "Epoch 641/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5330 - accuracy: 0.9798 - val_loss: 0.5329 - val_accuracy: 1.0000\n",
            "Epoch 642/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5328 - accuracy: 0.9798 - val_loss: 0.5336 - val_accuracy: 0.9545\n",
            "Epoch 643/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5325 - accuracy: 0.9798 - val_loss: 0.5339 - val_accuracy: 0.9545\n",
            "Epoch 644/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5324 - accuracy: 0.9798 - val_loss: 0.5337 - val_accuracy: 0.9545\n",
            "Epoch 645/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5322 - accuracy: 0.9798 - val_loss: 0.5329 - val_accuracy: 1.0000\n",
            "Epoch 646/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5319 - accuracy: 0.9798 - val_loss: 0.5319 - val_accuracy: 1.0000\n",
            "Epoch 647/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5318 - accuracy: 0.9798 - val_loss: 0.5318 - val_accuracy: 1.0000\n",
            "Epoch 648/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5317 - accuracy: 0.9798 - val_loss: 0.5324 - val_accuracy: 0.9545\n",
            "Epoch 649/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.5314 - accuracy: 0.9798 - val_loss: 0.5326 - val_accuracy: 0.9545\n",
            "Epoch 650/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5312 - accuracy: 0.9798 - val_loss: 0.5325 - val_accuracy: 0.9545\n",
            "Epoch 651/1000\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.5310 - accuracy: 0.9798 - val_loss: 0.5322 - val_accuracy: 0.9545\n",
            "Epoch 652/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.5308 - accuracy: 0.9798 - val_loss: 0.5316 - val_accuracy: 1.0000\n",
            "Epoch 653/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5306 - accuracy: 0.9798 - val_loss: 0.5311 - val_accuracy: 1.0000\n",
            "Epoch 654/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5304 - accuracy: 0.9798 - val_loss: 0.5313 - val_accuracy: 0.9545\n",
            "Epoch 655/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5302 - accuracy: 0.9798 - val_loss: 0.5313 - val_accuracy: 0.9545\n",
            "Epoch 656/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5300 - accuracy: 0.9798 - val_loss: 0.5310 - val_accuracy: 0.9545\n",
            "Epoch 657/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5298 - accuracy: 0.9798 - val_loss: 0.5304 - val_accuracy: 1.0000\n",
            "Epoch 658/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5297 - accuracy: 0.9798 - val_loss: 0.5306 - val_accuracy: 0.9545\n",
            "Epoch 659/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5295 - accuracy: 0.9798 - val_loss: 0.5305 - val_accuracy: 0.9545\n",
            "Epoch 660/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5293 - accuracy: 0.9798 - val_loss: 0.5301 - val_accuracy: 0.9545\n",
            "Epoch 661/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.5291 - accuracy: 0.9798 - val_loss: 0.5298 - val_accuracy: 1.0000\n",
            "Epoch 662/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5289 - accuracy: 0.9798 - val_loss: 0.5296 - val_accuracy: 1.0000\n",
            "Epoch 663/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5287 - accuracy: 0.9798 - val_loss: 0.5295 - val_accuracy: 1.0000\n",
            "Epoch 664/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5285 - accuracy: 0.9798 - val_loss: 0.5294 - val_accuracy: 0.9545\n",
            "Epoch 665/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5283 - accuracy: 0.9798 - val_loss: 0.5294 - val_accuracy: 0.9545\n",
            "Epoch 666/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5282 - accuracy: 0.9798 - val_loss: 0.5291 - val_accuracy: 0.9545\n",
            "Epoch 667/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5280 - accuracy: 0.9798 - val_loss: 0.5285 - val_accuracy: 1.0000\n",
            "Epoch 668/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5278 - accuracy: 0.9798 - val_loss: 0.5288 - val_accuracy: 0.9545\n",
            "Epoch 669/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5276 - accuracy: 0.9798 - val_loss: 0.5287 - val_accuracy: 0.9545\n",
            "Epoch 670/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5274 - accuracy: 0.9798 - val_loss: 0.5284 - val_accuracy: 0.9545\n",
            "Epoch 671/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5272 - accuracy: 0.9798 - val_loss: 0.5278 - val_accuracy: 1.0000\n",
            "Epoch 672/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5271 - accuracy: 0.9798 - val_loss: 0.5280 - val_accuracy: 0.9545\n",
            "Epoch 673/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5268 - accuracy: 0.9798 - val_loss: 0.5282 - val_accuracy: 0.9545\n",
            "Epoch 674/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5267 - accuracy: 0.9798 - val_loss: 0.5281 - val_accuracy: 0.9545\n",
            "Epoch 675/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5265 - accuracy: 0.9798 - val_loss: 0.5278 - val_accuracy: 0.9545\n",
            "Epoch 676/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5263 - accuracy: 0.9798 - val_loss: 0.5271 - val_accuracy: 0.9545\n",
            "Epoch 677/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5261 - accuracy: 0.9798 - val_loss: 0.5266 - val_accuracy: 1.0000\n",
            "Epoch 678/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5259 - accuracy: 0.9798 - val_loss: 0.5269 - val_accuracy: 0.9545\n",
            "Epoch 679/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5257 - accuracy: 0.9798 - val_loss: 0.5273 - val_accuracy: 0.9545\n",
            "Epoch 680/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5256 - accuracy: 0.9798 - val_loss: 0.5273 - val_accuracy: 0.9545\n",
            "Epoch 681/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5254 - accuracy: 0.9798 - val_loss: 0.5270 - val_accuracy: 0.9545\n",
            "Epoch 682/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5252 - accuracy: 0.9798 - val_loss: 0.5264 - val_accuracy: 0.9545\n",
            "Epoch 683/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5250 - accuracy: 0.9798 - val_loss: 0.5255 - val_accuracy: 1.0000\n",
            "Epoch 684/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5248 - accuracy: 0.9798 - val_loss: 0.5256 - val_accuracy: 1.0000\n",
            "Epoch 685/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5246 - accuracy: 0.9798 - val_loss: 0.5264 - val_accuracy: 0.9545\n",
            "Epoch 686/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5244 - accuracy: 0.9798 - val_loss: 0.5269 - val_accuracy: 0.9545\n",
            "Epoch 687/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5243 - accuracy: 0.9798 - val_loss: 0.5269 - val_accuracy: 0.9545\n",
            "Epoch 688/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.5241 - accuracy: 0.9798 - val_loss: 0.5262 - val_accuracy: 0.9545\n",
            "Epoch 689/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5239 - accuracy: 0.9798 - val_loss: 0.5253 - val_accuracy: 0.9545\n",
            "Epoch 690/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5237 - accuracy: 0.9798 - val_loss: 0.5242 - val_accuracy: 1.0000\n",
            "Epoch 691/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.5235 - accuracy: 0.9798 - val_loss: 0.5239 - val_accuracy: 1.0000\n",
            "Epoch 692/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5234 - accuracy: 0.9798 - val_loss: 0.5246 - val_accuracy: 0.9545\n",
            "Epoch 693/1000\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.5231 - accuracy: 0.9798 - val_loss: 0.5248 - val_accuracy: 0.9545\n",
            "Epoch 694/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5229 - accuracy: 0.9798 - val_loss: 0.5247 - val_accuracy: 0.9545\n",
            "Epoch 695/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5228 - accuracy: 0.9798 - val_loss: 0.5243 - val_accuracy: 0.9545\n",
            "Epoch 696/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5226 - accuracy: 0.9798 - val_loss: 0.5237 - val_accuracy: 0.9545\n",
            "Epoch 697/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5224 - accuracy: 0.9798 - val_loss: 0.5231 - val_accuracy: 1.0000\n",
            "Epoch 698/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5222 - accuracy: 0.9798 - val_loss: 0.5235 - val_accuracy: 0.9545\n",
            "Epoch 699/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5220 - accuracy: 0.9798 - val_loss: 0.5235 - val_accuracy: 0.9545\n",
            "Epoch 700/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.5218 - accuracy: 0.9798 - val_loss: 0.5232 - val_accuracy: 0.9545\n",
            "Epoch 701/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5216 - accuracy: 0.9798 - val_loss: 0.5226 - val_accuracy: 0.9545\n",
            "Epoch 702/1000\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.5214 - accuracy: 0.9798 - val_loss: 0.5228 - val_accuracy: 0.9545\n",
            "Epoch 703/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5213 - accuracy: 0.9798 - val_loss: 0.5228 - val_accuracy: 0.9545\n",
            "Epoch 704/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.5211 - accuracy: 0.9798 - val_loss: 0.5224 - val_accuracy: 0.9545\n",
            "Epoch 705/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5209 - accuracy: 0.9798 - val_loss: 0.5218 - val_accuracy: 0.9545\n",
            "Epoch 706/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.5207 - accuracy: 0.9798 - val_loss: 0.5220 - val_accuracy: 0.9545\n",
            "Epoch 707/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5205 - accuracy: 0.9798 - val_loss: 0.5220 - val_accuracy: 0.9545\n",
            "Epoch 708/1000\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.5203 - accuracy: 0.9798 - val_loss: 0.5216 - val_accuracy: 0.9545\n",
            "Epoch 709/1000\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.5201 - accuracy: 0.9798 - val_loss: 0.5209 - val_accuracy: 1.0000\n",
            "Epoch 710/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.5200 - accuracy: 0.9798 - val_loss: 0.5212 - val_accuracy: 0.9545\n",
            "Epoch 711/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5198 - accuracy: 0.9798 - val_loss: 0.5215 - val_accuracy: 0.9545\n",
            "Epoch 712/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5196 - accuracy: 0.9798 - val_loss: 0.5215 - val_accuracy: 0.9545\n",
            "Epoch 713/1000\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.5194 - accuracy: 0.9798 - val_loss: 0.5211 - val_accuracy: 0.9545\n",
            "Epoch 714/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5192 - accuracy: 0.9798 - val_loss: 0.5204 - val_accuracy: 0.9545\n",
            "Epoch 715/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5190 - accuracy: 0.9798 - val_loss: 0.5199 - val_accuracy: 0.9545\n",
            "Epoch 716/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5189 - accuracy: 0.9798 - val_loss: 0.5203 - val_accuracy: 0.9545\n",
            "Epoch 717/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.5187 - accuracy: 0.9798 - val_loss: 0.5204 - val_accuracy: 0.9545\n",
            "Epoch 718/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5185 - accuracy: 0.9798 - val_loss: 0.5201 - val_accuracy: 0.9545\n",
            "Epoch 719/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5183 - accuracy: 0.9798 - val_loss: 0.5195 - val_accuracy: 0.9545\n",
            "Epoch 720/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5181 - accuracy: 0.9798 - val_loss: 0.5191 - val_accuracy: 0.9545\n",
            "Epoch 721/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.5180 - accuracy: 0.9798 - val_loss: 0.5196 - val_accuracy: 0.9545\n",
            "Epoch 722/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5178 - accuracy: 0.9798 - val_loss: 0.5197 - val_accuracy: 0.9545\n",
            "Epoch 723/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5176 - accuracy: 0.9798 - val_loss: 0.5195 - val_accuracy: 0.9545\n",
            "Epoch 724/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.5174 - accuracy: 0.9798 - val_loss: 0.5189 - val_accuracy: 0.9545\n",
            "Epoch 725/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5172 - accuracy: 0.9798 - val_loss: 0.5181 - val_accuracy: 0.9545\n",
            "Epoch 726/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5171 - accuracy: 0.9798 - val_loss: 0.5183 - val_accuracy: 0.9545\n",
            "Epoch 727/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5168 - accuracy: 0.9798 - val_loss: 0.5185 - val_accuracy: 0.9545\n",
            "Epoch 728/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.5167 - accuracy: 0.9798 - val_loss: 0.5184 - val_accuracy: 0.9545\n",
            "Epoch 729/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5165 - accuracy: 0.9798 - val_loss: 0.5180 - val_accuracy: 0.9545\n",
            "Epoch 730/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5163 - accuracy: 0.9798 - val_loss: 0.5173 - val_accuracy: 0.9545\n",
            "Epoch 731/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5161 - accuracy: 0.9798 - val_loss: 0.5176 - val_accuracy: 0.9545\n",
            "Epoch 732/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.5159 - accuracy: 0.9798 - val_loss: 0.5179 - val_accuracy: 0.9545\n",
            "Epoch 733/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.5158 - accuracy: 0.9798 - val_loss: 0.5178 - val_accuracy: 0.9545\n",
            "Epoch 734/1000\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.5156 - accuracy: 0.9798 - val_loss: 0.5175 - val_accuracy: 0.9545\n",
            "Epoch 735/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.5154 - accuracy: 0.9798 - val_loss: 0.5168 - val_accuracy: 0.9545\n",
            "Epoch 736/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5152 - accuracy: 0.9798 - val_loss: 0.5163 - val_accuracy: 0.9545\n",
            "Epoch 737/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.5150 - accuracy: 0.9798 - val_loss: 0.5167 - val_accuracy: 0.9545\n",
            "Epoch 738/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.5148 - accuracy: 0.9798 - val_loss: 0.5168 - val_accuracy: 0.9545\n",
            "Epoch 739/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.5147 - accuracy: 0.9798 - val_loss: 0.5166 - val_accuracy: 0.9545\n",
            "Epoch 740/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5145 - accuracy: 0.9798 - val_loss: 0.5160 - val_accuracy: 0.9545\n",
            "Epoch 741/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5143 - accuracy: 0.9798 - val_loss: 0.5156 - val_accuracy: 0.9545\n",
            "Epoch 742/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5141 - accuracy: 0.9798 - val_loss: 0.5161 - val_accuracy: 0.9545\n",
            "Epoch 743/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.5139 - accuracy: 0.9798 - val_loss: 0.5163 - val_accuracy: 0.9545\n",
            "Epoch 744/1000\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.5138 - accuracy: 0.9798 - val_loss: 0.5161 - val_accuracy: 0.9545\n",
            "Epoch 745/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.5136 - accuracy: 0.9798 - val_loss: 0.5155 - val_accuracy: 0.9545\n",
            "Epoch 746/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5134 - accuracy: 0.9798 - val_loss: 0.5147 - val_accuracy: 0.9545\n",
            "Epoch 747/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5132 - accuracy: 0.9798 - val_loss: 0.5149 - val_accuracy: 0.9545\n",
            "Epoch 748/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.5130 - accuracy: 0.9798 - val_loss: 0.5148 - val_accuracy: 0.9545\n",
            "Epoch 749/1000\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.5128 - accuracy: 0.9798 - val_loss: 0.5143 - val_accuracy: 0.9545\n",
            "Epoch 750/1000\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.5127 - accuracy: 0.9798 - val_loss: 0.5140 - val_accuracy: 0.9545\n",
            "Epoch 751/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5125 - accuracy: 0.9798 - val_loss: 0.5147 - val_accuracy: 0.9545\n",
            "Epoch 752/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5123 - accuracy: 0.9798 - val_loss: 0.5149 - val_accuracy: 0.9545\n",
            "Epoch 753/1000\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.5122 - accuracy: 0.9798 - val_loss: 0.5148 - val_accuracy: 0.9545\n",
            "Epoch 754/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5120 - accuracy: 0.9798 - val_loss: 0.5143 - val_accuracy: 0.9545\n",
            "Epoch 755/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5118 - accuracy: 0.9798 - val_loss: 0.5136 - val_accuracy: 0.9545\n",
            "Epoch 756/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5116 - accuracy: 0.9798 - val_loss: 0.5126 - val_accuracy: 0.9545\n",
            "Epoch 757/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5115 - accuracy: 0.9798 - val_loss: 0.5126 - val_accuracy: 0.9545\n",
            "Epoch 758/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5113 - accuracy: 0.9798 - val_loss: 0.5136 - val_accuracy: 0.9545\n",
            "Epoch 759/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5111 - accuracy: 0.9798 - val_loss: 0.5142 - val_accuracy: 0.9545\n",
            "Epoch 760/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5109 - accuracy: 0.9798 - val_loss: 0.5144 - val_accuracy: 0.9545\n",
            "Epoch 761/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5108 - accuracy: 0.9798 - val_loss: 0.5142 - val_accuracy: 0.9545\n",
            "Epoch 762/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5106 - accuracy: 0.9798 - val_loss: 0.5136 - val_accuracy: 0.9545\n",
            "Epoch 763/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5104 - accuracy: 0.9798 - val_loss: 0.5127 - val_accuracy: 0.9545\n",
            "Epoch 764/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5102 - accuracy: 0.9798 - val_loss: 0.5115 - val_accuracy: 0.9545\n",
            "Epoch 765/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5100 - accuracy: 0.9798 - val_loss: 0.5115 - val_accuracy: 0.9545\n",
            "Epoch 766/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5098 - accuracy: 0.9798 - val_loss: 0.5124 - val_accuracy: 0.9545\n",
            "Epoch 767/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5096 - accuracy: 0.9798 - val_loss: 0.5129 - val_accuracy: 0.9545\n",
            "Epoch 768/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5095 - accuracy: 0.9798 - val_loss: 0.5130 - val_accuracy: 0.9545\n",
            "Epoch 769/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5093 - accuracy: 0.9798 - val_loss: 0.5127 - val_accuracy: 0.9545\n",
            "Epoch 770/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5091 - accuracy: 0.9798 - val_loss: 0.5120 - val_accuracy: 0.9545\n",
            "Epoch 771/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5089 - accuracy: 0.9798 - val_loss: 0.5111 - val_accuracy: 0.9545\n",
            "Epoch 772/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5087 - accuracy: 0.9798 - val_loss: 0.5099 - val_accuracy: 0.9545\n",
            "Epoch 773/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5086 - accuracy: 0.9798 - val_loss: 0.5098 - val_accuracy: 0.9545\n",
            "Epoch 774/1000\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.5084 - accuracy: 0.9798 - val_loss: 0.5108 - val_accuracy: 0.9545\n",
            "Epoch 775/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5082 - accuracy: 0.9798 - val_loss: 0.5113 - val_accuracy: 0.9545\n",
            "Epoch 776/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5080 - accuracy: 0.9798 - val_loss: 0.5114 - val_accuracy: 0.9545\n",
            "Epoch 777/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5079 - accuracy: 0.9798 - val_loss: 0.5112 - val_accuracy: 0.9545\n",
            "Epoch 778/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5077 - accuracy: 0.9798 - val_loss: 0.5105 - val_accuracy: 0.9545\n",
            "Epoch 779/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.5075 - accuracy: 0.9798 - val_loss: 0.5096 - val_accuracy: 0.9545\n",
            "Epoch 780/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5073 - accuracy: 0.9798 - val_loss: 0.5085 - val_accuracy: 0.9545\n",
            "Epoch 781/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5072 - accuracy: 0.9798 - val_loss: 0.5084 - val_accuracy: 0.9545\n",
            "Epoch 782/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5070 - accuracy: 0.9798 - val_loss: 0.5095 - val_accuracy: 0.9545\n",
            "Epoch 783/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5068 - accuracy: 0.9798 - val_loss: 0.5101 - val_accuracy: 0.9545\n",
            "Epoch 784/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5066 - accuracy: 0.9798 - val_loss: 0.5102 - val_accuracy: 0.9545\n",
            "Epoch 785/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5065 - accuracy: 0.9798 - val_loss: 0.5100 - val_accuracy: 0.9545\n",
            "Epoch 786/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5063 - accuracy: 0.9798 - val_loss: 0.5094 - val_accuracy: 0.9545\n",
            "Epoch 787/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5061 - accuracy: 0.9798 - val_loss: 0.5085 - val_accuracy: 0.9545\n",
            "Epoch 788/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5059 - accuracy: 0.9798 - val_loss: 0.5073 - val_accuracy: 0.9545\n",
            "Epoch 789/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5057 - accuracy: 0.9798 - val_loss: 0.5074 - val_accuracy: 0.9545\n",
            "Epoch 790/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5055 - accuracy: 0.9798 - val_loss: 0.5084 - val_accuracy: 0.9545\n",
            "Epoch 791/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5053 - accuracy: 0.9798 - val_loss: 0.5090 - val_accuracy: 0.9545\n",
            "Epoch 792/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5052 - accuracy: 0.9798 - val_loss: 0.5092 - val_accuracy: 0.9545\n",
            "Epoch 793/1000\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.5051 - accuracy: 0.9798 - val_loss: 0.5089 - val_accuracy: 0.9545\n",
            "Epoch 794/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5049 - accuracy: 0.9798 - val_loss: 0.5083 - val_accuracy: 0.9545\n",
            "Epoch 795/1000\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.5047 - accuracy: 0.9798 - val_loss: 0.5074 - val_accuracy: 0.9545\n",
            "Epoch 796/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.5044 - accuracy: 0.9798 - val_loss: 0.5062 - val_accuracy: 0.9545\n",
            "Epoch 797/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5043 - accuracy: 0.9798 - val_loss: 0.5062 - val_accuracy: 0.9545\n",
            "Epoch 798/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5041 - accuracy: 0.9798 - val_loss: 0.5073 - val_accuracy: 0.9545\n",
            "Epoch 799/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5039 - accuracy: 0.9798 - val_loss: 0.5079 - val_accuracy: 0.9545\n",
            "Epoch 800/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5038 - accuracy: 0.9798 - val_loss: 0.5080 - val_accuracy: 0.9545\n",
            "Epoch 801/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5037 - accuracy: 0.9798 - val_loss: 0.5077 - val_accuracy: 0.9545\n",
            "Epoch 802/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5035 - accuracy: 0.9798 - val_loss: 0.5071 - val_accuracy: 0.9545\n",
            "Epoch 803/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5033 - accuracy: 0.9798 - val_loss: 0.5061 - val_accuracy: 0.9545\n",
            "Epoch 804/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5030 - accuracy: 0.9798 - val_loss: 0.5049 - val_accuracy: 0.9545\n",
            "Epoch 805/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5029 - accuracy: 0.9798 - val_loss: 0.5049 - val_accuracy: 0.9545\n",
            "Epoch 806/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5027 - accuracy: 0.9798 - val_loss: 0.5051 - val_accuracy: 0.9545\n",
            "Epoch 807/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5025 - accuracy: 0.9798 - val_loss: 0.5048 - val_accuracy: 0.9545\n",
            "Epoch 808/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5023 - accuracy: 0.9798 - val_loss: 0.5048 - val_accuracy: 0.9545\n",
            "Epoch 809/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5021 - accuracy: 0.9798 - val_loss: 0.5044 - val_accuracy: 0.9545\n",
            "Epoch 810/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5020 - accuracy: 0.9798 - val_loss: 0.5041 - val_accuracy: 0.9545\n",
            "Epoch 811/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.5018 - accuracy: 0.9798 - val_loss: 0.5041 - val_accuracy: 0.9545\n",
            "Epoch 812/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.5016 - accuracy: 0.9798 - val_loss: 0.5041 - val_accuracy: 0.9545\n",
            "Epoch 813/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.5014 - accuracy: 0.9798 - val_loss: 0.5039 - val_accuracy: 0.9545\n",
            "Epoch 814/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5012 - accuracy: 0.9798 - val_loss: 0.5038 - val_accuracy: 0.9545\n",
            "Epoch 815/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.5011 - accuracy: 0.9798 - val_loss: 0.5033 - val_accuracy: 0.9545\n",
            "Epoch 816/1000\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.5009 - accuracy: 0.9798 - val_loss: 0.5031 - val_accuracy: 0.9545\n",
            "Epoch 817/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5007 - accuracy: 0.9798 - val_loss: 0.5040 - val_accuracy: 0.9545\n",
            "Epoch 818/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5006 - accuracy: 0.9798 - val_loss: 0.5044 - val_accuracy: 0.9545\n",
            "Epoch 819/1000\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.5004 - accuracy: 0.9798 - val_loss: 0.5044 - val_accuracy: 0.9545\n",
            "Epoch 820/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.5003 - accuracy: 0.9798 - val_loss: 0.5040 - val_accuracy: 0.9545\n",
            "Epoch 821/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.5001 - accuracy: 0.9798 - val_loss: 0.5033 - val_accuracy: 0.9545\n",
            "Epoch 822/1000\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.4999 - accuracy: 0.9798 - val_loss: 0.5023 - val_accuracy: 0.9545\n",
            "Epoch 823/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.4997 - accuracy: 0.9798 - val_loss: 0.5015 - val_accuracy: 0.9545\n",
            "Epoch 824/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.4996 - accuracy: 0.9798 - val_loss: 0.5020 - val_accuracy: 0.9545\n",
            "Epoch 825/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4993 - accuracy: 0.9798 - val_loss: 0.5026 - val_accuracy: 0.9545\n",
            "Epoch 826/1000\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.4992 - accuracy: 0.9798 - val_loss: 0.5027 - val_accuracy: 0.9545\n",
            "Epoch 827/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.4990 - accuracy: 0.9798 - val_loss: 0.5025 - val_accuracy: 0.9545\n",
            "Epoch 828/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4988 - accuracy: 0.9798 - val_loss: 0.5019 - val_accuracy: 0.9545\n",
            "Epoch 829/1000\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.4986 - accuracy: 0.9798 - val_loss: 0.5010 - val_accuracy: 0.9545\n",
            "Epoch 830/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.4985 - accuracy: 0.9798 - val_loss: 0.5014 - val_accuracy: 0.9545\n",
            "Epoch 831/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.4983 - accuracy: 0.9798 - val_loss: 0.5013 - val_accuracy: 0.9545\n",
            "Epoch 832/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.4981 - accuracy: 0.9798 - val_loss: 0.5009 - val_accuracy: 0.9545\n",
            "Epoch 833/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.4979 - accuracy: 0.9798 - val_loss: 0.5002 - val_accuracy: 0.9545\n",
            "Epoch 834/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.4978 - accuracy: 0.9798 - val_loss: 0.5008 - val_accuracy: 0.9545\n",
            "Epoch 835/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.4976 - accuracy: 0.9798 - val_loss: 0.5009 - val_accuracy: 0.9545\n",
            "Epoch 836/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.4974 - accuracy: 0.9798 - val_loss: 0.5007 - val_accuracy: 0.9545\n",
            "Epoch 837/1000\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.4973 - accuracy: 0.9798 - val_loss: 0.5001 - val_accuracy: 0.9545\n",
            "Epoch 838/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.4971 - accuracy: 0.9798 - val_loss: 0.4992 - val_accuracy: 0.9545\n",
            "Epoch 839/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.4970 - accuracy: 0.9798 - val_loss: 0.4996 - val_accuracy: 0.9545\n",
            "Epoch 840/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.4967 - accuracy: 0.9798 - val_loss: 0.5001 - val_accuracy: 0.9545\n",
            "Epoch 841/1000\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.4966 - accuracy: 0.9798 - val_loss: 0.5003 - val_accuracy: 0.9545\n",
            "Epoch 842/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.4964 - accuracy: 0.9798 - val_loss: 0.5000 - val_accuracy: 0.9545\n",
            "Epoch 843/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.4962 - accuracy: 0.9798 - val_loss: 0.4994 - val_accuracy: 0.9545\n",
            "Epoch 844/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.4960 - accuracy: 0.9798 - val_loss: 0.4984 - val_accuracy: 0.9545\n",
            "Epoch 845/1000\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.4959 - accuracy: 0.9798 - val_loss: 0.4988 - val_accuracy: 0.9545\n",
            "Epoch 846/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.4957 - accuracy: 0.9798 - val_loss: 0.4988 - val_accuracy: 0.9545\n",
            "Epoch 847/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.4955 - accuracy: 0.9798 - val_loss: 0.4985 - val_accuracy: 0.9545\n",
            "Epoch 848/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.4953 - accuracy: 0.9798 - val_loss: 0.4978 - val_accuracy: 0.9545\n",
            "Epoch 849/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.4952 - accuracy: 0.9798 - val_loss: 0.4984 - val_accuracy: 0.9545\n",
            "Epoch 850/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4950 - accuracy: 0.9798 - val_loss: 0.4986 - val_accuracy: 0.9545\n",
            "Epoch 851/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.4948 - accuracy: 0.9798 - val_loss: 0.4984 - val_accuracy: 0.9545\n",
            "Epoch 852/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.4947 - accuracy: 0.9798 - val_loss: 0.4979 - val_accuracy: 0.9545\n",
            "Epoch 853/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4945 - accuracy: 0.9798 - val_loss: 0.4970 - val_accuracy: 0.9545\n",
            "Epoch 854/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4943 - accuracy: 0.9798 - val_loss: 0.4975 - val_accuracy: 0.9545\n",
            "Epoch 855/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.4941 - accuracy: 0.9798 - val_loss: 0.4975 - val_accuracy: 0.9545\n",
            "Epoch 856/1000\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.4940 - accuracy: 0.9798 - val_loss: 0.4972 - val_accuracy: 0.9545\n",
            "Epoch 857/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.4938 - accuracy: 0.9798 - val_loss: 0.4966 - val_accuracy: 0.9545\n",
            "Epoch 858/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.4936 - accuracy: 0.9798 - val_loss: 0.4962 - val_accuracy: 0.9545\n",
            "Epoch 859/1000\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.4935 - accuracy: 0.9798 - val_loss: 0.4972 - val_accuracy: 0.9545\n",
            "Epoch 860/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.4933 - accuracy: 0.9798 - val_loss: 0.4976 - val_accuracy: 0.9545\n",
            "Epoch 861/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.4931 - accuracy: 0.9798 - val_loss: 0.4976 - val_accuracy: 0.9545\n",
            "Epoch 862/1000\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.4930 - accuracy: 0.9798 - val_loss: 0.4973 - val_accuracy: 0.9545\n",
            "Epoch 863/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4928 - accuracy: 0.9798 - val_loss: 0.4965 - val_accuracy: 0.9545\n",
            "Epoch 864/1000\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.4926 - accuracy: 0.9798 - val_loss: 0.4955 - val_accuracy: 0.9545\n",
            "Epoch 865/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.4924 - accuracy: 0.9798 - val_loss: 0.4947 - val_accuracy: 0.9545\n",
            "Epoch 866/1000\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.4924 - accuracy: 0.9798 - val_loss: 0.4958 - val_accuracy: 0.9545\n",
            "Epoch 867/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.4921 - accuracy: 0.9798 - val_loss: 0.4964 - val_accuracy: 0.9545\n",
            "Epoch 868/1000\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.4919 - accuracy: 0.9798 - val_loss: 0.4966 - val_accuracy: 0.9545\n",
            "Epoch 869/1000\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.4918 - accuracy: 0.9798 - val_loss: 0.4963 - val_accuracy: 0.9545\n",
            "Epoch 870/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.4916 - accuracy: 0.9798 - val_loss: 0.4956 - val_accuracy: 0.9545\n",
            "Epoch 871/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.4914 - accuracy: 0.9798 - val_loss: 0.4947 - val_accuracy: 0.9545\n",
            "Epoch 872/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.4912 - accuracy: 0.9798 - val_loss: 0.4940 - val_accuracy: 0.9545\n",
            "Epoch 873/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.4911 - accuracy: 0.9798 - val_loss: 0.4951 - val_accuracy: 0.9545\n",
            "Epoch 874/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.4909 - accuracy: 0.9798 - val_loss: 0.4958 - val_accuracy: 0.9545\n",
            "Epoch 875/1000\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.4908 - accuracy: 0.9798 - val_loss: 0.4960 - val_accuracy: 0.9545\n",
            "Epoch 876/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.4906 - accuracy: 0.9798 - val_loss: 0.4957 - val_accuracy: 0.9545\n",
            "Epoch 877/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.4904 - accuracy: 0.9798 - val_loss: 0.4951 - val_accuracy: 0.9545\n",
            "Epoch 878/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.4902 - accuracy: 0.9798 - val_loss: 0.4941 - val_accuracy: 0.9545\n",
            "Epoch 879/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.4900 - accuracy: 0.9798 - val_loss: 0.4928 - val_accuracy: 0.9545\n",
            "Epoch 880/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.4899 - accuracy: 0.9798 - val_loss: 0.4934 - val_accuracy: 0.9545\n",
            "Epoch 881/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.4897 - accuracy: 0.9798 - val_loss: 0.4937 - val_accuracy: 0.9545\n",
            "Epoch 882/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4895 - accuracy: 0.9798 - val_loss: 0.4935 - val_accuracy: 0.9545\n",
            "Epoch 883/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4893 - accuracy: 0.9798 - val_loss: 0.4930 - val_accuracy: 0.9545\n",
            "Epoch 884/1000\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.4892 - accuracy: 0.9798 - val_loss: 0.4921 - val_accuracy: 0.9545\n",
            "Epoch 885/1000\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.4891 - accuracy: 0.9798 - val_loss: 0.4931 - val_accuracy: 0.9545\n",
            "Epoch 886/1000\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.4888 - accuracy: 0.9798 - val_loss: 0.4937 - val_accuracy: 0.9545\n",
            "Epoch 887/1000\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.4887 - accuracy: 0.9798 - val_loss: 0.4938 - val_accuracy: 0.9545\n",
            "Epoch 888/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4885 - accuracy: 0.9798 - val_loss: 0.4935 - val_accuracy: 0.9545\n",
            "Epoch 889/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4884 - accuracy: 0.9798 - val_loss: 0.4928 - val_accuracy: 0.9545\n",
            "Epoch 890/1000\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.4882 - accuracy: 0.9798 - val_loss: 0.4918 - val_accuracy: 0.9545\n",
            "Epoch 891/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4880 - accuracy: 0.9798 - val_loss: 0.4915 - val_accuracy: 0.9545\n",
            "Epoch 892/1000\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.4878 - accuracy: 0.9798 - val_loss: 0.4919 - val_accuracy: 0.9545\n",
            "Epoch 893/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.4876 - accuracy: 0.9798 - val_loss: 0.4920 - val_accuracy: 0.9545\n",
            "Epoch 894/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4875 - accuracy: 0.9798 - val_loss: 0.4916 - val_accuracy: 0.9545\n",
            "Epoch 895/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.4873 - accuracy: 0.9798 - val_loss: 0.4909 - val_accuracy: 0.9545\n",
            "Epoch 896/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.4871 - accuracy: 0.9798 - val_loss: 0.4909 - val_accuracy: 0.9545\n",
            "Epoch 897/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.4870 - accuracy: 0.9798 - val_loss: 0.4910 - val_accuracy: 0.9545\n",
            "Epoch 898/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4868 - accuracy: 0.9798 - val_loss: 0.4908 - val_accuracy: 0.9545\n",
            "Epoch 899/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.4866 - accuracy: 0.9798 - val_loss: 0.4902 - val_accuracy: 0.9545\n",
            "Epoch 900/1000\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.4865 - accuracy: 0.9798 - val_loss: 0.4903 - val_accuracy: 0.9545\n",
            "Epoch 901/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.4863 - accuracy: 0.9798 - val_loss: 0.4905 - val_accuracy: 0.9545\n",
            "Epoch 902/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.4861 - accuracy: 0.9798 - val_loss: 0.4903 - val_accuracy: 0.9545\n",
            "Epoch 903/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.4860 - accuracy: 0.9798 - val_loss: 0.4898 - val_accuracy: 0.9545\n",
            "Epoch 904/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.4858 - accuracy: 0.9798 - val_loss: 0.4900 - val_accuracy: 0.9545\n",
            "Epoch 905/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.4856 - accuracy: 0.9798 - val_loss: 0.4898 - val_accuracy: 0.9545\n",
            "Epoch 906/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4855 - accuracy: 0.9798 - val_loss: 0.4897 - val_accuracy: 0.9545\n",
            "Epoch 907/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.4853 - accuracy: 0.9798 - val_loss: 0.4897 - val_accuracy: 0.9545\n",
            "Epoch 908/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.4851 - accuracy: 0.9798 - val_loss: 0.4893 - val_accuracy: 0.9545\n",
            "Epoch 909/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.4850 - accuracy: 0.9798 - val_loss: 0.4891 - val_accuracy: 0.9545\n",
            "Epoch 910/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.4848 - accuracy: 0.9798 - val_loss: 0.4889 - val_accuracy: 0.9545\n",
            "Epoch 911/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.4846 - accuracy: 0.9798 - val_loss: 0.4889 - val_accuracy: 0.9545\n",
            "Epoch 912/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.4845 - accuracy: 0.9798 - val_loss: 0.4890 - val_accuracy: 0.9545\n",
            "Epoch 913/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.4843 - accuracy: 0.9798 - val_loss: 0.4886 - val_accuracy: 0.9545\n",
            "Epoch 914/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.4841 - accuracy: 0.9798 - val_loss: 0.4884 - val_accuracy: 0.9545\n",
            "Epoch 915/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.4840 - accuracy: 0.9798 - val_loss: 0.4884 - val_accuracy: 0.9545\n",
            "Epoch 916/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.4838 - accuracy: 0.9798 - val_loss: 0.4884 - val_accuracy: 0.9545\n",
            "Epoch 917/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.4836 - accuracy: 0.9798 - val_loss: 0.4885 - val_accuracy: 0.9545\n",
            "Epoch 918/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.4835 - accuracy: 0.9798 - val_loss: 0.4882 - val_accuracy: 0.9545\n",
            "Epoch 919/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4833 - accuracy: 0.9798 - val_loss: 0.4875 - val_accuracy: 0.9545\n",
            "Epoch 920/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.4831 - accuracy: 0.9798 - val_loss: 0.4870 - val_accuracy: 0.9545\n",
            "Epoch 921/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.4830 - accuracy: 0.9798 - val_loss: 0.4873 - val_accuracy: 0.9545\n",
            "Epoch 922/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.4828 - accuracy: 0.9798 - val_loss: 0.4876 - val_accuracy: 0.9545\n",
            "Epoch 923/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.4826 - accuracy: 0.9798 - val_loss: 0.4875 - val_accuracy: 0.9545\n",
            "Epoch 924/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4825 - accuracy: 0.9798 - val_loss: 0.4871 - val_accuracy: 0.9545\n",
            "Epoch 925/1000\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.4823 - accuracy: 0.9798 - val_loss: 0.4867 - val_accuracy: 0.9545\n",
            "Epoch 926/1000\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.4821 - accuracy: 0.9798 - val_loss: 0.4865 - val_accuracy: 0.9545\n",
            "Epoch 927/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.4820 - accuracy: 0.9798 - val_loss: 0.4865 - val_accuracy: 0.9545\n",
            "Epoch 928/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.4818 - accuracy: 0.9798 - val_loss: 0.4865 - val_accuracy: 0.9545\n",
            "Epoch 929/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.4816 - accuracy: 0.9798 - val_loss: 0.4866 - val_accuracy: 0.9545\n",
            "Epoch 930/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4815 - accuracy: 0.9798 - val_loss: 0.4864 - val_accuracy: 0.9545\n",
            "Epoch 931/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.4813 - accuracy: 0.9798 - val_loss: 0.4862 - val_accuracy: 0.9545\n",
            "Epoch 932/1000\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.4811 - accuracy: 0.9798 - val_loss: 0.4861 - val_accuracy: 0.9545\n",
            "Epoch 933/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4810 - accuracy: 0.9798 - val_loss: 0.4862 - val_accuracy: 0.9545\n",
            "Epoch 934/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.4808 - accuracy: 0.9798 - val_loss: 0.4858 - val_accuracy: 0.9545\n",
            "Epoch 935/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.4807 - accuracy: 0.9798 - val_loss: 0.4856 - val_accuracy: 0.9545\n",
            "Epoch 936/1000\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.4805 - accuracy: 0.9798 - val_loss: 0.4855 - val_accuracy: 0.9545\n",
            "Epoch 937/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.4803 - accuracy: 0.9798 - val_loss: 0.4855 - val_accuracy: 0.9545\n",
            "Epoch 938/1000\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.4802 - accuracy: 0.9798 - val_loss: 0.4856 - val_accuracy: 0.9545\n",
            "Epoch 939/1000\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.4800 - accuracy: 0.9798 - val_loss: 0.4852 - val_accuracy: 0.9545\n",
            "Epoch 940/1000\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.4798 - accuracy: 0.9798 - val_loss: 0.4850 - val_accuracy: 0.9545\n",
            "Epoch 941/1000\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.4797 - accuracy: 0.9798 - val_loss: 0.4850 - val_accuracy: 0.9545\n",
            "Epoch 942/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.4795 - accuracy: 0.9798 - val_loss: 0.4850 - val_accuracy: 0.9545\n",
            "Epoch 943/1000\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.4793 - accuracy: 0.9798 - val_loss: 0.4846 - val_accuracy: 0.9545\n",
            "Epoch 944/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.4792 - accuracy: 0.9798 - val_loss: 0.4843 - val_accuracy: 0.9545\n",
            "Epoch 945/1000\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.4790 - accuracy: 0.9798 - val_loss: 0.4842 - val_accuracy: 0.9545\n",
            "Epoch 946/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.4788 - accuracy: 0.9798 - val_loss: 0.4841 - val_accuracy: 0.9545\n",
            "Epoch 947/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.4787 - accuracy: 0.9798 - val_loss: 0.4842 - val_accuracy: 0.9545\n",
            "Epoch 948/1000\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.4785 - accuracy: 0.9798 - val_loss: 0.4844 - val_accuracy: 0.9545\n",
            "Epoch 949/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.4784 - accuracy: 0.9798 - val_loss: 0.4841 - val_accuracy: 0.9545\n",
            "Epoch 950/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.4782 - accuracy: 0.9798 - val_loss: 0.4834 - val_accuracy: 0.9545\n",
            "Epoch 951/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.4780 - accuracy: 0.9798 - val_loss: 0.4829 - val_accuracy: 0.9545\n",
            "Epoch 952/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.4779 - accuracy: 0.9798 - val_loss: 0.4826 - val_accuracy: 0.9545\n",
            "Epoch 953/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.4777 - accuracy: 0.9798 - val_loss: 0.4824 - val_accuracy: 0.9545\n",
            "Epoch 954/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.4776 - accuracy: 0.9798 - val_loss: 0.4824 - val_accuracy: 0.9545\n",
            "Epoch 955/1000\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.4774 - accuracy: 0.9798 - val_loss: 0.4825 - val_accuracy: 0.9545\n",
            "Epoch 956/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.4772 - accuracy: 0.9798 - val_loss: 0.4827 - val_accuracy: 0.9545\n",
            "Epoch 957/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.4771 - accuracy: 0.9798 - val_loss: 0.4830 - val_accuracy: 0.9545\n",
            "Epoch 958/1000\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.4769 - accuracy: 0.9798 - val_loss: 0.4828 - val_accuracy: 0.9545\n",
            "Epoch 959/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.4767 - accuracy: 0.9798 - val_loss: 0.4827 - val_accuracy: 0.9545\n",
            "Epoch 960/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.4766 - accuracy: 0.9798 - val_loss: 0.4822 - val_accuracy: 0.9545\n",
            "Epoch 961/1000\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.4764 - accuracy: 0.9798 - val_loss: 0.4819 - val_accuracy: 0.9545\n",
            "Epoch 962/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4762 - accuracy: 0.9798 - val_loss: 0.4817 - val_accuracy: 0.9545\n",
            "Epoch 963/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.4761 - accuracy: 0.9798 - val_loss: 0.4817 - val_accuracy: 0.9545\n",
            "Epoch 964/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.4759 - accuracy: 0.9798 - val_loss: 0.4817 - val_accuracy: 0.9545\n",
            "Epoch 965/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4758 - accuracy: 0.9798 - val_loss: 0.4819 - val_accuracy: 0.9545\n",
            "Epoch 966/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.4756 - accuracy: 0.9798 - val_loss: 0.4822 - val_accuracy: 0.9545\n",
            "Epoch 967/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.4754 - accuracy: 0.9798 - val_loss: 0.4819 - val_accuracy: 0.9545\n",
            "Epoch 968/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4753 - accuracy: 0.9798 - val_loss: 0.4813 - val_accuracy: 0.9545\n",
            "Epoch 969/1000\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.4751 - accuracy: 0.9798 - val_loss: 0.4808 - val_accuracy: 0.9545\n",
            "Epoch 970/1000\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.4749 - accuracy: 0.9798 - val_loss: 0.4804 - val_accuracy: 0.9545\n",
            "Epoch 971/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.4748 - accuracy: 0.9798 - val_loss: 0.4803 - val_accuracy: 0.9545\n",
            "Epoch 972/1000\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.4746 - accuracy: 0.9798 - val_loss: 0.4802 - val_accuracy: 0.9545\n",
            "Epoch 973/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.4745 - accuracy: 0.9798 - val_loss: 0.4803 - val_accuracy: 0.9545\n",
            "Epoch 974/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.4743 - accuracy: 0.9798 - val_loss: 0.4806 - val_accuracy: 0.9545\n",
            "Epoch 975/1000\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.4741 - accuracy: 0.9798 - val_loss: 0.4809 - val_accuracy: 0.9545\n",
            "Epoch 976/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4740 - accuracy: 0.9798 - val_loss: 0.4807 - val_accuracy: 0.9545\n",
            "Epoch 977/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4738 - accuracy: 0.9798 - val_loss: 0.4800 - val_accuracy: 0.9545\n",
            "Epoch 978/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.4737 - accuracy: 0.9798 - val_loss: 0.4796 - val_accuracy: 0.9545\n",
            "Epoch 979/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.4735 - accuracy: 0.9798 - val_loss: 0.4793 - val_accuracy: 0.9545\n",
            "Epoch 980/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4733 - accuracy: 0.9798 - val_loss: 0.4791 - val_accuracy: 0.9545\n",
            "Epoch 981/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.4732 - accuracy: 0.9798 - val_loss: 0.4791 - val_accuracy: 0.9545\n",
            "Epoch 982/1000\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.4730 - accuracy: 0.9798 - val_loss: 0.4793 - val_accuracy: 0.9545\n",
            "Epoch 983/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.4729 - accuracy: 0.9798 - val_loss: 0.4795 - val_accuracy: 0.9545\n",
            "Epoch 984/1000\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.4727 - accuracy: 0.9798 - val_loss: 0.4799 - val_accuracy: 0.9545\n",
            "Epoch 985/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.4725 - accuracy: 0.9798 - val_loss: 0.4797 - val_accuracy: 0.9545\n",
            "Epoch 986/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.4724 - accuracy: 0.9798 - val_loss: 0.4790 - val_accuracy: 0.9545\n",
            "Epoch 987/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.4722 - accuracy: 0.9798 - val_loss: 0.4785 - val_accuracy: 0.9545\n",
            "Epoch 988/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4720 - accuracy: 0.9798 - val_loss: 0.4782 - val_accuracy: 0.9545\n",
            "Epoch 989/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.4719 - accuracy: 0.9798 - val_loss: 0.4781 - val_accuracy: 0.9545\n",
            "Epoch 990/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.4717 - accuracy: 0.9798 - val_loss: 0.4781 - val_accuracy: 0.9545\n",
            "Epoch 991/1000\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.4716 - accuracy: 0.9798 - val_loss: 0.4782 - val_accuracy: 0.9545\n",
            "Epoch 992/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.4714 - accuracy: 0.9798 - val_loss: 0.4785 - val_accuracy: 0.9545\n",
            "Epoch 993/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.4712 - accuracy: 0.9798 - val_loss: 0.4788 - val_accuracy: 0.9545\n",
            "Epoch 994/1000\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.4711 - accuracy: 0.9798 - val_loss: 0.4778 - val_accuracy: 0.9545\n",
            "Epoch 995/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.4709 - accuracy: 0.9798 - val_loss: 0.4770 - val_accuracy: 0.9545\n",
            "Epoch 996/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.4708 - accuracy: 0.9798 - val_loss: 0.4764 - val_accuracy: 0.9545\n",
            "Epoch 997/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.4706 - accuracy: 0.9798 - val_loss: 0.4761 - val_accuracy: 0.9545\n",
            "Epoch 998/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.4705 - accuracy: 0.9798 - val_loss: 0.4759 - val_accuracy: 0.9545\n",
            "Epoch 999/1000\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.4703 - accuracy: 0.9798 - val_loss: 0.4759 - val_accuracy: 0.9545\n",
            "Epoch 1000/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.4701 - accuracy: 0.9798 - val_loss: 0.4761 - val_accuracy: 0.9545\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "22n_VjV_L7Zp"
      },
      "source": [
        ""
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "ORCPEGgdOeFR",
        "outputId": "98ed9cfa-4bbe-4a45-fbc0-2ca39fd73c69"
      },
      "source": [
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# # summarize history for accuracy\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper right')\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "_iCTyEzJOd-B",
        "outputId": "ff7346a4-f43d-4145-8c3f-4a536239fe1a"
      },
      "source": [
        "#Visualize the models accuracy and loss\n",
        "plt.plot(history.history[\"accuracy\"])\n",
        "plt.plot(history.history[\"loss\"])\n",
        "plt.title(\"model accuracy & loss\")\n",
        "plt.ylabel(\"accuracy and loss\")\n",
        "plt.xlabel(\"epoch\")\n",
        "plt.legend(['acc', 'loss'], loc='lower right')\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KrKY6nCBOeMi",
        "outputId": "277cc3f8-e21d-491c-cca5-80560e58568a"
      },
      "source": [
        "print(\"---------------------------------------------------------\")\n",
        "print(\"Shape of training data: \", x_train.shape)\n",
        "print(\"Shape of test data    : \", x_test.shape )\n",
        "print(\"---------------------------------------------------------\")"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "---------------------------------------------------------\n",
            "Shape of training data:  (220, 5)\n",
            "Shape of test data    :  (56, 5)\n",
            "---------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p9I1F4YqPAgs",
        "outputId": "91ed701f-346c-421f-e3b6-61db47a5be9c"
      },
      "source": [
        "for model_file in glob.glob('model.h5'):\n",
        "  print(\"Model file: \", model_file)\n",
        "  model = load_model(model_file)\n",
        "  pred = model.predict(x_test)\n",
        "  pred = [1 if y>=0.5 else 0 for y in pred] #Threshold, transforming probabilities to either 0 or 1 depending if the probability is below or above 0.5\n",
        "  scores = model.evaluate(x_test, y_test)\n",
        "  print()\n",
        "  print(\"Original  : {0}\".format(\", \".join([str(x) for x in y_test])))\n",
        "  print()\n",
        "  print(\"Predicted : {0}\".format(\", \".join([str(x) for x in pred])))\n",
        "  print() \n",
        "  print(\"Scores    : loss = \", scores[0], \" acc = \", scores[1])\n",
        "  print(\"---------------------------------------------------------\")\n",
        "  print()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model file:  model.h5\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 0.4737 - accuracy: 0.9821\n",
            "\n",
            "Original  : 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0\n",
            "\n",
            "Predicted : 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0\n",
            "\n",
            "Scores    : loss =  0.47366005182266235  acc =  0.9821428656578064\n",
            "---------------------------------------------------------\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}