CirrhosisPrediction.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D1kU1oHPXRls"
      },
      "source": [
        "# Cirrhosis Prediction"
      ]
    },
    {
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      "execution_count": 54,
      "metadata": {
        "id": "SPwGwzh0XRlv"
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      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler,MinMaxScaler,RobustScaler,OneHotEncoder\n",
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier,GradientBoostingClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from google.colab import files\n",
        "import io\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gxQVReEXXRlw"
      },
      "source": [
        "## Retrieve the Data"
      ]
    },
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          "text": [
            "Saving cirrhosis.csv to cirrhosis (1).csv\n"
          ]
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              "      <th>ID</th>\n",
              "      <th>N_Days</th>\n",
              "      <th>Status</th>\n",
              "      <th>Drug</th>\n",
              "      <th>Age</th>\n",
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              "      <th>Ascites</th>\n",
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              "   ID  N_Days Status             Drug    Age Sex Ascites Hepatomegaly Spiders  \\\n",
              "0   1     400      D  D-penicillamine  21464   F       Y            Y       Y   \n",
              "1   2    4500      C  D-penicillamine  20617   F       N            Y       Y   \n",
              "2   3    1012      D  D-penicillamine  25594   M       N            N       N   \n",
              "3   4    1925      D  D-penicillamine  19994   F       N            Y       Y   \n",
              "4   5    1504     CL          Placebo  13918   F       N            Y       Y   \n",
              "\n",
              "  Edema  Bilirubin  Cholesterol  Albumin  Copper  Alk_Phos    SGOT  \\\n",
              "0     Y       14.5        261.0     2.60   156.0    1718.0  137.95   \n",
              "1     N        1.1        302.0     4.14    54.0    7394.8  113.52   \n",
              "2     S        1.4        176.0     3.48   210.0     516.0   96.10   \n",
              "3     S        1.8        244.0     2.54    64.0    6121.8   60.63   \n",
              "4     N        3.4        279.0     3.53   143.0     671.0  113.15   \n",
              "\n",
              "   Tryglicerides  Platelets  Prothrombin  Stage  \n",
              "0          172.0      190.0         12.2    4.0  \n",
              "1           88.0      221.0         10.6    3.0  \n",
              "2           55.0      151.0         12.0    4.0  \n",
              "3           92.0      183.0         10.3    4.0  \n",
              "4           72.0      136.0         10.9    3.0  "
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      "source": [
        "# Import the data\n",
        "uploaded = files.upload()\n",
        "df = pd.read_csv(io.StringIO(uploaded['cirrhosis.csv'].decode('utf-8')))\n",
        "df.head()"
      ]
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>N_Days</th>\n",
              "      <th>Status</th>\n",
              "      <th>Drug</th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Ascites</th>\n",
              "      <th>Hepatomegaly</th>\n",
              "      <th>Spiders</th>\n",
              "      <th>Edema</th>\n",
              "      <th>Bilirubin</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>Albumin</th>\n",
              "      <th>Copper</th>\n",
              "      <th>Alk_Phos</th>\n",
              "      <th>SGOT</th>\n",
              "      <th>Tryglicerides</th>\n",
              "      <th>Platelets</th>\n",
              "      <th>Prothrombin</th>\n",
              "      <th>Stage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>400</td>\n",
              "      <td>D</td>\n",
              "      <td>D-penicillamine</td>\n",
              "      <td>21464</td>\n",
              "      <td>F</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>14.5</td>\n",
              "      <td>261.0</td>\n",
              "      <td>2.60</td>\n",
              "      <td>156.0</td>\n",
              "      <td>1718.0</td>\n",
              "      <td>137.95</td>\n",
              "      <td>172.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>12.2</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4500</td>\n",
              "      <td>C</td>\n",
              "      <td>D-penicillamine</td>\n",
              "      <td>20617</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>N</td>\n",
              "      <td>1.1</td>\n",
              "      <td>302.0</td>\n",
              "      <td>4.14</td>\n",
              "      <td>54.0</td>\n",
              "      <td>7394.8</td>\n",
              "      <td>113.52</td>\n",
              "      <td>88.0</td>\n",
              "      <td>221.0</td>\n",
              "      <td>10.6</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1012</td>\n",
              "      <td>D</td>\n",
              "      <td>D-penicillamine</td>\n",
              "      <td>25594</td>\n",
              "      <td>M</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>S</td>\n",
              "      <td>1.4</td>\n",
              "      <td>176.0</td>\n",
              "      <td>3.48</td>\n",
              "      <td>210.0</td>\n",
              "      <td>516.0</td>\n",
              "      <td>96.10</td>\n",
              "      <td>55.0</td>\n",
              "      <td>151.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1925</td>\n",
              "      <td>D</td>\n",
              "      <td>D-penicillamine</td>\n",
              "      <td>19994</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>S</td>\n",
              "      <td>1.8</td>\n",
              "      <td>244.0</td>\n",
              "      <td>2.54</td>\n",
              "      <td>64.0</td>\n",
              "      <td>6121.8</td>\n",
              "      <td>60.63</td>\n",
              "      <td>92.0</td>\n",
              "      <td>183.0</td>\n",
              "      <td>10.3</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1504</td>\n",
              "      <td>CL</td>\n",
              "      <td>Placebo</td>\n",
              "      <td>13918</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>N</td>\n",
              "      <td>3.4</td>\n",
              "      <td>279.0</td>\n",
              "      <td>3.53</td>\n",
              "      <td>143.0</td>\n",
              "      <td>671.0</td>\n",
              "      <td>113.15</td>\n",
              "      <td>72.0</td>\n",
              "      <td>136.0</td>\n",
              "      <td>10.9</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>414</td>\n",
              "      <td>681</td>\n",
              "      <td>D</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24472</td>\n",
              "      <td>F</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>1.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.96</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>174.0</td>\n",
              "      <td>10.9</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>415</td>\n",
              "      <td>1103</td>\n",
              "      <td>C</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14245</td>\n",
              "      <td>F</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>0.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.83</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>180.0</td>\n",
              "      <td>11.2</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>416</td>\n",
              "      <td>1055</td>\n",
              "      <td>C</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20819</td>\n",
              "      <td>F</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>1.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.42</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>143.0</td>\n",
              "      <td>9.9</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>417</td>\n",
              "      <td>691</td>\n",
              "      <td>C</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21185</td>\n",
              "      <td>F</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>0.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>269.0</td>\n",
              "      <td>10.4</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>417</th>\n",
              "      <td>418</td>\n",
              "      <td>976</td>\n",
              "      <td>C</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19358</td>\n",
              "      <td>F</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>0.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.29</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>350.0</td>\n",
              "      <td>10.6</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>418 rows × 20 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-11e9628e-5399-4223-ab6b-26d6929cb134')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-11e9628e-5399-4223-ab6b-26d6929cb134 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-11e9628e-5399-4223-ab6b-26d6929cb134');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      ID  N_Days Status             Drug    Age Sex Ascites Hepatomegaly  \\\n",
              "0      1     400      D  D-penicillamine  21464   F       Y            Y   \n",
              "1      2    4500      C  D-penicillamine  20617   F       N            Y   \n",
              "2      3    1012      D  D-penicillamine  25594   M       N            N   \n",
              "3      4    1925      D  D-penicillamine  19994   F       N            Y   \n",
              "4      5    1504     CL          Placebo  13918   F       N            Y   \n",
              "..   ...     ...    ...              ...    ...  ..     ...          ...   \n",
              "413  414     681      D              NaN  24472   F     NaN          NaN   \n",
              "414  415    1103      C              NaN  14245   F     NaN          NaN   \n",
              "415  416    1055      C              NaN  20819   F     NaN          NaN   \n",
              "416  417     691      C              NaN  21185   F     NaN          NaN   \n",
              "417  418     976      C              NaN  19358   F     NaN          NaN   \n",
              "\n",
              "    Spiders Edema  Bilirubin  Cholesterol  Albumin  Copper  Alk_Phos    SGOT  \\\n",
              "0         Y     Y       14.5        261.0     2.60   156.0    1718.0  137.95   \n",
              "1         Y     N        1.1        302.0     4.14    54.0    7394.8  113.52   \n",
              "2         N     S        1.4        176.0     3.48   210.0     516.0   96.10   \n",
              "3         Y     S        1.8        244.0     2.54    64.0    6121.8   60.63   \n",
              "4         Y     N        3.4        279.0     3.53   143.0     671.0  113.15   \n",
              "..      ...   ...        ...          ...      ...     ...       ...     ...   \n",
              "413     NaN     N        1.2          NaN     2.96     NaN       NaN     NaN   \n",
              "414     NaN     N        0.9          NaN     3.83     NaN       NaN     NaN   \n",
              "415     NaN     N        1.6          NaN     3.42     NaN       NaN     NaN   \n",
              "416     NaN     N        0.8          NaN     3.75     NaN       NaN     NaN   \n",
              "417     NaN     N        0.7          NaN     3.29     NaN       NaN     NaN   \n",
              "\n",
              "     Tryglicerides  Platelets  Prothrombin  Stage  \n",
              "0            172.0      190.0         12.2    4.0  \n",
              "1             88.0      221.0         10.6    3.0  \n",
              "2             55.0      151.0         12.0    4.0  \n",
              "3             92.0      183.0         10.3    4.0  \n",
              "4             72.0      136.0         10.9    3.0  \n",
              "..             ...        ...          ...    ...  \n",
              "413            NaN      174.0         10.9    3.0  \n",
              "414            NaN      180.0         11.2    4.0  \n",
              "415            NaN      143.0          9.9    3.0  \n",
              "416            NaN      269.0         10.4    3.0  \n",
              "417            NaN      350.0         10.6    4.0  \n",
              "\n",
              "[418 rows x 20 columns]"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Drop duplicate data\n",
        "df = df.drop_duplicates()\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lBUgA82fujHI",
        "outputId": "2695473e-bf8f-49e1-91e4-7a7a968a7f62"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 418 entries, 0 to 417\n",
            "Data columns (total 20 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   ID             418 non-null    int64  \n",
            " 1   N_Days         418 non-null    int64  \n",
            " 2   Status         418 non-null    object \n",
            " 3   Drug           312 non-null    object \n",
            " 4   Age            418 non-null    int64  \n",
            " 5   Sex            418 non-null    object \n",
            " 6   Ascites        312 non-null    object \n",
            " 7   Hepatomegaly   312 non-null    object \n",
            " 8   Spiders        312 non-null    object \n",
            " 9   Edema          418 non-null    object \n",
            " 10  Bilirubin      418 non-null    float64\n",
            " 11  Cholesterol    284 non-null    float64\n",
            " 12  Albumin        418 non-null    float64\n",
            " 13  Copper         310 non-null    float64\n",
            " 14  Alk_Phos       312 non-null    float64\n",
            " 15  SGOT           312 non-null    float64\n",
            " 16  Tryglicerides  282 non-null    float64\n",
            " 17  Platelets      407 non-null    float64\n",
            " 18  Prothrombin    416 non-null    float64\n",
            " 19  Stage          412 non-null    float64\n",
            "dtypes: float64(10), int64(3), object(7)\n",
            "memory usage: 68.6+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LGjEhe3eXRly",
        "outputId": "f54deb37-20b3-47db-ff0f-35dc14a698d5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "ID                 0\n",
              "N_Days             0\n",
              "Status             0\n",
              "Drug             106\n",
              "Age                0\n",
              "Sex                0\n",
              "Ascites          106\n",
              "Hepatomegaly     106\n",
              "Spiders          106\n",
              "Edema              0\n",
              "Bilirubin          0\n",
              "Cholesterol      134\n",
              "Albumin            0\n",
              "Copper           108\n",
              "Alk_Phos         106\n",
              "SGOT             106\n",
              "Tryglicerides    136\n",
              "Platelets         11\n",
              "Prothrombin        2\n",
              "Stage              6\n",
              "dtype: int64"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Check null value\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "S6Kg4GsIXRly"
      },
      "outputs": [],
      "source": [
        "# Drop meanless columns\n",
        "df = df.drop(columns=['ID','Drug','Status'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "lM_rrP6wXRly"
      },
      "outputs": [],
      "source": [
        "# Check data structure to decide how to fill null value\n",
        "stage1_df = (df.loc[df['Stage']==1.0]).dropna()\n",
        "stage2_df = (df.loc[df['Stage']==2.0]).dropna()\n",
        "stage3_df = (df.loc[df['Stage']==3.0]).dropna()\n",
        "stage4_df = (df.loc[df['Stage']==4.0]).dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "fznyd6cMXRlz",
        "outputId": "3904069a-4f5f-45a9-d418-7bfdb471be11"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x720 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Check data structure to decide how to fill null value by stage1\n",
        "fig, axs = plt.subplots(nrows=3, ncols=3, figsize=(12, 10))\n",
        "fig.suptitle('Stage1', fontsize=16)\n",
        "axs[0, 0].hist(stage1_df['Bilirubin'],stage1_df['Bilirubin'].count())\n",
        "axs[0, 0].set_title('Bilirubin')\n",
        "axs[0, 1].hist(stage1_df['Cholesterol'],stage1_df['Cholesterol'].count())\n",
        "axs[0, 1].set_title('Cholesterol')\n",
        "axs[0, 2].hist(stage1_df['Albumin'],stage1_df['Albumin'].count())\n",
        "axs[0, 2].set_title('Albumin')\n",
        "axs[1, 0].hist(stage1_df['Copper'],stage1_df['Copper'].count())\n",
        "axs[1, 0].set_title('Copper')\n",
        "axs[1, 1].hist(stage1_df['Alk_Phos'],stage1_df['Alk_Phos'].count())\n",
        "axs[1, 1].set_title('Alk_Phos')\n",
        "axs[1, 2].hist(stage1_df['SGOT'],stage1_df['SGOT'].count())\n",
        "axs[1, 2].set_title('SGOT')\n",
        "axs[2, 0].hist(stage1_df['Tryglicerides'],stage1_df['Tryglicerides'].count())\n",
        "axs[2, 0].set_title('Tryglicerides')\n",
        "axs[2, 1].hist(stage1_df['Platelets'],stage1_df['Platelets'].count())\n",
        "axs[2, 1].set_title('Platelets')\n",
        "axs[2, 2].hist(stage1_df['Prothrombin'],stage1_df['Prothrombin'].count())\n",
        "axs[2, 2].set_title('Prothrombin');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "8ppTUoDNjWqP"
      },
      "outputs": [],
      "source": [
        "# To fill up null value(numeric columns:by mean(normal distribution) or median(long tail distribution), non-numeric columns: by mode)\\\n",
        "s1 = df['Stage']==1.0\n",
        "for col in ['Bilirubin','Cholesterol','Copper','Alk_Phos','SGOT','Tryglicerides','Prothrombin']:\n",
        "  df.loc[s1,col] = df.loc[s1,col].fillna(df.loc[s1,col].median())\n",
        "for col in ['Albumin','Platelets']:\n",
        "  df.loc[s1,col] = df.loc[s1,col].fillna(df.loc[s1,col].mean())\n",
        "for col in ['Ascites','Hepatomegaly','Spiders','Edema']:\n",
        "  df.loc[s1,col] = df.loc[s1,col].fillna(df.loc[s1,col].mode().values[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z9h7i_Ruklv3",
        "outputId": "b8ba851d-6d05-4da4-d5fd-9d8e6221ed42"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "N_Days           0\n",
              "Age              0\n",
              "Sex              0\n",
              "Ascites          0\n",
              "Hepatomegaly     0\n",
              "Spiders          0\n",
              "Edema            0\n",
              "Bilirubin        0\n",
              "Cholesterol      0\n",
              "Albumin          0\n",
              "Copper           0\n",
              "Alk_Phos         0\n",
              "SGOT             0\n",
              "Tryglicerides    0\n",
              "Platelets        0\n",
              "Prothrombin      0\n",
              "Stage            0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Check if fillna successfully\n",
        "df.loc[df['Stage']==1.0].isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "yXMS1U5NiOJ-",
        "outputId": "27c01ee2-8455-493b-e342-ff365676ca1f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x720 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Check data structure to decide how to fill null value by stage2\n",
        "fig, axs = plt.subplots(nrows=3, ncols=3, figsize=(12, 10))\n",
        "fig.suptitle('Stage2', fontsize=16)\n",
        "axs[0, 0].hist(stage2_df['Bilirubin'],stage2_df['Bilirubin'].count())\n",
        "axs[0, 0].set_title('Bilirubin')\n",
        "axs[0, 1].hist(stage2_df['Cholesterol'],stage2_df['Cholesterol'].count())\n",
        "axs[0, 1].set_title('Cholesterol')\n",
        "axs[0, 2].hist(stage2_df['Albumin'],stage2_df['Albumin'].count())\n",
        "axs[0, 2].set_title('Albumin')\n",
        "axs[1, 0].hist(stage2_df['Copper'],stage2_df['Copper'].count())\n",
        "axs[1, 0].set_title('Copper')\n",
        "axs[1, 1].hist(stage2_df['Alk_Phos'],stage2_df['Alk_Phos'].count())\n",
        "axs[1, 1].set_title('Alk_Phos')\n",
        "axs[1, 2].hist(stage2_df['SGOT'],stage2_df['SGOT'].count())\n",
        "axs[1, 2].set_title('SGOT')\n",
        "axs[2, 0].hist(stage2_df['Tryglicerides'],stage2_df['Tryglicerides'].count())\n",
        "axs[2, 0].set_title('Tryglicerides')\n",
        "axs[2, 1].hist(stage2_df['Platelets'],stage2_df['Platelets'].count())\n",
        "axs[2, 1].set_title('Platelets')\n",
        "axs[2, 2].hist(stage2_df['Prothrombin'],stage2_df['Prothrombin'].count())\n",
        "axs[2, 2].set_title('Prothrombin');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "Wy3HOVh7sU6Y"
      },
      "outputs": [],
      "source": [
        "# To fill up null value(numeric columns:by mean(normal distribution) or median(long tail distribution), non-numeric columns: by mode)\\\n",
        "s1 = df['Stage']==2.0\n",
        "for col in ['Bilirubin','Cholesterol','Copper','Alk_Phos','SGOT','Tryglicerides']:\n",
        "  df.loc[s1,col] = df.loc[s1,col].fillna(df.loc[s1,col].median())\n",
        "for col in ['Albumin','Platelets','Prothrombin']:\n",
        "  df.loc[s1,col] = df.loc[s1,col].fillna(df.loc[s1,col].mean())\n",
        "for col in ['Ascites','Hepatomegaly','Spiders','Edema']:\n",
        "  df.loc[s1,col] = df.loc[s1,col].fillna(df.loc[s1,col].mode().values[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tbZzfNqcsWxa",
        "outputId": "55b7727e-8aec-4619-820e-955c3b96ca4b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "N_Days           0\n",
              "Age              0\n",
              "Sex              0\n",
              "Ascites          0\n",
              "Hepatomegaly     0\n",
              "Spiders          0\n",
              "Edema            0\n",
              "Bilirubin        0\n",
              "Cholesterol      0\n",
              "Albumin          0\n",
              "Copper           0\n",
              "Alk_Phos         0\n",
              "SGOT             0\n",
              "Tryglicerides    0\n",
              "Platelets        0\n",
              "Prothrombin      0\n",
              "Stage            0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Check if fillna successfully\n",
        "df.loc[df['Stage']==2.0].isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "p8OEDSJJisDM",
        "outputId": "32a32af0-49bc-43aa-dbe2-19fb25d2e21c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x720 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Check data structure to decide how to fill null value by stage3\n",
        "fig, axs = plt.subplots(nrows=3, ncols=3, figsize=(12, 10))\n",
        "fig.suptitle('Stage3', fontsize=16)\n",
        "axs[0, 0].hist(stage3_df['Bilirubin'],stage3_df['Bilirubin'].count())\n",
        "axs[0, 0].set_title('Bilirubin')\n",
        "axs[0, 1].hist(stage3_df['Cholesterol'],stage3_df['Cholesterol'].count())\n",
        "axs[0, 1].set_title('Cholesterol')\n",
        "axs[0, 2].hist(stage3_df['Albumin'],stage3_df['Albumin'].count())\n",
        "axs[0, 2].set_title('Albumin')\n",
        "axs[1, 0].hist(stage3_df['Copper'],stage3_df['Copper'].count())\n",
        "axs[1, 0].set_title('Copper')\n",
        "axs[1, 1].hist(stage3_df['Alk_Phos'],stage3_df['Alk_Phos'].count())\n",
        "axs[1, 1].set_title('Alk_Phos')\n",
        "axs[1, 2].hist(stage3_df['SGOT'],stage3_df['SGOT'].count())\n",
        "axs[1, 2].set_title('SGOT')\n",
        "axs[2, 0].hist(stage3_df['Tryglicerides'],stage3_df['Tryglicerides'].count())\n",
        "axs[2, 0].set_title('Tryglicerides')\n",
        "axs[2, 1].hist(stage3_df['Platelets'],stage3_df['Platelets'].count())\n",
        "axs[2, 1].set_title('Platelets')\n",
        "axs[2, 2].hist(stage3_df['Prothrombin'],stage3_df['Prothrombin'].count())\n",
        "axs[2, 2].set_title('Prothrombin');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "awv2ouJZsmfd"
      },
      "outputs": [],
      "source": [
        "# To fill up null value(numeric columns:by mean(normal distribution) or median(long tail distribution), non-numeric columns: by mode)\\\n",
        "s1 = df['Stage']==3.0\n",
        "for col in ['Bilirubin','Cholesterol','Copper','Alk_Phos','Tryglicerides']:\n",
        "  df.loc[s1,col] = df.loc[s1,col].fillna(df.loc[s1,col].median())\n",
        "for col in ['Albumin','Platelets','Prothrombin','SGOT']:\n",
        "  df.loc[s1,col] = df.loc[s1,col].fillna(df.loc[s1,col].mean())\n",
        "for col in ['Ascites','Hepatomegaly','Spiders','Edema']:\n",
        "  df.loc[s1,col] = df.loc[s1,col].fillna(df.loc[s1,col].mode().values[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yn2EverQsnGy",
        "outputId": "ec9207d2-5b74-4580-ef77-9bb62c090bc6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "N_Days           0\n",
              "Age              0\n",
              "Sex              0\n",
              "Ascites          0\n",
              "Hepatomegaly     0\n",
              "Spiders          0\n",
              "Edema            0\n",
              "Bilirubin        0\n",
              "Cholesterol      0\n",
              "Albumin          0\n",
              "Copper           0\n",
              "Alk_Phos         0\n",
              "SGOT             0\n",
              "Tryglicerides    0\n",
              "Platelets        0\n",
              "Prothrombin      0\n",
              "Stage            0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 69,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Check if fillna successfully\n",
        "df.loc[df['Stage']==3.0].isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "T9g_Fdtai9UY",
        "outputId": "aebc1816-5d4d-4f9a-99b4-f31b3adefa3e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x720 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Check data structure to decide how to fill null value by stage4\n",
        "fig, axs = plt.subplots(nrows=3, ncols=3, figsize=(12, 10))\n",
        "fig.suptitle('Stage4', fontsize=16)\n",
        "axs[0, 0].hist(stage4_df['Bilirubin'],stage4_df['Bilirubin'].count())\n",
        "axs[0, 0].set_title('Bilirubin')\n",
        "axs[0, 1].hist(stage4_df['Cholesterol'],stage4_df['Cholesterol'].count())\n",
        "axs[0, 1].set_title('Cholesterol')\n",
        "axs[0, 2].hist(stage4_df['Albumin'],stage4_df['Albumin'].count())\n",
        "axs[0, 2].set_title('Albumin')\n",
        "axs[1, 0].hist(stage4_df['Copper'],stage4_df['Copper'].count())\n",
        "axs[1, 0].set_title('Copper')\n",
        "axs[1, 1].hist(stage4_df['Alk_Phos'],stage4_df['Alk_Phos'].count())\n",
        "axs[1, 1].set_title('Alk_Phos')\n",
        "axs[1, 2].hist(stage4_df['SGOT'],stage4_df['SGOT'].count())\n",
        "axs[1, 2].set_title('SGOT')\n",
        "axs[2, 0].hist(stage4_df['Tryglicerides'],stage4_df['Tryglicerides'].count())\n",
        "axs[2, 0].set_title('Tryglicerides')\n",
        "axs[2, 1].hist(stage4_df['Platelets'],stage4_df['Platelets'].count())\n",
        "axs[2, 1].set_title('Platelets')\n",
        "axs[2, 2].hist(stage4_df['Prothrombin'],stage4_df['Prothrombin'].count())\n",
        "axs[2, 2].set_title('Prothrombin');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "m1A5KTY9s_PJ"
      },
      "outputs": [],
      "source": [
        "# To fill up null value(numeric columns:by mean(normal distribution) or median(long tail distribution), non-numeric columns: by mode)\\\n",
        "s1 = df['Stage']==4.0\n",
        "for col in ['Bilirubin','Cholesterol','Copper','Alk_Phos','Tryglicerides','SGOT']:\n",
        "  df.loc[s1,col] = df.loc[s1,col].fillna(df.loc[s1,col].median())\n",
        "for col in ['Albumin','Platelets','Prothrombin']:\n",
        "  df.loc[s1,col] = df.loc[s1,col].fillna(df.loc[s1,col].mean())\n",
        "for col in ['Ascites','Hepatomegaly','Spiders','Edema']:\n",
        "  df.loc[s1,col] = df.loc[s1,col].fillna(df.loc[s1,col].mode().values[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "csy7hGiGtBYq",
        "outputId": "6eb13272-cafa-4ae0-8614-581b60005c27"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "N_Days           0\n",
              "Age              0\n",
              "Sex              0\n",
              "Ascites          0\n",
              "Hepatomegaly     0\n",
              "Spiders          0\n",
              "Edema            0\n",
              "Bilirubin        0\n",
              "Cholesterol      0\n",
              "Albumin          0\n",
              "Copper           0\n",
              "Alk_Phos         0\n",
              "SGOT             0\n",
              "Tryglicerides    0\n",
              "Platelets        0\n",
              "Prothrombin      0\n",
              "Stage            0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 72,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Check if fillna successfully\n",
        "df.loc[df['Stage']==4.0].isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tDXARL2rGNYz",
        "outputId": "1b0f8751-6883-45fc-ec11-fa64d50fd000"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "N_Days           0\n",
              "Age              0\n",
              "Sex              0\n",
              "Ascites          6\n",
              "Hepatomegaly     6\n",
              "Spiders          6\n",
              "Edema            0\n",
              "Bilirubin        0\n",
              "Cholesterol      6\n",
              "Albumin          0\n",
              "Copper           6\n",
              "Alk_Phos         6\n",
              "SGOT             6\n",
              "Tryglicerides    6\n",
              "Platelets        0\n",
              "Prothrombin      0\n",
              "Stage            6\n",
              "dtype: int64"
            ]
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "ttXy_G2OGTQE"
      },
      "outputs": [],
      "source": [
        "df = df.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pmHJ_NEKXRl1",
        "outputId": "efcda152-1c73-459f-d50d-88a572e1ad7d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-75-bd265b255853>:2: 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",
            "  df['Age'] = (df['Age']/365).round()\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "0      59.0\n",
              "1      56.0\n",
              "2      70.0\n",
              "3      55.0\n",
              "4      38.0\n",
              "       ... \n",
              "413    67.0\n",
              "414    39.0\n",
              "415    57.0\n",
              "416    58.0\n",
              "417    53.0\n",
              "Name: Age, Length: 412, dtype: float64"
            ]
          },
          "execution_count": 75,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Transfrom Age from days to years\n",
        "df['Age'] = (df['Age']/365).round()\n",
        "df['Age']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A2iEJduWEmTO"
      },
      "source": [
        "Visualize the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "DU3HZ69zXRl1",
        "outputId": "a528240d-9dce-4f0a-f5cd-5775e782af65"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1440x720 with 8 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axs = plt.subplots(2,4, figsize=(20, 10))\n",
        "sns.regplot(x=df['Bilirubin'], y=df['Stage'],scatter=True, color=\"skyblue\", ax=axs[0,0])\n",
        "sns.regplot(x=df['Cholesterol'], y=df['Stage'],scatter=True, color=\"olive\", ax=axs[0,1])\n",
        "sns.regplot(x=df['Copper'], y=df['Stage'],scatter=True, color=\"gold\", ax=axs[0,2])\n",
        "sns.regplot(x=df['Alk_Phos'], y=df['Stage'],scatter=True,color=\"teal\", ax=axs[0,3])\n",
        "sns.regplot(x=df['SGOT'], y=df['Stage'],scatter=True, color=\"yellow\", ax=axs[1,0])\n",
        "sns.regplot(x=df['Tryglicerides'], y=df['Stage'],scatter=True,color=\"green\", ax=axs[1,1])\n",
        "sns.regplot(x=df['Prothrombin'], y=df['Stage'],scatter=True, color=\"pink\", ax=axs[1,2])\n",
        "sns.regplot(x=df['Age'], y=df['Stage'],scatter=True,color=\"grey\", ax=axs[1,3]);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "l3ybq3qDXRl1",
        "outputId": "4560b282-4145-4bc3-978d-372aa5641b55"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1440x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "fig, axs = plt.subplots(2,2, figsize=(20, 10))\n",
        "# Ascites\n",
        "Ascites_df = df.groupby('Stage')['Ascites'].value_counts().reset_index(name='Count')\n",
        "sns.barplot(data=Ascites_df, x='Stage', y='Count',hue='Ascites',color=\"green\",ax=axs[0,0])\n",
        "\n",
        "# Hepatomegaly\n",
        "Hepatomegaly_df = df.groupby('Stage')['Hepatomegaly'].value_counts().reset_index(name='Count')\n",
        "sns.barplot(data=Hepatomegaly_df, x='Stage', y='Count',hue='Hepatomegaly',color=\"teal\",ax=axs[0,1])\n",
        "\n",
        "# Spiders\n",
        "Spiders_df = df.groupby('Stage')['Spiders'].value_counts().reset_index(name='Count')\n",
        "sns.barplot(data=Spiders_df, x='Stage', y='Count',hue='Spiders',color=\"olive\",ax=axs[1,0])\n",
        "\n",
        "# Edema\n",
        "Edema_df = df.groupby('Stage')['Edema'].value_counts().reset_index(name='Count')\n",
        "sns.barplot(data=Edema_df, x='Stage', y='Count',hue='Edema',color=\"pink\",ax=axs[1,1]);\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JYEU4ePpXRl2"
      },
      "source": [
        "## Create Models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q5yyQW0PXRl2"
      },
      "source": [
        "## Split the Data into Training and Testing Sets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "Da2vPxlI0Voi"
      },
      "outputs": [],
      "source": [
        "# Based on original reaserch, Stage 4.0 is Cirrhosis stage\n",
        "df['Stage'] = df['Stage'].replace({1.0:0,2.0:0,3.0:0,4.0:1}).astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GzfMSnogb0ma",
        "outputId": "8dda823a-3e6c-47c7-8b96-fa54a6db3605"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    268\n",
              "1    144\n",
              "Name: Stage, dtype: int64"
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y = df['Stage']\n",
        "y.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "MaXncoFFXRl2",
        "outputId": "daf3f51f-08d9-400e-86b9-f1e478af5079"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-0664fc56-92a5-4185-ab4d-3757ebc1f8dc\">\n",
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              "      <th>Age</th>\n",
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              "      <th>0</th>\n",
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              "      <td>Y</td>\n",
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              "      <td>2.60</td>\n",
              "      <td>156.0</td>\n",
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              "      <td>54.0</td>\n",
              "      <td>221.0</td>\n",
              "      <td>10.6</td>\n",
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              "      <td>151.0</td>\n",
              "      <td>12.0</td>\n",
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              "      <td>2.54</td>\n",
              "      <td>64.0</td>\n",
              "      <td>183.0</td>\n",
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              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>67.0</td>\n",
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              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>1.2</td>\n",
              "      <td>2.96</td>\n",
              "      <td>67.5</td>\n",
              "      <td>174.0</td>\n",
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              "      <th>414</th>\n",
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              "      <td>98.5</td>\n",
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              "      <td>57.0</td>\n",
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              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
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              "      <td>3.42</td>\n",
              "      <td>67.5</td>\n",
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              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>0.8</td>\n",
              "      <td>3.75</td>\n",
              "      <td>67.5</td>\n",
              "      <td>269.0</td>\n",
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              "      <td>53.0</td>\n",
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              "      <td>N</td>\n",
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              "      <td>3.29</td>\n",
              "      <td>98.5</td>\n",
              "      <td>350.0</td>\n",
              "      <td>10.6</td>\n",
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            "text/plain": [
              "      Age Ascites Hepatomegaly Spiders Edema  Bilirubin  Albumin  Copper  \\\n",
              "0    59.0       Y            Y       Y     Y       14.5     2.60   156.0   \n",
              "1    56.0       N            Y       Y     N        1.1     4.14    54.0   \n",
              "2    70.0       N            N       N     S        1.4     3.48   210.0   \n",
              "3    55.0       N            Y       Y     S        1.8     2.54    64.0   \n",
              "4    38.0       N            Y       Y     N        3.4     3.53   143.0   \n",
              "..    ...     ...          ...     ...   ...        ...      ...     ...   \n",
              "413  67.0       N            N       N     N        1.2     2.96    67.5   \n",
              "414  39.0       N            Y       N     N        0.9     3.83    98.5   \n",
              "415  57.0       N            N       N     N        1.6     3.42    67.5   \n",
              "416  58.0       N            N       N     N        0.8     3.75    67.5   \n",
              "417  53.0       N            Y       N     N        0.7     3.29    98.5   \n",
              "\n",
              "     Platelets  Prothrombin  \n",
              "0        190.0         12.2  \n",
              "1        221.0         10.6  \n",
              "2        151.0         12.0  \n",
              "3        183.0         10.3  \n",
              "4        136.0         10.9  \n",
              "..         ...          ...  \n",
              "413      174.0         10.9  \n",
              "414      180.0         11.2  \n",
              "415      143.0          9.9  \n",
              "416      269.0         10.4  \n",
              "417      350.0         10.6  \n",
              "\n",
              "[412 rows x 10 columns]"
            ]
          },
          "execution_count": 81,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X = df.drop(columns=['Stage','Cholesterol','Alk_Phos','SGOT','Tryglicerides','Sex','N_Days'],axis=1)\n",
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "0hPmkM8SD49O",
        "outputId": "aaceb087-a52a-4643-b6b2-c2cf6cef4c09"
      },
      "outputs": [
        {
          "data": {
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              "      <td>350.0</td>\n",
              "      <td>10.6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>412 rows × 15 columns</p>\n",
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              "  "
            ],
            "text/plain": [
              "      Age  Bilirubin  Albumin  Copper  Platelets  Prothrombin  Ascites_N  \\\n",
              "0    59.0       14.5     2.60   156.0      190.0         12.2          0   \n",
              "1    56.0        1.1     4.14    54.0      221.0         10.6          1   \n",
              "2    70.0        1.4     3.48   210.0      151.0         12.0          1   \n",
              "3    55.0        1.8     2.54    64.0      183.0         10.3          1   \n",
              "4    38.0        3.4     3.53   143.0      136.0         10.9          1   \n",
              "..    ...        ...      ...     ...        ...          ...        ...   \n",
              "413  67.0        1.2     2.96    67.5      174.0         10.9          1   \n",
              "414  39.0        0.9     3.83    98.5      180.0         11.2          1   \n",
              "415  57.0        1.6     3.42    67.5      143.0          9.9          1   \n",
              "416  58.0        0.8     3.75    67.5      269.0         10.4          1   \n",
              "417  53.0        0.7     3.29    98.5      350.0         10.6          1   \n",
              "\n",
              "     Ascites_Y  Hepatomegaly_N  Hepatomegaly_Y  Spiders_N  Spiders_Y  Edema_N  \\\n",
              "0            1               0               1          0          1        0   \n",
              "1            0               0               1          0          1        1   \n",
              "2            0               1               0          1          0        0   \n",
              "3            0               0               1          0          1        0   \n",
              "4            0               0               1          0          1        1   \n",
              "..         ...             ...             ...        ...        ...      ...   \n",
              "413          0               1               0          1          0        1   \n",
              "414          0               0               1          1          0        1   \n",
              "415          0               1               0          1          0        1   \n",
              "416          0               1               0          1          0        1   \n",
              "417          0               0               1          1          0        1   \n",
              "\n",
              "     Edema_S  Edema_Y  \n",
              "0          0        1  \n",
              "1          0        0  \n",
              "2          1        0  \n",
              "3          1        0  \n",
              "4          0        0  \n",
              "..       ...      ...  \n",
              "413        0        0  \n",
              "414        0        0  \n",
              "415        0        0  \n",
              "416        0        0  \n",
              "417        0        0  \n",
              "\n",
              "[412 rows x 15 columns]"
            ]
          },
          "execution_count": 82,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dummied_X = pd.get_dummies(X)\n",
        "dummied_X"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h4qnfxalXRl2"
      },
      "source": [
        "## Create, Fit and Compare Models\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "id": "GAbHrCRjFa3d"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(dummied_X, y,stratify = y, test_size=0.3,random_state=50)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7f7c8-XYgDDW"
      },
      "source": [
        "### <span style='font-family:Helvetica;color:darkorange'> Classifier Models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "id": "evgLWdX8ED6y"
      },
      "outputs": [],
      "source": [
        "Scaler = [StandardScaler(),MinMaxScaler(),RobustScaler()]\n",
        "models= [SVC(),DecisionTreeClassifier(),RandomForestClassifier(),LogisticRegression(max_iter=1000),AdaBoostClassifier(),KNeighborsClassifier(),GradientBoostingClassifier(n_estimators = 100, max_depth =1)]\n",
        "scores = []\n",
        "for i in range(0,len(Scaler)):\n",
        "  a = Scaler[i].fit(X_train)\n",
        "  X_train_scaled = a.transform(X_train)\n",
        "  X_test_scaled = a.transform(X_test)\n",
        "  for u in range(0,len(models)):\n",
        "    model = models[u]\n",
        "    model.fit(X_train_scaled, y_train) \n",
        "    accuracy = model.score(X_test_scaled, y_test)\n",
        "    y_true = y_test\n",
        "    y_pred = model.predict(X_test_scaled)\n",
        "    tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()\n",
        "    recall = tp / (tp+fn) \n",
        "    precision = tp/(tp+fp)\n",
        "    F1 = 2*(recall*precision)/(recall+precision)\n",
        "    term={'Scaler':Scaler[i],'Model':models[u],'Accuracy':accuracy,'Recall':recall,'Precision':precision,'F1':F1}\n",
        "    scores.append(term)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "CyEiW8wt55-e",
        "outputId": "5bdc4a63-9a61-4b02-85e0-6775bac54852"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Scaler</th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Precision</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>RobustScaler()</td>\n",
              "      <td>([DecisionTreeRegressor(criterion='friedman_ms...</td>\n",
              "      <td>0.870968</td>\n",
              "      <td>0.813953</td>\n",
              "      <td>0.813953</td>\n",
              "      <td>0.813953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>MinMaxScaler()</td>\n",
              "      <td>([DecisionTreeRegressor(criterion='friedman_ms...</td>\n",
              "      <td>0.870968</td>\n",
              "      <td>0.813953</td>\n",
              "      <td>0.813953</td>\n",
              "      <td>0.813953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>StandardScaler()</td>\n",
              "      <td>([DecisionTreeRegressor(criterion='friedman_ms...</td>\n",
              "      <td>0.870968</td>\n",
              "      <td>0.813953</td>\n",
              "      <td>0.813953</td>\n",
              "      <td>0.813953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>StandardScaler()</td>\n",
              "      <td>(DecisionTreeClassifier(max_features='auto', r...</td>\n",
              "      <td>0.822581</td>\n",
              "      <td>0.720930</td>\n",
              "      <td>0.756098</td>\n",
              "      <td>0.738095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>RobustScaler()</td>\n",
              "      <td>(DecisionTreeClassifier(max_features='auto', r...</td>\n",
              "      <td>0.822581</td>\n",
              "      <td>0.744186</td>\n",
              "      <td>0.744186</td>\n",
              "      <td>0.744186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>MinMaxScaler()</td>\n",
              "      <td>(DecisionTreeClassifier(max_features='auto', r...</td>\n",
              "      <td>0.806452</td>\n",
              "      <td>0.720930</td>\n",
              "      <td>0.720930</td>\n",
              "      <td>0.720930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>StandardScaler()</td>\n",
              "      <td>LogisticRegression(max_iter=1000)</td>\n",
              "      <td>0.790323</td>\n",
              "      <td>0.767442</td>\n",
              "      <td>0.673469</td>\n",
              "      <td>0.717391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>StandardScaler()</td>\n",
              "      <td>(DecisionTreeClassifier(max_depth=1, random_st...</td>\n",
              "      <td>0.790323</td>\n",
              "      <td>0.697674</td>\n",
              "      <td>0.697674</td>\n",
              "      <td>0.697674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>RobustScaler()</td>\n",
              "      <td>(DecisionTreeClassifier(max_depth=1, random_st...</td>\n",
              "      <td>0.790323</td>\n",
              "      <td>0.697674</td>\n",
              "      <td>0.697674</td>\n",
              "      <td>0.697674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>RobustScaler()</td>\n",
              "      <td>LogisticRegression(max_iter=1000)</td>\n",
              "      <td>0.790323</td>\n",
              "      <td>0.767442</td>\n",
              "      <td>0.673469</td>\n",
              "      <td>0.717391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>MinMaxScaler()</td>\n",
              "      <td>(DecisionTreeClassifier(max_depth=1, random_st...</td>\n",
              "      <td>0.790323</td>\n",
              "      <td>0.697674</td>\n",
              "      <td>0.697674</td>\n",
              "      <td>0.697674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>RobustScaler()</td>\n",
              "      <td>KNeighborsClassifier()</td>\n",
              "      <td>0.782258</td>\n",
              "      <td>0.674419</td>\n",
              "      <td>0.690476</td>\n",
              "      <td>0.682353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>MinMaxScaler()</td>\n",
              "      <td>LogisticRegression(max_iter=1000)</td>\n",
              "      <td>0.782258</td>\n",
              "      <td>0.813953</td>\n",
              "      <td>0.648148</td>\n",
              "      <td>0.721649</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>MinMaxScaler()</td>\n",
              "      <td>KNeighborsClassifier()</td>\n",
              "      <td>0.782258</td>\n",
              "      <td>0.720930</td>\n",
              "      <td>0.673913</td>\n",
              "      <td>0.696629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>RobustScaler()</td>\n",
              "      <td>SVC()</td>\n",
              "      <td>0.774194</td>\n",
              "      <td>0.790698</td>\n",
              "      <td>0.641509</td>\n",
              "      <td>0.708333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>StandardScaler()</td>\n",
              "      <td>KNeighborsClassifier()</td>\n",
              "      <td>0.766129</td>\n",
              "      <td>0.720930</td>\n",
              "      <td>0.645833</td>\n",
              "      <td>0.681319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>RobustScaler()</td>\n",
              "      <td>DecisionTreeClassifier()</td>\n",
              "      <td>0.758065</td>\n",
              "      <td>0.674419</td>\n",
              "      <td>0.644444</td>\n",
              "      <td>0.659091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>StandardScaler()</td>\n",
              "      <td>SVC()</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.813953</td>\n",
              "      <td>0.603448</td>\n",
              "      <td>0.693069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>MinMaxScaler()</td>\n",
              "      <td>DecisionTreeClassifier()</td>\n",
              "      <td>0.733871</td>\n",
              "      <td>0.651163</td>\n",
              "      <td>0.608696</td>\n",
              "      <td>0.629213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>MinMaxScaler()</td>\n",
              "      <td>SVC()</td>\n",
              "      <td>0.733871</td>\n",
              "      <td>0.883721</td>\n",
              "      <td>0.575758</td>\n",
              "      <td>0.697248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>StandardScaler()</td>\n",
              "      <td>DecisionTreeClassifier()</td>\n",
              "      <td>0.693548</td>\n",
              "      <td>0.604651</td>\n",
              "      <td>0.553191</td>\n",
              "      <td>0.577778</td>\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f6cb3c43-642d-4a6d-8741-da2c4cbafe19 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f6cb3c43-642d-4a6d-8741-da2c4cbafe19');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              Scaler                                              Model  \\\n",
              "20    RobustScaler()  ([DecisionTreeRegressor(criterion='friedman_ms...   \n",
              "13    MinMaxScaler()  ([DecisionTreeRegressor(criterion='friedman_ms...   \n",
              "6   StandardScaler()  ([DecisionTreeRegressor(criterion='friedman_ms...   \n",
              "2   StandardScaler()  (DecisionTreeClassifier(max_features='auto', r...   \n",
              "16    RobustScaler()  (DecisionTreeClassifier(max_features='auto', r...   \n",
              "9     MinMaxScaler()  (DecisionTreeClassifier(max_features='auto', r...   \n",
              "3   StandardScaler()                  LogisticRegression(max_iter=1000)   \n",
              "4   StandardScaler()  (DecisionTreeClassifier(max_depth=1, random_st...   \n",
              "18    RobustScaler()  (DecisionTreeClassifier(max_depth=1, random_st...   \n",
              "17    RobustScaler()                  LogisticRegression(max_iter=1000)   \n",
              "11    MinMaxScaler()  (DecisionTreeClassifier(max_depth=1, random_st...   \n",
              "19    RobustScaler()                             KNeighborsClassifier()   \n",
              "10    MinMaxScaler()                  LogisticRegression(max_iter=1000)   \n",
              "12    MinMaxScaler()                             KNeighborsClassifier()   \n",
              "14    RobustScaler()                                              SVC()   \n",
              "5   StandardScaler()                             KNeighborsClassifier()   \n",
              "15    RobustScaler()                           DecisionTreeClassifier()   \n",
              "0   StandardScaler()                                              SVC()   \n",
              "8     MinMaxScaler()                           DecisionTreeClassifier()   \n",
              "7     MinMaxScaler()                                              SVC()   \n",
              "1   StandardScaler()                           DecisionTreeClassifier()   \n",
              "\n",
              "    Accuracy    Recall  Precision        F1  \n",
              "20  0.870968  0.813953   0.813953  0.813953  \n",
              "13  0.870968  0.813953   0.813953  0.813953  \n",
              "6   0.870968  0.813953   0.813953  0.813953  \n",
              "2   0.822581  0.720930   0.756098  0.738095  \n",
              "16  0.822581  0.744186   0.744186  0.744186  \n",
              "9   0.806452  0.720930   0.720930  0.720930  \n",
              "3   0.790323  0.767442   0.673469  0.717391  \n",
              "4   0.790323  0.697674   0.697674  0.697674  \n",
              "18  0.790323  0.697674   0.697674  0.697674  \n",
              "17  0.790323  0.767442   0.673469  0.717391  \n",
              "11  0.790323  0.697674   0.697674  0.697674  \n",
              "19  0.782258  0.674419   0.690476  0.682353  \n",
              "10  0.782258  0.813953   0.648148  0.721649  \n",
              "12  0.782258  0.720930   0.673913  0.696629  \n",
              "14  0.774194  0.790698   0.641509  0.708333  \n",
              "5   0.766129  0.720930   0.645833  0.681319  \n",
              "15  0.758065  0.674419   0.644444  0.659091  \n",
              "0   0.750000  0.813953   0.603448  0.693069  \n",
              "8   0.733871  0.651163   0.608696  0.629213  \n",
              "7   0.733871  0.883721   0.575758  0.697248  \n",
              "1   0.693548  0.604651   0.553191  0.577778  "
            ]
          },
          "execution_count": 86,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "clfTestResult_df = pd.DataFrame(scores)\n",
        "clfTestResult_df = clfTestResult_df.sort_values('Accuracy',ascending=False)\n",
        "clfTestResult_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "XU8A8aBpcpYa",
        "outputId": "a7d66ec2-a376-4e53-a0ea-41706aa5198b"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": "download(\"download_a64ae36b-6739-4f1f-8b06-82c7daea3fb7\", \"clfTestResult.csv\", 2514)",
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              "<IPython.core.display.Javascript object>"
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          "metadata": {},
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      ],
      "source": [
        "clfTestResult_df.to_csv('clfTestResult.csv')\n",
        "files.download('clfTestResult.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E_xce8xUXRl2"
      },
      "source": [
        "### <span style='font-family:Helvetica;color:darkorange'> Neural Network Models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9hK-SM5wXRl3",
        "outputId": "d2123142-0dd1-49fa-d122-fd657cb48b51"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 2ms/step - loss: 0.6736 - accuracy: 0.6458 - recall_36: 0.4554 - precision_36: 0.4946\n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6388 - accuracy: 0.7569 - recall_36: 0.6139 - precision_36: 0.6667\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6084 - accuracy: 0.7604 - recall_36: 0.5842 - precision_36: 0.6860\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5757 - accuracy: 0.7708 - recall_36: 0.5842 - precision_36: 0.7108\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5437 - accuracy: 0.7847 - recall_36: 0.6139 - precision_36: 0.7294\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5154 - accuracy: 0.7882 - recall_36: 0.6139 - precision_36: 0.7381\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4918 - accuracy: 0.7917 - recall_36: 0.6040 - precision_36: 0.7531\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4714 - accuracy: 0.7917 - recall_36: 0.6436 - precision_36: 0.7303\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4534 - accuracy: 0.7951 - recall_36: 0.6535 - precision_36: 0.7333\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4390 - accuracy: 0.8090 - recall_36: 0.6832 - precision_36: 0.7500\n",
            "4/4 - 0s - loss: 0.4486 - accuracy: 0.8306 - recall_36: 0.7442 - precision_36: 0.7619 - 265ms/epoch - 66ms/step\n",
            "Epoch 1/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4265 - accuracy: 0.8125 - recall_36: 0.6931 - precision_36: 0.7527\n",
            "Epoch 2/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4157 - accuracy: 0.8333 - recall_36: 0.7525 - precision_36: 0.7677\n",
            "Epoch 3/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4061 - accuracy: 0.8299 - recall_36: 0.7525 - precision_36: 0.7600\n",
            "Epoch 4/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3978 - accuracy: 0.8299 - recall_36: 0.7525 - precision_36: 0.7600\n",
            "Epoch 5/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3910 - accuracy: 0.8368 - recall_36: 0.7723 - precision_36: 0.7647\n",
            "Epoch 6/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3840 - accuracy: 0.8299 - recall_36: 0.7525 - precision_36: 0.7600\n",
            "Epoch 7/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3780 - accuracy: 0.8368 - recall_36: 0.7822 - precision_36: 0.7596\n",
            "Epoch 8/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3735 - accuracy: 0.8368 - recall_36: 0.7723 - precision_36: 0.7647\n",
            "Epoch 9/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3700 - accuracy: 0.8403 - recall_36: 0.8119 - precision_36: 0.7523\n",
            "Epoch 10/20\n",
            "9/9 [==============================] - 0s 1ms/step - loss: 0.3628 - accuracy: 0.8403 - recall_36: 0.8119 - precision_36: 0.7523\n",
            "Epoch 11/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3586 - accuracy: 0.8438 - recall_36: 0.8020 - precision_36: 0.7642\n",
            "Epoch 12/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3544 - accuracy: 0.8403 - recall_36: 0.7921 - precision_36: 0.7619\n",
            "Epoch 13/20\n",
            "9/9 [==============================] - 0s 1ms/step - loss: 0.3510 - accuracy: 0.8403 - recall_36: 0.7921 - precision_36: 0.7619\n",
            "Epoch 14/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3462 - accuracy: 0.8438 - recall_36: 0.8020 - precision_36: 0.7642\n",
            "Epoch 15/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3425 - accuracy: 0.8507 - recall_36: 0.8020 - precision_36: 0.7788\n",
            "Epoch 16/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3381 - accuracy: 0.8507 - recall_36: 0.8020 - precision_36: 0.7788\n",
            "Epoch 17/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3347 - accuracy: 0.8542 - recall_36: 0.8218 - precision_36: 0.7757\n",
            "Epoch 18/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3306 - accuracy: 0.8507 - recall_36: 0.8020 - precision_36: 0.7788\n",
            "Epoch 19/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3271 - accuracy: 0.8576 - recall_36: 0.8218 - precision_36: 0.7830\n",
            "Epoch 20/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3242 - accuracy: 0.8646 - recall_36: 0.8218 - precision_36: 0.7981\n",
            "4/4 - 0s - loss: 0.4355 - accuracy: 0.8145 - recall_36: 0.7674 - precision_36: 0.7174 - 23ms/epoch - 6ms/step\n",
            "Epoch 1/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3202 - accuracy: 0.8646 - recall_36: 0.8218 - precision_36: 0.7981\n",
            "Epoch 2/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3176 - accuracy: 0.8681 - recall_36: 0.8317 - precision_36: 0.8000\n",
            "Epoch 3/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3133 - accuracy: 0.8785 - recall_36: 0.8614 - precision_36: 0.8056\n",
            "Epoch 4/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3104 - accuracy: 0.8785 - recall_36: 0.8515 - precision_36: 0.8113\n",
            "Epoch 5/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3076 - accuracy: 0.8785 - recall_36: 0.8515 - precision_36: 0.8113\n",
            "Epoch 6/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3044 - accuracy: 0.8785 - recall_36: 0.8515 - precision_36: 0.8113\n",
            "Epoch 7/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3022 - accuracy: 0.8785 - recall_36: 0.8515 - precision_36: 0.8113\n",
            "Epoch 8/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2988 - accuracy: 0.8785 - recall_36: 0.8614 - precision_36: 0.8056\n",
            "Epoch 9/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2957 - accuracy: 0.8819 - recall_36: 0.8614 - precision_36: 0.8131\n",
            "Epoch 10/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2936 - accuracy: 0.8854 - recall_36: 0.8614 - precision_36: 0.8208\n",
            "Epoch 11/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2908 - accuracy: 0.8819 - recall_36: 0.8614 - precision_36: 0.8131\n",
            "Epoch 12/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2878 - accuracy: 0.8854 - recall_36: 0.8614 - precision_36: 0.8208\n",
            "Epoch 13/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2864 - accuracy: 0.8819 - recall_36: 0.8614 - precision_36: 0.8131\n",
            "Epoch 14/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2823 - accuracy: 0.8819 - recall_36: 0.8614 - precision_36: 0.8131\n",
            "Epoch 15/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2830 - accuracy: 0.8924 - recall_36: 0.8614 - precision_36: 0.8365\n",
            "Epoch 16/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2799 - accuracy: 0.8924 - recall_36: 0.8614 - precision_36: 0.8365\n",
            "Epoch 17/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2763 - accuracy: 0.8854 - recall_36: 0.8614 - precision_36: 0.8208\n",
            "Epoch 18/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2730 - accuracy: 0.8958 - recall_36: 0.8614 - precision_36: 0.8447\n",
            "Epoch 19/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2705 - accuracy: 0.8958 - recall_36: 0.8614 - precision_36: 0.8447\n",
            "Epoch 20/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2693 - accuracy: 0.8958 - recall_36: 0.8614 - precision_36: 0.8447\n",
            "Epoch 21/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2658 - accuracy: 0.8958 - recall_36: 0.8614 - precision_36: 0.8447\n",
            "Epoch 22/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2629 - accuracy: 0.8958 - recall_36: 0.8614 - precision_36: 0.8447\n",
            "Epoch 23/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2604 - accuracy: 0.8958 - recall_36: 0.8614 - precision_36: 0.8447\n",
            "Epoch 24/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2593 - accuracy: 0.8958 - recall_36: 0.8614 - precision_36: 0.8447\n",
            "Epoch 25/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2558 - accuracy: 0.8958 - recall_36: 0.8614 - precision_36: 0.8447\n",
            "Epoch 26/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2535 - accuracy: 0.8958 - recall_36: 0.8515 - precision_36: 0.8515\n",
            "Epoch 27/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2517 - accuracy: 0.8958 - recall_36: 0.8515 - precision_36: 0.8515\n",
            "Epoch 28/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2482 - accuracy: 0.8958 - recall_36: 0.8515 - precision_36: 0.8515\n",
            "Epoch 29/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2464 - accuracy: 0.8958 - recall_36: 0.8515 - precision_36: 0.8515\n",
            "Epoch 30/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2443 - accuracy: 0.8958 - recall_36: 0.8515 - precision_36: 0.8515\n",
            "4/4 - 0s - loss: 0.4718 - accuracy: 0.8548 - recall_36: 0.7442 - precision_36: 0.8205 - 17ms/epoch - 4ms/step\n",
            "Epoch 1/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2417 - accuracy: 0.9028 - recall_36: 0.8713 - precision_36: 0.8544\n",
            "Epoch 2/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2417 - accuracy: 0.8993 - recall_36: 0.8614 - precision_36: 0.8529\n",
            "Epoch 3/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2373 - accuracy: 0.8958 - recall_36: 0.8515 - precision_36: 0.8515\n",
            "Epoch 4/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2354 - accuracy: 0.9028 - recall_36: 0.8614 - precision_36: 0.8614\n",
            "Epoch 5/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2329 - accuracy: 0.8958 - recall_36: 0.8515 - precision_36: 0.8515\n",
            "Epoch 6/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2305 - accuracy: 0.9097 - recall_36: 0.8713 - precision_36: 0.8713\n",
            "Epoch 7/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2310 - accuracy: 0.9028 - recall_36: 0.8614 - precision_36: 0.8614\n",
            "Epoch 8/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2280 - accuracy: 0.9062 - recall_36: 0.8713 - precision_36: 0.8627\n",
            "Epoch 9/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2254 - accuracy: 0.9028 - recall_36: 0.8614 - precision_36: 0.8614\n",
            "Epoch 10/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2226 - accuracy: 0.9028 - recall_36: 0.8614 - precision_36: 0.8614\n",
            "Epoch 11/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2207 - accuracy: 0.9062 - recall_36: 0.8713 - precision_36: 0.8627\n",
            "Epoch 12/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2189 - accuracy: 0.9062 - recall_36: 0.8713 - precision_36: 0.8627\n",
            "Epoch 13/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2172 - accuracy: 0.9062 - recall_36: 0.8713 - precision_36: 0.8627\n",
            "Epoch 14/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2153 - accuracy: 0.9132 - recall_36: 0.8911 - precision_36: 0.8654\n",
            "Epoch 15/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2141 - accuracy: 0.9062 - recall_36: 0.8713 - precision_36: 0.8627\n",
            "Epoch 16/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2125 - accuracy: 0.9097 - recall_36: 0.8812 - precision_36: 0.8641\n",
            "Epoch 17/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2099 - accuracy: 0.9132 - recall_36: 0.8812 - precision_36: 0.8725\n",
            "Epoch 18/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2091 - accuracy: 0.9167 - recall_36: 0.9010 - precision_36: 0.8667\n",
            "Epoch 19/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2063 - accuracy: 0.9167 - recall_36: 0.9010 - precision_36: 0.8667\n",
            "Epoch 20/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2050 - accuracy: 0.9132 - recall_36: 0.8911 - precision_36: 0.8654\n",
            "Epoch 21/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2034 - accuracy: 0.9236 - recall_36: 0.9010 - precision_36: 0.8835\n",
            "Epoch 22/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2010 - accuracy: 0.9236 - recall_36: 0.9109 - precision_36: 0.8762\n",
            "Epoch 23/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1997 - accuracy: 0.9167 - recall_36: 0.8812 - precision_36: 0.8812\n",
            "Epoch 24/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1981 - accuracy: 0.9201 - recall_36: 0.8911 - precision_36: 0.8824\n",
            "Epoch 25/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1973 - accuracy: 0.9236 - recall_36: 0.9010 - precision_36: 0.8835\n",
            "Epoch 26/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1947 - accuracy: 0.9271 - recall_36: 0.9109 - precision_36: 0.8846\n",
            "Epoch 27/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1929 - accuracy: 0.9271 - recall_36: 0.9010 - precision_36: 0.8922\n",
            "Epoch 28/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1913 - accuracy: 0.9236 - recall_36: 0.9010 - precision_36: 0.8835\n",
            "Epoch 29/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1900 - accuracy: 0.9271 - recall_36: 0.9010 - precision_36: 0.8922\n",
            "Epoch 30/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1883 - accuracy: 0.9271 - recall_36: 0.9010 - precision_36: 0.8922\n",
            "Epoch 31/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1867 - accuracy: 0.9271 - recall_36: 0.9010 - precision_36: 0.8922\n",
            "Epoch 32/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1846 - accuracy: 0.9306 - recall_36: 0.9109 - precision_36: 0.8932\n",
            "Epoch 33/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1845 - accuracy: 0.9271 - recall_36: 0.9010 - precision_36: 0.8922\n",
            "Epoch 34/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1821 - accuracy: 0.9306 - recall_36: 0.9109 - precision_36: 0.8932\n",
            "Epoch 35/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1805 - accuracy: 0.9306 - recall_36: 0.9109 - precision_36: 0.8932\n",
            "Epoch 36/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1791 - accuracy: 0.9306 - recall_36: 0.9109 - precision_36: 0.8932\n",
            "Epoch 37/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1780 - accuracy: 0.9306 - recall_36: 0.9109 - precision_36: 0.8932\n",
            "Epoch 38/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1794 - accuracy: 0.9271 - recall_36: 0.9010 - precision_36: 0.8922\n",
            "Epoch 39/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1745 - accuracy: 0.9340 - recall_36: 0.9208 - precision_36: 0.8942\n",
            "Epoch 40/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1737 - accuracy: 0.9340 - recall_36: 0.9208 - precision_36: 0.8942\n",
            "4/4 - 0s - loss: 0.5456 - accuracy: 0.8306 - recall_36: 0.6744 - precision_36: 0.8056 - 16ms/epoch - 4ms/step\n",
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 2ms/step - loss: 0.7129 - accuracy: 0.4965 - recall_37: 0.6733 - precision_37: 0.3778\n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6691 - accuracy: 0.6250 - recall_37: 0.6238 - precision_37: 0.4737\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6294 - accuracy: 0.7222 - recall_37: 0.6040 - precision_37: 0.6040\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5968 - accuracy: 0.7639 - recall_37: 0.6535 - precision_37: 0.6667\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5671 - accuracy: 0.7986 - recall_37: 0.6733 - precision_37: 0.7312\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5383 - accuracy: 0.7917 - recall_37: 0.6634 - precision_37: 0.7204\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5133 - accuracy: 0.8056 - recall_37: 0.6832 - precision_37: 0.7419\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4909 - accuracy: 0.8125 - recall_37: 0.7030 - precision_37: 0.7474\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4709 - accuracy: 0.8090 - recall_37: 0.7030 - precision_37: 0.7396\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4532 - accuracy: 0.8125 - recall_37: 0.7030 - precision_37: 0.7474\n",
            "4/4 - 0s - loss: 0.4449 - accuracy: 0.8065 - recall_37: 0.6744 - precision_37: 0.7436 - 285ms/epoch - 71ms/step\n",
            "Epoch 1/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4399 - accuracy: 0.8090 - recall_37: 0.7129 - precision_37: 0.7347\n",
            "Epoch 2/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4264 - accuracy: 0.8125 - recall_37: 0.7228 - precision_37: 0.7374\n",
            "Epoch 3/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4169 - accuracy: 0.8160 - recall_37: 0.7426 - precision_37: 0.7353\n",
            "Epoch 4/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4083 - accuracy: 0.8194 - recall_37: 0.7426 - precision_37: 0.7426\n",
            "Epoch 5/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3999 - accuracy: 0.8229 - recall_37: 0.7426 - precision_37: 0.7500\n",
            "Epoch 6/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3934 - accuracy: 0.8299 - recall_37: 0.7525 - precision_37: 0.7600\n",
            "Epoch 7/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3890 - accuracy: 0.8299 - recall_37: 0.7822 - precision_37: 0.7453\n",
            "Epoch 8/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3857 - accuracy: 0.8299 - recall_37: 0.7525 - precision_37: 0.7600\n",
            "Epoch 9/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3780 - accuracy: 0.8368 - recall_37: 0.7723 - precision_37: 0.7647\n",
            "Epoch 10/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3732 - accuracy: 0.8438 - recall_37: 0.7921 - precision_37: 0.7692\n",
            "Epoch 11/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3688 - accuracy: 0.8472 - recall_37: 0.8119 - precision_37: 0.7664\n",
            "Epoch 12/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3647 - accuracy: 0.8472 - recall_37: 0.8119 - precision_37: 0.7664\n",
            "Epoch 13/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3623 - accuracy: 0.8472 - recall_37: 0.8020 - precision_37: 0.7714\n",
            "Epoch 14/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3581 - accuracy: 0.8507 - recall_37: 0.8119 - precision_37: 0.7736\n",
            "Epoch 15/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3550 - accuracy: 0.8507 - recall_37: 0.8119 - precision_37: 0.7736\n",
            "Epoch 16/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3512 - accuracy: 0.8542 - recall_37: 0.8218 - precision_37: 0.7757\n",
            "Epoch 17/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3480 - accuracy: 0.8576 - recall_37: 0.8218 - precision_37: 0.7830\n",
            "Epoch 18/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3448 - accuracy: 0.8611 - recall_37: 0.8218 - precision_37: 0.7905\n",
            "Epoch 19/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3420 - accuracy: 0.8611 - recall_37: 0.8218 - precision_37: 0.7905\n",
            "Epoch 20/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3392 - accuracy: 0.8576 - recall_37: 0.8218 - precision_37: 0.7830\n",
            "4/4 - 0s - loss: 0.4239 - accuracy: 0.8065 - recall_37: 0.7209 - precision_37: 0.7209 - 20ms/epoch - 5ms/step\n",
            "Epoch 1/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3371 - accuracy: 0.8611 - recall_37: 0.8218 - precision_37: 0.7905\n",
            "Epoch 2/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3332 - accuracy: 0.8681 - recall_37: 0.8317 - precision_37: 0.8000\n",
            "Epoch 3/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3318 - accuracy: 0.8681 - recall_37: 0.8317 - precision_37: 0.8000\n",
            "Epoch 4/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3298 - accuracy: 0.8681 - recall_37: 0.8317 - precision_37: 0.8000\n",
            "Epoch 5/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3264 - accuracy: 0.8681 - recall_37: 0.8317 - precision_37: 0.8000\n",
            "Epoch 6/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3242 - accuracy: 0.8646 - recall_37: 0.8317 - precision_37: 0.7925\n",
            "Epoch 7/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3242 - accuracy: 0.8646 - recall_37: 0.8218 - precision_37: 0.7981\n",
            "Epoch 8/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3203 - accuracy: 0.8681 - recall_37: 0.8317 - precision_37: 0.8000\n",
            "Epoch 9/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3171 - accuracy: 0.8750 - recall_37: 0.8515 - precision_37: 0.8037\n",
            "Epoch 10/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3144 - accuracy: 0.8715 - recall_37: 0.8416 - precision_37: 0.8019\n",
            "Epoch 11/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3128 - accuracy: 0.8715 - recall_37: 0.8416 - precision_37: 0.8019\n",
            "Epoch 12/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3102 - accuracy: 0.8715 - recall_37: 0.8416 - precision_37: 0.8019\n",
            "Epoch 13/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3082 - accuracy: 0.8715 - recall_37: 0.8416 - precision_37: 0.8019\n",
            "Epoch 14/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3063 - accuracy: 0.8715 - recall_37: 0.8416 - precision_37: 0.8019\n",
            "Epoch 15/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3036 - accuracy: 0.8715 - recall_37: 0.8416 - precision_37: 0.8019\n",
            "Epoch 16/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3017 - accuracy: 0.8750 - recall_37: 0.8515 - precision_37: 0.8037\n",
            "Epoch 17/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3000 - accuracy: 0.8715 - recall_37: 0.8416 - precision_37: 0.8019\n",
            "Epoch 18/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3004 - accuracy: 0.8715 - recall_37: 0.8416 - precision_37: 0.8019\n",
            "Epoch 19/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2953 - accuracy: 0.8715 - recall_37: 0.8416 - precision_37: 0.8019\n",
            "Epoch 20/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2940 - accuracy: 0.8715 - recall_37: 0.8416 - precision_37: 0.8019\n",
            "Epoch 21/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2918 - accuracy: 0.8785 - recall_37: 0.8416 - precision_37: 0.8173\n",
            "Epoch 22/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2916 - accuracy: 0.8819 - recall_37: 0.8416 - precision_37: 0.8252\n",
            "Epoch 23/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2904 - accuracy: 0.8715 - recall_37: 0.8515 - precision_37: 0.7963\n",
            "Epoch 24/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2855 - accuracy: 0.8715 - recall_37: 0.8416 - precision_37: 0.8019\n",
            "Epoch 25/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2839 - accuracy: 0.8819 - recall_37: 0.8416 - precision_37: 0.8252\n",
            "Epoch 26/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2811 - accuracy: 0.8785 - recall_37: 0.8416 - precision_37: 0.8173\n",
            "Epoch 27/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2790 - accuracy: 0.8819 - recall_37: 0.8515 - precision_37: 0.8190\n",
            "Epoch 28/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2787 - accuracy: 0.8854 - recall_37: 0.8614 - precision_37: 0.8208\n",
            "Epoch 29/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2750 - accuracy: 0.8854 - recall_37: 0.8515 - precision_37: 0.8269\n",
            "Epoch 30/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2731 - accuracy: 0.8854 - recall_37: 0.8515 - precision_37: 0.8269\n",
            "4/4 - 0s - loss: 0.4768 - accuracy: 0.7984 - recall_37: 0.6977 - precision_37: 0.7143 - 19ms/epoch - 5ms/step\n",
            "Epoch 1/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2710 - accuracy: 0.8785 - recall_37: 0.8515 - precision_37: 0.8113\n",
            "Epoch 2/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2697 - accuracy: 0.8750 - recall_37: 0.8416 - precision_37: 0.8095\n",
            "Epoch 3/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2678 - accuracy: 0.8819 - recall_37: 0.8515 - precision_37: 0.8190\n",
            "Epoch 4/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2655 - accuracy: 0.8819 - recall_37: 0.8515 - precision_37: 0.8190\n",
            "Epoch 5/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2641 - accuracy: 0.8785 - recall_37: 0.8515 - precision_37: 0.8113\n",
            "Epoch 6/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2618 - accuracy: 0.8785 - recall_37: 0.8416 - precision_37: 0.8173\n",
            "Epoch 7/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2601 - accuracy: 0.8785 - recall_37: 0.8416 - precision_37: 0.8173\n",
            "Epoch 8/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2578 - accuracy: 0.8785 - recall_37: 0.8416 - precision_37: 0.8173\n",
            "Epoch 9/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2561 - accuracy: 0.8819 - recall_37: 0.8416 - precision_37: 0.8252\n",
            "Epoch 10/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2545 - accuracy: 0.8819 - recall_37: 0.8416 - precision_37: 0.8252\n",
            "Epoch 11/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2529 - accuracy: 0.8785 - recall_37: 0.8515 - precision_37: 0.8113\n",
            "Epoch 12/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2511 - accuracy: 0.8819 - recall_37: 0.8416 - precision_37: 0.8252\n",
            "Epoch 13/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2498 - accuracy: 0.8854 - recall_37: 0.8317 - precision_37: 0.8400\n",
            "Epoch 14/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2475 - accuracy: 0.8819 - recall_37: 0.8317 - precision_37: 0.8317\n",
            "Epoch 15/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2449 - accuracy: 0.8785 - recall_37: 0.8317 - precision_37: 0.8235\n",
            "Epoch 16/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2436 - accuracy: 0.8854 - recall_37: 0.8317 - precision_37: 0.8400\n",
            "Epoch 17/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2412 - accuracy: 0.8819 - recall_37: 0.8317 - precision_37: 0.8317\n",
            "Epoch 18/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2397 - accuracy: 0.8819 - recall_37: 0.8317 - precision_37: 0.8317\n",
            "Epoch 19/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2386 - accuracy: 0.8819 - recall_37: 0.8317 - precision_37: 0.8317\n",
            "Epoch 20/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2378 - accuracy: 0.8924 - recall_37: 0.8713 - precision_37: 0.8302\n",
            "Epoch 21/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2351 - accuracy: 0.8924 - recall_37: 0.8416 - precision_37: 0.8500\n",
            "Epoch 22/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2338 - accuracy: 0.8958 - recall_37: 0.8317 - precision_37: 0.8660\n",
            "Epoch 23/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2317 - accuracy: 0.8889 - recall_37: 0.8416 - precision_37: 0.8416\n",
            "Epoch 24/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2313 - accuracy: 0.8924 - recall_37: 0.8515 - precision_37: 0.8431\n",
            "Epoch 25/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2286 - accuracy: 0.8958 - recall_37: 0.8416 - precision_37: 0.8586\n",
            "Epoch 26/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2274 - accuracy: 0.8924 - recall_37: 0.8614 - precision_37: 0.8365\n",
            "Epoch 27/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2272 - accuracy: 0.8958 - recall_37: 0.8416 - precision_37: 0.8586\n",
            "Epoch 28/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2235 - accuracy: 0.8958 - recall_37: 0.8515 - precision_37: 0.8515\n",
            "Epoch 29/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2224 - accuracy: 0.9028 - recall_37: 0.8614 - precision_37: 0.8614\n",
            "Epoch 30/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2223 - accuracy: 0.9062 - recall_37: 0.8614 - precision_37: 0.8700\n",
            "Epoch 31/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2201 - accuracy: 0.8993 - recall_37: 0.8614 - precision_37: 0.8529\n",
            "Epoch 32/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2182 - accuracy: 0.8993 - recall_37: 0.8614 - precision_37: 0.8529\n",
            "Epoch 33/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2161 - accuracy: 0.9028 - recall_37: 0.8614 - precision_37: 0.8614\n",
            "Epoch 34/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2140 - accuracy: 0.9062 - recall_37: 0.8614 - precision_37: 0.8700\n",
            "Epoch 35/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2126 - accuracy: 0.9062 - recall_37: 0.8614 - precision_37: 0.8700\n",
            "Epoch 36/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2113 - accuracy: 0.9132 - recall_37: 0.8614 - precision_37: 0.8878\n",
            "Epoch 37/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2113 - accuracy: 0.9062 - recall_37: 0.8614 - precision_37: 0.8700\n",
            "Epoch 38/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2093 - accuracy: 0.9062 - recall_37: 0.8614 - precision_37: 0.8700\n",
            "Epoch 39/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2073 - accuracy: 0.9097 - recall_37: 0.8614 - precision_37: 0.8788\n",
            "Epoch 40/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2061 - accuracy: 0.9097 - recall_37: 0.8713 - precision_37: 0.8713\n",
            "4/4 - 0s - loss: 0.5556 - accuracy: 0.8145 - recall_37: 0.7674 - precision_37: 0.7174 - 18ms/epoch - 5ms/step\n",
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 2ms/step - loss: 0.7301 - accuracy: 0.3646 - recall_38: 0.8812 - precision_38: 0.3423\n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6972 - accuracy: 0.4653 - recall_38: 0.8119 - precision_38: 0.3779\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6784 - accuracy: 0.6354 - recall_38: 0.7723 - precision_38: 0.4875\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6651 - accuracy: 0.7500 - recall_38: 0.7327 - precision_38: 0.6218\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6540 - accuracy: 0.7465 - recall_38: 0.7426 - precision_38: 0.6148\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.6432 - accuracy: 0.7847 - recall_38: 0.8020 - precision_38: 0.6585\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.6316 - accuracy: 0.7917 - recall_38: 0.8119 - precision_38: 0.6667\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6203 - accuracy: 0.8056 - recall_38: 0.8218 - precision_38: 0.6860\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6087 - accuracy: 0.8090 - recall_38: 0.8119 - precision_38: 0.6949\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5962 - accuracy: 0.8090 - recall_38: 0.8020 - precision_38: 0.6983\n",
            "4/4 - 0s - loss: 0.6058 - accuracy: 0.7500 - recall_38: 0.7674 - precision_38: 0.6111 - 303ms/epoch - 76ms/step\n",
            "Epoch 1/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5829 - accuracy: 0.8194 - recall_38: 0.8020 - precision_38: 0.7168\n",
            "Epoch 2/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5689 - accuracy: 0.8160 - recall_38: 0.7921 - precision_38: 0.7143\n",
            "Epoch 3/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5548 - accuracy: 0.8194 - recall_38: 0.7921 - precision_38: 0.7207\n",
            "Epoch 4/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5406 - accuracy: 0.8194 - recall_38: 0.7624 - precision_38: 0.7333\n",
            "Epoch 5/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5250 - accuracy: 0.8194 - recall_38: 0.7525 - precision_38: 0.7379\n",
            "Epoch 6/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5100 - accuracy: 0.8125 - recall_38: 0.7525 - precision_38: 0.7238\n",
            "Epoch 7/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4948 - accuracy: 0.8194 - recall_38: 0.7426 - precision_38: 0.7426\n",
            "Epoch 8/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4807 - accuracy: 0.8299 - recall_38: 0.7426 - precision_38: 0.7653\n",
            "Epoch 9/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4678 - accuracy: 0.8264 - recall_38: 0.7426 - precision_38: 0.7576\n",
            "Epoch 10/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4535 - accuracy: 0.8333 - recall_38: 0.7525 - precision_38: 0.7677\n",
            "Epoch 11/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4409 - accuracy: 0.8299 - recall_38: 0.7327 - precision_38: 0.7708\n",
            "Epoch 12/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4300 - accuracy: 0.8264 - recall_38: 0.7228 - precision_38: 0.7684\n",
            "Epoch 13/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4190 - accuracy: 0.8264 - recall_38: 0.7327 - precision_38: 0.7629\n",
            "Epoch 14/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4110 - accuracy: 0.8299 - recall_38: 0.7426 - precision_38: 0.7653\n",
            "Epoch 15/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3999 - accuracy: 0.8333 - recall_38: 0.7525 - precision_38: 0.7677\n",
            "Epoch 16/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3910 - accuracy: 0.8299 - recall_38: 0.7426 - precision_38: 0.7653\n",
            "Epoch 17/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3825 - accuracy: 0.8333 - recall_38: 0.7525 - precision_38: 0.7677\n",
            "Epoch 18/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3761 - accuracy: 0.8299 - recall_38: 0.7426 - precision_38: 0.7653\n",
            "Epoch 19/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3693 - accuracy: 0.8403 - recall_38: 0.7624 - precision_38: 0.7778\n",
            "Epoch 20/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3634 - accuracy: 0.8333 - recall_38: 0.7426 - precision_38: 0.7732\n",
            "4/4 - 0s - loss: 0.4308 - accuracy: 0.7903 - recall_38: 0.6977 - precision_38: 0.6977 - 19ms/epoch - 5ms/step\n",
            "Epoch 1/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3577 - accuracy: 0.8333 - recall_38: 0.7426 - precision_38: 0.7732\n",
            "Epoch 2/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3538 - accuracy: 0.8368 - recall_38: 0.7525 - precision_38: 0.7755\n",
            "Epoch 3/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3480 - accuracy: 0.8368 - recall_38: 0.7525 - precision_38: 0.7755\n",
            "Epoch 4/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3441 - accuracy: 0.8333 - recall_38: 0.7426 - precision_38: 0.7732\n",
            "Epoch 5/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3404 - accuracy: 0.8438 - recall_38: 0.7723 - precision_38: 0.7800\n",
            "Epoch 6/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3362 - accuracy: 0.8542 - recall_38: 0.8020 - precision_38: 0.7864\n",
            "Epoch 7/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3335 - accuracy: 0.8542 - recall_38: 0.8020 - precision_38: 0.7864\n",
            "Epoch 8/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3289 - accuracy: 0.8576 - recall_38: 0.8020 - precision_38: 0.7941\n",
            "Epoch 9/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3257 - accuracy: 0.8542 - recall_38: 0.8020 - precision_38: 0.7864\n",
            "Epoch 10/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3231 - accuracy: 0.8542 - recall_38: 0.7822 - precision_38: 0.7980\n",
            "Epoch 11/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3188 - accuracy: 0.8611 - recall_38: 0.8020 - precision_38: 0.8020\n",
            "Epoch 12/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3166 - accuracy: 0.8646 - recall_38: 0.8218 - precision_38: 0.7981\n",
            "Epoch 13/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3133 - accuracy: 0.8646 - recall_38: 0.8218 - precision_38: 0.7981\n",
            "Epoch 14/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3108 - accuracy: 0.8646 - recall_38: 0.8119 - precision_38: 0.8039\n",
            "Epoch 15/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3088 - accuracy: 0.8646 - recall_38: 0.8119 - precision_38: 0.8039\n",
            "Epoch 16/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3050 - accuracy: 0.8646 - recall_38: 0.8119 - precision_38: 0.8039\n",
            "Epoch 17/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3028 - accuracy: 0.8715 - recall_38: 0.8218 - precision_38: 0.8137\n",
            "Epoch 18/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2999 - accuracy: 0.8715 - recall_38: 0.8218 - precision_38: 0.8137\n",
            "Epoch 19/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2973 - accuracy: 0.8715 - recall_38: 0.8218 - precision_38: 0.8137\n",
            "Epoch 20/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2954 - accuracy: 0.8715 - recall_38: 0.8218 - precision_38: 0.8137\n",
            "Epoch 21/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2927 - accuracy: 0.8715 - recall_38: 0.8218 - precision_38: 0.8137\n",
            "Epoch 22/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2905 - accuracy: 0.8750 - recall_38: 0.8218 - precision_38: 0.8218\n",
            "Epoch 23/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2881 - accuracy: 0.8750 - recall_38: 0.8218 - precision_38: 0.8218\n",
            "Epoch 24/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2860 - accuracy: 0.8715 - recall_38: 0.8218 - precision_38: 0.8137\n",
            "Epoch 25/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2841 - accuracy: 0.8750 - recall_38: 0.8317 - precision_38: 0.8155\n",
            "Epoch 26/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2826 - accuracy: 0.8715 - recall_38: 0.8218 - precision_38: 0.8137\n",
            "Epoch 27/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2797 - accuracy: 0.8750 - recall_38: 0.8218 - precision_38: 0.8218\n",
            "Epoch 28/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2779 - accuracy: 0.8750 - recall_38: 0.8317 - precision_38: 0.8155\n",
            "Epoch 29/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2761 - accuracy: 0.8819 - recall_38: 0.8317 - precision_38: 0.8317\n",
            "Epoch 30/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2740 - accuracy: 0.8819 - recall_38: 0.8317 - precision_38: 0.8317\n",
            "4/4 - 0s - loss: 0.4469 - accuracy: 0.8145 - recall_38: 0.7442 - precision_38: 0.7273 - 16ms/epoch - 4ms/step\n",
            "Epoch 1/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2723 - accuracy: 0.8785 - recall_38: 0.8317 - precision_38: 0.8235\n",
            "Epoch 2/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2701 - accuracy: 0.8889 - recall_38: 0.8317 - precision_38: 0.8485\n",
            "Epoch 3/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2682 - accuracy: 0.8889 - recall_38: 0.8317 - precision_38: 0.8485\n",
            "Epoch 4/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2665 - accuracy: 0.8889 - recall_38: 0.8317 - precision_38: 0.8485\n",
            "Epoch 5/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2647 - accuracy: 0.8889 - recall_38: 0.8317 - precision_38: 0.8485\n",
            "Epoch 6/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2634 - accuracy: 0.8889 - recall_38: 0.8317 - precision_38: 0.8485\n",
            "Epoch 7/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2615 - accuracy: 0.8889 - recall_38: 0.8317 - precision_38: 0.8485\n",
            "Epoch 8/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2601 - accuracy: 0.8889 - recall_38: 0.8317 - precision_38: 0.8485\n",
            "Epoch 9/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2583 - accuracy: 0.8958 - recall_38: 0.8317 - precision_38: 0.8660\n",
            "Epoch 10/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.2569 - accuracy: 0.8958 - recall_38: 0.8317 - precision_38: 0.8660\n",
            "Epoch 11/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2553 - accuracy: 0.8924 - recall_38: 0.8317 - precision_38: 0.8571\n",
            "Epoch 12/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2534 - accuracy: 0.8924 - recall_38: 0.8317 - precision_38: 0.8571\n",
            "Epoch 13/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2524 - accuracy: 0.8958 - recall_38: 0.8317 - precision_38: 0.8660\n",
            "Epoch 14/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2506 - accuracy: 0.8958 - recall_38: 0.8317 - precision_38: 0.8660\n",
            "Epoch 15/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2494 - accuracy: 0.8958 - recall_38: 0.8317 - precision_38: 0.8660\n",
            "Epoch 16/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2486 - accuracy: 0.8993 - recall_38: 0.8317 - precision_38: 0.8750\n",
            "Epoch 17/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2464 - accuracy: 0.8958 - recall_38: 0.8317 - precision_38: 0.8660\n",
            "Epoch 18/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2457 - accuracy: 0.8993 - recall_38: 0.8416 - precision_38: 0.8673\n",
            "Epoch 19/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2439 - accuracy: 0.8993 - recall_38: 0.8317 - precision_38: 0.8750\n",
            "Epoch 20/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2424 - accuracy: 0.9062 - recall_38: 0.8416 - precision_38: 0.8854\n",
            "Epoch 21/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2409 - accuracy: 0.9062 - recall_38: 0.8416 - precision_38: 0.8854\n",
            "Epoch 22/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2394 - accuracy: 0.9062 - recall_38: 0.8416 - precision_38: 0.8854\n",
            "Epoch 23/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2383 - accuracy: 0.9028 - recall_38: 0.8416 - precision_38: 0.8763\n",
            "Epoch 24/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2369 - accuracy: 0.9028 - recall_38: 0.8416 - precision_38: 0.8763\n",
            "Epoch 25/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2357 - accuracy: 0.9062 - recall_38: 0.8416 - precision_38: 0.8854\n",
            "Epoch 26/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2341 - accuracy: 0.9097 - recall_38: 0.8416 - precision_38: 0.8947\n",
            "Epoch 27/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2328 - accuracy: 0.9167 - recall_38: 0.8416 - precision_38: 0.9140\n",
            "Epoch 28/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2316 - accuracy: 0.9097 - recall_38: 0.8416 - precision_38: 0.8947\n",
            "Epoch 29/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2312 - accuracy: 0.9097 - recall_38: 0.8416 - precision_38: 0.8947\n",
            "Epoch 30/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2295 - accuracy: 0.9132 - recall_38: 0.8515 - precision_38: 0.8958\n",
            "Epoch 31/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2284 - accuracy: 0.9097 - recall_38: 0.8416 - precision_38: 0.8947\n",
            "Epoch 32/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2277 - accuracy: 0.9167 - recall_38: 0.8416 - precision_38: 0.9140\n",
            "Epoch 33/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2259 - accuracy: 0.9167 - recall_38: 0.8515 - precision_38: 0.9053\n",
            "Epoch 34/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2250 - accuracy: 0.9132 - recall_38: 0.8515 - precision_38: 0.8958\n",
            "Epoch 35/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2237 - accuracy: 0.9201 - recall_38: 0.8515 - precision_38: 0.9149\n",
            "Epoch 36/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2230 - accuracy: 0.9236 - recall_38: 0.8515 - precision_38: 0.9247\n",
            "Epoch 37/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2215 - accuracy: 0.9236 - recall_38: 0.8515 - precision_38: 0.9247\n",
            "Epoch 38/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2213 - accuracy: 0.9236 - recall_38: 0.8515 - precision_38: 0.9247\n",
            "Epoch 39/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2204 - accuracy: 0.9201 - recall_38: 0.8416 - precision_38: 0.9239\n",
            "Epoch 40/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2186 - accuracy: 0.9236 - recall_38: 0.8515 - precision_38: 0.9247\n",
            "4/4 - 0s - loss: 0.4994 - accuracy: 0.7903 - recall_38: 0.7209 - precision_38: 0.6889 - 21ms/epoch - 5ms/step\n",
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 2ms/step - loss: 0.6455 - accuracy: 0.6944 - recall_39: 0.5644 - precision_39: 0.5644\n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6181 - accuracy: 0.7361 - recall_39: 0.5941 - precision_39: 0.6316\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5956 - accuracy: 0.7535 - recall_39: 0.6139 - precision_39: 0.6596\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5736 - accuracy: 0.7569 - recall_39: 0.6337 - precision_39: 0.6598\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5524 - accuracy: 0.7708 - recall_39: 0.6436 - precision_39: 0.6842\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5317 - accuracy: 0.7951 - recall_39: 0.6832 - precision_39: 0.7188\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5130 - accuracy: 0.8021 - recall_39: 0.6832 - precision_39: 0.7340\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4945 - accuracy: 0.8021 - recall_39: 0.6931 - precision_39: 0.7292\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4778 - accuracy: 0.8090 - recall_39: 0.7228 - precision_39: 0.7300\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4619 - accuracy: 0.8090 - recall_39: 0.7327 - precision_39: 0.7255\n",
            "4/4 - 0s - loss: 0.4815 - accuracy: 0.8065 - recall_39: 0.6977 - precision_39: 0.7317 - 275ms/epoch - 69ms/step\n",
            "Epoch 1/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4479 - accuracy: 0.8021 - recall_39: 0.7129 - precision_39: 0.7200\n",
            "Epoch 2/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4369 - accuracy: 0.8160 - recall_39: 0.7525 - precision_39: 0.7308\n",
            "Epoch 3/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4263 - accuracy: 0.8125 - recall_39: 0.7426 - precision_39: 0.7282\n",
            "Epoch 4/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4167 - accuracy: 0.8264 - recall_39: 0.7624 - precision_39: 0.7476\n",
            "Epoch 5/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4081 - accuracy: 0.8264 - recall_39: 0.7624 - precision_39: 0.7476\n",
            "Epoch 6/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4010 - accuracy: 0.8229 - recall_39: 0.7624 - precision_39: 0.7404\n",
            "Epoch 7/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3957 - accuracy: 0.8299 - recall_39: 0.7723 - precision_39: 0.7500\n",
            "Epoch 8/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3889 - accuracy: 0.8299 - recall_39: 0.7822 - precision_39: 0.7453\n",
            "Epoch 9/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3835 - accuracy: 0.8333 - recall_39: 0.7921 - precision_39: 0.7477\n",
            "Epoch 10/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3785 - accuracy: 0.8438 - recall_39: 0.8020 - precision_39: 0.7642\n",
            "Epoch 11/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3742 - accuracy: 0.8438 - recall_39: 0.8020 - precision_39: 0.7642\n",
            "Epoch 12/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3702 - accuracy: 0.8403 - recall_39: 0.8020 - precision_39: 0.7570\n",
            "Epoch 13/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3672 - accuracy: 0.8403 - recall_39: 0.8020 - precision_39: 0.7570\n",
            "Epoch 14/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3629 - accuracy: 0.8472 - recall_39: 0.8020 - precision_39: 0.7714\n",
            "Epoch 15/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3596 - accuracy: 0.8438 - recall_39: 0.7822 - precision_39: 0.7745\n",
            "Epoch 16/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3564 - accuracy: 0.8438 - recall_39: 0.7921 - precision_39: 0.7692\n",
            "Epoch 17/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3537 - accuracy: 0.8438 - recall_39: 0.7921 - precision_39: 0.7692\n",
            "Epoch 18/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3514 - accuracy: 0.8472 - recall_39: 0.7822 - precision_39: 0.7822\n",
            "Epoch 19/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3483 - accuracy: 0.8472 - recall_39: 0.8020 - precision_39: 0.7714\n",
            "Epoch 20/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3451 - accuracy: 0.8507 - recall_39: 0.8119 - precision_39: 0.7736\n",
            "4/4 - 0s - loss: 0.4227 - accuracy: 0.8468 - recall_39: 0.7674 - precision_39: 0.7857 - 16ms/epoch - 4ms/step\n",
            "Epoch 1/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3432 - accuracy: 0.8507 - recall_39: 0.7921 - precision_39: 0.7843\n",
            "Epoch 2/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3394 - accuracy: 0.8542 - recall_39: 0.8020 - precision_39: 0.7864\n",
            "Epoch 3/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3366 - accuracy: 0.8576 - recall_39: 0.8119 - precision_39: 0.7885\n",
            "Epoch 4/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3344 - accuracy: 0.8576 - recall_39: 0.8119 - precision_39: 0.7885\n",
            "Epoch 5/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3316 - accuracy: 0.8611 - recall_39: 0.8218 - precision_39: 0.7905\n",
            "Epoch 6/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3287 - accuracy: 0.8611 - recall_39: 0.8218 - precision_39: 0.7905\n",
            "Epoch 7/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3273 - accuracy: 0.8611 - recall_39: 0.8119 - precision_39: 0.7961\n",
            "Epoch 8/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3248 - accuracy: 0.8646 - recall_39: 0.8218 - precision_39: 0.7981\n",
            "Epoch 9/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3215 - accuracy: 0.8611 - recall_39: 0.8218 - precision_39: 0.7905\n",
            "Epoch 10/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3194 - accuracy: 0.8611 - recall_39: 0.8119 - precision_39: 0.7961\n",
            "Epoch 11/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3166 - accuracy: 0.8611 - recall_39: 0.8119 - precision_39: 0.7961\n",
            "Epoch 12/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3146 - accuracy: 0.8646 - recall_39: 0.8119 - precision_39: 0.8039\n",
            "Epoch 13/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3118 - accuracy: 0.8646 - recall_39: 0.8119 - precision_39: 0.8039\n",
            "Epoch 14/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3090 - accuracy: 0.8715 - recall_39: 0.8317 - precision_39: 0.8077\n",
            "Epoch 15/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3067 - accuracy: 0.8715 - recall_39: 0.8317 - precision_39: 0.8077\n",
            "Epoch 16/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3050 - accuracy: 0.8750 - recall_39: 0.8416 - precision_39: 0.8095\n",
            "Epoch 17/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3027 - accuracy: 0.8750 - recall_39: 0.8317 - precision_39: 0.8155\n",
            "Epoch 18/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3015 - accuracy: 0.8785 - recall_39: 0.8416 - precision_39: 0.8173\n",
            "Epoch 19/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2982 - accuracy: 0.8750 - recall_39: 0.8317 - precision_39: 0.8155\n",
            "Epoch 20/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2961 - accuracy: 0.8785 - recall_39: 0.8317 - precision_39: 0.8235\n",
            "Epoch 21/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2939 - accuracy: 0.8750 - recall_39: 0.8317 - precision_39: 0.8155\n",
            "Epoch 22/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2924 - accuracy: 0.8785 - recall_39: 0.8416 - precision_39: 0.8173\n",
            "Epoch 23/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2899 - accuracy: 0.8785 - recall_39: 0.8317 - precision_39: 0.8235\n",
            "Epoch 24/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2886 - accuracy: 0.8785 - recall_39: 0.8416 - precision_39: 0.8173\n",
            "Epoch 25/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2852 - accuracy: 0.8819 - recall_39: 0.8515 - precision_39: 0.8190\n",
            "Epoch 26/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2835 - accuracy: 0.8785 - recall_39: 0.8317 - precision_39: 0.8235\n",
            "Epoch 27/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2809 - accuracy: 0.8819 - recall_39: 0.8416 - precision_39: 0.8252\n",
            "Epoch 28/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2787 - accuracy: 0.8819 - recall_39: 0.8416 - precision_39: 0.8252\n",
            "Epoch 29/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2770 - accuracy: 0.8854 - recall_39: 0.8416 - precision_39: 0.8333\n",
            "Epoch 30/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2746 - accuracy: 0.8889 - recall_39: 0.8515 - precision_39: 0.8350\n",
            "4/4 - 0s - loss: 0.4503 - accuracy: 0.8226 - recall_39: 0.6977 - precision_39: 0.7692 - 20ms/epoch - 5ms/step\n",
            "Epoch 1/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2741 - accuracy: 0.8854 - recall_39: 0.8515 - precision_39: 0.8269\n",
            "Epoch 2/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2706 - accuracy: 0.8889 - recall_39: 0.8614 - precision_39: 0.8286\n",
            "Epoch 3/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.2697 - accuracy: 0.8854 - recall_39: 0.8416 - precision_39: 0.8333\n",
            "Epoch 4/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2672 - accuracy: 0.8889 - recall_39: 0.8416 - precision_39: 0.8416\n",
            "Epoch 5/40\n",
            "9/9 [==============================] - 0s 5ms/step - loss: 0.2654 - accuracy: 0.8889 - recall_39: 0.8416 - precision_39: 0.8416\n",
            "Epoch 6/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.2631 - accuracy: 0.8889 - recall_39: 0.8416 - precision_39: 0.8416\n",
            "Epoch 7/40\n",
            "9/9 [==============================] - 0s 5ms/step - loss: 0.2610 - accuracy: 0.8889 - recall_39: 0.8416 - precision_39: 0.8416\n",
            "Epoch 8/40\n",
            "9/9 [==============================] - 0s 7ms/step - loss: 0.2591 - accuracy: 0.8889 - recall_39: 0.8416 - precision_39: 0.8416\n",
            "Epoch 9/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.2579 - accuracy: 0.8924 - recall_39: 0.8416 - precision_39: 0.8500\n",
            "Epoch 10/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.2559 - accuracy: 0.8958 - recall_39: 0.8515 - precision_39: 0.8515\n",
            "Epoch 11/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2538 - accuracy: 0.8958 - recall_39: 0.8614 - precision_39: 0.8447\n",
            "Epoch 12/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.2521 - accuracy: 0.8993 - recall_39: 0.8515 - precision_39: 0.8600\n",
            "Epoch 13/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2502 - accuracy: 0.8958 - recall_39: 0.8416 - precision_39: 0.8586\n",
            "Epoch 14/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2497 - accuracy: 0.9062 - recall_39: 0.8713 - precision_39: 0.8627\n",
            "Epoch 15/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2463 - accuracy: 0.9028 - recall_39: 0.8614 - precision_39: 0.8614\n",
            "Epoch 16/40\n",
            "9/9 [==============================] - 0s 10ms/step - loss: 0.2468 - accuracy: 0.9062 - recall_39: 0.8713 - precision_39: 0.8627\n",
            "Epoch 17/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.2459 - accuracy: 0.8993 - recall_39: 0.8515 - precision_39: 0.8600\n",
            "Epoch 18/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.2413 - accuracy: 0.9028 - recall_39: 0.8614 - precision_39: 0.8614\n",
            "Epoch 19/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2399 - accuracy: 0.9097 - recall_39: 0.8812 - precision_39: 0.8641\n",
            "Epoch 20/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2378 - accuracy: 0.9132 - recall_39: 0.8911 - precision_39: 0.8654\n",
            "Epoch 21/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.2363 - accuracy: 0.9097 - recall_39: 0.8812 - precision_39: 0.8641\n",
            "Epoch 22/40\n",
            "9/9 [==============================] - 0s 5ms/step - loss: 0.2352 - accuracy: 0.9132 - recall_39: 0.8911 - precision_39: 0.8654\n",
            "Epoch 23/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2329 - accuracy: 0.9132 - recall_39: 0.8911 - precision_39: 0.8654\n",
            "Epoch 24/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.2310 - accuracy: 0.9167 - recall_39: 0.8911 - precision_39: 0.8738\n",
            "Epoch 25/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.2299 - accuracy: 0.9132 - recall_39: 0.8911 - precision_39: 0.8654\n",
            "Epoch 26/40\n",
            "9/9 [==============================] - 0s 6ms/step - loss: 0.2276 - accuracy: 0.9167 - recall_39: 0.8911 - precision_39: 0.8738\n",
            "Epoch 27/40\n",
            "9/9 [==============================] - 0s 5ms/step - loss: 0.2258 - accuracy: 0.9167 - recall_39: 0.8911 - precision_39: 0.8738\n",
            "Epoch 28/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2244 - accuracy: 0.9167 - recall_39: 0.8911 - precision_39: 0.8738\n",
            "Epoch 29/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.2233 - accuracy: 0.9201 - recall_39: 0.8812 - precision_39: 0.8900\n",
            "Epoch 30/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2214 - accuracy: 0.9167 - recall_39: 0.8911 - precision_39: 0.8738\n",
            "Epoch 31/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.2194 - accuracy: 0.9167 - recall_39: 0.8911 - precision_39: 0.8738\n",
            "Epoch 32/40\n",
            "9/9 [==============================] - 0s 7ms/step - loss: 0.2173 - accuracy: 0.9236 - recall_39: 0.8911 - precision_39: 0.8911\n",
            "Epoch 33/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2158 - accuracy: 0.9201 - recall_39: 0.8812 - precision_39: 0.8900\n",
            "Epoch 34/40\n",
            "9/9 [==============================] - 0s 7ms/step - loss: 0.2140 - accuracy: 0.9236 - recall_39: 0.8911 - precision_39: 0.8911\n",
            "Epoch 35/40\n",
            "9/9 [==============================] - 0s 6ms/step - loss: 0.2126 - accuracy: 0.9271 - recall_39: 0.8911 - precision_39: 0.9000\n",
            "Epoch 36/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.2112 - accuracy: 0.9236 - recall_39: 0.8911 - precision_39: 0.8911\n",
            "Epoch 37/40\n",
            "9/9 [==============================] - 0s 6ms/step - loss: 0.2093 - accuracy: 0.9236 - recall_39: 0.8911 - precision_39: 0.8911\n",
            "Epoch 38/40\n",
            "9/9 [==============================] - 0s 5ms/step - loss: 0.2078 - accuracy: 0.9271 - recall_39: 0.8911 - precision_39: 0.9000\n",
            "Epoch 39/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2059 - accuracy: 0.9271 - recall_39: 0.8911 - precision_39: 0.9000\n",
            "Epoch 40/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2041 - accuracy: 0.9306 - recall_39: 0.8911 - precision_39: 0.9091\n",
            "4/4 - 0s - loss: 0.5145 - accuracy: 0.8065 - recall_39: 0.6977 - precision_39: 0.7317 - 47ms/epoch - 12ms/step\n",
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 2ms/step - loss: 0.7607 - accuracy: 0.5799 - recall_40: 0.0990 - precision_40: 0.2500\n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.7129 - accuracy: 0.6215 - recall_40: 0.2178 - precision_40: 0.4231\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.6671 - accuracy: 0.6562 - recall_40: 0.3366 - precision_40: 0.5152\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6357 - accuracy: 0.7083 - recall_40: 0.5050 - precision_40: 0.6000\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6095 - accuracy: 0.7361 - recall_40: 0.6040 - precision_40: 0.6289\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5895 - accuracy: 0.7396 - recall_40: 0.6634 - precision_40: 0.6204\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5685 - accuracy: 0.7604 - recall_40: 0.7129 - precision_40: 0.6429\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5492 - accuracy: 0.7812 - recall_40: 0.7327 - precision_40: 0.6727\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5313 - accuracy: 0.7951 - recall_40: 0.7327 - precision_40: 0.6981\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5136 - accuracy: 0.7951 - recall_40: 0.7228 - precision_40: 0.7019\n",
            "4/4 - 0s - loss: 0.5266 - accuracy: 0.7984 - recall_40: 0.7209 - precision_40: 0.7045 - 247ms/epoch - 62ms/step\n",
            "Epoch 1/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4974 - accuracy: 0.8021 - recall_40: 0.7426 - precision_40: 0.7075\n",
            "Epoch 2/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4831 - accuracy: 0.8090 - recall_40: 0.7525 - precision_40: 0.7170\n",
            "Epoch 3/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4688 - accuracy: 0.8160 - recall_40: 0.7624 - precision_40: 0.7264\n",
            "Epoch 4/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4564 - accuracy: 0.8194 - recall_40: 0.7723 - precision_40: 0.7290\n",
            "Epoch 5/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4446 - accuracy: 0.8264 - recall_40: 0.7822 - precision_40: 0.7383\n",
            "Epoch 6/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4338 - accuracy: 0.8229 - recall_40: 0.7525 - precision_40: 0.7451\n",
            "Epoch 7/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4242 - accuracy: 0.8229 - recall_40: 0.7525 - precision_40: 0.7451\n",
            "Epoch 8/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4148 - accuracy: 0.8264 - recall_40: 0.7525 - precision_40: 0.7525\n",
            "Epoch 9/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4076 - accuracy: 0.8333 - recall_40: 0.7525 - precision_40: 0.7677\n",
            "Epoch 10/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4001 - accuracy: 0.8299 - recall_40: 0.7525 - precision_40: 0.7600\n",
            "Epoch 11/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3927 - accuracy: 0.8333 - recall_40: 0.7525 - precision_40: 0.7677\n",
            "Epoch 12/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3870 - accuracy: 0.8333 - recall_40: 0.7525 - precision_40: 0.7677\n",
            "Epoch 13/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3818 - accuracy: 0.8403 - recall_40: 0.7723 - precision_40: 0.7723\n",
            "Epoch 14/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3764 - accuracy: 0.8403 - recall_40: 0.7723 - precision_40: 0.7723\n",
            "Epoch 15/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3716 - accuracy: 0.8403 - recall_40: 0.7723 - precision_40: 0.7723\n",
            "Epoch 16/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3677 - accuracy: 0.8403 - recall_40: 0.7723 - precision_40: 0.7723\n",
            "Epoch 17/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3631 - accuracy: 0.8403 - recall_40: 0.7723 - precision_40: 0.7723\n",
            "Epoch 18/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3593 - accuracy: 0.8438 - recall_40: 0.7822 - precision_40: 0.7745\n",
            "Epoch 19/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3556 - accuracy: 0.8472 - recall_40: 0.7921 - precision_40: 0.7767\n",
            "Epoch 20/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3524 - accuracy: 0.8438 - recall_40: 0.7822 - precision_40: 0.7745\n",
            "4/4 - 0s - loss: 0.4341 - accuracy: 0.7903 - recall_40: 0.7209 - precision_40: 0.6889 - 17ms/epoch - 4ms/step\n",
            "Epoch 1/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3497 - accuracy: 0.8403 - recall_40: 0.7723 - precision_40: 0.7723\n",
            "Epoch 2/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3462 - accuracy: 0.8507 - recall_40: 0.8020 - precision_40: 0.7788\n",
            "Epoch 3/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3430 - accuracy: 0.8507 - recall_40: 0.8020 - precision_40: 0.7788\n",
            "Epoch 4/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3406 - accuracy: 0.8542 - recall_40: 0.8119 - precision_40: 0.7810\n",
            "Epoch 5/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3379 - accuracy: 0.8542 - recall_40: 0.8119 - precision_40: 0.7810\n",
            "Epoch 6/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3352 - accuracy: 0.8542 - recall_40: 0.8119 - precision_40: 0.7810\n",
            "Epoch 7/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3337 - accuracy: 0.8507 - recall_40: 0.8020 - precision_40: 0.7788\n",
            "Epoch 8/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3309 - accuracy: 0.8542 - recall_40: 0.8119 - precision_40: 0.7810\n",
            "Epoch 9/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3286 - accuracy: 0.8507 - recall_40: 0.8020 - precision_40: 0.7788\n",
            "Epoch 10/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3268 - accuracy: 0.8542 - recall_40: 0.8020 - precision_40: 0.7864\n",
            "Epoch 11/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3245 - accuracy: 0.8576 - recall_40: 0.8119 - precision_40: 0.7885\n",
            "Epoch 12/30\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3223 - accuracy: 0.8576 - recall_40: 0.8119 - precision_40: 0.7885\n",
            "Epoch 13/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3206 - accuracy: 0.8646 - recall_40: 0.8317 - precision_40: 0.7925\n",
            "Epoch 14/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3183 - accuracy: 0.8611 - recall_40: 0.8218 - precision_40: 0.7905\n",
            "Epoch 15/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3167 - accuracy: 0.8576 - recall_40: 0.8119 - precision_40: 0.7885\n",
            "Epoch 16/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3146 - accuracy: 0.8646 - recall_40: 0.8317 - precision_40: 0.7925\n",
            "Epoch 17/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3130 - accuracy: 0.8576 - recall_40: 0.8119 - precision_40: 0.7885\n",
            "Epoch 18/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3111 - accuracy: 0.8576 - recall_40: 0.8119 - precision_40: 0.7885\n",
            "Epoch 19/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3090 - accuracy: 0.8611 - recall_40: 0.8119 - precision_40: 0.7961\n",
            "Epoch 20/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3080 - accuracy: 0.8576 - recall_40: 0.8119 - precision_40: 0.7885\n",
            "Epoch 21/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3057 - accuracy: 0.8576 - recall_40: 0.8119 - precision_40: 0.7885\n",
            "Epoch 22/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3034 - accuracy: 0.8646 - recall_40: 0.8119 - precision_40: 0.8039\n",
            "Epoch 23/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3020 - accuracy: 0.8611 - recall_40: 0.8020 - precision_40: 0.8020\n",
            "Epoch 24/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3006 - accuracy: 0.8646 - recall_40: 0.8119 - precision_40: 0.8039\n",
            "Epoch 25/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2990 - accuracy: 0.8681 - recall_40: 0.8119 - precision_40: 0.8119\n",
            "Epoch 26/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2971 - accuracy: 0.8750 - recall_40: 0.8317 - precision_40: 0.8155\n",
            "Epoch 27/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2949 - accuracy: 0.8750 - recall_40: 0.8317 - precision_40: 0.8155\n",
            "Epoch 28/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2930 - accuracy: 0.8715 - recall_40: 0.8218 - precision_40: 0.8137\n",
            "Epoch 29/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2919 - accuracy: 0.8681 - recall_40: 0.8020 - precision_40: 0.8182\n",
            "Epoch 30/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2905 - accuracy: 0.8715 - recall_40: 0.8119 - precision_40: 0.8200\n",
            "4/4 - 0s - loss: 0.4670 - accuracy: 0.8145 - recall_40: 0.7442 - precision_40: 0.7273 - 16ms/epoch - 4ms/step\n",
            "Epoch 1/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2888 - accuracy: 0.8715 - recall_40: 0.8119 - precision_40: 0.8200\n",
            "Epoch 2/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2864 - accuracy: 0.8750 - recall_40: 0.8218 - precision_40: 0.8218\n",
            "Epoch 3/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2853 - accuracy: 0.8785 - recall_40: 0.8317 - precision_40: 0.8235\n",
            "Epoch 4/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2836 - accuracy: 0.8785 - recall_40: 0.8317 - precision_40: 0.8235\n",
            "Epoch 5/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2818 - accuracy: 0.8819 - recall_40: 0.8416 - precision_40: 0.8252\n",
            "Epoch 6/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2804 - accuracy: 0.8819 - recall_40: 0.8416 - precision_40: 0.8252\n",
            "Epoch 7/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2784 - accuracy: 0.8785 - recall_40: 0.8317 - precision_40: 0.8235\n",
            "Epoch 8/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2766 - accuracy: 0.8785 - recall_40: 0.8317 - precision_40: 0.8235\n",
            "Epoch 9/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2753 - accuracy: 0.8819 - recall_40: 0.8416 - precision_40: 0.8252\n",
            "Epoch 10/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2735 - accuracy: 0.8819 - recall_40: 0.8416 - precision_40: 0.8252\n",
            "Epoch 11/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2719 - accuracy: 0.8889 - recall_40: 0.8614 - precision_40: 0.8286\n",
            "Epoch 12/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2712 - accuracy: 0.8889 - recall_40: 0.8614 - precision_40: 0.8286\n",
            "Epoch 13/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2692 - accuracy: 0.8889 - recall_40: 0.8614 - precision_40: 0.8286\n",
            "Epoch 14/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2679 - accuracy: 0.8889 - recall_40: 0.8614 - precision_40: 0.8286\n",
            "Epoch 15/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2657 - accuracy: 0.8889 - recall_40: 0.8614 - precision_40: 0.8286\n",
            "Epoch 16/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2645 - accuracy: 0.8924 - recall_40: 0.8713 - precision_40: 0.8302\n",
            "Epoch 17/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2627 - accuracy: 0.8924 - recall_40: 0.8713 - precision_40: 0.8302\n",
            "Epoch 18/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2612 - accuracy: 0.8924 - recall_40: 0.8713 - precision_40: 0.8302\n",
            "Epoch 19/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2599 - accuracy: 0.8924 - recall_40: 0.8713 - precision_40: 0.8302\n",
            "Epoch 20/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2586 - accuracy: 0.8924 - recall_40: 0.8713 - precision_40: 0.8302\n",
            "Epoch 21/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2570 - accuracy: 0.8924 - recall_40: 0.8713 - precision_40: 0.8302\n",
            "Epoch 22/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2554 - accuracy: 0.8924 - recall_40: 0.8713 - precision_40: 0.8302\n",
            "Epoch 23/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2540 - accuracy: 0.8958 - recall_40: 0.8713 - precision_40: 0.8381\n",
            "Epoch 24/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2529 - accuracy: 0.8958 - recall_40: 0.8713 - precision_40: 0.8381\n",
            "Epoch 25/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2514 - accuracy: 0.8958 - recall_40: 0.8713 - precision_40: 0.8381\n",
            "Epoch 26/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2498 - accuracy: 0.8958 - recall_40: 0.8713 - precision_40: 0.8381\n",
            "Epoch 27/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2482 - accuracy: 0.8958 - recall_40: 0.8713 - precision_40: 0.8381\n",
            "Epoch 28/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2470 - accuracy: 0.8958 - recall_40: 0.8713 - precision_40: 0.8381\n",
            "Epoch 29/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2454 - accuracy: 0.8958 - recall_40: 0.8713 - precision_40: 0.8381\n",
            "Epoch 30/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2442 - accuracy: 0.8993 - recall_40: 0.8713 - precision_40: 0.8462\n",
            "Epoch 31/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2433 - accuracy: 0.8993 - recall_40: 0.8713 - precision_40: 0.8462\n",
            "Epoch 32/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2415 - accuracy: 0.8993 - recall_40: 0.8713 - precision_40: 0.8462\n",
            "Epoch 33/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2397 - accuracy: 0.8958 - recall_40: 0.8713 - precision_40: 0.8381\n",
            "Epoch 34/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2384 - accuracy: 0.8993 - recall_40: 0.8713 - precision_40: 0.8462\n",
            "Epoch 35/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2370 - accuracy: 0.8993 - recall_40: 0.8713 - precision_40: 0.8462\n",
            "Epoch 36/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2359 - accuracy: 0.9028 - recall_40: 0.8812 - precision_40: 0.8476\n",
            "Epoch 37/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2344 - accuracy: 0.8993 - recall_40: 0.8713 - precision_40: 0.8462\n",
            "Epoch 38/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2332 - accuracy: 0.8993 - recall_40: 0.8713 - precision_40: 0.8462\n",
            "Epoch 39/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2327 - accuracy: 0.9028 - recall_40: 0.8713 - precision_40: 0.8544\n",
            "Epoch 40/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2305 - accuracy: 0.9062 - recall_40: 0.8812 - precision_40: 0.8558\n",
            "4/4 - 0s - loss: 0.5264 - accuracy: 0.8387 - recall_40: 0.7674 - precision_40: 0.7674 - 33ms/epoch - 8ms/step\n",
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 2ms/step - loss: 0.6343 - accuracy: 0.6562 - recall_41: 0.1485 - precision_41: 0.5357\n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6133 - accuracy: 0.6944 - recall_41: 0.2772 - precision_41: 0.6512\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5967 - accuracy: 0.7222 - recall_41: 0.3762 - precision_41: 0.6909\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5796 - accuracy: 0.7257 - recall_41: 0.3861 - precision_41: 0.6964\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5661 - accuracy: 0.7431 - recall_41: 0.4257 - precision_41: 0.7288\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5516 - accuracy: 0.7604 - recall_41: 0.4653 - precision_41: 0.7581\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5380 - accuracy: 0.7639 - recall_41: 0.4752 - precision_41: 0.7619\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5251 - accuracy: 0.7604 - recall_41: 0.4950 - precision_41: 0.7353\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5129 - accuracy: 0.7569 - recall_41: 0.5050 - precision_41: 0.7183\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5018 - accuracy: 0.7674 - recall_41: 0.5644 - precision_41: 0.7125\n",
            "4/4 - 0s - loss: 0.5006 - accuracy: 0.7984 - recall_41: 0.5349 - precision_41: 0.8214 - 273ms/epoch - 68ms/step\n",
            "Epoch 1/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4901 - accuracy: 0.7708 - recall_41: 0.5743 - precision_41: 0.7160\n",
            "Epoch 2/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4801 - accuracy: 0.7847 - recall_41: 0.6139 - precision_41: 0.7294\n",
            "Epoch 3/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4704 - accuracy: 0.7847 - recall_41: 0.6436 - precision_41: 0.7143\n",
            "Epoch 4/20\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.4615 - accuracy: 0.7951 - recall_41: 0.6634 - precision_41: 0.7283\n",
            "Epoch 5/20\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.4527 - accuracy: 0.7882 - recall_41: 0.6436 - precision_41: 0.7222\n",
            "Epoch 6/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4444 - accuracy: 0.7917 - recall_41: 0.6535 - precision_41: 0.7253\n",
            "Epoch 7/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4372 - accuracy: 0.7986 - recall_41: 0.6733 - precision_41: 0.7312\n",
            "Epoch 8/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4305 - accuracy: 0.8021 - recall_41: 0.7030 - precision_41: 0.7245\n",
            "Epoch 9/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4239 - accuracy: 0.8125 - recall_41: 0.7426 - precision_41: 0.7282\n",
            "Epoch 10/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4175 - accuracy: 0.8194 - recall_41: 0.7525 - precision_41: 0.7379\n",
            "Epoch 11/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4123 - accuracy: 0.8160 - recall_41: 0.7426 - precision_41: 0.7353\n",
            "Epoch 12/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4067 - accuracy: 0.8194 - recall_41: 0.7525 - precision_41: 0.7379\n",
            "Epoch 13/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4020 - accuracy: 0.8229 - recall_41: 0.7723 - precision_41: 0.7358\n",
            "Epoch 14/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3973 - accuracy: 0.8264 - recall_41: 0.7921 - precision_41: 0.7339\n",
            "Epoch 15/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3932 - accuracy: 0.8264 - recall_41: 0.7921 - precision_41: 0.7339\n",
            "Epoch 16/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3891 - accuracy: 0.8299 - recall_41: 0.8020 - precision_41: 0.7364\n",
            "Epoch 17/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3847 - accuracy: 0.8299 - recall_41: 0.8020 - precision_41: 0.7364\n",
            "Epoch 18/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3811 - accuracy: 0.8299 - recall_41: 0.8020 - precision_41: 0.7364\n",
            "Epoch 19/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3775 - accuracy: 0.8229 - recall_41: 0.7921 - precision_41: 0.7273\n",
            "Epoch 20/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3743 - accuracy: 0.8299 - recall_41: 0.8020 - precision_41: 0.7364\n",
            "4/4 - 0s - loss: 0.4313 - accuracy: 0.8065 - recall_41: 0.7442 - precision_41: 0.7111 - 23ms/epoch - 6ms/step\n",
            "Epoch 1/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3712 - accuracy: 0.8333 - recall_41: 0.8119 - precision_41: 0.7387\n",
            "Epoch 2/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3682 - accuracy: 0.8333 - recall_41: 0.8119 - precision_41: 0.7387\n",
            "Epoch 3/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3658 - accuracy: 0.8368 - recall_41: 0.8119 - precision_41: 0.7455\n",
            "Epoch 4/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3632 - accuracy: 0.8368 - recall_41: 0.8119 - precision_41: 0.7455\n",
            "Epoch 5/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3606 - accuracy: 0.8368 - recall_41: 0.8119 - precision_41: 0.7455\n",
            "Epoch 6/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3580 - accuracy: 0.8299 - recall_41: 0.8020 - precision_41: 0.7364\n",
            "Epoch 7/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3565 - accuracy: 0.8333 - recall_41: 0.8020 - precision_41: 0.7431\n",
            "Epoch 8/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3534 - accuracy: 0.8333 - recall_41: 0.8119 - precision_41: 0.7387\n",
            "Epoch 9/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3514 - accuracy: 0.8368 - recall_41: 0.8218 - precision_41: 0.7411\n",
            "Epoch 10/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3495 - accuracy: 0.8368 - recall_41: 0.8218 - precision_41: 0.7411\n",
            "Epoch 11/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3476 - accuracy: 0.8403 - recall_41: 0.8317 - precision_41: 0.7434\n",
            "Epoch 12/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3455 - accuracy: 0.8438 - recall_41: 0.8317 - precision_41: 0.7500\n",
            "Epoch 13/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3436 - accuracy: 0.8507 - recall_41: 0.8515 - precision_41: 0.7544\n",
            "Epoch 14/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3416 - accuracy: 0.8507 - recall_41: 0.8416 - precision_41: 0.7589\n",
            "Epoch 15/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3399 - accuracy: 0.8507 - recall_41: 0.8416 - precision_41: 0.7589\n",
            "Epoch 16/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3379 - accuracy: 0.8507 - recall_41: 0.8416 - precision_41: 0.7589\n",
            "Epoch 17/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3369 - accuracy: 0.8507 - recall_41: 0.8515 - precision_41: 0.7544\n",
            "Epoch 18/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3350 - accuracy: 0.8611 - recall_41: 0.8614 - precision_41: 0.7699\n",
            "Epoch 19/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3332 - accuracy: 0.8611 - recall_41: 0.8614 - precision_41: 0.7699\n",
            "Epoch 20/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3314 - accuracy: 0.8611 - recall_41: 0.8515 - precision_41: 0.7748\n",
            "Epoch 21/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3303 - accuracy: 0.8611 - recall_41: 0.8515 - precision_41: 0.7748\n",
            "Epoch 22/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3284 - accuracy: 0.8646 - recall_41: 0.8614 - precision_41: 0.7768\n",
            "Epoch 23/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3271 - accuracy: 0.8646 - recall_41: 0.8614 - precision_41: 0.7768\n",
            "Epoch 24/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3254 - accuracy: 0.8611 - recall_41: 0.8515 - precision_41: 0.7748\n",
            "Epoch 25/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3238 - accuracy: 0.8646 - recall_41: 0.8614 - precision_41: 0.7768\n",
            "Epoch 26/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3227 - accuracy: 0.8646 - recall_41: 0.8614 - precision_41: 0.7768\n",
            "Epoch 27/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3215 - accuracy: 0.8646 - recall_41: 0.8614 - precision_41: 0.7768\n",
            "Epoch 28/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3199 - accuracy: 0.8611 - recall_41: 0.8416 - precision_41: 0.7798\n",
            "Epoch 29/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3182 - accuracy: 0.8646 - recall_41: 0.8416 - precision_41: 0.7870\n",
            "Epoch 30/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3175 - accuracy: 0.8681 - recall_41: 0.8515 - precision_41: 0.7890\n",
            "4/4 - 0s - loss: 0.4544 - accuracy: 0.8226 - recall_41: 0.7674 - precision_41: 0.7333 - 18ms/epoch - 4ms/step\n",
            "Epoch 1/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3164 - accuracy: 0.8646 - recall_41: 0.8416 - precision_41: 0.7870\n",
            "Epoch 2/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3146 - accuracy: 0.8646 - recall_41: 0.8416 - precision_41: 0.7870\n",
            "Epoch 3/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3134 - accuracy: 0.8646 - recall_41: 0.8416 - precision_41: 0.7870\n",
            "Epoch 4/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3128 - accuracy: 0.8646 - recall_41: 0.8416 - precision_41: 0.7870\n",
            "Epoch 5/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3118 - accuracy: 0.8646 - recall_41: 0.8416 - precision_41: 0.7870\n",
            "Epoch 6/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3100 - accuracy: 0.8646 - recall_41: 0.8416 - precision_41: 0.7870\n",
            "Epoch 7/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3089 - accuracy: 0.8681 - recall_41: 0.8416 - precision_41: 0.7944\n",
            "Epoch 8/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3082 - accuracy: 0.8681 - recall_41: 0.8515 - precision_41: 0.7890\n",
            "Epoch 9/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3070 - accuracy: 0.8715 - recall_41: 0.8515 - precision_41: 0.7963\n",
            "Epoch 10/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3056 - accuracy: 0.8750 - recall_41: 0.8515 - precision_41: 0.8037\n",
            "Epoch 11/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3051 - accuracy: 0.8750 - recall_41: 0.8515 - precision_41: 0.8037\n",
            "Epoch 12/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3031 - accuracy: 0.8750 - recall_41: 0.8515 - precision_41: 0.8037\n",
            "Epoch 13/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3021 - accuracy: 0.8750 - recall_41: 0.8515 - precision_41: 0.8037\n",
            "Epoch 14/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3007 - accuracy: 0.8750 - recall_41: 0.8515 - precision_41: 0.8037\n",
            "Epoch 15/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3003 - accuracy: 0.8750 - recall_41: 0.8515 - precision_41: 0.8037\n",
            "Epoch 16/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2984 - accuracy: 0.8750 - recall_41: 0.8515 - precision_41: 0.8037\n",
            "Epoch 17/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2975 - accuracy: 0.8750 - recall_41: 0.8515 - precision_41: 0.8037\n",
            "Epoch 18/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2962 - accuracy: 0.8750 - recall_41: 0.8515 - precision_41: 0.8037\n",
            "Epoch 19/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2957 - accuracy: 0.8750 - recall_41: 0.8515 - precision_41: 0.8037\n",
            "Epoch 20/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2945 - accuracy: 0.8750 - recall_41: 0.8515 - precision_41: 0.8037\n",
            "Epoch 21/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2931 - accuracy: 0.8750 - recall_41: 0.8515 - precision_41: 0.8037\n",
            "Epoch 22/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2918 - accuracy: 0.8750 - recall_41: 0.8515 - precision_41: 0.8037\n",
            "Epoch 23/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2910 - accuracy: 0.8715 - recall_41: 0.8416 - precision_41: 0.8019\n",
            "Epoch 24/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2899 - accuracy: 0.8785 - recall_41: 0.8515 - precision_41: 0.8113\n",
            "Epoch 25/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2886 - accuracy: 0.8785 - recall_41: 0.8515 - precision_41: 0.8113\n",
            "Epoch 26/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2873 - accuracy: 0.8785 - recall_41: 0.8515 - precision_41: 0.8113\n",
            "Epoch 27/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2865 - accuracy: 0.8750 - recall_41: 0.8416 - precision_41: 0.8095\n",
            "Epoch 28/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2850 - accuracy: 0.8785 - recall_41: 0.8515 - precision_41: 0.8113\n",
            "Epoch 29/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2838 - accuracy: 0.8785 - recall_41: 0.8515 - precision_41: 0.8113\n",
            "Epoch 30/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2828 - accuracy: 0.8785 - recall_41: 0.8515 - precision_41: 0.8113\n",
            "Epoch 31/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2819 - accuracy: 0.8750 - recall_41: 0.8416 - precision_41: 0.8095\n",
            "Epoch 32/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2804 - accuracy: 0.8750 - recall_41: 0.8416 - precision_41: 0.8095\n",
            "Epoch 33/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2801 - accuracy: 0.8750 - recall_41: 0.8416 - precision_41: 0.8095\n",
            "Epoch 34/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2786 - accuracy: 0.8785 - recall_41: 0.8416 - precision_41: 0.8173\n",
            "Epoch 35/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2774 - accuracy: 0.8854 - recall_41: 0.8515 - precision_41: 0.8269\n",
            "Epoch 36/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2758 - accuracy: 0.8819 - recall_41: 0.8614 - precision_41: 0.8131\n",
            "Epoch 37/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2758 - accuracy: 0.8819 - recall_41: 0.8614 - precision_41: 0.8131\n",
            "Epoch 38/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2745 - accuracy: 0.8854 - recall_41: 0.8614 - precision_41: 0.8208\n",
            "Epoch 39/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2726 - accuracy: 0.8889 - recall_41: 0.8614 - precision_41: 0.8286\n",
            "Epoch 40/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2721 - accuracy: 0.8854 - recall_41: 0.8614 - precision_41: 0.8208\n",
            "4/4 - 0s - loss: 0.4774 - accuracy: 0.8145 - recall_41: 0.7209 - precision_41: 0.7381 - 20ms/epoch - 5ms/step\n",
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 2ms/step - loss: 0.6825 - accuracy: 0.6528 - recall_42: 0.6040 - precision_42: 0.5041\n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.6106 - accuracy: 0.6944 - recall_42: 0.6931 - precision_42: 0.5512\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5640 - accuracy: 0.7222 - recall_42: 0.7228 - precision_42: 0.5840\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5276 - accuracy: 0.7431 - recall_42: 0.7327 - precision_42: 0.6116\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5034 - accuracy: 0.7743 - recall_42: 0.7327 - precision_42: 0.6607\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4822 - accuracy: 0.7847 - recall_42: 0.7327 - precision_42: 0.6789\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4652 - accuracy: 0.8021 - recall_42: 0.7624 - precision_42: 0.7000\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4500 - accuracy: 0.8056 - recall_42: 0.7822 - precision_42: 0.6991\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4382 - accuracy: 0.8056 - recall_42: 0.7723 - precision_42: 0.7027\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4285 - accuracy: 0.8160 - recall_42: 0.7822 - precision_42: 0.7182\n",
            "4/4 - 1s - loss: 0.4729 - accuracy: 0.7581 - recall_42: 0.6744 - precision_42: 0.6444 - 737ms/epoch - 184ms/step\n",
            "Epoch 1/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4170 - accuracy: 0.8229 - recall_42: 0.7822 - precision_42: 0.7315\n",
            "Epoch 2/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4106 - accuracy: 0.8229 - recall_42: 0.7822 - precision_42: 0.7315\n",
            "Epoch 3/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4047 - accuracy: 0.8299 - recall_42: 0.7822 - precision_42: 0.7453\n",
            "Epoch 4/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3961 - accuracy: 0.8368 - recall_42: 0.7921 - precision_42: 0.7547\n",
            "Epoch 5/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3922 - accuracy: 0.8333 - recall_42: 0.7921 - precision_42: 0.7477\n",
            "Epoch 6/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3893 - accuracy: 0.8333 - recall_42: 0.8020 - precision_42: 0.7431\n",
            "Epoch 7/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3866 - accuracy: 0.8333 - recall_42: 0.7822 - precision_42: 0.7524\n",
            "Epoch 8/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3818 - accuracy: 0.8403 - recall_42: 0.7822 - precision_42: 0.7670\n",
            "Epoch 9/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3784 - accuracy: 0.8403 - recall_42: 0.7921 - precision_42: 0.7619\n",
            "Epoch 10/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3757 - accuracy: 0.8368 - recall_42: 0.7921 - precision_42: 0.7547\n",
            "Epoch 11/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3738 - accuracy: 0.8368 - recall_42: 0.8020 - precision_42: 0.7500\n",
            "Epoch 12/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3719 - accuracy: 0.8368 - recall_42: 0.8020 - precision_42: 0.7500\n",
            "Epoch 13/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3699 - accuracy: 0.8368 - recall_42: 0.8020 - precision_42: 0.7500\n",
            "Epoch 14/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3689 - accuracy: 0.8368 - recall_42: 0.7723 - precision_42: 0.7647\n",
            "Epoch 15/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3657 - accuracy: 0.8368 - recall_42: 0.7624 - precision_42: 0.7700\n",
            "Epoch 16/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3648 - accuracy: 0.8403 - recall_42: 0.7822 - precision_42: 0.7670\n",
            "Epoch 17/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3646 - accuracy: 0.8299 - recall_42: 0.7822 - precision_42: 0.7453\n",
            "Epoch 18/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3650 - accuracy: 0.8333 - recall_42: 0.7822 - precision_42: 0.7524\n",
            "Epoch 19/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3608 - accuracy: 0.8438 - recall_42: 0.8020 - precision_42: 0.7642\n",
            "Epoch 20/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3589 - accuracy: 0.8333 - recall_42: 0.7723 - precision_42: 0.7573\n",
            "4/4 - 0s - loss: 0.4414 - accuracy: 0.7742 - recall_42: 0.6977 - precision_42: 0.6667 - 25ms/epoch - 6ms/step\n",
            "Epoch 1/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3576 - accuracy: 0.8403 - recall_42: 0.7723 - precision_42: 0.7723\n",
            "Epoch 2/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3552 - accuracy: 0.8403 - recall_42: 0.7822 - precision_42: 0.7670\n",
            "Epoch 3/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3541 - accuracy: 0.8438 - recall_42: 0.8119 - precision_42: 0.7593\n",
            "Epoch 4/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3533 - accuracy: 0.8403 - recall_42: 0.7822 - precision_42: 0.7670\n",
            "Epoch 5/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3508 - accuracy: 0.8368 - recall_42: 0.7723 - precision_42: 0.7647\n",
            "Epoch 6/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3508 - accuracy: 0.8368 - recall_42: 0.7723 - precision_42: 0.7647\n",
            "Epoch 7/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3485 - accuracy: 0.8472 - recall_42: 0.8020 - precision_42: 0.7714\n",
            "Epoch 8/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3475 - accuracy: 0.8542 - recall_42: 0.7921 - precision_42: 0.7921\n",
            "Epoch 9/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3463 - accuracy: 0.8472 - recall_42: 0.7921 - precision_42: 0.7767\n",
            "Epoch 10/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3459 - accuracy: 0.8472 - recall_42: 0.7822 - precision_42: 0.7822\n",
            "Epoch 11/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3427 - accuracy: 0.8542 - recall_42: 0.7921 - precision_42: 0.7921\n",
            "Epoch 12/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3420 - accuracy: 0.8542 - recall_42: 0.8119 - precision_42: 0.7810\n",
            "Epoch 13/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3405 - accuracy: 0.8542 - recall_42: 0.8020 - precision_42: 0.7864\n",
            "Epoch 14/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3399 - accuracy: 0.8611 - recall_42: 0.8020 - precision_42: 0.8020\n",
            "Epoch 15/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3388 - accuracy: 0.8472 - recall_42: 0.7822 - precision_42: 0.7822\n",
            "Epoch 16/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3361 - accuracy: 0.8472 - recall_42: 0.7921 - precision_42: 0.7767\n",
            "Epoch 17/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3337 - accuracy: 0.8507 - recall_42: 0.7822 - precision_42: 0.7900\n",
            "Epoch 18/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3337 - accuracy: 0.8542 - recall_42: 0.7822 - precision_42: 0.7980\n",
            "Epoch 19/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3311 - accuracy: 0.8542 - recall_42: 0.7921 - precision_42: 0.7921\n",
            "Epoch 20/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3300 - accuracy: 0.8576 - recall_42: 0.8020 - precision_42: 0.7941\n",
            "Epoch 21/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3285 - accuracy: 0.8646 - recall_42: 0.8218 - precision_42: 0.7981\n",
            "Epoch 22/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3290 - accuracy: 0.8542 - recall_42: 0.7921 - precision_42: 0.7921\n",
            "Epoch 23/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3263 - accuracy: 0.8576 - recall_42: 0.7921 - precision_42: 0.8000\n",
            "Epoch 24/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3255 - accuracy: 0.8576 - recall_42: 0.7822 - precision_42: 0.8061\n",
            "Epoch 25/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3240 - accuracy: 0.8681 - recall_42: 0.8119 - precision_42: 0.8119\n",
            "Epoch 26/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3222 - accuracy: 0.8681 - recall_42: 0.8317 - precision_42: 0.8000\n",
            "Epoch 27/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3200 - accuracy: 0.8646 - recall_42: 0.8119 - precision_42: 0.8039\n",
            "Epoch 28/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3198 - accuracy: 0.8576 - recall_42: 0.7822 - precision_42: 0.8061\n",
            "Epoch 29/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3181 - accuracy: 0.8646 - recall_42: 0.8020 - precision_42: 0.8100\n",
            "Epoch 30/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3162 - accuracy: 0.8715 - recall_42: 0.8317 - precision_42: 0.8077\n",
            "4/4 - 0s - loss: 0.4445 - accuracy: 0.8226 - recall_42: 0.8140 - precision_42: 0.7143 - 18ms/epoch - 5ms/step\n",
            "Epoch 1/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3149 - accuracy: 0.8611 - recall_42: 0.8020 - precision_42: 0.8020\n",
            "Epoch 2/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3146 - accuracy: 0.8611 - recall_42: 0.7921 - precision_42: 0.8081\n",
            "Epoch 3/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3118 - accuracy: 0.8646 - recall_42: 0.8020 - precision_42: 0.8100\n",
            "Epoch 4/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3102 - accuracy: 0.8576 - recall_42: 0.7822 - precision_42: 0.8061\n",
            "Epoch 5/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3092 - accuracy: 0.8646 - recall_42: 0.8020 - precision_42: 0.8100\n",
            "Epoch 6/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3094 - accuracy: 0.8646 - recall_42: 0.8020 - precision_42: 0.8100\n",
            "Epoch 7/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3069 - accuracy: 0.8646 - recall_42: 0.8020 - precision_42: 0.8100\n",
            "Epoch 8/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3052 - accuracy: 0.8681 - recall_42: 0.8119 - precision_42: 0.8119\n",
            "Epoch 9/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3046 - accuracy: 0.8611 - recall_42: 0.7921 - precision_42: 0.8081\n",
            "Epoch 10/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3024 - accuracy: 0.8576 - recall_42: 0.7822 - precision_42: 0.8061\n",
            "Epoch 11/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3011 - accuracy: 0.8646 - recall_42: 0.8020 - precision_42: 0.8100\n",
            "Epoch 12/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2996 - accuracy: 0.8611 - recall_42: 0.7921 - precision_42: 0.8081\n",
            "Epoch 13/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2986 - accuracy: 0.8681 - recall_42: 0.8119 - precision_42: 0.8119\n",
            "Epoch 14/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2973 - accuracy: 0.8681 - recall_42: 0.8119 - precision_42: 0.8119\n",
            "Epoch 15/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2969 - accuracy: 0.8611 - recall_42: 0.7822 - precision_42: 0.8144\n",
            "Epoch 16/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2958 - accuracy: 0.8611 - recall_42: 0.7822 - precision_42: 0.8144\n",
            "Epoch 17/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2939 - accuracy: 0.8681 - recall_42: 0.7921 - precision_42: 0.8247\n",
            "Epoch 18/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2936 - accuracy: 0.8576 - recall_42: 0.7723 - precision_42: 0.8125\n",
            "Epoch 19/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2921 - accuracy: 0.8611 - recall_42: 0.7921 - precision_42: 0.8081\n",
            "Epoch 20/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2899 - accuracy: 0.8611 - recall_42: 0.7822 - precision_42: 0.8144\n",
            "Epoch 21/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2886 - accuracy: 0.8611 - recall_42: 0.7822 - precision_42: 0.8144\n",
            "Epoch 22/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2869 - accuracy: 0.8646 - recall_42: 0.7921 - precision_42: 0.8163\n",
            "Epoch 23/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2855 - accuracy: 0.8646 - recall_42: 0.7822 - precision_42: 0.8229\n",
            "Epoch 24/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2836 - accuracy: 0.8646 - recall_42: 0.7921 - precision_42: 0.8163\n",
            "Epoch 25/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2831 - accuracy: 0.8576 - recall_42: 0.7921 - precision_42: 0.8000\n",
            "Epoch 26/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2824 - accuracy: 0.8646 - recall_42: 0.7921 - precision_42: 0.8163\n",
            "Epoch 27/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2805 - accuracy: 0.8681 - recall_42: 0.7822 - precision_42: 0.8316\n",
            "Epoch 28/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2804 - accuracy: 0.8576 - recall_42: 0.7822 - precision_42: 0.8061\n",
            "Epoch 29/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2789 - accuracy: 0.8646 - recall_42: 0.7822 - precision_42: 0.8229\n",
            "Epoch 30/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2767 - accuracy: 0.8681 - recall_42: 0.7822 - precision_42: 0.8316\n",
            "Epoch 31/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2753 - accuracy: 0.8646 - recall_42: 0.7921 - precision_42: 0.8163\n",
            "Epoch 32/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2737 - accuracy: 0.8715 - recall_42: 0.8020 - precision_42: 0.8265\n",
            "Epoch 33/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2732 - accuracy: 0.8715 - recall_42: 0.7921 - precision_42: 0.8333\n",
            "Epoch 34/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2721 - accuracy: 0.8715 - recall_42: 0.7822 - precision_42: 0.8404\n",
            "Epoch 35/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2699 - accuracy: 0.8715 - recall_42: 0.7921 - precision_42: 0.8333\n",
            "Epoch 36/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2697 - accuracy: 0.8750 - recall_42: 0.7921 - precision_42: 0.8421\n",
            "Epoch 37/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2683 - accuracy: 0.8715 - recall_42: 0.7822 - precision_42: 0.8404\n",
            "Epoch 38/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2669 - accuracy: 0.8715 - recall_42: 0.7822 - precision_42: 0.8404\n",
            "Epoch 39/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2674 - accuracy: 0.8750 - recall_42: 0.8119 - precision_42: 0.8283\n",
            "Epoch 40/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2651 - accuracy: 0.8785 - recall_42: 0.8020 - precision_42: 0.8438\n",
            "4/4 - 0s - loss: 0.4798 - accuracy: 0.8306 - recall_42: 0.7674 - precision_42: 0.7500 - 24ms/epoch - 6ms/step\n",
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 3ms/step - loss: 0.7131 - accuracy: 0.4722 - recall_43: 0.2079 - precision_43: 0.2258\n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.6374 - accuracy: 0.7153 - recall_43: 0.6139 - precision_43: 0.5905\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5906 - accuracy: 0.7396 - recall_43: 0.7327 - precision_43: 0.6066\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.5527 - accuracy: 0.7500 - recall_43: 0.7426 - precision_43: 0.6198\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5266 - accuracy: 0.7604 - recall_43: 0.7228 - precision_43: 0.6404\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5014 - accuracy: 0.7708 - recall_43: 0.7129 - precision_43: 0.6606\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4819 - accuracy: 0.7778 - recall_43: 0.7030 - precision_43: 0.6762\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4676 - accuracy: 0.7951 - recall_43: 0.7129 - precision_43: 0.7059\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4535 - accuracy: 0.7986 - recall_43: 0.7327 - precision_43: 0.7048\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4420 - accuracy: 0.8090 - recall_43: 0.7525 - precision_43: 0.7170\n",
            "4/4 - 0s - loss: 0.4723 - accuracy: 0.7823 - recall_43: 0.7209 - precision_43: 0.6739 - 280ms/epoch - 70ms/step\n",
            "Epoch 1/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4316 - accuracy: 0.8056 - recall_43: 0.7426 - precision_43: 0.7143\n",
            "Epoch 2/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4239 - accuracy: 0.8125 - recall_43: 0.7525 - precision_43: 0.7238\n",
            "Epoch 3/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4168 - accuracy: 0.8160 - recall_43: 0.7525 - precision_43: 0.7308\n",
            "Epoch 4/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4102 - accuracy: 0.8264 - recall_43: 0.7723 - precision_43: 0.7429\n",
            "Epoch 5/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4052 - accuracy: 0.8264 - recall_43: 0.7624 - precision_43: 0.7476\n",
            "Epoch 6/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4008 - accuracy: 0.8264 - recall_43: 0.7624 - precision_43: 0.7476\n",
            "Epoch 7/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3961 - accuracy: 0.8403 - recall_43: 0.7921 - precision_43: 0.7619\n",
            "Epoch 8/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3935 - accuracy: 0.8368 - recall_43: 0.7921 - precision_43: 0.7547\n",
            "Epoch 9/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3908 - accuracy: 0.8368 - recall_43: 0.7921 - precision_43: 0.7547\n",
            "Epoch 10/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3876 - accuracy: 0.8403 - recall_43: 0.7822 - precision_43: 0.7670\n",
            "Epoch 11/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3858 - accuracy: 0.8403 - recall_43: 0.7822 - precision_43: 0.7670\n",
            "Epoch 12/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3835 - accuracy: 0.8368 - recall_43: 0.7723 - precision_43: 0.7647\n",
            "Epoch 13/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3825 - accuracy: 0.8368 - recall_43: 0.7822 - precision_43: 0.7596\n",
            "Epoch 14/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3811 - accuracy: 0.8333 - recall_43: 0.7822 - precision_43: 0.7524\n",
            "Epoch 15/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3793 - accuracy: 0.8368 - recall_43: 0.7822 - precision_43: 0.7596\n",
            "Epoch 16/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3777 - accuracy: 0.8368 - recall_43: 0.7723 - precision_43: 0.7647\n",
            "Epoch 17/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3762 - accuracy: 0.8333 - recall_43: 0.7624 - precision_43: 0.7624\n",
            "Epoch 18/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3764 - accuracy: 0.8299 - recall_43: 0.7723 - precision_43: 0.7500\n",
            "Epoch 19/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3755 - accuracy: 0.8403 - recall_43: 0.7921 - precision_43: 0.7619\n",
            "Epoch 20/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3725 - accuracy: 0.8368 - recall_43: 0.7822 - precision_43: 0.7596\n",
            "4/4 - 0s - loss: 0.4374 - accuracy: 0.7984 - recall_43: 0.7674 - precision_43: 0.6875 - 36ms/epoch - 9ms/step\n",
            "Epoch 1/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3719 - accuracy: 0.8368 - recall_43: 0.7723 - precision_43: 0.7647\n",
            "Epoch 2/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3716 - accuracy: 0.8368 - recall_43: 0.7723 - precision_43: 0.7647\n",
            "Epoch 3/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3695 - accuracy: 0.8368 - recall_43: 0.7723 - precision_43: 0.7647\n",
            "Epoch 4/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3684 - accuracy: 0.8403 - recall_43: 0.7822 - precision_43: 0.7670\n",
            "Epoch 5/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3678 - accuracy: 0.8368 - recall_43: 0.7921 - precision_43: 0.7547\n",
            "Epoch 6/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3675 - accuracy: 0.8333 - recall_43: 0.7723 - precision_43: 0.7573\n",
            "Epoch 7/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3653 - accuracy: 0.8403 - recall_43: 0.7822 - precision_43: 0.7670\n",
            "Epoch 8/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3641 - accuracy: 0.8403 - recall_43: 0.7921 - precision_43: 0.7619\n",
            "Epoch 9/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3634 - accuracy: 0.8403 - recall_43: 0.7921 - precision_43: 0.7619\n",
            "Epoch 10/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3621 - accuracy: 0.8403 - recall_43: 0.7921 - precision_43: 0.7619\n",
            "Epoch 11/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3621 - accuracy: 0.8403 - recall_43: 0.7921 - precision_43: 0.7619\n",
            "Epoch 12/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3600 - accuracy: 0.8438 - recall_43: 0.8020 - precision_43: 0.7642\n",
            "Epoch 13/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3601 - accuracy: 0.8507 - recall_43: 0.8020 - precision_43: 0.7788\n",
            "Epoch 14/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3584 - accuracy: 0.8507 - recall_43: 0.8020 - precision_43: 0.7788\n",
            "Epoch 15/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3581 - accuracy: 0.8507 - recall_43: 0.7921 - precision_43: 0.7843\n",
            "Epoch 16/30\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3564 - accuracy: 0.8507 - recall_43: 0.8119 - precision_43: 0.7736\n",
            "Epoch 17/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3560 - accuracy: 0.8472 - recall_43: 0.8020 - precision_43: 0.7714\n",
            "Epoch 18/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3549 - accuracy: 0.8507 - recall_43: 0.7921 - precision_43: 0.7843\n",
            "Epoch 19/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3540 - accuracy: 0.8542 - recall_43: 0.8020 - precision_43: 0.7864\n",
            "Epoch 20/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3553 - accuracy: 0.8507 - recall_43: 0.7822 - precision_43: 0.7900\n",
            "Epoch 21/30\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3506 - accuracy: 0.8542 - recall_43: 0.8020 - precision_43: 0.7864\n",
            "Epoch 22/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3522 - accuracy: 0.8542 - recall_43: 0.8218 - precision_43: 0.7757\n",
            "Epoch 23/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3502 - accuracy: 0.8542 - recall_43: 0.8218 - precision_43: 0.7757\n",
            "Epoch 24/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3482 - accuracy: 0.8542 - recall_43: 0.8119 - precision_43: 0.7810\n",
            "Epoch 25/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3474 - accuracy: 0.8507 - recall_43: 0.8020 - precision_43: 0.7788\n",
            "Epoch 26/30\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3458 - accuracy: 0.8576 - recall_43: 0.8020 - precision_43: 0.7941\n",
            "Epoch 27/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3456 - accuracy: 0.8576 - recall_43: 0.8119 - precision_43: 0.7885\n",
            "Epoch 28/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3447 - accuracy: 0.8576 - recall_43: 0.8119 - precision_43: 0.7885\n",
            "Epoch 29/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3433 - accuracy: 0.8542 - recall_43: 0.7921 - precision_43: 0.7921\n",
            "Epoch 30/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3421 - accuracy: 0.8576 - recall_43: 0.8218 - precision_43: 0.7830\n",
            "4/4 - 0s - loss: 0.4509 - accuracy: 0.7903 - recall_43: 0.7442 - precision_43: 0.6809 - 18ms/epoch - 5ms/step\n",
            "Epoch 1/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3411 - accuracy: 0.8611 - recall_43: 0.8218 - precision_43: 0.7905\n",
            "Epoch 2/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3404 - accuracy: 0.8542 - recall_43: 0.8218 - precision_43: 0.7757\n",
            "Epoch 3/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3389 - accuracy: 0.8646 - recall_43: 0.8218 - precision_43: 0.7981\n",
            "Epoch 4/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3385 - accuracy: 0.8611 - recall_43: 0.8218 - precision_43: 0.7905\n",
            "Epoch 5/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3376 - accuracy: 0.8646 - recall_43: 0.8218 - precision_43: 0.7981\n",
            "Epoch 6/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3375 - accuracy: 0.8542 - recall_43: 0.8218 - precision_43: 0.7757\n",
            "Epoch 7/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3353 - accuracy: 0.8646 - recall_43: 0.8218 - precision_43: 0.7981\n",
            "Epoch 8/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3345 - accuracy: 0.8646 - recall_43: 0.8218 - precision_43: 0.7981\n",
            "Epoch 9/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3359 - accuracy: 0.8611 - recall_43: 0.8218 - precision_43: 0.7905\n",
            "Epoch 10/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3318 - accuracy: 0.8646 - recall_43: 0.8317 - precision_43: 0.7925\n",
            "Epoch 11/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3309 - accuracy: 0.8646 - recall_43: 0.8218 - precision_43: 0.7981\n",
            "Epoch 12/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3302 - accuracy: 0.8646 - recall_43: 0.8218 - precision_43: 0.7981\n",
            "Epoch 13/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3282 - accuracy: 0.8646 - recall_43: 0.8218 - precision_43: 0.7981\n",
            "Epoch 14/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3276 - accuracy: 0.8646 - recall_43: 0.8218 - precision_43: 0.7981\n",
            "Epoch 15/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3272 - accuracy: 0.8611 - recall_43: 0.8218 - precision_43: 0.7905\n",
            "Epoch 16/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3253 - accuracy: 0.8646 - recall_43: 0.8218 - precision_43: 0.7981\n",
            "Epoch 17/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3256 - accuracy: 0.8611 - recall_43: 0.8119 - precision_43: 0.7961\n",
            "Epoch 18/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3231 - accuracy: 0.8646 - recall_43: 0.8119 - precision_43: 0.8039\n",
            "Epoch 19/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3246 - accuracy: 0.8646 - recall_43: 0.8218 - precision_43: 0.7981\n",
            "Epoch 20/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3208 - accuracy: 0.8646 - recall_43: 0.8218 - precision_43: 0.7981\n",
            "Epoch 21/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3197 - accuracy: 0.8681 - recall_43: 0.8218 - precision_43: 0.8058\n",
            "Epoch 22/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3206 - accuracy: 0.8715 - recall_43: 0.8218 - precision_43: 0.8137\n",
            "Epoch 23/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3189 - accuracy: 0.8646 - recall_43: 0.8218 - precision_43: 0.7981\n",
            "Epoch 24/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3172 - accuracy: 0.8681 - recall_43: 0.8218 - precision_43: 0.8058\n",
            "Epoch 25/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3163 - accuracy: 0.8611 - recall_43: 0.8119 - precision_43: 0.7961\n",
            "Epoch 26/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3155 - accuracy: 0.8611 - recall_43: 0.8119 - precision_43: 0.7961\n",
            "Epoch 27/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3159 - accuracy: 0.8681 - recall_43: 0.8218 - precision_43: 0.8058\n",
            "Epoch 28/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3128 - accuracy: 0.8681 - recall_43: 0.8218 - precision_43: 0.8058\n",
            "Epoch 29/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3121 - accuracy: 0.8715 - recall_43: 0.8218 - precision_43: 0.8137\n",
            "Epoch 30/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3115 - accuracy: 0.8681 - recall_43: 0.8119 - precision_43: 0.8119\n",
            "Epoch 31/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3104 - accuracy: 0.8715 - recall_43: 0.8119 - precision_43: 0.8200\n",
            "Epoch 32/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3097 - accuracy: 0.8715 - recall_43: 0.8218 - precision_43: 0.8137\n",
            "Epoch 33/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3077 - accuracy: 0.8681 - recall_43: 0.8218 - precision_43: 0.8058\n",
            "Epoch 34/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3071 - accuracy: 0.8681 - recall_43: 0.8119 - precision_43: 0.8119\n",
            "Epoch 35/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3055 - accuracy: 0.8715 - recall_43: 0.8119 - precision_43: 0.8200\n",
            "Epoch 36/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3047 - accuracy: 0.8681 - recall_43: 0.8119 - precision_43: 0.8119\n",
            "Epoch 37/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3036 - accuracy: 0.8681 - recall_43: 0.8119 - precision_43: 0.8119\n",
            "Epoch 38/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3016 - accuracy: 0.8715 - recall_43: 0.8119 - precision_43: 0.8200\n",
            "Epoch 39/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3014 - accuracy: 0.8715 - recall_43: 0.8119 - precision_43: 0.8200\n",
            "Epoch 40/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3002 - accuracy: 0.8750 - recall_43: 0.8119 - precision_43: 0.8283\n",
            "4/4 - 0s - loss: 0.4699 - accuracy: 0.8226 - recall_43: 0.7674 - precision_43: 0.7333 - 21ms/epoch - 5ms/step\n",
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 3ms/step - loss: 0.7156 - accuracy: 0.5938 - recall_44: 0.5149 - precision_44: 0.4333\n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6379 - accuracy: 0.6771 - recall_44: 0.6436 - precision_44: 0.5328\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5865 - accuracy: 0.7153 - recall_44: 0.6832 - precision_44: 0.5798\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5469 - accuracy: 0.7361 - recall_44: 0.6931 - precision_44: 0.6087\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5094 - accuracy: 0.7569 - recall_44: 0.7129 - precision_44: 0.6372\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4863 - accuracy: 0.7743 - recall_44: 0.7129 - precision_44: 0.6667\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4647 - accuracy: 0.7847 - recall_44: 0.7129 - precision_44: 0.6857\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4494 - accuracy: 0.8021 - recall_44: 0.7525 - precision_44: 0.7037\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4392 - accuracy: 0.8090 - recall_44: 0.7327 - precision_44: 0.7255\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4270 - accuracy: 0.8194 - recall_44: 0.7624 - precision_44: 0.7333\n",
            "4/4 - 0s - loss: 0.4548 - accuracy: 0.7742 - recall_44: 0.6744 - precision_44: 0.6744 - 291ms/epoch - 73ms/step\n",
            "Epoch 1/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4195 - accuracy: 0.8264 - recall_44: 0.7822 - precision_44: 0.7383\n",
            "Epoch 2/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4115 - accuracy: 0.8333 - recall_44: 0.7921 - precision_44: 0.7477\n",
            "Epoch 3/20\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.4055 - accuracy: 0.8403 - recall_44: 0.7921 - precision_44: 0.7619\n",
            "Epoch 4/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4002 - accuracy: 0.8403 - recall_44: 0.7822 - precision_44: 0.7670\n",
            "Epoch 5/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3956 - accuracy: 0.8368 - recall_44: 0.7822 - precision_44: 0.7596\n",
            "Epoch 6/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3913 - accuracy: 0.8403 - recall_44: 0.7921 - precision_44: 0.7619\n",
            "Epoch 7/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3881 - accuracy: 0.8403 - recall_44: 0.8020 - precision_44: 0.7570\n",
            "Epoch 8/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3860 - accuracy: 0.8438 - recall_44: 0.8020 - precision_44: 0.7642\n",
            "Epoch 9/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3813 - accuracy: 0.8403 - recall_44: 0.7921 - precision_44: 0.7619\n",
            "Epoch 10/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3789 - accuracy: 0.8368 - recall_44: 0.7921 - precision_44: 0.7547\n",
            "Epoch 11/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3761 - accuracy: 0.8403 - recall_44: 0.8020 - precision_44: 0.7570\n",
            "Epoch 12/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3740 - accuracy: 0.8403 - recall_44: 0.8020 - precision_44: 0.7570\n",
            "Epoch 13/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3725 - accuracy: 0.8438 - recall_44: 0.8020 - precision_44: 0.7642\n",
            "Epoch 14/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3708 - accuracy: 0.8438 - recall_44: 0.8020 - precision_44: 0.7642\n",
            "Epoch 15/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3700 - accuracy: 0.8403 - recall_44: 0.8020 - precision_44: 0.7570\n",
            "Epoch 16/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3676 - accuracy: 0.8438 - recall_44: 0.8020 - precision_44: 0.7642\n",
            "Epoch 17/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3659 - accuracy: 0.8438 - recall_44: 0.8020 - precision_44: 0.7642\n",
            "Epoch 18/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3637 - accuracy: 0.8438 - recall_44: 0.8020 - precision_44: 0.7642\n",
            "Epoch 19/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3630 - accuracy: 0.8438 - recall_44: 0.8020 - precision_44: 0.7642\n",
            "Epoch 20/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3616 - accuracy: 0.8472 - recall_44: 0.8119 - precision_44: 0.7664\n",
            "4/4 - 0s - loss: 0.4362 - accuracy: 0.7984 - recall_44: 0.7674 - precision_44: 0.6875 - 23ms/epoch - 6ms/step\n",
            "Epoch 1/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3603 - accuracy: 0.8472 - recall_44: 0.8020 - precision_44: 0.7714\n",
            "Epoch 2/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3585 - accuracy: 0.8472 - recall_44: 0.8020 - precision_44: 0.7714\n",
            "Epoch 3/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3575 - accuracy: 0.8542 - recall_44: 0.8119 - precision_44: 0.7810\n",
            "Epoch 4/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3564 - accuracy: 0.8507 - recall_44: 0.8119 - precision_44: 0.7736\n",
            "Epoch 5/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3547 - accuracy: 0.8542 - recall_44: 0.8119 - precision_44: 0.7810\n",
            "Epoch 6/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3536 - accuracy: 0.8542 - recall_44: 0.8119 - precision_44: 0.7810\n",
            "Epoch 7/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3524 - accuracy: 0.8542 - recall_44: 0.8218 - precision_44: 0.7757\n",
            "Epoch 8/30\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3523 - accuracy: 0.8542 - recall_44: 0.8119 - precision_44: 0.7810\n",
            "Epoch 9/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3499 - accuracy: 0.8576 - recall_44: 0.8218 - precision_44: 0.7830\n",
            "Epoch 10/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3492 - accuracy: 0.8576 - recall_44: 0.8218 - precision_44: 0.7830\n",
            "Epoch 11/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3483 - accuracy: 0.8542 - recall_44: 0.8119 - precision_44: 0.7810\n",
            "Epoch 12/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3469 - accuracy: 0.8576 - recall_44: 0.8218 - precision_44: 0.7830\n",
            "Epoch 13/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3463 - accuracy: 0.8542 - recall_44: 0.8218 - precision_44: 0.7757\n",
            "Epoch 14/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3455 - accuracy: 0.8611 - recall_44: 0.8317 - precision_44: 0.7850\n",
            "Epoch 15/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3447 - accuracy: 0.8611 - recall_44: 0.8317 - precision_44: 0.7850\n",
            "Epoch 16/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3433 - accuracy: 0.8576 - recall_44: 0.8218 - precision_44: 0.7830\n",
            "Epoch 17/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3422 - accuracy: 0.8576 - recall_44: 0.8218 - precision_44: 0.7830\n",
            "Epoch 18/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3419 - accuracy: 0.8611 - recall_44: 0.8218 - precision_44: 0.7905\n",
            "Epoch 19/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3402 - accuracy: 0.8611 - recall_44: 0.8218 - precision_44: 0.7905\n",
            "Epoch 20/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3396 - accuracy: 0.8611 - recall_44: 0.8218 - precision_44: 0.7905\n",
            "Epoch 21/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3400 - accuracy: 0.8646 - recall_44: 0.8317 - precision_44: 0.7925\n",
            "Epoch 22/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3369 - accuracy: 0.8681 - recall_44: 0.8416 - precision_44: 0.7944\n",
            "Epoch 23/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3373 - accuracy: 0.8611 - recall_44: 0.8119 - precision_44: 0.7961\n",
            "Epoch 24/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3353 - accuracy: 0.8611 - recall_44: 0.8020 - precision_44: 0.8020\n",
            "Epoch 25/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3354 - accuracy: 0.8646 - recall_44: 0.8317 - precision_44: 0.7925\n",
            "Epoch 26/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3353 - accuracy: 0.8646 - recall_44: 0.8317 - precision_44: 0.7925\n",
            "Epoch 27/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3331 - accuracy: 0.8611 - recall_44: 0.8119 - precision_44: 0.7961\n",
            "Epoch 28/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3323 - accuracy: 0.8646 - recall_44: 0.8317 - precision_44: 0.7925\n",
            "Epoch 29/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3313 - accuracy: 0.8611 - recall_44: 0.8218 - precision_44: 0.7905\n",
            "Epoch 30/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3299 - accuracy: 0.8646 - recall_44: 0.8317 - precision_44: 0.7925\n",
            "4/4 - 0s - loss: 0.4508 - accuracy: 0.8145 - recall_44: 0.7442 - precision_44: 0.7273 - 24ms/epoch - 6ms/step\n",
            "Epoch 1/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3293 - accuracy: 0.8646 - recall_44: 0.8317 - precision_44: 0.7925\n",
            "Epoch 2/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3281 - accuracy: 0.8646 - recall_44: 0.8317 - precision_44: 0.7925\n",
            "Epoch 3/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3277 - accuracy: 0.8542 - recall_44: 0.7921 - precision_44: 0.7921\n",
            "Epoch 4/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3266 - accuracy: 0.8611 - recall_44: 0.8119 - precision_44: 0.7961\n",
            "Epoch 5/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3259 - accuracy: 0.8646 - recall_44: 0.8317 - precision_44: 0.7925\n",
            "Epoch 6/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3249 - accuracy: 0.8646 - recall_44: 0.8317 - precision_44: 0.7925\n",
            "Epoch 7/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3257 - accuracy: 0.8646 - recall_44: 0.8218 - precision_44: 0.7981\n",
            "Epoch 8/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3245 - accuracy: 0.8681 - recall_44: 0.8317 - precision_44: 0.8000\n",
            "Epoch 9/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3227 - accuracy: 0.8681 - recall_44: 0.8317 - precision_44: 0.8000\n",
            "Epoch 10/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3221 - accuracy: 0.8611 - recall_44: 0.8119 - precision_44: 0.7961\n",
            "Epoch 11/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3219 - accuracy: 0.8681 - recall_44: 0.8119 - precision_44: 0.8119\n",
            "Epoch 12/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3209 - accuracy: 0.8576 - recall_44: 0.8119 - precision_44: 0.7885\n",
            "Epoch 13/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3198 - accuracy: 0.8576 - recall_44: 0.8119 - precision_44: 0.7885\n",
            "Epoch 14/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3193 - accuracy: 0.8507 - recall_44: 0.7822 - precision_44: 0.7900\n",
            "Epoch 15/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3178 - accuracy: 0.8576 - recall_44: 0.8020 - precision_44: 0.7941\n",
            "Epoch 16/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3170 - accuracy: 0.8611 - recall_44: 0.8020 - precision_44: 0.8020\n",
            "Epoch 17/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3174 - accuracy: 0.8646 - recall_44: 0.8020 - precision_44: 0.8100\n",
            "Epoch 18/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3144 - accuracy: 0.8646 - recall_44: 0.8119 - precision_44: 0.8039\n",
            "Epoch 19/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3149 - accuracy: 0.8611 - recall_44: 0.8119 - precision_44: 0.7961\n",
            "Epoch 20/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3141 - accuracy: 0.8611 - recall_44: 0.8119 - precision_44: 0.7961\n",
            "Epoch 21/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3130 - accuracy: 0.8576 - recall_44: 0.8020 - precision_44: 0.7941\n",
            "Epoch 22/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3129 - accuracy: 0.8646 - recall_44: 0.8020 - precision_44: 0.8100\n",
            "Epoch 23/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3113 - accuracy: 0.8646 - recall_44: 0.8020 - precision_44: 0.8100\n",
            "Epoch 24/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3109 - accuracy: 0.8646 - recall_44: 0.8020 - precision_44: 0.8100\n",
            "Epoch 25/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3106 - accuracy: 0.8542 - recall_44: 0.8020 - precision_44: 0.7864\n",
            "Epoch 26/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3089 - accuracy: 0.8611 - recall_44: 0.8020 - precision_44: 0.8020\n",
            "Epoch 27/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3083 - accuracy: 0.8646 - recall_44: 0.8020 - precision_44: 0.8100\n",
            "Epoch 28/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3079 - accuracy: 0.8646 - recall_44: 0.8020 - precision_44: 0.8100\n",
            "Epoch 29/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3062 - accuracy: 0.8646 - recall_44: 0.8020 - precision_44: 0.8100\n",
            "Epoch 30/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3062 - accuracy: 0.8611 - recall_44: 0.8020 - precision_44: 0.8020\n",
            "Epoch 31/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3061 - accuracy: 0.8611 - recall_44: 0.8020 - precision_44: 0.8020\n",
            "Epoch 32/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3054 - accuracy: 0.8681 - recall_44: 0.8020 - precision_44: 0.8182\n",
            "Epoch 33/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3031 - accuracy: 0.8681 - recall_44: 0.8020 - precision_44: 0.8182\n",
            "Epoch 34/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3027 - accuracy: 0.8646 - recall_44: 0.8020 - precision_44: 0.8100\n",
            "Epoch 35/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3023 - accuracy: 0.8681 - recall_44: 0.8020 - precision_44: 0.8182\n",
            "Epoch 36/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3008 - accuracy: 0.8681 - recall_44: 0.8020 - precision_44: 0.8182\n",
            "Epoch 37/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2999 - accuracy: 0.8681 - recall_44: 0.8020 - precision_44: 0.8182\n",
            "Epoch 38/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2990 - accuracy: 0.8646 - recall_44: 0.8020 - precision_44: 0.8100\n",
            "Epoch 39/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2990 - accuracy: 0.8646 - recall_44: 0.8020 - precision_44: 0.8100\n",
            "Epoch 40/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2984 - accuracy: 0.8681 - recall_44: 0.8020 - precision_44: 0.8182\n",
            "4/4 - 0s - loss: 0.4753 - accuracy: 0.8226 - recall_44: 0.6977 - precision_44: 0.7692 - 26ms/epoch - 7ms/step\n",
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 2ms/step - loss: 0.7477 - accuracy: 0.5521 - recall_45: 0.5842 - precision_45: 0.4041\n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6744 - accuracy: 0.6146 - recall_45: 0.5545 - precision_45: 0.4590\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6189 - accuracy: 0.6806 - recall_45: 0.5842 - precision_45: 0.5413\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5767 - accuracy: 0.7222 - recall_45: 0.5941 - precision_45: 0.6061\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5398 - accuracy: 0.7396 - recall_45: 0.6238 - precision_45: 0.6300\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.5108 - accuracy: 0.7604 - recall_45: 0.6535 - precision_45: 0.6600\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4888 - accuracy: 0.7674 - recall_45: 0.6931 - precision_45: 0.6604\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4701 - accuracy: 0.7708 - recall_45: 0.7030 - precision_45: 0.6636\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4547 - accuracy: 0.7847 - recall_45: 0.7129 - precision_45: 0.6857\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4413 - accuracy: 0.8056 - recall_45: 0.7327 - precision_45: 0.7184\n",
            "4/4 - 0s - loss: 0.4791 - accuracy: 0.7500 - recall_45: 0.6744 - precision_45: 0.6304 - 330ms/epoch - 83ms/step\n",
            "Epoch 1/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4299 - accuracy: 0.8090 - recall_45: 0.7327 - precision_45: 0.7255\n",
            "Epoch 2/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4214 - accuracy: 0.8160 - recall_45: 0.7327 - precision_45: 0.7400\n",
            "Epoch 3/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4129 - accuracy: 0.8264 - recall_45: 0.7525 - precision_45: 0.7525\n",
            "Epoch 4/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4072 - accuracy: 0.8368 - recall_45: 0.7822 - precision_45: 0.7596\n",
            "Epoch 5/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4009 - accuracy: 0.8368 - recall_45: 0.7822 - precision_45: 0.7596\n",
            "Epoch 6/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3969 - accuracy: 0.8333 - recall_45: 0.7624 - precision_45: 0.7624\n",
            "Epoch 7/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3924 - accuracy: 0.8333 - recall_45: 0.7624 - precision_45: 0.7624\n",
            "Epoch 8/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3885 - accuracy: 0.8333 - recall_45: 0.7525 - precision_45: 0.7677\n",
            "Epoch 9/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3853 - accuracy: 0.8368 - recall_45: 0.7624 - precision_45: 0.7700\n",
            "Epoch 10/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3820 - accuracy: 0.8368 - recall_45: 0.7723 - precision_45: 0.7647\n",
            "Epoch 11/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3806 - accuracy: 0.8368 - recall_45: 0.7723 - precision_45: 0.7647\n",
            "Epoch 12/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3779 - accuracy: 0.8333 - recall_45: 0.7624 - precision_45: 0.7624\n",
            "Epoch 13/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3758 - accuracy: 0.8333 - recall_45: 0.7624 - precision_45: 0.7624\n",
            "Epoch 14/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3742 - accuracy: 0.8368 - recall_45: 0.7723 - precision_45: 0.7647\n",
            "Epoch 15/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3722 - accuracy: 0.8333 - recall_45: 0.7723 - precision_45: 0.7573\n",
            "Epoch 16/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3708 - accuracy: 0.8333 - recall_45: 0.7723 - precision_45: 0.7573\n",
            "Epoch 17/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3690 - accuracy: 0.8368 - recall_45: 0.7723 - precision_45: 0.7647\n",
            "Epoch 18/20\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3682 - accuracy: 0.8403 - recall_45: 0.7723 - precision_45: 0.7723\n",
            "Epoch 19/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3674 - accuracy: 0.8368 - recall_45: 0.7723 - precision_45: 0.7647\n",
            "Epoch 20/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3671 - accuracy: 0.8368 - recall_45: 0.7723 - precision_45: 0.7647\n",
            "4/4 - 0s - loss: 0.4433 - accuracy: 0.7984 - recall_45: 0.7442 - precision_45: 0.6957 - 30ms/epoch - 8ms/step\n",
            "Epoch 1/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3647 - accuracy: 0.8333 - recall_45: 0.7723 - precision_45: 0.7573\n",
            "Epoch 2/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3638 - accuracy: 0.8403 - recall_45: 0.7822 - precision_45: 0.7670\n",
            "Epoch 3/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3639 - accuracy: 0.8403 - recall_45: 0.7723 - precision_45: 0.7723\n",
            "Epoch 4/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3610 - accuracy: 0.8368 - recall_45: 0.7723 - precision_45: 0.7647\n",
            "Epoch 5/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3612 - accuracy: 0.8403 - recall_45: 0.7921 - precision_45: 0.7619\n",
            "Epoch 6/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3598 - accuracy: 0.8368 - recall_45: 0.7822 - precision_45: 0.7596\n",
            "Epoch 7/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3586 - accuracy: 0.8403 - recall_45: 0.7723 - precision_45: 0.7723\n",
            "Epoch 8/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3584 - accuracy: 0.8438 - recall_45: 0.7822 - precision_45: 0.7745\n",
            "Epoch 9/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3563 - accuracy: 0.8438 - recall_45: 0.8020 - precision_45: 0.7642\n",
            "Epoch 10/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3554 - accuracy: 0.8472 - recall_45: 0.7921 - precision_45: 0.7767\n",
            "Epoch 11/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3546 - accuracy: 0.8472 - recall_45: 0.8020 - precision_45: 0.7714\n",
            "Epoch 12/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3536 - accuracy: 0.8472 - recall_45: 0.8020 - precision_45: 0.7714\n",
            "Epoch 13/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3532 - accuracy: 0.8507 - recall_45: 0.8119 - precision_45: 0.7736\n",
            "Epoch 14/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3517 - accuracy: 0.8507 - recall_45: 0.8119 - precision_45: 0.7736\n",
            "Epoch 15/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3511 - accuracy: 0.8507 - recall_45: 0.8119 - precision_45: 0.7736\n",
            "Epoch 16/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3494 - accuracy: 0.8507 - recall_45: 0.8119 - precision_45: 0.7736\n",
            "Epoch 17/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3487 - accuracy: 0.8542 - recall_45: 0.8119 - precision_45: 0.7810\n",
            "Epoch 18/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3500 - accuracy: 0.8507 - recall_45: 0.8020 - precision_45: 0.7788\n",
            "Epoch 19/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3472 - accuracy: 0.8507 - recall_45: 0.8119 - precision_45: 0.7736\n",
            "Epoch 20/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3466 - accuracy: 0.8576 - recall_45: 0.8119 - precision_45: 0.7885\n",
            "Epoch 21/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3445 - accuracy: 0.8576 - recall_45: 0.8119 - precision_45: 0.7885\n",
            "Epoch 22/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3433 - accuracy: 0.8542 - recall_45: 0.8119 - precision_45: 0.7810\n",
            "Epoch 23/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3428 - accuracy: 0.8576 - recall_45: 0.8218 - precision_45: 0.7830\n",
            "Epoch 24/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3417 - accuracy: 0.8576 - recall_45: 0.8218 - precision_45: 0.7830\n",
            "Epoch 25/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3402 - accuracy: 0.8542 - recall_45: 0.8119 - precision_45: 0.7810\n",
            "Epoch 26/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3400 - accuracy: 0.8576 - recall_45: 0.8119 - precision_45: 0.7885\n",
            "Epoch 27/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3405 - accuracy: 0.8611 - recall_45: 0.8119 - precision_45: 0.7961\n",
            "Epoch 28/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3388 - accuracy: 0.8542 - recall_45: 0.8119 - precision_45: 0.7810\n",
            "Epoch 29/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3371 - accuracy: 0.8576 - recall_45: 0.8218 - precision_45: 0.7830\n",
            "Epoch 30/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3352 - accuracy: 0.8646 - recall_45: 0.8218 - precision_45: 0.7981\n",
            "4/4 - 0s - loss: 0.4431 - accuracy: 0.8065 - recall_45: 0.7209 - precision_45: 0.7209 - 24ms/epoch - 6ms/step\n",
            "Epoch 1/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3355 - accuracy: 0.8646 - recall_45: 0.8119 - precision_45: 0.8039\n",
            "Epoch 2/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3358 - accuracy: 0.8576 - recall_45: 0.8119 - precision_45: 0.7885\n",
            "Epoch 3/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3327 - accuracy: 0.8646 - recall_45: 0.8218 - precision_45: 0.7981\n",
            "Epoch 4/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3328 - accuracy: 0.8611 - recall_45: 0.8119 - precision_45: 0.7961\n",
            "Epoch 5/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3314 - accuracy: 0.8646 - recall_45: 0.8218 - precision_45: 0.7981\n",
            "Epoch 6/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3305 - accuracy: 0.8646 - recall_45: 0.8218 - precision_45: 0.7981\n",
            "Epoch 7/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3305 - accuracy: 0.8646 - recall_45: 0.8119 - precision_45: 0.8039\n",
            "Epoch 8/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3287 - accuracy: 0.8611 - recall_45: 0.8119 - precision_45: 0.7961\n",
            "Epoch 9/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3266 - accuracy: 0.8611 - recall_45: 0.8218 - precision_45: 0.7905\n",
            "Epoch 10/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3262 - accuracy: 0.8646 - recall_45: 0.8119 - precision_45: 0.8039\n",
            "Epoch 11/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3251 - accuracy: 0.8611 - recall_45: 0.8119 - precision_45: 0.7961\n",
            "Epoch 12/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3251 - accuracy: 0.8611 - recall_45: 0.8218 - precision_45: 0.7905\n",
            "Epoch 13/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3225 - accuracy: 0.8611 - recall_45: 0.8218 - precision_45: 0.7905\n",
            "Epoch 14/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3218 - accuracy: 0.8646 - recall_45: 0.8218 - precision_45: 0.7981\n",
            "Epoch 15/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3212 - accuracy: 0.8646 - recall_45: 0.8218 - precision_45: 0.7981\n",
            "Epoch 16/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3195 - accuracy: 0.8646 - recall_45: 0.8218 - precision_45: 0.7981\n",
            "Epoch 17/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3205 - accuracy: 0.8646 - recall_45: 0.8218 - precision_45: 0.7981\n",
            "Epoch 18/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3172 - accuracy: 0.8646 - recall_45: 0.8218 - precision_45: 0.7981\n",
            "Epoch 19/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3169 - accuracy: 0.8646 - recall_45: 0.8218 - precision_45: 0.7981\n",
            "Epoch 20/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3159 - accuracy: 0.8681 - recall_45: 0.8218 - precision_45: 0.8058\n",
            "Epoch 21/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3156 - accuracy: 0.8646 - recall_45: 0.8218 - precision_45: 0.7981\n",
            "Epoch 22/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3137 - accuracy: 0.8646 - recall_45: 0.8218 - precision_45: 0.7981\n",
            "Epoch 23/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3149 - accuracy: 0.8646 - recall_45: 0.8218 - precision_45: 0.7981\n",
            "Epoch 24/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3130 - accuracy: 0.8750 - recall_45: 0.8218 - precision_45: 0.8218\n",
            "Epoch 25/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3097 - accuracy: 0.8681 - recall_45: 0.8218 - precision_45: 0.8058\n",
            "Epoch 26/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3106 - accuracy: 0.8611 - recall_45: 0.8218 - precision_45: 0.7905\n",
            "Epoch 27/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3114 - accuracy: 0.8646 - recall_45: 0.8218 - precision_45: 0.7981\n",
            "Epoch 28/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3081 - accuracy: 0.8681 - recall_45: 0.8218 - precision_45: 0.8058\n",
            "Epoch 29/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3077 - accuracy: 0.8681 - recall_45: 0.8218 - precision_45: 0.8058\n",
            "Epoch 30/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3060 - accuracy: 0.8646 - recall_45: 0.8218 - precision_45: 0.7981\n",
            "Epoch 31/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3055 - accuracy: 0.8715 - recall_45: 0.8218 - precision_45: 0.8137\n",
            "Epoch 32/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3056 - accuracy: 0.8646 - recall_45: 0.8218 - precision_45: 0.7981\n",
            "Epoch 33/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3038 - accuracy: 0.8646 - recall_45: 0.8218 - precision_45: 0.7981\n",
            "Epoch 34/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3036 - accuracy: 0.8750 - recall_45: 0.8218 - precision_45: 0.8218\n",
            "Epoch 35/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3013 - accuracy: 0.8750 - recall_45: 0.8218 - precision_45: 0.8218\n",
            "Epoch 36/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3005 - accuracy: 0.8750 - recall_45: 0.8218 - precision_45: 0.8218\n",
            "Epoch 37/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2999 - accuracy: 0.8681 - recall_45: 0.8218 - precision_45: 0.8058\n",
            "Epoch 38/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2985 - accuracy: 0.8750 - recall_45: 0.8218 - precision_45: 0.8218\n",
            "Epoch 39/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2975 - accuracy: 0.8750 - recall_45: 0.8218 - precision_45: 0.8218\n",
            "Epoch 40/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2965 - accuracy: 0.8715 - recall_45: 0.8218 - precision_45: 0.8137\n",
            "4/4 - 0s - loss: 0.4561 - accuracy: 0.8306 - recall_45: 0.7674 - precision_45: 0.7500 - 25ms/epoch - 6ms/step\n",
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 2ms/step - loss: 0.7489 - accuracy: 0.5938 - recall_46: 0.7525 - precision_46: 0.4524\n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.6921 - accuracy: 0.6389 - recall_46: 0.7228 - precision_46: 0.4899\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6444 - accuracy: 0.6875 - recall_46: 0.7030 - precision_46: 0.5420\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.6077 - accuracy: 0.6979 - recall_46: 0.6931 - precision_46: 0.5556\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5755 - accuracy: 0.7118 - recall_46: 0.7327 - precision_46: 0.5692\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5509 - accuracy: 0.7188 - recall_46: 0.7327 - precision_46: 0.5781\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5296 - accuracy: 0.7535 - recall_46: 0.7327 - precision_46: 0.6271\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5125 - accuracy: 0.7708 - recall_46: 0.7327 - precision_46: 0.6549\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4965 - accuracy: 0.7743 - recall_46: 0.7228 - precision_46: 0.6636\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4833 - accuracy: 0.7778 - recall_46: 0.6931 - precision_46: 0.6796\n",
            "4/4 - 0s - loss: 0.4805 - accuracy: 0.7984 - recall_46: 0.7907 - precision_46: 0.6800 - 380ms/epoch - 95ms/step\n",
            "Epoch 1/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4707 - accuracy: 0.7986 - recall_46: 0.7327 - precision_46: 0.7048\n",
            "Epoch 2/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4609 - accuracy: 0.8056 - recall_46: 0.7426 - precision_46: 0.7143\n",
            "Epoch 3/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4516 - accuracy: 0.7951 - recall_46: 0.7327 - precision_46: 0.6981\n",
            "Epoch 4/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4427 - accuracy: 0.8021 - recall_46: 0.7426 - precision_46: 0.7075\n",
            "Epoch 5/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4350 - accuracy: 0.8090 - recall_46: 0.7624 - precision_46: 0.7130\n",
            "Epoch 6/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4290 - accuracy: 0.8125 - recall_46: 0.7624 - precision_46: 0.7196\n",
            "Epoch 7/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4236 - accuracy: 0.8160 - recall_46: 0.7723 - precision_46: 0.7222\n",
            "Epoch 8/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4186 - accuracy: 0.8125 - recall_46: 0.7723 - precision_46: 0.7156\n",
            "Epoch 9/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4139 - accuracy: 0.8194 - recall_46: 0.7822 - precision_46: 0.7248\n",
            "Epoch 10/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4093 - accuracy: 0.8264 - recall_46: 0.7921 - precision_46: 0.7339\n",
            "Epoch 11/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4049 - accuracy: 0.8264 - recall_46: 0.7822 - precision_46: 0.7383\n",
            "Epoch 12/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4023 - accuracy: 0.8299 - recall_46: 0.7723 - precision_46: 0.7500\n",
            "Epoch 13/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3989 - accuracy: 0.8333 - recall_46: 0.7822 - precision_46: 0.7524\n",
            "Epoch 14/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3970 - accuracy: 0.8299 - recall_46: 0.7822 - precision_46: 0.7453\n",
            "Epoch 15/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3940 - accuracy: 0.8299 - recall_46: 0.7822 - precision_46: 0.7453\n",
            "Epoch 16/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3911 - accuracy: 0.8264 - recall_46: 0.7723 - precision_46: 0.7429\n",
            "Epoch 17/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3889 - accuracy: 0.8264 - recall_46: 0.7723 - precision_46: 0.7429\n",
            "Epoch 18/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3871 - accuracy: 0.8299 - recall_46: 0.7723 - precision_46: 0.7500\n",
            "Epoch 19/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3855 - accuracy: 0.8299 - recall_46: 0.7723 - precision_46: 0.7500\n",
            "Epoch 20/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3845 - accuracy: 0.8264 - recall_46: 0.7624 - precision_46: 0.7476\n",
            "4/4 - 0s - loss: 0.4352 - accuracy: 0.8065 - recall_46: 0.7209 - precision_46: 0.7209 - 24ms/epoch - 6ms/step\n",
            "Epoch 1/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3818 - accuracy: 0.8264 - recall_46: 0.7723 - precision_46: 0.7429\n",
            "Epoch 2/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3802 - accuracy: 0.8299 - recall_46: 0.7921 - precision_46: 0.7407\n",
            "Epoch 3/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3783 - accuracy: 0.8299 - recall_46: 0.7723 - precision_46: 0.7500\n",
            "Epoch 4/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3771 - accuracy: 0.8229 - recall_46: 0.7624 - precision_46: 0.7404\n",
            "Epoch 5/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3763 - accuracy: 0.8333 - recall_46: 0.7921 - precision_46: 0.7477\n",
            "Epoch 6/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3750 - accuracy: 0.8299 - recall_46: 0.7822 - precision_46: 0.7453\n",
            "Epoch 7/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3737 - accuracy: 0.8299 - recall_46: 0.7822 - precision_46: 0.7453\n",
            "Epoch 8/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3725 - accuracy: 0.8299 - recall_46: 0.7723 - precision_46: 0.7500\n",
            "Epoch 9/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3707 - accuracy: 0.8264 - recall_46: 0.7723 - precision_46: 0.7429\n",
            "Epoch 10/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3692 - accuracy: 0.8264 - recall_46: 0.7723 - precision_46: 0.7429\n",
            "Epoch 11/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3694 - accuracy: 0.8299 - recall_46: 0.7723 - precision_46: 0.7500\n",
            "Epoch 12/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3673 - accuracy: 0.8299 - recall_46: 0.7723 - precision_46: 0.7500\n",
            "Epoch 13/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3663 - accuracy: 0.8299 - recall_46: 0.7723 - precision_46: 0.7500\n",
            "Epoch 14/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3653 - accuracy: 0.8264 - recall_46: 0.7723 - precision_46: 0.7429\n",
            "Epoch 15/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3641 - accuracy: 0.8333 - recall_46: 0.7822 - precision_46: 0.7524\n",
            "Epoch 16/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3626 - accuracy: 0.8333 - recall_46: 0.7822 - precision_46: 0.7524\n",
            "Epoch 17/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3615 - accuracy: 0.8299 - recall_46: 0.7723 - precision_46: 0.7500\n",
            "Epoch 18/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3608 - accuracy: 0.8333 - recall_46: 0.7822 - precision_46: 0.7524\n",
            "Epoch 19/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3594 - accuracy: 0.8333 - recall_46: 0.7822 - precision_46: 0.7524\n",
            "Epoch 20/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3589 - accuracy: 0.8333 - recall_46: 0.7723 - precision_46: 0.7573\n",
            "Epoch 21/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3575 - accuracy: 0.8333 - recall_46: 0.7822 - precision_46: 0.7524\n",
            "Epoch 22/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3563 - accuracy: 0.8368 - recall_46: 0.7822 - precision_46: 0.7596\n",
            "Epoch 23/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3570 - accuracy: 0.8333 - recall_46: 0.7723 - precision_46: 0.7573\n",
            "Epoch 24/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3546 - accuracy: 0.8368 - recall_46: 0.7822 - precision_46: 0.7596\n",
            "Epoch 25/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3543 - accuracy: 0.8403 - recall_46: 0.7921 - precision_46: 0.7619\n",
            "Epoch 26/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3530 - accuracy: 0.8403 - recall_46: 0.7921 - precision_46: 0.7619\n",
            "Epoch 27/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3517 - accuracy: 0.8403 - recall_46: 0.7921 - precision_46: 0.7619\n",
            "Epoch 28/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3508 - accuracy: 0.8403 - recall_46: 0.7921 - precision_46: 0.7619\n",
            "Epoch 29/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3506 - accuracy: 0.8403 - recall_46: 0.7921 - precision_46: 0.7619\n",
            "Epoch 30/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3508 - accuracy: 0.8438 - recall_46: 0.7921 - precision_46: 0.7692\n",
            "4/4 - 0s - loss: 0.4342 - accuracy: 0.8065 - recall_46: 0.7442 - precision_46: 0.7111 - 25ms/epoch - 6ms/step\n",
            "Epoch 1/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3485 - accuracy: 0.8438 - recall_46: 0.7921 - precision_46: 0.7692\n",
            "Epoch 2/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3477 - accuracy: 0.8438 - recall_46: 0.7921 - precision_46: 0.7692\n",
            "Epoch 3/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3465 - accuracy: 0.8472 - recall_46: 0.7921 - precision_46: 0.7767\n",
            "Epoch 4/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3460 - accuracy: 0.8438 - recall_46: 0.7921 - precision_46: 0.7692\n",
            "Epoch 5/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3453 - accuracy: 0.8403 - recall_46: 0.7921 - precision_46: 0.7619\n",
            "Epoch 6/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3437 - accuracy: 0.8438 - recall_46: 0.7921 - precision_46: 0.7692\n",
            "Epoch 7/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3433 - accuracy: 0.8472 - recall_46: 0.7921 - precision_46: 0.7767\n",
            "Epoch 8/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3421 - accuracy: 0.8472 - recall_46: 0.7921 - precision_46: 0.7767\n",
            "Epoch 9/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3416 - accuracy: 0.8472 - recall_46: 0.7921 - precision_46: 0.7767\n",
            "Epoch 10/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3410 - accuracy: 0.8438 - recall_46: 0.7921 - precision_46: 0.7692\n",
            "Epoch 11/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3395 - accuracy: 0.8438 - recall_46: 0.7921 - precision_46: 0.7692\n",
            "Epoch 12/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3387 - accuracy: 0.8438 - recall_46: 0.7921 - precision_46: 0.7692\n",
            "Epoch 13/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3377 - accuracy: 0.8438 - recall_46: 0.7921 - precision_46: 0.7692\n",
            "Epoch 14/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3383 - accuracy: 0.8472 - recall_46: 0.7921 - precision_46: 0.7767\n",
            "Epoch 15/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3360 - accuracy: 0.8472 - recall_46: 0.7921 - precision_46: 0.7767\n",
            "Epoch 16/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3355 - accuracy: 0.8507 - recall_46: 0.7921 - precision_46: 0.7843\n",
            "Epoch 17/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3349 - accuracy: 0.8507 - recall_46: 0.7921 - precision_46: 0.7843\n",
            "Epoch 18/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3335 - accuracy: 0.8507 - recall_46: 0.7921 - precision_46: 0.7843\n",
            "Epoch 19/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3328 - accuracy: 0.8507 - recall_46: 0.8020 - precision_46: 0.7788\n",
            "Epoch 20/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3320 - accuracy: 0.8507 - recall_46: 0.8020 - precision_46: 0.7788\n",
            "Epoch 21/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3314 - accuracy: 0.8472 - recall_46: 0.7921 - precision_46: 0.7767\n",
            "Epoch 22/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3308 - accuracy: 0.8472 - recall_46: 0.7921 - precision_46: 0.7767\n",
            "Epoch 23/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3299 - accuracy: 0.8542 - recall_46: 0.8020 - precision_46: 0.7864\n",
            "Epoch 24/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3291 - accuracy: 0.8472 - recall_46: 0.7921 - precision_46: 0.7767\n",
            "Epoch 25/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3286 - accuracy: 0.8507 - recall_46: 0.8020 - precision_46: 0.7788\n",
            "Epoch 26/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3277 - accuracy: 0.8507 - recall_46: 0.8020 - precision_46: 0.7788\n",
            "Epoch 27/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3264 - accuracy: 0.8507 - recall_46: 0.7921 - precision_46: 0.7843\n",
            "Epoch 28/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3256 - accuracy: 0.8507 - recall_46: 0.7921 - precision_46: 0.7843\n",
            "Epoch 29/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3248 - accuracy: 0.8507 - recall_46: 0.7921 - precision_46: 0.7843\n",
            "Epoch 30/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3240 - accuracy: 0.8507 - recall_46: 0.7921 - precision_46: 0.7843\n",
            "Epoch 31/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3237 - accuracy: 0.8542 - recall_46: 0.8020 - precision_46: 0.7864\n",
            "Epoch 32/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3226 - accuracy: 0.8542 - recall_46: 0.8020 - precision_46: 0.7864\n",
            "Epoch 33/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3221 - accuracy: 0.8542 - recall_46: 0.8020 - precision_46: 0.7864\n",
            "Epoch 34/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3214 - accuracy: 0.8507 - recall_46: 0.7921 - precision_46: 0.7843\n",
            "Epoch 35/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3206 - accuracy: 0.8507 - recall_46: 0.7921 - precision_46: 0.7843\n",
            "Epoch 36/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3193 - accuracy: 0.8576 - recall_46: 0.8119 - precision_46: 0.7885\n",
            "Epoch 37/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3184 - accuracy: 0.8542 - recall_46: 0.8020 - precision_46: 0.7864\n",
            "Epoch 38/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3177 - accuracy: 0.8542 - recall_46: 0.8020 - precision_46: 0.7864\n",
            "Epoch 39/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3172 - accuracy: 0.8542 - recall_46: 0.8020 - precision_46: 0.7864\n",
            "Epoch 40/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3159 - accuracy: 0.8542 - recall_46: 0.8020 - precision_46: 0.7864\n",
            "4/4 - 0s - loss: 0.4420 - accuracy: 0.8226 - recall_46: 0.7907 - precision_46: 0.7234 - 24ms/epoch - 6ms/step\n",
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 2ms/step - loss: 0.6999 - accuracy: 0.5486 - recall_47: 0.5446 - precision_47: 0.3957\n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6549 - accuracy: 0.6597 - recall_47: 0.7723 - precision_47: 0.5098\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6195 - accuracy: 0.7292 - recall_47: 0.8614 - precision_47: 0.5762\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5895 - accuracy: 0.7500 - recall_47: 0.8515 - precision_47: 0.6014\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5672 - accuracy: 0.7604 - recall_47: 0.8218 - precision_47: 0.6194\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5461 - accuracy: 0.7847 - recall_47: 0.8020 - precision_47: 0.6585\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5296 - accuracy: 0.7951 - recall_47: 0.8020 - precision_47: 0.6750\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5140 - accuracy: 0.7986 - recall_47: 0.7822 - precision_47: 0.6870\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5003 - accuracy: 0.8021 - recall_47: 0.7822 - precision_47: 0.6930\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4880 - accuracy: 0.8056 - recall_47: 0.7723 - precision_47: 0.7027\n",
            "4/4 - 0s - loss: 0.5296 - accuracy: 0.7419 - recall_47: 0.7209 - precision_47: 0.6078 - 332ms/epoch - 83ms/step\n",
            "Epoch 1/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4762 - accuracy: 0.8160 - recall_47: 0.7723 - precision_47: 0.7222\n",
            "Epoch 2/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4664 - accuracy: 0.8160 - recall_47: 0.7624 - precision_47: 0.7264\n",
            "Epoch 3/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4571 - accuracy: 0.8160 - recall_47: 0.7723 - precision_47: 0.7222\n",
            "Epoch 4/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4482 - accuracy: 0.8229 - recall_47: 0.7723 - precision_47: 0.7358\n",
            "Epoch 5/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4415 - accuracy: 0.8264 - recall_47: 0.7723 - precision_47: 0.7429\n",
            "Epoch 6/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4335 - accuracy: 0.8264 - recall_47: 0.7723 - precision_47: 0.7429\n",
            "Epoch 7/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4266 - accuracy: 0.8333 - recall_47: 0.7723 - precision_47: 0.7573\n",
            "Epoch 8/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4216 - accuracy: 0.8368 - recall_47: 0.7723 - precision_47: 0.7647\n",
            "Epoch 9/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4157 - accuracy: 0.8368 - recall_47: 0.7723 - precision_47: 0.7647\n",
            "Epoch 10/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4105 - accuracy: 0.8333 - recall_47: 0.7723 - precision_47: 0.7573\n",
            "Epoch 11/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4070 - accuracy: 0.8368 - recall_47: 0.7822 - precision_47: 0.7596\n",
            "Epoch 12/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4022 - accuracy: 0.8403 - recall_47: 0.7822 - precision_47: 0.7670\n",
            "Epoch 13/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3998 - accuracy: 0.8368 - recall_47: 0.7723 - precision_47: 0.7647\n",
            "Epoch 14/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3949 - accuracy: 0.8368 - recall_47: 0.7723 - precision_47: 0.7647\n",
            "Epoch 15/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3940 - accuracy: 0.8368 - recall_47: 0.7822 - precision_47: 0.7596\n",
            "Epoch 16/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3896 - accuracy: 0.8368 - recall_47: 0.7723 - precision_47: 0.7647\n",
            "Epoch 17/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3872 - accuracy: 0.8403 - recall_47: 0.7723 - precision_47: 0.7723\n",
            "Epoch 18/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3854 - accuracy: 0.8333 - recall_47: 0.7525 - precision_47: 0.7677\n",
            "Epoch 19/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3829 - accuracy: 0.8299 - recall_47: 0.7525 - precision_47: 0.7600\n",
            "Epoch 20/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3806 - accuracy: 0.8403 - recall_47: 0.7822 - precision_47: 0.7670\n",
            "4/4 - 0s - loss: 0.4427 - accuracy: 0.7661 - recall_47: 0.6744 - precision_47: 0.6591 - 24ms/epoch - 6ms/step\n",
            "Epoch 1/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3794 - accuracy: 0.8438 - recall_47: 0.7921 - precision_47: 0.7692\n",
            "Epoch 2/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3777 - accuracy: 0.8368 - recall_47: 0.7723 - precision_47: 0.7647\n",
            "Epoch 3/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3759 - accuracy: 0.8368 - recall_47: 0.7723 - precision_47: 0.7647\n",
            "Epoch 4/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3753 - accuracy: 0.8368 - recall_47: 0.7723 - precision_47: 0.7647\n",
            "Epoch 5/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3737 - accuracy: 0.8438 - recall_47: 0.7723 - precision_47: 0.7800\n",
            "Epoch 6/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3725 - accuracy: 0.8438 - recall_47: 0.7723 - precision_47: 0.7800\n",
            "Epoch 7/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3715 - accuracy: 0.8403 - recall_47: 0.7822 - precision_47: 0.7670\n",
            "Epoch 8/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3698 - accuracy: 0.8368 - recall_47: 0.7723 - precision_47: 0.7647\n",
            "Epoch 9/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3690 - accuracy: 0.8403 - recall_47: 0.7723 - precision_47: 0.7723\n",
            "Epoch 10/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3671 - accuracy: 0.8403 - recall_47: 0.7723 - precision_47: 0.7723\n",
            "Epoch 11/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3664 - accuracy: 0.8403 - recall_47: 0.7822 - precision_47: 0.7670\n",
            "Epoch 12/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3659 - accuracy: 0.8472 - recall_47: 0.8020 - precision_47: 0.7714\n",
            "Epoch 13/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3658 - accuracy: 0.8438 - recall_47: 0.7822 - precision_47: 0.7745\n",
            "Epoch 14/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3633 - accuracy: 0.8472 - recall_47: 0.7723 - precision_47: 0.7879\n",
            "Epoch 15/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3630 - accuracy: 0.8472 - recall_47: 0.7921 - precision_47: 0.7767\n",
            "Epoch 16/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3625 - accuracy: 0.8507 - recall_47: 0.8020 - precision_47: 0.7788\n",
            "Epoch 17/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3625 - accuracy: 0.8507 - recall_47: 0.8020 - precision_47: 0.7788\n",
            "Epoch 18/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3599 - accuracy: 0.8507 - recall_47: 0.7921 - precision_47: 0.7843\n",
            "Epoch 19/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3591 - accuracy: 0.8507 - recall_47: 0.8020 - precision_47: 0.7788\n",
            "Epoch 20/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3583 - accuracy: 0.8542 - recall_47: 0.8020 - precision_47: 0.7864\n",
            "Epoch 21/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3576 - accuracy: 0.8542 - recall_47: 0.7921 - precision_47: 0.7921\n",
            "Epoch 22/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3571 - accuracy: 0.8542 - recall_47: 0.7921 - precision_47: 0.7921\n",
            "Epoch 23/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3556 - accuracy: 0.8576 - recall_47: 0.8020 - precision_47: 0.7941\n",
            "Epoch 24/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3551 - accuracy: 0.8576 - recall_47: 0.8119 - precision_47: 0.7885\n",
            "Epoch 25/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3544 - accuracy: 0.8611 - recall_47: 0.8020 - precision_47: 0.8020\n",
            "Epoch 26/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3536 - accuracy: 0.8542 - recall_47: 0.7921 - precision_47: 0.7921\n",
            "Epoch 27/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3522 - accuracy: 0.8576 - recall_47: 0.8020 - precision_47: 0.7941\n",
            "Epoch 28/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3517 - accuracy: 0.8611 - recall_47: 0.8119 - precision_47: 0.7961\n",
            "Epoch 29/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3515 - accuracy: 0.8542 - recall_47: 0.7921 - precision_47: 0.7921\n",
            "Epoch 30/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3517 - accuracy: 0.8611 - recall_47: 0.8020 - precision_47: 0.8020\n",
            "4/4 - 0s - loss: 0.4299 - accuracy: 0.8145 - recall_47: 0.7209 - precision_47: 0.7381 - 23ms/epoch - 6ms/step\n",
            "Epoch 1/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3497 - accuracy: 0.8611 - recall_47: 0.8119 - precision_47: 0.7961\n",
            "Epoch 2/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3486 - accuracy: 0.8576 - recall_47: 0.8020 - precision_47: 0.7941\n",
            "Epoch 3/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3472 - accuracy: 0.8611 - recall_47: 0.8119 - precision_47: 0.7961\n",
            "Epoch 4/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3470 - accuracy: 0.8576 - recall_47: 0.8020 - precision_47: 0.7941\n",
            "Epoch 5/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3455 - accuracy: 0.8542 - recall_47: 0.7921 - precision_47: 0.7921\n",
            "Epoch 6/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3450 - accuracy: 0.8611 - recall_47: 0.8119 - precision_47: 0.7961\n",
            "Epoch 7/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3448 - accuracy: 0.8576 - recall_47: 0.8020 - precision_47: 0.7941\n",
            "Epoch 8/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3447 - accuracy: 0.8611 - recall_47: 0.8119 - precision_47: 0.7961\n",
            "Epoch 9/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3428 - accuracy: 0.8611 - recall_47: 0.8119 - precision_47: 0.7961\n",
            "Epoch 10/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3419 - accuracy: 0.8611 - recall_47: 0.8119 - precision_47: 0.7961\n",
            "Epoch 11/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3419 - accuracy: 0.8576 - recall_47: 0.8020 - precision_47: 0.7941\n",
            "Epoch 12/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3415 - accuracy: 0.8611 - recall_47: 0.8119 - precision_47: 0.7961\n",
            "Epoch 13/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3404 - accuracy: 0.8611 - recall_47: 0.8119 - precision_47: 0.7961\n",
            "Epoch 14/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3404 - accuracy: 0.8576 - recall_47: 0.8020 - precision_47: 0.7941\n",
            "Epoch 15/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3388 - accuracy: 0.8611 - recall_47: 0.8119 - precision_47: 0.7961\n",
            "Epoch 16/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3378 - accuracy: 0.8611 - recall_47: 0.8119 - precision_47: 0.7961\n",
            "Epoch 17/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3366 - accuracy: 0.8611 - recall_47: 0.8119 - precision_47: 0.7961\n",
            "Epoch 18/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3367 - accuracy: 0.8576 - recall_47: 0.8020 - precision_47: 0.7941\n",
            "Epoch 19/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3356 - accuracy: 0.8611 - recall_47: 0.8119 - precision_47: 0.7961\n",
            "Epoch 20/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3350 - accuracy: 0.8611 - recall_47: 0.8119 - precision_47: 0.7961\n",
            "Epoch 21/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3335 - accuracy: 0.8646 - recall_47: 0.8218 - precision_47: 0.7981\n",
            "Epoch 22/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3337 - accuracy: 0.8611 - recall_47: 0.8119 - precision_47: 0.7961\n",
            "Epoch 23/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3335 - accuracy: 0.8646 - recall_47: 0.8218 - precision_47: 0.7981\n",
            "Epoch 24/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3316 - accuracy: 0.8611 - recall_47: 0.8218 - precision_47: 0.7905\n",
            "Epoch 25/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3303 - accuracy: 0.8646 - recall_47: 0.8218 - precision_47: 0.7981\n",
            "Epoch 26/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3300 - accuracy: 0.8646 - recall_47: 0.8119 - precision_47: 0.8039\n",
            "Epoch 27/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3298 - accuracy: 0.8646 - recall_47: 0.8218 - precision_47: 0.7981\n",
            "Epoch 28/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3292 - accuracy: 0.8646 - recall_47: 0.8218 - precision_47: 0.7981\n",
            "Epoch 29/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3276 - accuracy: 0.8646 - recall_47: 0.8218 - precision_47: 0.7981\n",
            "Epoch 30/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3275 - accuracy: 0.8646 - recall_47: 0.8218 - precision_47: 0.7981\n",
            "Epoch 31/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3259 - accuracy: 0.8646 - recall_47: 0.8218 - precision_47: 0.7981\n",
            "Epoch 32/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3253 - accuracy: 0.8681 - recall_47: 0.8218 - precision_47: 0.8058\n",
            "Epoch 33/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3248 - accuracy: 0.8681 - recall_47: 0.8218 - precision_47: 0.8058\n",
            "Epoch 34/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3240 - accuracy: 0.8646 - recall_47: 0.8218 - precision_47: 0.7981\n",
            "Epoch 35/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3235 - accuracy: 0.8646 - recall_47: 0.8218 - precision_47: 0.7981\n",
            "Epoch 36/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3233 - accuracy: 0.8681 - recall_47: 0.8218 - precision_47: 0.8058\n",
            "Epoch 37/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3221 - accuracy: 0.8681 - recall_47: 0.8218 - precision_47: 0.8058\n",
            "Epoch 38/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3213 - accuracy: 0.8715 - recall_47: 0.8218 - precision_47: 0.8137\n",
            "Epoch 39/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3204 - accuracy: 0.8715 - recall_47: 0.8218 - precision_47: 0.8137\n",
            "Epoch 40/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3195 - accuracy: 0.8681 - recall_47: 0.8218 - precision_47: 0.8058\n",
            "4/4 - 0s - loss: 0.4381 - accuracy: 0.8065 - recall_47: 0.6977 - precision_47: 0.7317 - 25ms/epoch - 6ms/step\n",
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 2ms/step - loss: 0.6807 - accuracy: 0.5451 - recall_48: 0.3267 - precision_48: 0.3438    \n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.6112 - accuracy: 0.7257 - recall_48: 0.6931 - precision_48: 0.5932\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5702 - accuracy: 0.7361 - recall_48: 0.7921 - precision_48: 0.5926\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.5391 - accuracy: 0.7465 - recall_48: 0.8020 - precision_48: 0.6045\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5155 - accuracy: 0.7465 - recall_48: 0.7822 - precision_48: 0.6077\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4939 - accuracy: 0.7674 - recall_48: 0.7624 - precision_48: 0.6417\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4772 - accuracy: 0.7882 - recall_48: 0.7624 - precision_48: 0.6754\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4618 - accuracy: 0.7882 - recall_48: 0.7525 - precision_48: 0.6786\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4490 - accuracy: 0.8021 - recall_48: 0.7525 - precision_48: 0.7037\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4370 - accuracy: 0.8056 - recall_48: 0.7624 - precision_48: 0.7064\n",
            "4/4 - 0s - loss: 0.4801 - accuracy: 0.7339 - recall_48: 0.6977 - precision_48: 0.6000 - 337ms/epoch - 84ms/step\n",
            "Epoch 1/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4270 - accuracy: 0.8090 - recall_48: 0.7822 - precision_48: 0.7054\n",
            "Epoch 2/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4186 - accuracy: 0.8194 - recall_48: 0.7921 - precision_48: 0.7207\n",
            "Epoch 3/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4103 - accuracy: 0.8229 - recall_48: 0.7822 - precision_48: 0.7315\n",
            "Epoch 4/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4041 - accuracy: 0.8333 - recall_48: 0.7822 - precision_48: 0.7524\n",
            "Epoch 5/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3986 - accuracy: 0.8333 - recall_48: 0.8020 - precision_48: 0.7431\n",
            "Epoch 6/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3938 - accuracy: 0.8333 - recall_48: 0.7822 - precision_48: 0.7524\n",
            "Epoch 7/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3887 - accuracy: 0.8368 - recall_48: 0.7723 - precision_48: 0.7647\n",
            "Epoch 8/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3849 - accuracy: 0.8368 - recall_48: 0.7723 - precision_48: 0.7647\n",
            "Epoch 9/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3835 - accuracy: 0.8472 - recall_48: 0.8119 - precision_48: 0.7664\n",
            "Epoch 10/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3789 - accuracy: 0.8438 - recall_48: 0.7921 - precision_48: 0.7692\n",
            "Epoch 11/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3775 - accuracy: 0.8333 - recall_48: 0.7822 - precision_48: 0.7524\n",
            "Epoch 12/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3742 - accuracy: 0.8472 - recall_48: 0.8119 - precision_48: 0.7664\n",
            "Epoch 13/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3721 - accuracy: 0.8368 - recall_48: 0.7822 - precision_48: 0.7596\n",
            "Epoch 14/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3707 - accuracy: 0.8403 - recall_48: 0.7921 - precision_48: 0.7619\n",
            "Epoch 15/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3684 - accuracy: 0.8403 - recall_48: 0.7921 - precision_48: 0.7619\n",
            "Epoch 16/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3675 - accuracy: 0.8403 - recall_48: 0.7921 - precision_48: 0.7619\n",
            "Epoch 17/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3653 - accuracy: 0.8438 - recall_48: 0.7723 - precision_48: 0.7800\n",
            "Epoch 18/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3642 - accuracy: 0.8472 - recall_48: 0.7822 - precision_48: 0.7822\n",
            "Epoch 19/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3631 - accuracy: 0.8472 - recall_48: 0.7921 - precision_48: 0.7767\n",
            "Epoch 20/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3605 - accuracy: 0.8472 - recall_48: 0.7921 - precision_48: 0.7767\n",
            "4/4 - 0s - loss: 0.4233 - accuracy: 0.7984 - recall_48: 0.7674 - precision_48: 0.6875 - 32ms/epoch - 8ms/step\n",
            "Epoch 1/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3596 - accuracy: 0.8507 - recall_48: 0.8218 - precision_48: 0.7685\n",
            "Epoch 2/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3584 - accuracy: 0.8507 - recall_48: 0.7921 - precision_48: 0.7843\n",
            "Epoch 3/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3562 - accuracy: 0.8576 - recall_48: 0.8020 - precision_48: 0.7941\n",
            "Epoch 4/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3545 - accuracy: 0.8611 - recall_48: 0.8119 - precision_48: 0.7961\n",
            "Epoch 5/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3554 - accuracy: 0.8507 - recall_48: 0.7822 - precision_48: 0.7900\n",
            "Epoch 6/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3523 - accuracy: 0.8542 - recall_48: 0.8119 - precision_48: 0.7810\n",
            "Epoch 7/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3504 - accuracy: 0.8611 - recall_48: 0.8218 - precision_48: 0.7905\n",
            "Epoch 8/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3499 - accuracy: 0.8576 - recall_48: 0.8020 - precision_48: 0.7941\n",
            "Epoch 9/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3513 - accuracy: 0.8646 - recall_48: 0.8416 - precision_48: 0.7870\n",
            "Epoch 10/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3464 - accuracy: 0.8646 - recall_48: 0.8218 - precision_48: 0.7981\n",
            "Epoch 11/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3442 - accuracy: 0.8611 - recall_48: 0.8119 - precision_48: 0.7961\n",
            "Epoch 12/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3431 - accuracy: 0.8576 - recall_48: 0.8020 - precision_48: 0.7941\n",
            "Epoch 13/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3423 - accuracy: 0.8646 - recall_48: 0.8218 - precision_48: 0.7981\n",
            "Epoch 14/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3434 - accuracy: 0.8576 - recall_48: 0.8020 - precision_48: 0.7941\n",
            "Epoch 15/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3387 - accuracy: 0.8611 - recall_48: 0.8119 - precision_48: 0.7961\n",
            "Epoch 16/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3372 - accuracy: 0.8646 - recall_48: 0.8317 - precision_48: 0.7925\n",
            "Epoch 17/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3354 - accuracy: 0.8681 - recall_48: 0.8317 - precision_48: 0.8000\n",
            "Epoch 18/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3342 - accuracy: 0.8646 - recall_48: 0.8119 - precision_48: 0.8039\n",
            "Epoch 19/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3330 - accuracy: 0.8646 - recall_48: 0.8119 - precision_48: 0.8039\n",
            "Epoch 20/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3315 - accuracy: 0.8681 - recall_48: 0.8218 - precision_48: 0.8058\n",
            "Epoch 21/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3301 - accuracy: 0.8715 - recall_48: 0.8317 - precision_48: 0.8077\n",
            "Epoch 22/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3286 - accuracy: 0.8715 - recall_48: 0.8317 - precision_48: 0.8077\n",
            "Epoch 23/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3276 - accuracy: 0.8715 - recall_48: 0.8317 - precision_48: 0.8077\n",
            "Epoch 24/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3267 - accuracy: 0.8750 - recall_48: 0.8218 - precision_48: 0.8218\n",
            "Epoch 25/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3249 - accuracy: 0.8750 - recall_48: 0.8218 - precision_48: 0.8218\n",
            "Epoch 26/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3241 - accuracy: 0.8750 - recall_48: 0.8317 - precision_48: 0.8155\n",
            "Epoch 27/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3222 - accuracy: 0.8785 - recall_48: 0.8317 - precision_48: 0.8235\n",
            "Epoch 28/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3211 - accuracy: 0.8750 - recall_48: 0.8317 - precision_48: 0.8155\n",
            "Epoch 29/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3198 - accuracy: 0.8750 - recall_48: 0.8218 - precision_48: 0.8218\n",
            "Epoch 30/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3195 - accuracy: 0.8715 - recall_48: 0.8218 - precision_48: 0.8137\n",
            "4/4 - 0s - loss: 0.4122 - accuracy: 0.8226 - recall_48: 0.8140 - precision_48: 0.7143 - 22ms/epoch - 6ms/step\n",
            "Epoch 1/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3182 - accuracy: 0.8750 - recall_48: 0.8317 - precision_48: 0.8155\n",
            "Epoch 2/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3165 - accuracy: 0.8785 - recall_48: 0.8317 - precision_48: 0.8235\n",
            "Epoch 3/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3156 - accuracy: 0.8785 - recall_48: 0.8317 - precision_48: 0.8235\n",
            "Epoch 4/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3130 - accuracy: 0.8785 - recall_48: 0.8317 - precision_48: 0.8235\n",
            "Epoch 5/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3120 - accuracy: 0.8785 - recall_48: 0.8317 - precision_48: 0.8235\n",
            "Epoch 6/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3105 - accuracy: 0.8785 - recall_48: 0.8317 - precision_48: 0.8235\n",
            "Epoch 7/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3095 - accuracy: 0.8785 - recall_48: 0.8317 - precision_48: 0.8235\n",
            "Epoch 8/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3090 - accuracy: 0.8750 - recall_48: 0.8218 - precision_48: 0.8218\n",
            "Epoch 9/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3071 - accuracy: 0.8750 - recall_48: 0.8218 - precision_48: 0.8218\n",
            "Epoch 10/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3070 - accuracy: 0.8785 - recall_48: 0.8317 - precision_48: 0.8235\n",
            "Epoch 11/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3050 - accuracy: 0.8785 - recall_48: 0.8317 - precision_48: 0.8235\n",
            "Epoch 12/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3034 - accuracy: 0.8785 - recall_48: 0.8317 - precision_48: 0.8235\n",
            "Epoch 13/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3024 - accuracy: 0.8785 - recall_48: 0.8317 - precision_48: 0.8235\n",
            "Epoch 14/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3012 - accuracy: 0.8785 - recall_48: 0.8317 - precision_48: 0.8235\n",
            "Epoch 15/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3015 - accuracy: 0.8750 - recall_48: 0.8218 - precision_48: 0.8218\n",
            "Epoch 16/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2983 - accuracy: 0.8785 - recall_48: 0.8317 - precision_48: 0.8235\n",
            "Epoch 17/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2970 - accuracy: 0.8785 - recall_48: 0.8317 - precision_48: 0.8235\n",
            "Epoch 18/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2970 - accuracy: 0.8785 - recall_48: 0.8317 - precision_48: 0.8235\n",
            "Epoch 19/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2955 - accuracy: 0.8819 - recall_48: 0.8416 - precision_48: 0.8252\n",
            "Epoch 20/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2946 - accuracy: 0.8785 - recall_48: 0.8317 - precision_48: 0.8235\n",
            "Epoch 21/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2925 - accuracy: 0.8819 - recall_48: 0.8317 - precision_48: 0.8317\n",
            "Epoch 22/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2916 - accuracy: 0.8819 - recall_48: 0.8317 - precision_48: 0.8317\n",
            "Epoch 23/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2899 - accuracy: 0.8785 - recall_48: 0.8317 - precision_48: 0.8235\n",
            "Epoch 24/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2887 - accuracy: 0.8785 - recall_48: 0.8317 - precision_48: 0.8235\n",
            "Epoch 25/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2877 - accuracy: 0.8785 - recall_48: 0.8317 - precision_48: 0.8235\n",
            "Epoch 26/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2861 - accuracy: 0.8785 - recall_48: 0.8317 - precision_48: 0.8235\n",
            "Epoch 27/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2847 - accuracy: 0.8819 - recall_48: 0.8416 - precision_48: 0.8252\n",
            "Epoch 28/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2839 - accuracy: 0.8819 - recall_48: 0.8317 - precision_48: 0.8317\n",
            "Epoch 29/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2822 - accuracy: 0.8819 - recall_48: 0.8317 - precision_48: 0.8317\n",
            "Epoch 30/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2814 - accuracy: 0.8785 - recall_48: 0.8317 - precision_48: 0.8235\n",
            "Epoch 31/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2802 - accuracy: 0.8819 - recall_48: 0.8416 - precision_48: 0.8252\n",
            "Epoch 32/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2787 - accuracy: 0.8819 - recall_48: 0.8416 - precision_48: 0.8252\n",
            "Epoch 33/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2770 - accuracy: 0.8889 - recall_48: 0.8317 - precision_48: 0.8485\n",
            "Epoch 34/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2759 - accuracy: 0.8819 - recall_48: 0.8317 - precision_48: 0.8317\n",
            "Epoch 35/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2746 - accuracy: 0.8889 - recall_48: 0.8515 - precision_48: 0.8350\n",
            "Epoch 36/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2737 - accuracy: 0.8819 - recall_48: 0.8416 - precision_48: 0.8252\n",
            "Epoch 37/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2728 - accuracy: 0.8924 - recall_48: 0.8416 - precision_48: 0.8500\n",
            "Epoch 38/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2734 - accuracy: 0.8924 - recall_48: 0.8317 - precision_48: 0.8571\n",
            "Epoch 39/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2694 - accuracy: 0.8889 - recall_48: 0.8416 - precision_48: 0.8416\n",
            "Epoch 40/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2693 - accuracy: 0.8819 - recall_48: 0.8416 - precision_48: 0.8252\n",
            "4/4 - 0s - loss: 0.4239 - accuracy: 0.8468 - recall_48: 0.8140 - precision_48: 0.7609 - 23ms/epoch - 6ms/step\n",
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 2ms/step - loss: 0.6843 - accuracy: 0.5729 - recall_49: 0.4158 - precision_49: 0.3962\n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5924 - accuracy: 0.6979 - recall_49: 0.6733 - precision_49: 0.5574\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5429 - accuracy: 0.7326 - recall_49: 0.7624 - precision_49: 0.5923\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5114 - accuracy: 0.7569 - recall_49: 0.7723 - precision_49: 0.6240\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4883 - accuracy: 0.7708 - recall_49: 0.7723 - precision_49: 0.6446\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4707 - accuracy: 0.7778 - recall_49: 0.7723 - precision_49: 0.6555\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4546 - accuracy: 0.7951 - recall_49: 0.7624 - precision_49: 0.6875\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4412 - accuracy: 0.8056 - recall_49: 0.7822 - precision_49: 0.6991\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4302 - accuracy: 0.8125 - recall_49: 0.7822 - precision_49: 0.7117\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4219 - accuracy: 0.8090 - recall_49: 0.7723 - precision_49: 0.7091\n",
            "4/4 - 0s - loss: 0.4503 - accuracy: 0.7661 - recall_49: 0.7209 - precision_49: 0.6458 - 350ms/epoch - 88ms/step\n",
            "Epoch 1/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4141 - accuracy: 0.8160 - recall_49: 0.7723 - precision_49: 0.7222\n",
            "Epoch 2/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4075 - accuracy: 0.8160 - recall_49: 0.7723 - precision_49: 0.7222\n",
            "Epoch 3/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4026 - accuracy: 0.8299 - recall_49: 0.8020 - precision_49: 0.7364\n",
            "Epoch 4/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3964 - accuracy: 0.8333 - recall_49: 0.7921 - precision_49: 0.7477\n",
            "Epoch 5/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3921 - accuracy: 0.8333 - recall_49: 0.7822 - precision_49: 0.7524\n",
            "Epoch 6/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3884 - accuracy: 0.8368 - recall_49: 0.7921 - precision_49: 0.7547\n",
            "Epoch 7/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3854 - accuracy: 0.8368 - recall_49: 0.7921 - precision_49: 0.7547\n",
            "Epoch 8/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3827 - accuracy: 0.8368 - recall_49: 0.7822 - precision_49: 0.7596\n",
            "Epoch 9/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3794 - accuracy: 0.8438 - recall_49: 0.7921 - precision_49: 0.7692\n",
            "Epoch 10/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3759 - accuracy: 0.8438 - recall_49: 0.7921 - precision_49: 0.7692\n",
            "Epoch 11/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3743 - accuracy: 0.8438 - recall_49: 0.7921 - precision_49: 0.7692\n",
            "Epoch 12/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3729 - accuracy: 0.8438 - recall_49: 0.7921 - precision_49: 0.7692\n",
            "Epoch 13/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3722 - accuracy: 0.8438 - recall_49: 0.7921 - precision_49: 0.7692\n",
            "Epoch 14/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3685 - accuracy: 0.8507 - recall_49: 0.8020 - precision_49: 0.7788\n",
            "Epoch 15/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3658 - accuracy: 0.8472 - recall_49: 0.8020 - precision_49: 0.7714\n",
            "Epoch 16/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3644 - accuracy: 0.8507 - recall_49: 0.8020 - precision_49: 0.7788\n",
            "Epoch 17/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3626 - accuracy: 0.8542 - recall_49: 0.8119 - precision_49: 0.7810\n",
            "Epoch 18/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3617 - accuracy: 0.8507 - recall_49: 0.7921 - precision_49: 0.7843\n",
            "Epoch 19/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3590 - accuracy: 0.8507 - recall_49: 0.7921 - precision_49: 0.7843\n",
            "Epoch 20/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3584 - accuracy: 0.8507 - recall_49: 0.7921 - precision_49: 0.7843\n",
            "4/4 - 0s - loss: 0.4122 - accuracy: 0.7984 - recall_49: 0.7442 - precision_49: 0.6957 - 22ms/epoch - 6ms/step\n",
            "Epoch 1/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3558 - accuracy: 0.8542 - recall_49: 0.8020 - precision_49: 0.7864\n",
            "Epoch 2/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3543 - accuracy: 0.8576 - recall_49: 0.8020 - precision_49: 0.7941\n",
            "Epoch 3/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3548 - accuracy: 0.8542 - recall_49: 0.7921 - precision_49: 0.7921\n",
            "Epoch 4/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3507 - accuracy: 0.8576 - recall_49: 0.8218 - precision_49: 0.7830\n",
            "Epoch 5/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3504 - accuracy: 0.8611 - recall_49: 0.8317 - precision_49: 0.7850\n",
            "Epoch 6/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3475 - accuracy: 0.8576 - recall_49: 0.8020 - precision_49: 0.7941\n",
            "Epoch 7/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3470 - accuracy: 0.8611 - recall_49: 0.8020 - precision_49: 0.8020\n",
            "Epoch 8/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3459 - accuracy: 0.8576 - recall_49: 0.8020 - precision_49: 0.7941\n",
            "Epoch 9/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3439 - accuracy: 0.8611 - recall_49: 0.8119 - precision_49: 0.7961\n",
            "Epoch 10/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3419 - accuracy: 0.8646 - recall_49: 0.8119 - precision_49: 0.8039\n",
            "Epoch 11/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3409 - accuracy: 0.8646 - recall_49: 0.8119 - precision_49: 0.8039\n",
            "Epoch 12/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3398 - accuracy: 0.8646 - recall_49: 0.8119 - precision_49: 0.8039\n",
            "Epoch 13/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3381 - accuracy: 0.8646 - recall_49: 0.8119 - precision_49: 0.8039\n",
            "Epoch 14/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3368 - accuracy: 0.8646 - recall_49: 0.8119 - precision_49: 0.8039\n",
            "Epoch 15/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3351 - accuracy: 0.8750 - recall_49: 0.8416 - precision_49: 0.8095\n",
            "Epoch 16/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3347 - accuracy: 0.8611 - recall_49: 0.8218 - precision_49: 0.7905\n",
            "Epoch 17/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3326 - accuracy: 0.8715 - recall_49: 0.8317 - precision_49: 0.8077\n",
            "Epoch 18/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3316 - accuracy: 0.8715 - recall_49: 0.8317 - precision_49: 0.8077\n",
            "Epoch 19/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3301 - accuracy: 0.8681 - recall_49: 0.8218 - precision_49: 0.8058\n",
            "Epoch 20/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3297 - accuracy: 0.8715 - recall_49: 0.8317 - precision_49: 0.8077\n",
            "Epoch 21/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3271 - accuracy: 0.8715 - recall_49: 0.8317 - precision_49: 0.8077\n",
            "Epoch 22/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3257 - accuracy: 0.8715 - recall_49: 0.8317 - precision_49: 0.8077\n",
            "Epoch 23/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3238 - accuracy: 0.8715 - recall_49: 0.8317 - precision_49: 0.8077\n",
            "Epoch 24/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3229 - accuracy: 0.8750 - recall_49: 0.8416 - precision_49: 0.8095\n",
            "Epoch 25/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3209 - accuracy: 0.8750 - recall_49: 0.8416 - precision_49: 0.8095\n",
            "Epoch 26/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3204 - accuracy: 0.8785 - recall_49: 0.8416 - precision_49: 0.8173\n",
            "Epoch 27/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3221 - accuracy: 0.8750 - recall_49: 0.8416 - precision_49: 0.8095\n",
            "Epoch 28/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3174 - accuracy: 0.8750 - recall_49: 0.8416 - precision_49: 0.8095\n",
            "Epoch 29/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3161 - accuracy: 0.8785 - recall_49: 0.8416 - precision_49: 0.8173\n",
            "Epoch 30/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3153 - accuracy: 0.8750 - recall_49: 0.8416 - precision_49: 0.8095\n",
            "4/4 - 0s - loss: 0.3989 - accuracy: 0.8226 - recall_49: 0.7907 - precision_49: 0.7234 - 24ms/epoch - 6ms/step\n",
            "Epoch 1/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3135 - accuracy: 0.8785 - recall_49: 0.8416 - precision_49: 0.8173\n",
            "Epoch 2/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3125 - accuracy: 0.8785 - recall_49: 0.8416 - precision_49: 0.8173\n",
            "Epoch 3/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3113 - accuracy: 0.8819 - recall_49: 0.8515 - precision_49: 0.8190\n",
            "Epoch 4/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3101 - accuracy: 0.8715 - recall_49: 0.8317 - precision_49: 0.8077\n",
            "Epoch 5/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3091 - accuracy: 0.8785 - recall_49: 0.8317 - precision_49: 0.8235\n",
            "Epoch 6/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3093 - accuracy: 0.8750 - recall_49: 0.8416 - precision_49: 0.8095\n",
            "Epoch 7/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3062 - accuracy: 0.8819 - recall_49: 0.8515 - precision_49: 0.8190\n",
            "Epoch 8/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3054 - accuracy: 0.8819 - recall_49: 0.8416 - precision_49: 0.8252\n",
            "Epoch 9/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3040 - accuracy: 0.8819 - recall_49: 0.8416 - precision_49: 0.8252\n",
            "Epoch 10/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3023 - accuracy: 0.8819 - recall_49: 0.8515 - precision_49: 0.8190\n",
            "Epoch 11/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3016 - accuracy: 0.8819 - recall_49: 0.8515 - precision_49: 0.8190\n",
            "Epoch 12/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3006 - accuracy: 0.8819 - recall_49: 0.8515 - precision_49: 0.8190\n",
            "Epoch 13/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2995 - accuracy: 0.8785 - recall_49: 0.8416 - precision_49: 0.8173\n",
            "Epoch 14/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2981 - accuracy: 0.8819 - recall_49: 0.8416 - precision_49: 0.8252\n",
            "Epoch 15/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2974 - accuracy: 0.8785 - recall_49: 0.8416 - precision_49: 0.8173\n",
            "Epoch 16/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2956 - accuracy: 0.8854 - recall_49: 0.8515 - precision_49: 0.8269\n",
            "Epoch 17/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2956 - accuracy: 0.8819 - recall_49: 0.8416 - precision_49: 0.8252\n",
            "Epoch 18/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2942 - accuracy: 0.8819 - recall_49: 0.8416 - precision_49: 0.8252\n",
            "Epoch 19/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.2924 - accuracy: 0.8854 - recall_49: 0.8416 - precision_49: 0.8333\n",
            "Epoch 20/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2919 - accuracy: 0.8854 - recall_49: 0.8416 - precision_49: 0.8333\n",
            "Epoch 21/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2911 - accuracy: 0.8819 - recall_49: 0.8416 - precision_49: 0.8252\n",
            "Epoch 22/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2905 - accuracy: 0.8854 - recall_49: 0.8416 - precision_49: 0.8333\n",
            "Epoch 23/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2887 - accuracy: 0.8854 - recall_49: 0.8416 - precision_49: 0.8333\n",
            "Epoch 24/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2897 - accuracy: 0.8854 - recall_49: 0.8515 - precision_49: 0.8269\n",
            "Epoch 25/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2864 - accuracy: 0.8819 - recall_49: 0.8416 - precision_49: 0.8252\n",
            "Epoch 26/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2855 - accuracy: 0.8854 - recall_49: 0.8416 - precision_49: 0.8333\n",
            "Epoch 27/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2834 - accuracy: 0.8854 - recall_49: 0.8416 - precision_49: 0.8333\n",
            "Epoch 28/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2833 - accuracy: 0.8854 - recall_49: 0.8416 - precision_49: 0.8333\n",
            "Epoch 29/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2824 - accuracy: 0.8854 - recall_49: 0.8416 - precision_49: 0.8333\n",
            "Epoch 30/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2813 - accuracy: 0.8854 - recall_49: 0.8416 - precision_49: 0.8333\n",
            "Epoch 31/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2806 - accuracy: 0.8854 - recall_49: 0.8416 - precision_49: 0.8333\n",
            "Epoch 32/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2787 - accuracy: 0.8854 - recall_49: 0.8416 - precision_49: 0.8333\n",
            "Epoch 33/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2777 - accuracy: 0.8854 - recall_49: 0.8416 - precision_49: 0.8333\n",
            "Epoch 34/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2786 - accuracy: 0.8889 - recall_49: 0.8416 - precision_49: 0.8416\n",
            "Epoch 35/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2755 - accuracy: 0.8854 - recall_49: 0.8416 - precision_49: 0.8333\n",
            "Epoch 36/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2744 - accuracy: 0.8854 - recall_49: 0.8416 - precision_49: 0.8333\n",
            "Epoch 37/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2746 - accuracy: 0.8889 - recall_49: 0.8515 - precision_49: 0.8350\n",
            "Epoch 38/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2727 - accuracy: 0.8854 - recall_49: 0.8416 - precision_49: 0.8333\n",
            "Epoch 39/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2718 - accuracy: 0.8854 - recall_49: 0.8416 - precision_49: 0.8333\n",
            "Epoch 40/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2705 - accuracy: 0.8854 - recall_49: 0.8416 - precision_49: 0.8333\n",
            "4/4 - 0s - loss: 0.3974 - accuracy: 0.8145 - recall_49: 0.7907 - precision_49: 0.7083 - 22ms/epoch - 5ms/step\n",
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 2ms/step - loss: 0.6378 - accuracy: 0.6354 - recall_50: 0.5743 - precision_50: 0.4833\n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5717 - accuracy: 0.7188 - recall_50: 0.7129 - precision_50: 0.5806\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5361 - accuracy: 0.7535 - recall_50: 0.7624 - precision_50: 0.6210\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5094 - accuracy: 0.7674 - recall_50: 0.7624 - precision_50: 0.6417\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4883 - accuracy: 0.7708 - recall_50: 0.7525 - precision_50: 0.6496\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4713 - accuracy: 0.7917 - recall_50: 0.7624 - precision_50: 0.6814\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4581 - accuracy: 0.8021 - recall_50: 0.7624 - precision_50: 0.7000\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4465 - accuracy: 0.8021 - recall_50: 0.7624 - precision_50: 0.7000\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4365 - accuracy: 0.8160 - recall_50: 0.7723 - precision_50: 0.7222\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4273 - accuracy: 0.8160 - recall_50: 0.7723 - precision_50: 0.7222\n",
            "4/4 - 0s - loss: 0.4740 - accuracy: 0.7339 - recall_50: 0.6977 - precision_50: 0.6000 - 362ms/epoch - 91ms/step\n",
            "Epoch 1/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4205 - accuracy: 0.8160 - recall_50: 0.7822 - precision_50: 0.7182\n",
            "Epoch 2/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4138 - accuracy: 0.8194 - recall_50: 0.7921 - precision_50: 0.7207\n",
            "Epoch 3/20\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.4085 - accuracy: 0.8229 - recall_50: 0.7921 - precision_50: 0.7273\n",
            "Epoch 4/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4038 - accuracy: 0.8368 - recall_50: 0.7921 - precision_50: 0.7547\n",
            "Epoch 5/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4006 - accuracy: 0.8368 - recall_50: 0.7822 - precision_50: 0.7596\n",
            "Epoch 6/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3953 - accuracy: 0.8368 - recall_50: 0.7723 - precision_50: 0.7647\n",
            "Epoch 7/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3922 - accuracy: 0.8438 - recall_50: 0.8020 - precision_50: 0.7642\n",
            "Epoch 8/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3894 - accuracy: 0.8438 - recall_50: 0.8218 - precision_50: 0.7545\n",
            "Epoch 9/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3873 - accuracy: 0.8438 - recall_50: 0.8020 - precision_50: 0.7642\n",
            "Epoch 10/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3839 - accuracy: 0.8438 - recall_50: 0.7921 - precision_50: 0.7692\n",
            "Epoch 11/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3816 - accuracy: 0.8438 - recall_50: 0.7921 - precision_50: 0.7692\n",
            "Epoch 12/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3806 - accuracy: 0.8333 - recall_50: 0.7624 - precision_50: 0.7624\n",
            "Epoch 13/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3770 - accuracy: 0.8403 - recall_50: 0.8020 - precision_50: 0.7570\n",
            "Epoch 14/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3770 - accuracy: 0.8403 - recall_50: 0.7921 - precision_50: 0.7619\n",
            "Epoch 15/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3757 - accuracy: 0.8368 - recall_50: 0.7921 - precision_50: 0.7547\n",
            "Epoch 16/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3723 - accuracy: 0.8333 - recall_50: 0.7822 - precision_50: 0.7524\n",
            "Epoch 17/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3701 - accuracy: 0.8299 - recall_50: 0.7723 - precision_50: 0.7500\n",
            "Epoch 18/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3698 - accuracy: 0.8403 - recall_50: 0.8020 - precision_50: 0.7570\n",
            "Epoch 19/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3677 - accuracy: 0.8438 - recall_50: 0.7822 - precision_50: 0.7745\n",
            "Epoch 20/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3656 - accuracy: 0.8438 - recall_50: 0.7822 - precision_50: 0.7745\n",
            "4/4 - 0s - loss: 0.4165 - accuracy: 0.8065 - recall_50: 0.7907 - precision_50: 0.6939 - 23ms/epoch - 6ms/step\n",
            "Epoch 1/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3640 - accuracy: 0.8368 - recall_50: 0.7723 - precision_50: 0.7647\n",
            "Epoch 2/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3638 - accuracy: 0.8368 - recall_50: 0.7723 - precision_50: 0.7647\n",
            "Epoch 3/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3622 - accuracy: 0.8438 - recall_50: 0.8020 - precision_50: 0.7642\n",
            "Epoch 4/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3616 - accuracy: 0.8368 - recall_50: 0.7723 - precision_50: 0.7647\n",
            "Epoch 5/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3607 - accuracy: 0.8403 - recall_50: 0.7921 - precision_50: 0.7619\n",
            "Epoch 6/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3591 - accuracy: 0.8472 - recall_50: 0.8119 - precision_50: 0.7664\n",
            "Epoch 7/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3562 - accuracy: 0.8542 - recall_50: 0.8020 - precision_50: 0.7864\n",
            "Epoch 8/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3566 - accuracy: 0.8542 - recall_50: 0.7921 - precision_50: 0.7921\n",
            "Epoch 9/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3547 - accuracy: 0.8507 - recall_50: 0.7921 - precision_50: 0.7843\n",
            "Epoch 10/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3544 - accuracy: 0.8611 - recall_50: 0.8317 - precision_50: 0.7850\n",
            "Epoch 11/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3520 - accuracy: 0.8507 - recall_50: 0.7921 - precision_50: 0.7843\n",
            "Epoch 12/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3508 - accuracy: 0.8507 - recall_50: 0.7822 - precision_50: 0.7900\n",
            "Epoch 13/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3500 - accuracy: 0.8542 - recall_50: 0.7921 - precision_50: 0.7921\n",
            "Epoch 14/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3480 - accuracy: 0.8542 - recall_50: 0.7921 - precision_50: 0.7921\n",
            "Epoch 15/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3473 - accuracy: 0.8576 - recall_50: 0.8020 - precision_50: 0.7941\n",
            "Epoch 16/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3465 - accuracy: 0.8681 - recall_50: 0.8317 - precision_50: 0.8000\n",
            "Epoch 17/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3456 - accuracy: 0.8611 - recall_50: 0.8218 - precision_50: 0.7905\n",
            "Epoch 18/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3444 - accuracy: 0.8576 - recall_50: 0.8020 - precision_50: 0.7941\n",
            "Epoch 19/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3431 - accuracy: 0.8542 - recall_50: 0.7921 - precision_50: 0.7921\n",
            "Epoch 20/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3417 - accuracy: 0.8611 - recall_50: 0.8119 - precision_50: 0.7961\n",
            "Epoch 21/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3410 - accuracy: 0.8646 - recall_50: 0.8218 - precision_50: 0.7981\n",
            "Epoch 22/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3396 - accuracy: 0.8611 - recall_50: 0.8119 - precision_50: 0.7961\n",
            "Epoch 23/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3395 - accuracy: 0.8646 - recall_50: 0.8218 - precision_50: 0.7981\n",
            "Epoch 24/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3375 - accuracy: 0.8611 - recall_50: 0.8218 - precision_50: 0.7905\n",
            "Epoch 25/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3365 - accuracy: 0.8646 - recall_50: 0.8317 - precision_50: 0.7925\n",
            "Epoch 26/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3356 - accuracy: 0.8681 - recall_50: 0.8317 - precision_50: 0.8000\n",
            "Epoch 27/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3356 - accuracy: 0.8681 - recall_50: 0.8020 - precision_50: 0.8182\n",
            "Epoch 28/30\n",
            "9/9 [==============================] - 0s 5ms/step - loss: 0.3347 - accuracy: 0.8681 - recall_50: 0.8119 - precision_50: 0.8119\n",
            "Epoch 29/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3347 - accuracy: 0.8750 - recall_50: 0.8317 - precision_50: 0.8155\n",
            "Epoch 30/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3328 - accuracy: 0.8715 - recall_50: 0.8317 - precision_50: 0.8077\n",
            "4/4 - 0s - loss: 0.4154 - accuracy: 0.8387 - recall_50: 0.8140 - precision_50: 0.7447 - 26ms/epoch - 6ms/step\n",
            "Epoch 1/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3322 - accuracy: 0.8750 - recall_50: 0.8119 - precision_50: 0.8283\n",
            "Epoch 2/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3305 - accuracy: 0.8715 - recall_50: 0.8119 - precision_50: 0.8200\n",
            "Epoch 3/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3305 - accuracy: 0.8681 - recall_50: 0.8317 - precision_50: 0.8000\n",
            "Epoch 4/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3291 - accuracy: 0.8715 - recall_50: 0.8317 - precision_50: 0.8077\n",
            "Epoch 5/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3295 - accuracy: 0.8646 - recall_50: 0.7921 - precision_50: 0.8163\n",
            "Epoch 6/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3272 - accuracy: 0.8750 - recall_50: 0.8218 - precision_50: 0.8218\n",
            "Epoch 7/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3282 - accuracy: 0.8750 - recall_50: 0.8416 - precision_50: 0.8095\n",
            "Epoch 8/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3262 - accuracy: 0.8750 - recall_50: 0.8416 - precision_50: 0.8095\n",
            "Epoch 9/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3256 - accuracy: 0.8750 - recall_50: 0.8218 - precision_50: 0.8218\n",
            "Epoch 10/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3246 - accuracy: 0.8681 - recall_50: 0.8020 - precision_50: 0.8182\n",
            "Epoch 11/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3243 - accuracy: 0.8750 - recall_50: 0.8416 - precision_50: 0.8095\n",
            "Epoch 12/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3227 - accuracy: 0.8819 - recall_50: 0.8416 - precision_50: 0.8252\n",
            "Epoch 13/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3223 - accuracy: 0.8785 - recall_50: 0.8317 - precision_50: 0.8235\n",
            "Epoch 14/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3217 - accuracy: 0.8750 - recall_50: 0.8119 - precision_50: 0.8283\n",
            "Epoch 15/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3208 - accuracy: 0.8750 - recall_50: 0.8218 - precision_50: 0.8218\n",
            "Epoch 16/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3199 - accuracy: 0.8819 - recall_50: 0.8416 - precision_50: 0.8252\n",
            "Epoch 17/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3197 - accuracy: 0.8819 - recall_50: 0.8416 - precision_50: 0.8252\n",
            "Epoch 18/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3193 - accuracy: 0.8819 - recall_50: 0.8416 - precision_50: 0.8252\n",
            "Epoch 19/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3187 - accuracy: 0.8750 - recall_50: 0.8218 - precision_50: 0.8218\n",
            "Epoch 20/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3184 - accuracy: 0.8785 - recall_50: 0.8317 - precision_50: 0.8235\n",
            "Epoch 21/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3175 - accuracy: 0.8785 - recall_50: 0.8317 - precision_50: 0.8235\n",
            "Epoch 22/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3168 - accuracy: 0.8750 - recall_50: 0.8218 - precision_50: 0.8218\n",
            "Epoch 23/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3162 - accuracy: 0.8785 - recall_50: 0.8218 - precision_50: 0.8300\n",
            "Epoch 24/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3160 - accuracy: 0.8785 - recall_50: 0.8317 - precision_50: 0.8235\n",
            "Epoch 25/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3146 - accuracy: 0.8750 - recall_50: 0.8218 - precision_50: 0.8218\n",
            "Epoch 26/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3143 - accuracy: 0.8785 - recall_50: 0.8218 - precision_50: 0.8300\n",
            "Epoch 27/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3138 - accuracy: 0.8750 - recall_50: 0.8119 - precision_50: 0.8283\n",
            "Epoch 28/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3130 - accuracy: 0.8785 - recall_50: 0.8218 - precision_50: 0.8300\n",
            "Epoch 29/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3128 - accuracy: 0.8785 - recall_50: 0.8317 - precision_50: 0.8235\n",
            "Epoch 30/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3121 - accuracy: 0.8785 - recall_50: 0.8317 - precision_50: 0.8235\n",
            "Epoch 31/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3127 - accuracy: 0.8750 - recall_50: 0.8218 - precision_50: 0.8218\n",
            "Epoch 32/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3121 - accuracy: 0.8785 - recall_50: 0.8317 - precision_50: 0.8235\n",
            "Epoch 33/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3101 - accuracy: 0.8785 - recall_50: 0.8317 - precision_50: 0.8235\n",
            "Epoch 34/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3094 - accuracy: 0.8785 - recall_50: 0.8218 - precision_50: 0.8300\n",
            "Epoch 35/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3095 - accuracy: 0.8819 - recall_50: 0.8317 - precision_50: 0.8317\n",
            "Epoch 36/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3087 - accuracy: 0.8785 - recall_50: 0.8317 - precision_50: 0.8235\n",
            "Epoch 37/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3082 - accuracy: 0.8785 - recall_50: 0.8317 - precision_50: 0.8235\n",
            "Epoch 38/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3086 - accuracy: 0.8819 - recall_50: 0.8218 - precision_50: 0.8384\n",
            "Epoch 39/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3069 - accuracy: 0.8785 - recall_50: 0.8218 - precision_50: 0.8300\n",
            "Epoch 40/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3086 - accuracy: 0.8785 - recall_50: 0.8317 - precision_50: 0.8235\n",
            "4/4 - 0s - loss: 0.4228 - accuracy: 0.8226 - recall_50: 0.7907 - precision_50: 0.7234 - 25ms/epoch - 6ms/step\n",
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 3ms/step - loss: 0.6359 - accuracy: 0.6979 - recall_51: 0.9406 - precision_51: 0.5398\n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5969 - accuracy: 0.7014 - recall_51: 0.8812 - precision_51: 0.5460\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5671 - accuracy: 0.7118 - recall_51: 0.8515 - precision_51: 0.5584\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5413 - accuracy: 0.7326 - recall_51: 0.8218 - precision_51: 0.5845\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5218 - accuracy: 0.7361 - recall_51: 0.7921 - precision_51: 0.5926\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5050 - accuracy: 0.7569 - recall_51: 0.7822 - precision_51: 0.6220\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4899 - accuracy: 0.7708 - recall_51: 0.7723 - precision_51: 0.6446\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4771 - accuracy: 0.7847 - recall_51: 0.7723 - precision_51: 0.6667\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4662 - accuracy: 0.7917 - recall_51: 0.7723 - precision_51: 0.6783\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4547 - accuracy: 0.7951 - recall_51: 0.7723 - precision_51: 0.6842\n",
            "4/4 - 0s - loss: 0.4956 - accuracy: 0.7097 - recall_51: 0.6977 - precision_51: 0.5660 - 365ms/epoch - 91ms/step\n",
            "Epoch 1/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4449 - accuracy: 0.8021 - recall_51: 0.7822 - precision_51: 0.6930\n",
            "Epoch 2/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4381 - accuracy: 0.8056 - recall_51: 0.7624 - precision_51: 0.7064\n",
            "Epoch 3/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4299 - accuracy: 0.8125 - recall_51: 0.7822 - precision_51: 0.7117\n",
            "Epoch 4/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4228 - accuracy: 0.8160 - recall_51: 0.7921 - precision_51: 0.7143\n",
            "Epoch 5/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4156 - accuracy: 0.8264 - recall_51: 0.8020 - precision_51: 0.7297\n",
            "Epoch 6/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4115 - accuracy: 0.8333 - recall_51: 0.8119 - precision_51: 0.7387\n",
            "Epoch 7/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4064 - accuracy: 0.8264 - recall_51: 0.7921 - precision_51: 0.7339\n",
            "Epoch 8/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4018 - accuracy: 0.8368 - recall_51: 0.8020 - precision_51: 0.7500\n",
            "Epoch 9/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3977 - accuracy: 0.8368 - recall_51: 0.7921 - precision_51: 0.7547\n",
            "Epoch 10/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3943 - accuracy: 0.8368 - recall_51: 0.7822 - precision_51: 0.7596\n",
            "Epoch 11/20\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3905 - accuracy: 0.8403 - recall_51: 0.7921 - precision_51: 0.7619\n",
            "Epoch 12/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3875 - accuracy: 0.8438 - recall_51: 0.8020 - precision_51: 0.7642\n",
            "Epoch 13/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3851 - accuracy: 0.8438 - recall_51: 0.8020 - precision_51: 0.7642\n",
            "Epoch 14/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3825 - accuracy: 0.8403 - recall_51: 0.7921 - precision_51: 0.7619\n",
            "Epoch 15/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3797 - accuracy: 0.8472 - recall_51: 0.8020 - precision_51: 0.7714\n",
            "Epoch 16/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3772 - accuracy: 0.8438 - recall_51: 0.7921 - precision_51: 0.7692\n",
            "Epoch 17/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3757 - accuracy: 0.8472 - recall_51: 0.7921 - precision_51: 0.7767\n",
            "Epoch 18/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3737 - accuracy: 0.8472 - recall_51: 0.8020 - precision_51: 0.7714\n",
            "Epoch 19/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3713 - accuracy: 0.8542 - recall_51: 0.8218 - precision_51: 0.7757\n",
            "Epoch 20/20\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3698 - accuracy: 0.8576 - recall_51: 0.8119 - precision_51: 0.7885\n",
            "4/4 - 0s - loss: 0.4372 - accuracy: 0.7823 - recall_51: 0.7209 - precision_51: 0.6739 - 23ms/epoch - 6ms/step\n",
            "Epoch 1/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3682 - accuracy: 0.8542 - recall_51: 0.8020 - precision_51: 0.7864\n",
            "Epoch 2/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3661 - accuracy: 0.8472 - recall_51: 0.7921 - precision_51: 0.7767\n",
            "Epoch 3/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3642 - accuracy: 0.8438 - recall_51: 0.7822 - precision_51: 0.7745\n",
            "Epoch 4/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3621 - accuracy: 0.8507 - recall_51: 0.8020 - precision_51: 0.7788\n",
            "Epoch 5/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3611 - accuracy: 0.8472 - recall_51: 0.7921 - precision_51: 0.7767\n",
            "Epoch 6/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3587 - accuracy: 0.8507 - recall_51: 0.7921 - precision_51: 0.7843\n",
            "Epoch 7/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3574 - accuracy: 0.8542 - recall_51: 0.8119 - precision_51: 0.7810\n",
            "Epoch 8/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3558 - accuracy: 0.8542 - recall_51: 0.8218 - precision_51: 0.7757\n",
            "Epoch 9/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3548 - accuracy: 0.8576 - recall_51: 0.8218 - precision_51: 0.7830\n",
            "Epoch 10/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3527 - accuracy: 0.8576 - recall_51: 0.8218 - precision_51: 0.7830\n",
            "Epoch 11/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3517 - accuracy: 0.8542 - recall_51: 0.8119 - precision_51: 0.7810\n",
            "Epoch 12/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3501 - accuracy: 0.8611 - recall_51: 0.8317 - precision_51: 0.7850\n",
            "Epoch 13/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3486 - accuracy: 0.8611 - recall_51: 0.8317 - precision_51: 0.7850\n",
            "Epoch 14/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3473 - accuracy: 0.8542 - recall_51: 0.8119 - precision_51: 0.7810\n",
            "Epoch 15/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3455 - accuracy: 0.8576 - recall_51: 0.8218 - precision_51: 0.7830\n",
            "Epoch 16/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3450 - accuracy: 0.8542 - recall_51: 0.8119 - precision_51: 0.7810\n",
            "Epoch 17/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3432 - accuracy: 0.8611 - recall_51: 0.8218 - precision_51: 0.7905\n",
            "Epoch 18/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3412 - accuracy: 0.8576 - recall_51: 0.8119 - precision_51: 0.7885\n",
            "Epoch 19/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3400 - accuracy: 0.8542 - recall_51: 0.8119 - precision_51: 0.7810\n",
            "Epoch 20/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3386 - accuracy: 0.8646 - recall_51: 0.8317 - precision_51: 0.7925\n",
            "Epoch 21/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3375 - accuracy: 0.8646 - recall_51: 0.8317 - precision_51: 0.7925\n",
            "Epoch 22/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3361 - accuracy: 0.8681 - recall_51: 0.8317 - precision_51: 0.8000\n",
            "Epoch 23/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3345 - accuracy: 0.8681 - recall_51: 0.8317 - precision_51: 0.8000\n",
            "Epoch 24/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3334 - accuracy: 0.8715 - recall_51: 0.8317 - precision_51: 0.8077\n",
            "Epoch 25/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3323 - accuracy: 0.8715 - recall_51: 0.8317 - precision_51: 0.8077\n",
            "Epoch 26/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3315 - accuracy: 0.8715 - recall_51: 0.8416 - precision_51: 0.8019\n",
            "Epoch 27/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3298 - accuracy: 0.8681 - recall_51: 0.8317 - precision_51: 0.8000\n",
            "Epoch 28/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3282 - accuracy: 0.8681 - recall_51: 0.8317 - precision_51: 0.8000\n",
            "Epoch 29/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3271 - accuracy: 0.8715 - recall_51: 0.8317 - precision_51: 0.8077\n",
            "Epoch 30/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3264 - accuracy: 0.8681 - recall_51: 0.8218 - precision_51: 0.8058\n",
            "4/4 - 0s - loss: 0.4273 - accuracy: 0.8065 - recall_51: 0.7442 - precision_51: 0.7111 - 20ms/epoch - 5ms/step\n",
            "Epoch 1/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3249 - accuracy: 0.8715 - recall_51: 0.8317 - precision_51: 0.8077\n",
            "Epoch 2/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3243 - accuracy: 0.8715 - recall_51: 0.8317 - precision_51: 0.8077\n",
            "Epoch 3/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3229 - accuracy: 0.8715 - recall_51: 0.8317 - precision_51: 0.8077\n",
            "Epoch 4/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3228 - accuracy: 0.8750 - recall_51: 0.8317 - precision_51: 0.8155\n",
            "Epoch 5/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3203 - accuracy: 0.8750 - recall_51: 0.8317 - precision_51: 0.8155\n",
            "Epoch 6/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3204 - accuracy: 0.8750 - recall_51: 0.8317 - precision_51: 0.8155\n",
            "Epoch 7/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3183 - accuracy: 0.8785 - recall_51: 0.8317 - precision_51: 0.8235\n",
            "Epoch 8/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3171 - accuracy: 0.8715 - recall_51: 0.8317 - precision_51: 0.8077\n",
            "Epoch 9/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3166 - accuracy: 0.8750 - recall_51: 0.8416 - precision_51: 0.8095\n",
            "Epoch 10/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3154 - accuracy: 0.8750 - recall_51: 0.8416 - precision_51: 0.8095\n",
            "Epoch 11/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3149 - accuracy: 0.8750 - recall_51: 0.8317 - precision_51: 0.8155\n",
            "Epoch 12/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3134 - accuracy: 0.8750 - recall_51: 0.8317 - precision_51: 0.8155\n",
            "Epoch 13/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3122 - accuracy: 0.8750 - recall_51: 0.8416 - precision_51: 0.8095\n",
            "Epoch 14/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3113 - accuracy: 0.8715 - recall_51: 0.8416 - precision_51: 0.8019\n",
            "Epoch 15/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3097 - accuracy: 0.8750 - recall_51: 0.8317 - precision_51: 0.8155\n",
            "Epoch 16/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3100 - accuracy: 0.8785 - recall_51: 0.8317 - precision_51: 0.8235\n",
            "Epoch 17/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3074 - accuracy: 0.8819 - recall_51: 0.8416 - precision_51: 0.8252\n",
            "Epoch 18/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3069 - accuracy: 0.8819 - recall_51: 0.8416 - precision_51: 0.8252\n",
            "Epoch 19/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3055 - accuracy: 0.8785 - recall_51: 0.8416 - precision_51: 0.8173\n",
            "Epoch 20/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3044 - accuracy: 0.8785 - recall_51: 0.8416 - precision_51: 0.8173\n",
            "Epoch 21/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3033 - accuracy: 0.8819 - recall_51: 0.8416 - precision_51: 0.8252\n",
            "Epoch 22/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3022 - accuracy: 0.8785 - recall_51: 0.8416 - precision_51: 0.8173\n",
            "Epoch 23/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3019 - accuracy: 0.8854 - recall_51: 0.8416 - precision_51: 0.8333\n",
            "Epoch 24/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3006 - accuracy: 0.8889 - recall_51: 0.8416 - precision_51: 0.8416\n",
            "Epoch 25/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2992 - accuracy: 0.8819 - recall_51: 0.8416 - precision_51: 0.8252\n",
            "Epoch 26/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2988 - accuracy: 0.8785 - recall_51: 0.8416 - precision_51: 0.8173\n",
            "Epoch 27/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2977 - accuracy: 0.8785 - recall_51: 0.8416 - precision_51: 0.8173\n",
            "Epoch 28/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2969 - accuracy: 0.8889 - recall_51: 0.8416 - precision_51: 0.8416\n",
            "Epoch 29/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.2953 - accuracy: 0.8889 - recall_51: 0.8416 - precision_51: 0.8416\n",
            "Epoch 30/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2945 - accuracy: 0.8854 - recall_51: 0.8416 - precision_51: 0.8333\n",
            "Epoch 31/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2939 - accuracy: 0.8785 - recall_51: 0.8416 - precision_51: 0.8173\n",
            "Epoch 32/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2942 - accuracy: 0.8889 - recall_51: 0.8416 - precision_51: 0.8416\n",
            "Epoch 33/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2928 - accuracy: 0.8924 - recall_51: 0.8317 - precision_51: 0.8571\n",
            "Epoch 34/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2908 - accuracy: 0.8889 - recall_51: 0.8416 - precision_51: 0.8416\n",
            "Epoch 35/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2901 - accuracy: 0.8854 - recall_51: 0.8515 - precision_51: 0.8269\n",
            "Epoch 36/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2887 - accuracy: 0.8854 - recall_51: 0.8416 - precision_51: 0.8333\n",
            "Epoch 37/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2886 - accuracy: 0.8889 - recall_51: 0.8218 - precision_51: 0.8557\n",
            "Epoch 38/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2870 - accuracy: 0.8958 - recall_51: 0.8416 - precision_51: 0.8586\n",
            "Epoch 39/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2862 - accuracy: 0.8889 - recall_51: 0.8416 - precision_51: 0.8416\n",
            "Epoch 40/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2847 - accuracy: 0.8958 - recall_51: 0.8416 - precision_51: 0.8586\n",
            "4/4 - 0s - loss: 0.4375 - accuracy: 0.8226 - recall_51: 0.7674 - precision_51: 0.7333 - 21ms/epoch - 5ms/step\n",
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 2ms/step - loss: 0.6997 - accuracy: 0.5590 - recall_52: 0.8119 - precision_52: 0.4316\n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6517 - accuracy: 0.6181 - recall_52: 0.8416 - precision_52: 0.4749\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6125 - accuracy: 0.6701 - recall_52: 0.8416 - precision_52: 0.5183\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5824 - accuracy: 0.7257 - recall_52: 0.8614 - precision_52: 0.5724\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5567 - accuracy: 0.7535 - recall_52: 0.8416 - precision_52: 0.6071\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5354 - accuracy: 0.7639 - recall_52: 0.8020 - precision_52: 0.6279\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5184 - accuracy: 0.7778 - recall_52: 0.7921 - precision_52: 0.6504\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5034 - accuracy: 0.7847 - recall_52: 0.7822 - precision_52: 0.6639\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4915 - accuracy: 0.7882 - recall_52: 0.7723 - precision_52: 0.6724\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4793 - accuracy: 0.7986 - recall_52: 0.7822 - precision_52: 0.6870\n",
            "4/4 - 0s - loss: 0.5264 - accuracy: 0.7097 - recall_52: 0.7209 - precision_52: 0.5636 - 376ms/epoch - 94ms/step\n",
            "Epoch 1/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4697 - accuracy: 0.7917 - recall_52: 0.7624 - precision_52: 0.6814\n",
            "Epoch 2/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4603 - accuracy: 0.8021 - recall_52: 0.7723 - precision_52: 0.6964\n",
            "Epoch 3/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4527 - accuracy: 0.8125 - recall_52: 0.7921 - precision_52: 0.7080\n",
            "Epoch 4/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4449 - accuracy: 0.8229 - recall_52: 0.8020 - precision_52: 0.7232\n",
            "Epoch 5/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4379 - accuracy: 0.8229 - recall_52: 0.7921 - precision_52: 0.7273\n",
            "Epoch 6/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4328 - accuracy: 0.8229 - recall_52: 0.7921 - precision_52: 0.7273\n",
            "Epoch 7/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4267 - accuracy: 0.8160 - recall_52: 0.7624 - precision_52: 0.7264\n",
            "Epoch 8/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4210 - accuracy: 0.8229 - recall_52: 0.7723 - precision_52: 0.7358\n",
            "Epoch 9/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4171 - accuracy: 0.8264 - recall_52: 0.7822 - precision_52: 0.7383\n",
            "Epoch 10/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4126 - accuracy: 0.8299 - recall_52: 0.7822 - precision_52: 0.7453\n",
            "Epoch 11/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4085 - accuracy: 0.8299 - recall_52: 0.7822 - precision_52: 0.7453\n",
            "Epoch 12/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4053 - accuracy: 0.8299 - recall_52: 0.7822 - precision_52: 0.7453\n",
            "Epoch 13/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4022 - accuracy: 0.8368 - recall_52: 0.7921 - precision_52: 0.7547\n",
            "Epoch 14/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3994 - accuracy: 0.8299 - recall_52: 0.7723 - precision_52: 0.7500\n",
            "Epoch 15/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3965 - accuracy: 0.8299 - recall_52: 0.7723 - precision_52: 0.7500\n",
            "Epoch 16/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3940 - accuracy: 0.8299 - recall_52: 0.7723 - precision_52: 0.7500\n",
            "Epoch 17/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3909 - accuracy: 0.8333 - recall_52: 0.7822 - precision_52: 0.7524\n",
            "Epoch 18/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3890 - accuracy: 0.8333 - recall_52: 0.7822 - precision_52: 0.7524\n",
            "Epoch 19/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3877 - accuracy: 0.8333 - recall_52: 0.7822 - precision_52: 0.7524\n",
            "Epoch 20/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3858 - accuracy: 0.8333 - recall_52: 0.7921 - precision_52: 0.7477\n",
            "4/4 - 0s - loss: 0.4424 - accuracy: 0.7903 - recall_52: 0.7907 - precision_52: 0.6667 - 24ms/epoch - 6ms/step\n",
            "Epoch 1/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3841 - accuracy: 0.8264 - recall_52: 0.7723 - precision_52: 0.7429\n",
            "Epoch 2/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3821 - accuracy: 0.8299 - recall_52: 0.7624 - precision_52: 0.7549\n",
            "Epoch 3/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3803 - accuracy: 0.8299 - recall_52: 0.7723 - precision_52: 0.7500\n",
            "Epoch 4/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3789 - accuracy: 0.8264 - recall_52: 0.7624 - precision_52: 0.7476\n",
            "Epoch 5/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3770 - accuracy: 0.8368 - recall_52: 0.7822 - precision_52: 0.7596\n",
            "Epoch 6/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3755 - accuracy: 0.8368 - recall_52: 0.7822 - precision_52: 0.7596\n",
            "Epoch 7/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3737 - accuracy: 0.8368 - recall_52: 0.7822 - precision_52: 0.7596\n",
            "Epoch 8/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3727 - accuracy: 0.8403 - recall_52: 0.7822 - precision_52: 0.7670\n",
            "Epoch 9/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3718 - accuracy: 0.8368 - recall_52: 0.7822 - precision_52: 0.7596\n",
            "Epoch 10/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3705 - accuracy: 0.8438 - recall_52: 0.7822 - precision_52: 0.7745\n",
            "Epoch 11/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3697 - accuracy: 0.8472 - recall_52: 0.7822 - precision_52: 0.7822\n",
            "Epoch 12/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3679 - accuracy: 0.8438 - recall_52: 0.7822 - precision_52: 0.7745\n",
            "Epoch 13/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3670 - accuracy: 0.8403 - recall_52: 0.7723 - precision_52: 0.7723\n",
            "Epoch 14/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3660 - accuracy: 0.8438 - recall_52: 0.7822 - precision_52: 0.7745\n",
            "Epoch 15/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3641 - accuracy: 0.8438 - recall_52: 0.7921 - precision_52: 0.7692\n",
            "Epoch 16/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3628 - accuracy: 0.8507 - recall_52: 0.8020 - precision_52: 0.7788\n",
            "Epoch 17/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3622 - accuracy: 0.8472 - recall_52: 0.7921 - precision_52: 0.7767\n",
            "Epoch 18/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3608 - accuracy: 0.8472 - recall_52: 0.8020 - precision_52: 0.7714\n",
            "Epoch 19/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3603 - accuracy: 0.8472 - recall_52: 0.7921 - precision_52: 0.7767\n",
            "Epoch 20/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3598 - accuracy: 0.8472 - recall_52: 0.7921 - precision_52: 0.7767\n",
            "Epoch 21/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3574 - accuracy: 0.8472 - recall_52: 0.8020 - precision_52: 0.7714\n",
            "Epoch 22/30\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3562 - accuracy: 0.8507 - recall_52: 0.8020 - precision_52: 0.7788\n",
            "Epoch 23/30\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3554 - accuracy: 0.8507 - recall_52: 0.8020 - precision_52: 0.7788\n",
            "Epoch 24/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3546 - accuracy: 0.8507 - recall_52: 0.7921 - precision_52: 0.7843\n",
            "Epoch 25/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3541 - accuracy: 0.8472 - recall_52: 0.7921 - precision_52: 0.7767\n",
            "Epoch 26/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3519 - accuracy: 0.8472 - recall_52: 0.8020 - precision_52: 0.7714\n",
            "Epoch 27/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3516 - accuracy: 0.8472 - recall_52: 0.8020 - precision_52: 0.7714\n",
            "Epoch 28/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3500 - accuracy: 0.8472 - recall_52: 0.8020 - precision_52: 0.7714\n",
            "Epoch 29/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3488 - accuracy: 0.8472 - recall_52: 0.8020 - precision_52: 0.7714\n",
            "Epoch 30/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3477 - accuracy: 0.8472 - recall_52: 0.8020 - precision_52: 0.7714\n",
            "4/4 - 0s - loss: 0.4251 - accuracy: 0.7903 - recall_52: 0.7674 - precision_52: 0.6735 - 33ms/epoch - 8ms/step\n",
            "Epoch 1/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3467 - accuracy: 0.8576 - recall_52: 0.8020 - precision_52: 0.7941\n",
            "Epoch 2/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3468 - accuracy: 0.8472 - recall_52: 0.7921 - precision_52: 0.7767\n",
            "Epoch 3/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3443 - accuracy: 0.8507 - recall_52: 0.8020 - precision_52: 0.7788\n",
            "Epoch 4/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3445 - accuracy: 0.8507 - recall_52: 0.7921 - precision_52: 0.7843\n",
            "Epoch 5/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3429 - accuracy: 0.8576 - recall_52: 0.7921 - precision_52: 0.8000\n",
            "Epoch 6/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3418 - accuracy: 0.8576 - recall_52: 0.8218 - precision_52: 0.7830\n",
            "Epoch 7/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3420 - accuracy: 0.8542 - recall_52: 0.8020 - precision_52: 0.7864\n",
            "Epoch 8/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3399 - accuracy: 0.8576 - recall_52: 0.8020 - precision_52: 0.7941\n",
            "Epoch 9/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3385 - accuracy: 0.8542 - recall_52: 0.8218 - precision_52: 0.7757\n",
            "Epoch 10/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3379 - accuracy: 0.8611 - recall_52: 0.8020 - precision_52: 0.8020\n",
            "Epoch 11/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3366 - accuracy: 0.8576 - recall_52: 0.7921 - precision_52: 0.8000\n",
            "Epoch 12/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3350 - accuracy: 0.8611 - recall_52: 0.8020 - precision_52: 0.8020\n",
            "Epoch 13/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3346 - accuracy: 0.8576 - recall_52: 0.8020 - precision_52: 0.7941\n",
            "Epoch 14/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3337 - accuracy: 0.8576 - recall_52: 0.8119 - precision_52: 0.7885\n",
            "Epoch 15/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3323 - accuracy: 0.8611 - recall_52: 0.8119 - precision_52: 0.7961\n",
            "Epoch 16/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3325 - accuracy: 0.8681 - recall_52: 0.8317 - precision_52: 0.8000\n",
            "Epoch 17/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3300 - accuracy: 0.8646 - recall_52: 0.8218 - precision_52: 0.7981\n",
            "Epoch 18/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3296 - accuracy: 0.8611 - recall_52: 0.8119 - precision_52: 0.7961\n",
            "Epoch 19/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3283 - accuracy: 0.8576 - recall_52: 0.7921 - precision_52: 0.8000\n",
            "Epoch 20/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3272 - accuracy: 0.8646 - recall_52: 0.8119 - precision_52: 0.8039\n",
            "Epoch 21/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3269 - accuracy: 0.8611 - recall_52: 0.8119 - precision_52: 0.7961\n",
            "Epoch 22/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3253 - accuracy: 0.8611 - recall_52: 0.8119 - precision_52: 0.7961\n",
            "Epoch 23/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3251 - accuracy: 0.8611 - recall_52: 0.8119 - precision_52: 0.7961\n",
            "Epoch 24/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3250 - accuracy: 0.8646 - recall_52: 0.8020 - precision_52: 0.8100\n",
            "Epoch 25/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3223 - accuracy: 0.8611 - recall_52: 0.7921 - precision_52: 0.8081\n",
            "Epoch 26/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3212 - accuracy: 0.8646 - recall_52: 0.8119 - precision_52: 0.8039\n",
            "Epoch 27/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3209 - accuracy: 0.8715 - recall_52: 0.8416 - precision_52: 0.8019\n",
            "Epoch 28/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3192 - accuracy: 0.8715 - recall_52: 0.8416 - precision_52: 0.8019\n",
            "Epoch 29/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3184 - accuracy: 0.8681 - recall_52: 0.8119 - precision_52: 0.8119\n",
            "Epoch 30/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3181 - accuracy: 0.8611 - recall_52: 0.8119 - precision_52: 0.7961\n",
            "Epoch 31/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3165 - accuracy: 0.8646 - recall_52: 0.8218 - precision_52: 0.7981\n",
            "Epoch 32/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3163 - accuracy: 0.8646 - recall_52: 0.8218 - precision_52: 0.7981\n",
            "Epoch 33/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3152 - accuracy: 0.8681 - recall_52: 0.8119 - precision_52: 0.8119\n",
            "Epoch 34/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3135 - accuracy: 0.8681 - recall_52: 0.8218 - precision_52: 0.8058\n",
            "Epoch 35/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3134 - accuracy: 0.8681 - recall_52: 0.8317 - precision_52: 0.8000\n",
            "Epoch 36/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3121 - accuracy: 0.8681 - recall_52: 0.8218 - precision_52: 0.8058\n",
            "Epoch 37/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3111 - accuracy: 0.8715 - recall_52: 0.8119 - precision_52: 0.8200\n",
            "Epoch 38/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3108 - accuracy: 0.8715 - recall_52: 0.8119 - precision_52: 0.8200\n",
            "Epoch 39/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3096 - accuracy: 0.8646 - recall_52: 0.8218 - precision_52: 0.7981\n",
            "Epoch 40/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3079 - accuracy: 0.8681 - recall_52: 0.8218 - precision_52: 0.8058\n",
            "4/4 - 0s - loss: 0.4274 - accuracy: 0.8065 - recall_52: 0.7442 - precision_52: 0.7111 - 33ms/epoch - 8ms/step\n",
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 2ms/step - loss: 0.7919 - accuracy: 0.3958 - recall_53: 0.2970 - precision_53: 0.2256\n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.7226 - accuracy: 0.5208 - recall_53: 0.4950 - precision_53: 0.3650\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6641 - accuracy: 0.6042 - recall_53: 0.5941 - precision_53: 0.4511\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6209 - accuracy: 0.6771 - recall_53: 0.7030 - precision_53: 0.5299\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5886 - accuracy: 0.7083 - recall_53: 0.7624 - precision_53: 0.5620\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5624 - accuracy: 0.7500 - recall_53: 0.7723 - precision_53: 0.6142\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5419 - accuracy: 0.7778 - recall_53: 0.7723 - precision_53: 0.6555\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5263 - accuracy: 0.8090 - recall_53: 0.8020 - precision_53: 0.6983\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5123 - accuracy: 0.8160 - recall_53: 0.7921 - precision_53: 0.7143\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5001 - accuracy: 0.8090 - recall_53: 0.7723 - precision_53: 0.7091\n",
            "4/4 - 0s - loss: 0.5233 - accuracy: 0.7258 - recall_53: 0.6977 - precision_53: 0.5882 - 370ms/epoch - 93ms/step\n",
            "Epoch 1/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4896 - accuracy: 0.8090 - recall_53: 0.7723 - precision_53: 0.7091\n",
            "Epoch 2/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4804 - accuracy: 0.8125 - recall_53: 0.7822 - precision_53: 0.7117\n",
            "Epoch 3/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4728 - accuracy: 0.8229 - recall_53: 0.7921 - precision_53: 0.7273\n",
            "Epoch 4/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4651 - accuracy: 0.8299 - recall_53: 0.7921 - precision_53: 0.7407\n",
            "Epoch 5/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4587 - accuracy: 0.8333 - recall_53: 0.7921 - precision_53: 0.7477\n",
            "Epoch 6/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4530 - accuracy: 0.8368 - recall_53: 0.7921 - precision_53: 0.7547\n",
            "Epoch 7/20\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.4473 - accuracy: 0.8333 - recall_53: 0.7921 - precision_53: 0.7477\n",
            "Epoch 8/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4435 - accuracy: 0.8438 - recall_53: 0.8020 - precision_53: 0.7642\n",
            "Epoch 9/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4389 - accuracy: 0.8438 - recall_53: 0.8020 - precision_53: 0.7642\n",
            "Epoch 10/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4337 - accuracy: 0.8403 - recall_53: 0.8020 - precision_53: 0.7570\n",
            "Epoch 11/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4301 - accuracy: 0.8438 - recall_53: 0.8020 - precision_53: 0.7642\n",
            "Epoch 12/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4262 - accuracy: 0.8438 - recall_53: 0.8020 - precision_53: 0.7642\n",
            "Epoch 13/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4227 - accuracy: 0.8438 - recall_53: 0.8020 - precision_53: 0.7642\n",
            "Epoch 14/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4196 - accuracy: 0.8438 - recall_53: 0.8020 - precision_53: 0.7642\n",
            "Epoch 15/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4162 - accuracy: 0.8438 - recall_53: 0.8020 - precision_53: 0.7642\n",
            "Epoch 16/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4133 - accuracy: 0.8438 - recall_53: 0.8020 - precision_53: 0.7642\n",
            "Epoch 17/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4110 - accuracy: 0.8438 - recall_53: 0.8020 - precision_53: 0.7642\n",
            "Epoch 18/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4080 - accuracy: 0.8403 - recall_53: 0.7921 - precision_53: 0.7619\n",
            "Epoch 19/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4054 - accuracy: 0.8368 - recall_53: 0.7822 - precision_53: 0.7596\n",
            "Epoch 20/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4034 - accuracy: 0.8368 - recall_53: 0.7822 - precision_53: 0.7596\n",
            "4/4 - 0s - loss: 0.4489 - accuracy: 0.7581 - recall_53: 0.6977 - precision_53: 0.6383 - 22ms/epoch - 5ms/step\n",
            "Epoch 1/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4009 - accuracy: 0.8368 - recall_53: 0.7822 - precision_53: 0.7596\n",
            "Epoch 2/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3988 - accuracy: 0.8403 - recall_53: 0.7921 - precision_53: 0.7619\n",
            "Epoch 3/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3969 - accuracy: 0.8368 - recall_53: 0.7822 - precision_53: 0.7596\n",
            "Epoch 4/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3949 - accuracy: 0.8403 - recall_53: 0.7822 - precision_53: 0.7670\n",
            "Epoch 5/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3929 - accuracy: 0.8403 - recall_53: 0.7822 - precision_53: 0.7670\n",
            "Epoch 6/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3913 - accuracy: 0.8403 - recall_53: 0.7822 - precision_53: 0.7670\n",
            "Epoch 7/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3895 - accuracy: 0.8403 - recall_53: 0.7822 - precision_53: 0.7670\n",
            "Epoch 8/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3878 - accuracy: 0.8403 - recall_53: 0.7822 - precision_53: 0.7670\n",
            "Epoch 9/30\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3873 - accuracy: 0.8403 - recall_53: 0.7822 - precision_53: 0.7670\n",
            "Epoch 10/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3841 - accuracy: 0.8438 - recall_53: 0.7921 - precision_53: 0.7692\n",
            "Epoch 11/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3829 - accuracy: 0.8403 - recall_53: 0.7822 - precision_53: 0.7670\n",
            "Epoch 12/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3814 - accuracy: 0.8403 - recall_53: 0.7822 - precision_53: 0.7670\n",
            "Epoch 13/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3797 - accuracy: 0.8403 - recall_53: 0.7822 - precision_53: 0.7670\n",
            "Epoch 14/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3788 - accuracy: 0.8403 - recall_53: 0.7822 - precision_53: 0.7670\n",
            "Epoch 15/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3771 - accuracy: 0.8403 - recall_53: 0.7822 - precision_53: 0.7670\n",
            "Epoch 16/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3755 - accuracy: 0.8438 - recall_53: 0.7921 - precision_53: 0.7692\n",
            "Epoch 17/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3746 - accuracy: 0.8438 - recall_53: 0.7921 - precision_53: 0.7692\n",
            "Epoch 18/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3732 - accuracy: 0.8438 - recall_53: 0.7921 - precision_53: 0.7692\n",
            "Epoch 19/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3728 - accuracy: 0.8507 - recall_53: 0.8119 - precision_53: 0.7736\n",
            "Epoch 20/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3709 - accuracy: 0.8507 - recall_53: 0.8119 - precision_53: 0.7736\n",
            "Epoch 21/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3726 - accuracy: 0.8368 - recall_53: 0.7723 - precision_53: 0.7647\n",
            "Epoch 22/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3697 - accuracy: 0.8507 - recall_53: 0.8020 - precision_53: 0.7788\n",
            "Epoch 23/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3671 - accuracy: 0.8542 - recall_53: 0.8119 - precision_53: 0.7810\n",
            "Epoch 24/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3660 - accuracy: 0.8542 - recall_53: 0.8119 - precision_53: 0.7810\n",
            "Epoch 25/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3661 - accuracy: 0.8438 - recall_53: 0.7822 - precision_53: 0.7745\n",
            "Epoch 26/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3645 - accuracy: 0.8507 - recall_53: 0.8020 - precision_53: 0.7788\n",
            "Epoch 27/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3629 - accuracy: 0.8507 - recall_53: 0.8020 - precision_53: 0.7788\n",
            "Epoch 28/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3615 - accuracy: 0.8611 - recall_53: 0.8317 - precision_53: 0.7850\n",
            "Epoch 29/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3603 - accuracy: 0.8646 - recall_53: 0.8317 - precision_53: 0.7925\n",
            "Epoch 30/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3592 - accuracy: 0.8646 - recall_53: 0.8317 - precision_53: 0.7925\n",
            "4/4 - 0s - loss: 0.4298 - accuracy: 0.7823 - recall_53: 0.7442 - precision_53: 0.6667 - 26ms/epoch - 7ms/step\n",
            "Epoch 1/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3583 - accuracy: 0.8576 - recall_53: 0.8119 - precision_53: 0.7885\n",
            "Epoch 2/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3574 - accuracy: 0.8611 - recall_53: 0.8119 - precision_53: 0.7961\n",
            "Epoch 3/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3563 - accuracy: 0.8646 - recall_53: 0.8317 - precision_53: 0.7925\n",
            "Epoch 4/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3549 - accuracy: 0.8681 - recall_53: 0.8317 - precision_53: 0.8000\n",
            "Epoch 5/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3551 - accuracy: 0.8646 - recall_53: 0.8317 - precision_53: 0.7925\n",
            "Epoch 6/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3535 - accuracy: 0.8646 - recall_53: 0.8317 - precision_53: 0.7925\n",
            "Epoch 7/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3523 - accuracy: 0.8715 - recall_53: 0.8317 - precision_53: 0.8077\n",
            "Epoch 8/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3520 - accuracy: 0.8681 - recall_53: 0.8317 - precision_53: 0.8000\n",
            "Epoch 9/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3500 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 10/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3492 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 11/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3484 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 12/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3483 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 13/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3462 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 14/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3455 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 15/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3443 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 16/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3431 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 17/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3424 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 18/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3413 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 19/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3403 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 20/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3391 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 21/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3383 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 22/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3372 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 23/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3363 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 24/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3355 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 25/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3355 - accuracy: 0.8715 - recall_53: 0.8218 - precision_53: 0.8137\n",
            "Epoch 26/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3341 - accuracy: 0.8715 - recall_53: 0.8218 - precision_53: 0.8137\n",
            "Epoch 27/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3327 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 28/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3315 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 29/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3306 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 30/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3295 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 31/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3288 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 32/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3276 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 33/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3282 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 34/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3253 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 35/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3254 - accuracy: 0.8785 - recall_53: 0.8317 - precision_53: 0.8235\n",
            "Epoch 36/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3235 - accuracy: 0.8785 - recall_53: 0.8317 - precision_53: 0.8235\n",
            "Epoch 37/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3221 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 38/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3215 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 39/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3212 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "Epoch 40/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3195 - accuracy: 0.8750 - recall_53: 0.8317 - precision_53: 0.8155\n",
            "4/4 - 0s - loss: 0.4371 - accuracy: 0.7984 - recall_53: 0.7674 - precision_53: 0.6875 - 25ms/epoch - 6ms/step\n",
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 2ms/step - loss: 0.7294 - accuracy: 0.5556 - recall_54: 0.3564 - precision_54: 0.3636\n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6621 - accuracy: 0.6389 - recall_54: 0.5050 - precision_54: 0.4857\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6228 - accuracy: 0.6667 - recall_54: 0.5842 - precision_54: 0.5221\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5907 - accuracy: 0.7049 - recall_54: 0.6238 - precision_54: 0.5727\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5619 - accuracy: 0.7431 - recall_54: 0.6634 - precision_54: 0.6262\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5350 - accuracy: 0.7708 - recall_54: 0.6733 - precision_54: 0.6733\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5109 - accuracy: 0.7674 - recall_54: 0.6634 - precision_54: 0.6700\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4916 - accuracy: 0.7778 - recall_54: 0.6733 - precision_54: 0.6869\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4729 - accuracy: 0.7917 - recall_54: 0.6733 - precision_54: 0.7158\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4571 - accuracy: 0.7951 - recall_54: 0.6832 - precision_54: 0.7188\n",
            "4/4 - 0s - loss: 0.4594 - accuracy: 0.7742 - recall_54: 0.6744 - precision_54: 0.6744 - 368ms/epoch - 92ms/step\n",
            "Epoch 1/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4434 - accuracy: 0.8056 - recall_54: 0.6931 - precision_54: 0.7368\n",
            "Epoch 2/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4321 - accuracy: 0.8021 - recall_54: 0.7228 - precision_54: 0.7157\n",
            "Epoch 3/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4202 - accuracy: 0.8194 - recall_54: 0.7228 - precision_54: 0.7526\n",
            "Epoch 4/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4123 - accuracy: 0.8194 - recall_54: 0.7228 - precision_54: 0.7526\n",
            "Epoch 5/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4039 - accuracy: 0.8229 - recall_54: 0.7228 - precision_54: 0.7604\n",
            "Epoch 6/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3965 - accuracy: 0.8229 - recall_54: 0.7228 - precision_54: 0.7604\n",
            "Epoch 7/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3921 - accuracy: 0.8299 - recall_54: 0.7624 - precision_54: 0.7549\n",
            "Epoch 8/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3855 - accuracy: 0.8333 - recall_54: 0.7624 - precision_54: 0.7624\n",
            "Epoch 9/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3811 - accuracy: 0.8368 - recall_54: 0.7624 - precision_54: 0.7700\n",
            "Epoch 10/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3773 - accuracy: 0.8403 - recall_54: 0.7822 - precision_54: 0.7670\n",
            "Epoch 11/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3735 - accuracy: 0.8403 - recall_54: 0.7822 - precision_54: 0.7670\n",
            "Epoch 12/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3709 - accuracy: 0.8368 - recall_54: 0.7723 - precision_54: 0.7647\n",
            "Epoch 13/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3673 - accuracy: 0.8403 - recall_54: 0.7822 - precision_54: 0.7670\n",
            "Epoch 14/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3646 - accuracy: 0.8403 - recall_54: 0.7822 - precision_54: 0.7670\n",
            "Epoch 15/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3626 - accuracy: 0.8368 - recall_54: 0.7723 - precision_54: 0.7647\n",
            "Epoch 16/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3594 - accuracy: 0.8403 - recall_54: 0.7723 - precision_54: 0.7723\n",
            "Epoch 17/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3575 - accuracy: 0.8368 - recall_54: 0.7822 - precision_54: 0.7596\n",
            "Epoch 18/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3544 - accuracy: 0.8368 - recall_54: 0.7822 - precision_54: 0.7596\n",
            "Epoch 19/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3529 - accuracy: 0.8403 - recall_54: 0.7921 - precision_54: 0.7619\n",
            "Epoch 20/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3501 - accuracy: 0.8333 - recall_54: 0.7723 - precision_54: 0.7573\n",
            "4/4 - 0s - loss: 0.4287 - accuracy: 0.8065 - recall_54: 0.7442 - precision_54: 0.7111 - 22ms/epoch - 5ms/step\n",
            "Epoch 1/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3482 - accuracy: 0.8368 - recall_54: 0.7723 - precision_54: 0.7647\n",
            "Epoch 2/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3450 - accuracy: 0.8403 - recall_54: 0.7822 - precision_54: 0.7670\n",
            "Epoch 3/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3451 - accuracy: 0.8403 - recall_54: 0.7822 - precision_54: 0.7670\n",
            "Epoch 4/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3421 - accuracy: 0.8438 - recall_54: 0.7921 - precision_54: 0.7692\n",
            "Epoch 5/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3450 - accuracy: 0.8403 - recall_54: 0.8119 - precision_54: 0.7523\n",
            "Epoch 6/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3363 - accuracy: 0.8472 - recall_54: 0.7921 - precision_54: 0.7767\n",
            "Epoch 7/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3377 - accuracy: 0.8507 - recall_54: 0.7921 - precision_54: 0.7843\n",
            "Epoch 8/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3353 - accuracy: 0.8368 - recall_54: 0.7624 - precision_54: 0.7700\n",
            "Epoch 9/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3331 - accuracy: 0.8403 - recall_54: 0.8020 - precision_54: 0.7570\n",
            "Epoch 10/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3314 - accuracy: 0.8507 - recall_54: 0.8020 - precision_54: 0.7788\n",
            "Epoch 11/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3290 - accuracy: 0.8507 - recall_54: 0.7921 - precision_54: 0.7843\n",
            "Epoch 12/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3273 - accuracy: 0.8472 - recall_54: 0.7921 - precision_54: 0.7767\n",
            "Epoch 13/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3251 - accuracy: 0.8507 - recall_54: 0.8020 - precision_54: 0.7788\n",
            "Epoch 14/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3236 - accuracy: 0.8507 - recall_54: 0.8119 - precision_54: 0.7736\n",
            "Epoch 15/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3213 - accuracy: 0.8576 - recall_54: 0.8218 - precision_54: 0.7830\n",
            "Epoch 16/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3196 - accuracy: 0.8542 - recall_54: 0.7921 - precision_54: 0.7921\n",
            "Epoch 17/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3180 - accuracy: 0.8542 - recall_54: 0.7921 - precision_54: 0.7921\n",
            "Epoch 18/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3166 - accuracy: 0.8542 - recall_54: 0.7921 - precision_54: 0.7921\n",
            "Epoch 19/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3155 - accuracy: 0.8611 - recall_54: 0.8218 - precision_54: 0.7905\n",
            "Epoch 20/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3137 - accuracy: 0.8576 - recall_54: 0.8020 - precision_54: 0.7941\n",
            "Epoch 21/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3120 - accuracy: 0.8646 - recall_54: 0.8218 - precision_54: 0.7981\n",
            "Epoch 22/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3142 - accuracy: 0.8507 - recall_54: 0.7822 - precision_54: 0.7900\n",
            "Epoch 23/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3089 - accuracy: 0.8611 - recall_54: 0.8119 - precision_54: 0.7961\n",
            "Epoch 24/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3065 - accuracy: 0.8646 - recall_54: 0.8218 - precision_54: 0.7981\n",
            "Epoch 25/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3049 - accuracy: 0.8611 - recall_54: 0.8119 - precision_54: 0.7961\n",
            "Epoch 26/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3090 - accuracy: 0.8542 - recall_54: 0.7921 - precision_54: 0.7921\n",
            "Epoch 27/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3007 - accuracy: 0.8542 - recall_54: 0.8020 - precision_54: 0.7864\n",
            "Epoch 28/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3003 - accuracy: 0.8611 - recall_54: 0.8218 - precision_54: 0.7905\n",
            "Epoch 29/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2983 - accuracy: 0.8611 - recall_54: 0.8119 - precision_54: 0.7961\n",
            "Epoch 30/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2973 - accuracy: 0.8611 - recall_54: 0.8119 - precision_54: 0.7961\n",
            "4/4 - 0s - loss: 0.4495 - accuracy: 0.8145 - recall_54: 0.7209 - precision_54: 0.7381 - 31ms/epoch - 8ms/step\n",
            "Epoch 1/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2951 - accuracy: 0.8611 - recall_54: 0.8119 - precision_54: 0.7961\n",
            "Epoch 2/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2931 - accuracy: 0.8611 - recall_54: 0.8119 - precision_54: 0.7961\n",
            "Epoch 3/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2935 - accuracy: 0.8681 - recall_54: 0.8317 - precision_54: 0.8000\n",
            "Epoch 4/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2915 - accuracy: 0.8646 - recall_54: 0.8119 - precision_54: 0.8039\n",
            "Epoch 5/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2885 - accuracy: 0.8646 - recall_54: 0.8119 - precision_54: 0.8039\n",
            "Epoch 6/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2872 - accuracy: 0.8681 - recall_54: 0.8218 - precision_54: 0.8058\n",
            "Epoch 7/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2845 - accuracy: 0.8646 - recall_54: 0.8119 - precision_54: 0.8039\n",
            "Epoch 8/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2840 - accuracy: 0.8646 - recall_54: 0.8119 - precision_54: 0.8039\n",
            "Epoch 9/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2824 - accuracy: 0.8681 - recall_54: 0.8218 - precision_54: 0.8058\n",
            "Epoch 10/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2809 - accuracy: 0.8715 - recall_54: 0.8317 - precision_54: 0.8077\n",
            "Epoch 11/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2796 - accuracy: 0.8646 - recall_54: 0.8218 - precision_54: 0.7981\n",
            "Epoch 12/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2779 - accuracy: 0.8715 - recall_54: 0.8317 - precision_54: 0.8077\n",
            "Epoch 13/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2754 - accuracy: 0.8715 - recall_54: 0.8317 - precision_54: 0.8077\n",
            "Epoch 14/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2738 - accuracy: 0.8681 - recall_54: 0.8218 - precision_54: 0.8058\n",
            "Epoch 15/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2721 - accuracy: 0.8715 - recall_54: 0.8317 - precision_54: 0.8077\n",
            "Epoch 16/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2711 - accuracy: 0.8715 - recall_54: 0.8317 - precision_54: 0.8077\n",
            "Epoch 17/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2701 - accuracy: 0.8715 - recall_54: 0.8317 - precision_54: 0.8077\n",
            "Epoch 18/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2684 - accuracy: 0.8681 - recall_54: 0.8317 - precision_54: 0.8000\n",
            "Epoch 19/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2655 - accuracy: 0.8750 - recall_54: 0.8317 - precision_54: 0.8155\n",
            "Epoch 20/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2640 - accuracy: 0.8785 - recall_54: 0.8317 - precision_54: 0.8235\n",
            "Epoch 21/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2634 - accuracy: 0.8715 - recall_54: 0.8317 - precision_54: 0.8077\n",
            "Epoch 22/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2644 - accuracy: 0.8715 - recall_54: 0.8218 - precision_54: 0.8137\n",
            "Epoch 23/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2611 - accuracy: 0.8715 - recall_54: 0.8218 - precision_54: 0.8137\n",
            "Epoch 24/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2618 - accuracy: 0.8819 - recall_54: 0.8416 - precision_54: 0.8252\n",
            "Epoch 25/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2569 - accuracy: 0.8785 - recall_54: 0.8515 - precision_54: 0.8113\n",
            "Epoch 26/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2563 - accuracy: 0.8854 - recall_54: 0.8416 - precision_54: 0.8333\n",
            "Epoch 27/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2529 - accuracy: 0.8889 - recall_54: 0.8416 - precision_54: 0.8416\n",
            "Epoch 28/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2529 - accuracy: 0.8854 - recall_54: 0.8614 - precision_54: 0.8208\n",
            "Epoch 29/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2530 - accuracy: 0.8924 - recall_54: 0.8614 - precision_54: 0.8365\n",
            "Epoch 30/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2504 - accuracy: 0.8854 - recall_54: 0.8416 - precision_54: 0.8333\n",
            "Epoch 31/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2471 - accuracy: 0.8924 - recall_54: 0.8614 - precision_54: 0.8365\n",
            "Epoch 32/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2473 - accuracy: 0.8889 - recall_54: 0.8416 - precision_54: 0.8416\n",
            "Epoch 33/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2454 - accuracy: 0.8889 - recall_54: 0.8416 - precision_54: 0.8416\n",
            "Epoch 34/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2450 - accuracy: 0.8819 - recall_54: 0.8515 - precision_54: 0.8190\n",
            "Epoch 35/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2422 - accuracy: 0.8924 - recall_54: 0.8515 - precision_54: 0.8431\n",
            "Epoch 36/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2404 - accuracy: 0.8924 - recall_54: 0.8515 - precision_54: 0.8431\n",
            "Epoch 37/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2423 - accuracy: 0.8889 - recall_54: 0.8614 - precision_54: 0.8286\n",
            "Epoch 38/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2364 - accuracy: 0.8958 - recall_54: 0.8614 - precision_54: 0.8447\n",
            "Epoch 39/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2384 - accuracy: 0.8924 - recall_54: 0.8416 - precision_54: 0.8500\n",
            "Epoch 40/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2354 - accuracy: 0.8958 - recall_54: 0.8614 - precision_54: 0.8447\n",
            "4/4 - 0s - loss: 0.4802 - accuracy: 0.8306 - recall_54: 0.7674 - precision_54: 0.7500 - 24ms/epoch - 6ms/step\n",
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 2ms/step - loss: 0.6713 - accuracy: 0.6215 - recall_55: 0.8020 - precision_55: 0.4765\n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.6234 - accuracy: 0.7326 - recall_55: 0.7723 - precision_55: 0.5909\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5891 - accuracy: 0.7604 - recall_55: 0.7822 - precision_55: 0.6270\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5577 - accuracy: 0.7743 - recall_55: 0.7525 - precision_55: 0.6552\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5318 - accuracy: 0.7882 - recall_55: 0.7426 - precision_55: 0.6818\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5097 - accuracy: 0.7847 - recall_55: 0.7327 - precision_55: 0.6789\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4937 - accuracy: 0.7951 - recall_55: 0.7228 - precision_55: 0.7019\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4769 - accuracy: 0.8090 - recall_55: 0.7327 - precision_55: 0.7255\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4652 - accuracy: 0.7986 - recall_55: 0.7525 - precision_55: 0.6972\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4527 - accuracy: 0.8021 - recall_55: 0.7525 - precision_55: 0.7037\n",
            "4/4 - 0s - loss: 0.4742 - accuracy: 0.7742 - recall_55: 0.6977 - precision_55: 0.6667 - 328ms/epoch - 82ms/step\n",
            "Epoch 1/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4424 - accuracy: 0.8160 - recall_55: 0.7525 - precision_55: 0.7308\n",
            "Epoch 2/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4345 - accuracy: 0.8160 - recall_55: 0.7525 - precision_55: 0.7308\n",
            "Epoch 3/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4264 - accuracy: 0.8160 - recall_55: 0.7525 - precision_55: 0.7308\n",
            "Epoch 4/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4184 - accuracy: 0.8299 - recall_55: 0.7525 - precision_55: 0.7600\n",
            "Epoch 5/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4128 - accuracy: 0.8368 - recall_55: 0.7525 - precision_55: 0.7755\n",
            "Epoch 6/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4069 - accuracy: 0.8403 - recall_55: 0.7525 - precision_55: 0.7835\n",
            "Epoch 7/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4010 - accuracy: 0.8368 - recall_55: 0.7525 - precision_55: 0.7755\n",
            "Epoch 8/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3971 - accuracy: 0.8368 - recall_55: 0.7624 - precision_55: 0.7700\n",
            "Epoch 9/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3935 - accuracy: 0.8368 - recall_55: 0.7624 - precision_55: 0.7700\n",
            "Epoch 10/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3893 - accuracy: 0.8403 - recall_55: 0.7624 - precision_55: 0.7778\n",
            "Epoch 11/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3868 - accuracy: 0.8299 - recall_55: 0.7624 - precision_55: 0.7549\n",
            "Epoch 12/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3829 - accuracy: 0.8333 - recall_55: 0.7624 - precision_55: 0.7624\n",
            "Epoch 13/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3814 - accuracy: 0.8403 - recall_55: 0.7624 - precision_55: 0.7778\n",
            "Epoch 14/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3782 - accuracy: 0.8438 - recall_55: 0.7624 - precision_55: 0.7857\n",
            "Epoch 15/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3754 - accuracy: 0.8368 - recall_55: 0.7624 - precision_55: 0.7700\n",
            "Epoch 16/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3733 - accuracy: 0.8368 - recall_55: 0.7525 - precision_55: 0.7755\n",
            "Epoch 17/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3723 - accuracy: 0.8403 - recall_55: 0.7426 - precision_55: 0.7895\n",
            "Epoch 18/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3697 - accuracy: 0.8368 - recall_55: 0.7426 - precision_55: 0.7812\n",
            "Epoch 19/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3704 - accuracy: 0.8403 - recall_55: 0.7822 - precision_55: 0.7670\n",
            "Epoch 20/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3658 - accuracy: 0.8368 - recall_55: 0.7624 - precision_55: 0.7700\n",
            "4/4 - 0s - loss: 0.4143 - accuracy: 0.8387 - recall_55: 0.7442 - precision_55: 0.7805 - 21ms/epoch - 5ms/step\n",
            "Epoch 1/30\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3640 - accuracy: 0.8368 - recall_55: 0.7426 - precision_55: 0.7812\n",
            "Epoch 2/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3631 - accuracy: 0.8368 - recall_55: 0.7426 - precision_55: 0.7812\n",
            "Epoch 3/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3621 - accuracy: 0.8403 - recall_55: 0.7723 - precision_55: 0.7723\n",
            "Epoch 4/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3592 - accuracy: 0.8368 - recall_55: 0.7723 - precision_55: 0.7647\n",
            "Epoch 5/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3569 - accuracy: 0.8368 - recall_55: 0.7525 - precision_55: 0.7755\n",
            "Epoch 6/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3544 - accuracy: 0.8403 - recall_55: 0.7525 - precision_55: 0.7835\n",
            "Epoch 7/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3539 - accuracy: 0.8403 - recall_55: 0.7723 - precision_55: 0.7723\n",
            "Epoch 8/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3534 - accuracy: 0.8438 - recall_55: 0.7822 - precision_55: 0.7745\n",
            "Epoch 9/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3506 - accuracy: 0.8438 - recall_55: 0.7723 - precision_55: 0.7800\n",
            "Epoch 10/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3502 - accuracy: 0.8438 - recall_55: 0.7624 - precision_55: 0.7857\n",
            "Epoch 11/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3463 - accuracy: 0.8403 - recall_55: 0.7822 - precision_55: 0.7670\n",
            "Epoch 12/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3467 - accuracy: 0.8438 - recall_55: 0.7822 - precision_55: 0.7745\n",
            "Epoch 13/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3449 - accuracy: 0.8333 - recall_55: 0.7525 - precision_55: 0.7677\n",
            "Epoch 14/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3429 - accuracy: 0.8438 - recall_55: 0.7525 - precision_55: 0.7917\n",
            "Epoch 15/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3408 - accuracy: 0.8507 - recall_55: 0.7723 - precision_55: 0.7959\n",
            "Epoch 16/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3402 - accuracy: 0.8438 - recall_55: 0.7921 - precision_55: 0.7692\n",
            "Epoch 17/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3393 - accuracy: 0.8472 - recall_55: 0.7921 - precision_55: 0.7767\n",
            "Epoch 18/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3394 - accuracy: 0.8542 - recall_55: 0.7723 - precision_55: 0.8041\n",
            "Epoch 19/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3358 - accuracy: 0.8576 - recall_55: 0.7921 - precision_55: 0.8000\n",
            "Epoch 20/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3343 - accuracy: 0.8542 - recall_55: 0.7822 - precision_55: 0.7980\n",
            "Epoch 21/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3333 - accuracy: 0.8576 - recall_55: 0.7921 - precision_55: 0.8000\n",
            "Epoch 22/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3322 - accuracy: 0.8507 - recall_55: 0.7723 - precision_55: 0.7959\n",
            "Epoch 23/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3311 - accuracy: 0.8472 - recall_55: 0.7723 - precision_55: 0.7879\n",
            "Epoch 24/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3287 - accuracy: 0.8576 - recall_55: 0.8020 - precision_55: 0.7941\n",
            "Epoch 25/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3271 - accuracy: 0.8611 - recall_55: 0.7921 - precision_55: 0.8081\n",
            "Epoch 26/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3262 - accuracy: 0.8576 - recall_55: 0.7921 - precision_55: 0.8000\n",
            "Epoch 27/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3252 - accuracy: 0.8542 - recall_55: 0.8020 - precision_55: 0.7864\n",
            "Epoch 28/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3262 - accuracy: 0.8576 - recall_55: 0.7723 - precision_55: 0.8125\n",
            "Epoch 29/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3211 - accuracy: 0.8611 - recall_55: 0.7921 - precision_55: 0.8081\n",
            "Epoch 30/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3210 - accuracy: 0.8611 - recall_55: 0.8119 - precision_55: 0.7961\n",
            "4/4 - 0s - loss: 0.4173 - accuracy: 0.8306 - recall_55: 0.7674 - precision_55: 0.7500 - 28ms/epoch - 7ms/step\n",
            "Epoch 1/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3188 - accuracy: 0.8681 - recall_55: 0.8119 - precision_55: 0.8119\n",
            "Epoch 2/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3181 - accuracy: 0.8646 - recall_55: 0.7921 - precision_55: 0.8163\n",
            "Epoch 3/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3170 - accuracy: 0.8611 - recall_55: 0.7921 - precision_55: 0.8081\n",
            "Epoch 4/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3152 - accuracy: 0.8681 - recall_55: 0.8218 - precision_55: 0.8058\n",
            "Epoch 5/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3141 - accuracy: 0.8681 - recall_55: 0.8119 - precision_55: 0.8119\n",
            "Epoch 6/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3117 - accuracy: 0.8681 - recall_55: 0.8020 - precision_55: 0.8182\n",
            "Epoch 7/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3107 - accuracy: 0.8681 - recall_55: 0.8020 - precision_55: 0.8182\n",
            "Epoch 8/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3138 - accuracy: 0.8611 - recall_55: 0.8119 - precision_55: 0.7961\n",
            "Epoch 9/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3079 - accuracy: 0.8715 - recall_55: 0.8119 - precision_55: 0.8200\n",
            "Epoch 10/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3066 - accuracy: 0.8750 - recall_55: 0.8119 - precision_55: 0.8283\n",
            "Epoch 11/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3064 - accuracy: 0.8681 - recall_55: 0.8119 - precision_55: 0.8119\n",
            "Epoch 12/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3050 - accuracy: 0.8750 - recall_55: 0.8119 - precision_55: 0.8283\n",
            "Epoch 13/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3027 - accuracy: 0.8750 - recall_55: 0.8218 - precision_55: 0.8218\n",
            "Epoch 14/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3020 - accuracy: 0.8750 - recall_55: 0.8218 - precision_55: 0.8218\n",
            "Epoch 15/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3012 - accuracy: 0.8715 - recall_55: 0.8218 - precision_55: 0.8137\n",
            "Epoch 16/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2993 - accuracy: 0.8785 - recall_55: 0.8218 - precision_55: 0.8300\n",
            "Epoch 17/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2983 - accuracy: 0.8785 - recall_55: 0.8218 - precision_55: 0.8300\n",
            "Epoch 18/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2974 - accuracy: 0.8715 - recall_55: 0.8218 - precision_55: 0.8137\n",
            "Epoch 19/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2949 - accuracy: 0.8750 - recall_55: 0.8218 - precision_55: 0.8218\n",
            "Epoch 20/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2980 - accuracy: 0.8750 - recall_55: 0.8119 - precision_55: 0.8283\n",
            "Epoch 21/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2926 - accuracy: 0.8750 - recall_55: 0.8218 - precision_55: 0.8218\n",
            "Epoch 22/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2922 - accuracy: 0.8785 - recall_55: 0.8218 - precision_55: 0.8300\n",
            "Epoch 23/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2908 - accuracy: 0.8819 - recall_55: 0.8218 - precision_55: 0.8384\n",
            "Epoch 24/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2909 - accuracy: 0.8750 - recall_55: 0.8317 - precision_55: 0.8155\n",
            "Epoch 25/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2872 - accuracy: 0.8750 - recall_55: 0.8218 - precision_55: 0.8218\n",
            "Epoch 26/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2885 - accuracy: 0.8785 - recall_55: 0.8119 - precision_55: 0.8367\n",
            "Epoch 27/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2855 - accuracy: 0.8819 - recall_55: 0.8218 - precision_55: 0.8384\n",
            "Epoch 28/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2863 - accuracy: 0.8750 - recall_55: 0.8317 - precision_55: 0.8155\n",
            "Epoch 29/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2844 - accuracy: 0.8819 - recall_55: 0.8218 - precision_55: 0.8384\n",
            "Epoch 30/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2819 - accuracy: 0.8819 - recall_55: 0.8218 - precision_55: 0.8384\n",
            "Epoch 31/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2813 - accuracy: 0.8819 - recall_55: 0.8218 - precision_55: 0.8384\n",
            "Epoch 32/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2791 - accuracy: 0.8750 - recall_55: 0.8218 - precision_55: 0.8218\n",
            "Epoch 33/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2777 - accuracy: 0.8785 - recall_55: 0.8218 - precision_55: 0.8300\n",
            "Epoch 34/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2763 - accuracy: 0.8785 - recall_55: 0.8218 - precision_55: 0.8300\n",
            "Epoch 35/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2777 - accuracy: 0.8750 - recall_55: 0.8218 - precision_55: 0.8218\n",
            "Epoch 36/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2749 - accuracy: 0.8785 - recall_55: 0.8218 - precision_55: 0.8300\n",
            "Epoch 37/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2737 - accuracy: 0.8819 - recall_55: 0.8317 - precision_55: 0.8317\n",
            "Epoch 38/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2717 - accuracy: 0.8785 - recall_55: 0.8218 - precision_55: 0.8300\n",
            "Epoch 39/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2717 - accuracy: 0.8785 - recall_55: 0.8218 - precision_55: 0.8300\n",
            "Epoch 40/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2701 - accuracy: 0.8819 - recall_55: 0.8218 - precision_55: 0.8384\n",
            "4/4 - 0s - loss: 0.4456 - accuracy: 0.8548 - recall_55: 0.7674 - precision_55: 0.8049 - 21ms/epoch - 5ms/step\n",
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 3ms/step - loss: 0.7024 - accuracy: 0.5208 - recall_56: 0.6337 - precision_56: 0.3879\n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.6680 - accuracy: 0.6493 - recall_56: 0.6040 - precision_56: 0.5000\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6386 - accuracy: 0.7500 - recall_56: 0.7624 - precision_56: 0.6160\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6153 - accuracy: 0.7708 - recall_56: 0.7426 - precision_56: 0.6522\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5907 - accuracy: 0.7882 - recall_56: 0.7327 - precision_56: 0.6852\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5693 - accuracy: 0.7951 - recall_56: 0.7525 - precision_56: 0.6909\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5485 - accuracy: 0.8090 - recall_56: 0.7822 - precision_56: 0.7054\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5308 - accuracy: 0.7986 - recall_56: 0.7327 - precision_56: 0.7048\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5111 - accuracy: 0.7986 - recall_56: 0.7327 - precision_56: 0.7048\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4952 - accuracy: 0.8021 - recall_56: 0.7525 - precision_56: 0.7037\n",
            "4/4 - 0s - loss: 0.5128 - accuracy: 0.7903 - recall_56: 0.7674 - precision_56: 0.6735 - 340ms/epoch - 85ms/step\n",
            "Epoch 1/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4779 - accuracy: 0.8160 - recall_56: 0.7624 - precision_56: 0.7264\n",
            "Epoch 2/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4648 - accuracy: 0.8194 - recall_56: 0.7426 - precision_56: 0.7426\n",
            "Epoch 3/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4522 - accuracy: 0.8229 - recall_56: 0.7525 - precision_56: 0.7451\n",
            "Epoch 4/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4402 - accuracy: 0.8264 - recall_56: 0.7525 - precision_56: 0.7525\n",
            "Epoch 5/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4303 - accuracy: 0.8333 - recall_56: 0.7525 - precision_56: 0.7677\n",
            "Epoch 6/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4218 - accuracy: 0.8299 - recall_56: 0.7426 - precision_56: 0.7653\n",
            "Epoch 7/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4135 - accuracy: 0.8333 - recall_56: 0.7426 - precision_56: 0.7732\n",
            "Epoch 8/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4071 - accuracy: 0.8333 - recall_56: 0.7426 - precision_56: 0.7732\n",
            "Epoch 9/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4012 - accuracy: 0.8368 - recall_56: 0.7525 - precision_56: 0.7755\n",
            "Epoch 10/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3950 - accuracy: 0.8299 - recall_56: 0.7327 - precision_56: 0.7708\n",
            "Epoch 11/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3912 - accuracy: 0.8368 - recall_56: 0.7426 - precision_56: 0.7812\n",
            "Epoch 12/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3881 - accuracy: 0.8368 - recall_56: 0.7525 - precision_56: 0.7755\n",
            "Epoch 13/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3839 - accuracy: 0.8368 - recall_56: 0.7525 - precision_56: 0.7755\n",
            "Epoch 14/20\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3801 - accuracy: 0.8368 - recall_56: 0.7525 - precision_56: 0.7755\n",
            "Epoch 15/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3775 - accuracy: 0.8368 - recall_56: 0.7426 - precision_56: 0.7812\n",
            "Epoch 16/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3764 - accuracy: 0.8368 - recall_56: 0.7426 - precision_56: 0.7812\n",
            "Epoch 17/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3756 - accuracy: 0.8299 - recall_56: 0.7525 - precision_56: 0.7600\n",
            "Epoch 18/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3716 - accuracy: 0.8333 - recall_56: 0.7624 - precision_56: 0.7624\n",
            "Epoch 19/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3692 - accuracy: 0.8438 - recall_56: 0.7525 - precision_56: 0.7917\n",
            "Epoch 20/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3710 - accuracy: 0.8333 - recall_56: 0.7525 - precision_56: 0.7677\n",
            "4/4 - 0s - loss: 0.4178 - accuracy: 0.8226 - recall_56: 0.7442 - precision_56: 0.7442 - 21ms/epoch - 5ms/step\n",
            "Epoch 1/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3652 - accuracy: 0.8299 - recall_56: 0.7525 - precision_56: 0.7600\n",
            "Epoch 2/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3637 - accuracy: 0.8403 - recall_56: 0.7426 - precision_56: 0.7895\n",
            "Epoch 3/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3615 - accuracy: 0.8403 - recall_56: 0.7525 - precision_56: 0.7835\n",
            "Epoch 4/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3608 - accuracy: 0.8368 - recall_56: 0.7723 - precision_56: 0.7647\n",
            "Epoch 5/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3618 - accuracy: 0.8368 - recall_56: 0.7624 - precision_56: 0.7700\n",
            "Epoch 6/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3578 - accuracy: 0.8472 - recall_56: 0.7624 - precision_56: 0.7938\n",
            "Epoch 7/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3561 - accuracy: 0.8472 - recall_56: 0.7624 - precision_56: 0.7938\n",
            "Epoch 8/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3542 - accuracy: 0.8472 - recall_56: 0.7624 - precision_56: 0.7938\n",
            "Epoch 9/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3529 - accuracy: 0.8507 - recall_56: 0.7723 - precision_56: 0.7959\n",
            "Epoch 10/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3516 - accuracy: 0.8507 - recall_56: 0.7723 - precision_56: 0.7959\n",
            "Epoch 11/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3503 - accuracy: 0.8472 - recall_56: 0.7723 - precision_56: 0.7879\n",
            "Epoch 12/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3496 - accuracy: 0.8507 - recall_56: 0.7822 - precision_56: 0.7900\n",
            "Epoch 13/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3482 - accuracy: 0.8576 - recall_56: 0.7822 - precision_56: 0.8061\n",
            "Epoch 14/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3471 - accuracy: 0.8507 - recall_56: 0.7921 - precision_56: 0.7843\n",
            "Epoch 15/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3450 - accuracy: 0.8576 - recall_56: 0.7921 - precision_56: 0.8000\n",
            "Epoch 16/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3437 - accuracy: 0.8611 - recall_56: 0.7921 - precision_56: 0.8081\n",
            "Epoch 17/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3429 - accuracy: 0.8542 - recall_56: 0.7822 - precision_56: 0.7980\n",
            "Epoch 18/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3413 - accuracy: 0.8542 - recall_56: 0.7822 - precision_56: 0.7980\n",
            "Epoch 19/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3403 - accuracy: 0.8611 - recall_56: 0.7921 - precision_56: 0.8081\n",
            "Epoch 20/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3379 - accuracy: 0.8576 - recall_56: 0.7822 - precision_56: 0.8061\n",
            "Epoch 21/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3395 - accuracy: 0.8438 - recall_56: 0.7921 - precision_56: 0.7692\n",
            "Epoch 22/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3362 - accuracy: 0.8611 - recall_56: 0.8020 - precision_56: 0.8020\n",
            "Epoch 23/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3352 - accuracy: 0.8611 - recall_56: 0.7822 - precision_56: 0.8144\n",
            "Epoch 24/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3328 - accuracy: 0.8542 - recall_56: 0.7921 - precision_56: 0.7921\n",
            "Epoch 25/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3319 - accuracy: 0.8576 - recall_56: 0.7921 - precision_56: 0.8000\n",
            "Epoch 26/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3311 - accuracy: 0.8611 - recall_56: 0.7921 - precision_56: 0.8081\n",
            "Epoch 27/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3283 - accuracy: 0.8542 - recall_56: 0.7921 - precision_56: 0.7921\n",
            "Epoch 28/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3285 - accuracy: 0.8472 - recall_56: 0.7921 - precision_56: 0.7767\n",
            "Epoch 29/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3265 - accuracy: 0.8542 - recall_56: 0.7921 - precision_56: 0.7921\n",
            "Epoch 30/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3255 - accuracy: 0.8611 - recall_56: 0.7921 - precision_56: 0.8081\n",
            "4/4 - 0s - loss: 0.4271 - accuracy: 0.8306 - recall_56: 0.6977 - precision_56: 0.7895 - 22ms/epoch - 5ms/step\n",
            "Epoch 1/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3237 - accuracy: 0.8576 - recall_56: 0.7921 - precision_56: 0.8000\n",
            "Epoch 2/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3229 - accuracy: 0.8576 - recall_56: 0.8020 - precision_56: 0.7941\n",
            "Epoch 3/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3212 - accuracy: 0.8576 - recall_56: 0.7921 - precision_56: 0.8000\n",
            "Epoch 4/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3193 - accuracy: 0.8611 - recall_56: 0.7921 - precision_56: 0.8081\n",
            "Epoch 5/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3187 - accuracy: 0.8681 - recall_56: 0.7921 - precision_56: 0.8247\n",
            "Epoch 6/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3188 - accuracy: 0.8646 - recall_56: 0.7921 - precision_56: 0.8163\n",
            "Epoch 7/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3164 - accuracy: 0.8611 - recall_56: 0.7921 - precision_56: 0.8081\n",
            "Epoch 8/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3148 - accuracy: 0.8611 - recall_56: 0.7921 - precision_56: 0.8081\n",
            "Epoch 9/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3139 - accuracy: 0.8681 - recall_56: 0.7921 - precision_56: 0.8247\n",
            "Epoch 10/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3122 - accuracy: 0.8681 - recall_56: 0.7921 - precision_56: 0.8247\n",
            "Epoch 11/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3113 - accuracy: 0.8681 - recall_56: 0.7921 - precision_56: 0.8247\n",
            "Epoch 12/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3096 - accuracy: 0.8715 - recall_56: 0.8020 - precision_56: 0.8265\n",
            "Epoch 13/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3102 - accuracy: 0.8681 - recall_56: 0.8119 - precision_56: 0.8119\n",
            "Epoch 14/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3087 - accuracy: 0.8681 - recall_56: 0.7921 - precision_56: 0.8247\n",
            "Epoch 15/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3073 - accuracy: 0.8681 - recall_56: 0.7921 - precision_56: 0.8247\n",
            "Epoch 16/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3045 - accuracy: 0.8750 - recall_56: 0.8119 - precision_56: 0.8283\n",
            "Epoch 17/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3045 - accuracy: 0.8750 - recall_56: 0.8317 - precision_56: 0.8155\n",
            "Epoch 18/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3052 - accuracy: 0.8681 - recall_56: 0.7921 - precision_56: 0.8247\n",
            "Epoch 19/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3017 - accuracy: 0.8715 - recall_56: 0.8020 - precision_56: 0.8265\n",
            "Epoch 20/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3014 - accuracy: 0.8750 - recall_56: 0.8317 - precision_56: 0.8155\n",
            "Epoch 21/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2999 - accuracy: 0.8715 - recall_56: 0.8020 - precision_56: 0.8265\n",
            "Epoch 22/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2986 - accuracy: 0.8750 - recall_56: 0.8119 - precision_56: 0.8283\n",
            "Epoch 23/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2966 - accuracy: 0.8785 - recall_56: 0.8218 - precision_56: 0.8300\n",
            "Epoch 24/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2988 - accuracy: 0.8750 - recall_56: 0.7921 - precision_56: 0.8421\n",
            "Epoch 25/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2961 - accuracy: 0.8750 - recall_56: 0.8119 - precision_56: 0.8283\n",
            "Epoch 26/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2925 - accuracy: 0.8785 - recall_56: 0.8218 - precision_56: 0.8300\n",
            "Epoch 27/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2915 - accuracy: 0.8854 - recall_56: 0.8218 - precision_56: 0.8469\n",
            "Epoch 28/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2917 - accuracy: 0.8819 - recall_56: 0.8317 - precision_56: 0.8317\n",
            "Epoch 29/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2887 - accuracy: 0.8785 - recall_56: 0.8218 - precision_56: 0.8300\n",
            "Epoch 30/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2881 - accuracy: 0.8819 - recall_56: 0.8218 - precision_56: 0.8384\n",
            "Epoch 31/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2867 - accuracy: 0.8854 - recall_56: 0.8218 - precision_56: 0.8469\n",
            "Epoch 32/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2853 - accuracy: 0.8819 - recall_56: 0.8218 - precision_56: 0.8384\n",
            "Epoch 33/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2847 - accuracy: 0.8819 - recall_56: 0.8218 - precision_56: 0.8384\n",
            "Epoch 34/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2835 - accuracy: 0.8785 - recall_56: 0.8317 - precision_56: 0.8235\n",
            "Epoch 35/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2839 - accuracy: 0.8889 - recall_56: 0.8317 - precision_56: 0.8485\n",
            "Epoch 36/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2802 - accuracy: 0.8889 - recall_56: 0.8317 - precision_56: 0.8485\n",
            "Epoch 37/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2799 - accuracy: 0.8819 - recall_56: 0.8317 - precision_56: 0.8317\n",
            "Epoch 38/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2777 - accuracy: 0.8854 - recall_56: 0.8317 - precision_56: 0.8400\n",
            "Epoch 39/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2780 - accuracy: 0.8889 - recall_56: 0.8218 - precision_56: 0.8557\n",
            "Epoch 40/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2770 - accuracy: 0.8854 - recall_56: 0.8218 - precision_56: 0.8469\n",
            "4/4 - 0s - loss: 0.4444 - accuracy: 0.8387 - recall_56: 0.7209 - precision_56: 0.7949 - 22ms/epoch - 6ms/step\n",
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 4ms/step - loss: 0.7081 - accuracy: 0.4965 - recall_57: 0.1881 - precision_57: 0.2317\n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.6747 - accuracy: 0.5903 - recall_57: 0.2970 - precision_57: 0.3896\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6478 - accuracy: 0.6736 - recall_57: 0.4554 - precision_57: 0.5412\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.6257 - accuracy: 0.7118 - recall_57: 0.5743 - precision_57: 0.5918\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6047 - accuracy: 0.7257 - recall_57: 0.5842 - precision_57: 0.6146\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5850 - accuracy: 0.7465 - recall_57: 0.6238 - precision_57: 0.6429\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5651 - accuracy: 0.7604 - recall_57: 0.6238 - precision_57: 0.6702\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5458 - accuracy: 0.7674 - recall_57: 0.6337 - precision_57: 0.6809\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5270 - accuracy: 0.7917 - recall_57: 0.6436 - precision_57: 0.7303\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5086 - accuracy: 0.7812 - recall_57: 0.6238 - precision_57: 0.7159\n",
            "4/4 - 0s - loss: 0.5533 - accuracy: 0.7339 - recall_57: 0.5814 - precision_57: 0.6250 - 365ms/epoch - 91ms/step\n",
            "Epoch 1/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4915 - accuracy: 0.7882 - recall_57: 0.6436 - precision_57: 0.7222\n",
            "Epoch 2/20\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.4750 - accuracy: 0.7917 - recall_57: 0.6535 - precision_57: 0.7253\n",
            "Epoch 3/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4606 - accuracy: 0.7986 - recall_57: 0.6733 - precision_57: 0.7312\n",
            "Epoch 4/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4467 - accuracy: 0.7986 - recall_57: 0.6931 - precision_57: 0.7216\n",
            "Epoch 5/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4347 - accuracy: 0.8021 - recall_57: 0.7030 - precision_57: 0.7245\n",
            "Epoch 6/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4249 - accuracy: 0.8125 - recall_57: 0.7129 - precision_57: 0.7423\n",
            "Epoch 7/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4142 - accuracy: 0.8299 - recall_57: 0.7228 - precision_57: 0.7766\n",
            "Epoch 8/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4072 - accuracy: 0.8264 - recall_57: 0.7327 - precision_57: 0.7629\n",
            "Epoch 9/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3994 - accuracy: 0.8333 - recall_57: 0.7525 - precision_57: 0.7677\n",
            "Epoch 10/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3938 - accuracy: 0.8368 - recall_57: 0.7624 - precision_57: 0.7700\n",
            "Epoch 11/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3890 - accuracy: 0.8438 - recall_57: 0.7723 - precision_57: 0.7800\n",
            "Epoch 12/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3848 - accuracy: 0.8438 - recall_57: 0.7723 - precision_57: 0.7800\n",
            "Epoch 13/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3806 - accuracy: 0.8438 - recall_57: 0.7723 - precision_57: 0.7800\n",
            "Epoch 14/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3770 - accuracy: 0.8438 - recall_57: 0.7723 - precision_57: 0.7800\n",
            "Epoch 15/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3741 - accuracy: 0.8438 - recall_57: 0.7822 - precision_57: 0.7745\n",
            "Epoch 16/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3705 - accuracy: 0.8403 - recall_57: 0.7822 - precision_57: 0.7670\n",
            "Epoch 17/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3683 - accuracy: 0.8438 - recall_57: 0.7921 - precision_57: 0.7692\n",
            "Epoch 18/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3670 - accuracy: 0.8507 - recall_57: 0.7921 - precision_57: 0.7843\n",
            "Epoch 19/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3643 - accuracy: 0.8472 - recall_57: 0.7921 - precision_57: 0.7767\n",
            "Epoch 20/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3618 - accuracy: 0.8472 - recall_57: 0.8020 - precision_57: 0.7714\n",
            "4/4 - 0s - loss: 0.4580 - accuracy: 0.7903 - recall_57: 0.6977 - precision_57: 0.6977 - 23ms/epoch - 6ms/step\n",
            "Epoch 1/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3598 - accuracy: 0.8507 - recall_57: 0.8020 - precision_57: 0.7788\n",
            "Epoch 2/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3585 - accuracy: 0.8542 - recall_57: 0.8020 - precision_57: 0.7864\n",
            "Epoch 3/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3562 - accuracy: 0.8576 - recall_57: 0.8119 - precision_57: 0.7885\n",
            "Epoch 4/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3545 - accuracy: 0.8542 - recall_57: 0.8119 - precision_57: 0.7810\n",
            "Epoch 5/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3531 - accuracy: 0.8542 - recall_57: 0.8119 - precision_57: 0.7810\n",
            "Epoch 6/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3521 - accuracy: 0.8542 - recall_57: 0.8119 - precision_57: 0.7810\n",
            "Epoch 7/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3498 - accuracy: 0.8542 - recall_57: 0.8119 - precision_57: 0.7810\n",
            "Epoch 8/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3483 - accuracy: 0.8542 - recall_57: 0.8119 - precision_57: 0.7810\n",
            "Epoch 9/30\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3473 - accuracy: 0.8576 - recall_57: 0.8119 - precision_57: 0.7885\n",
            "Epoch 10/30\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3451 - accuracy: 0.8576 - recall_57: 0.8119 - precision_57: 0.7885\n",
            "Epoch 11/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3445 - accuracy: 0.8542 - recall_57: 0.8119 - precision_57: 0.7810\n",
            "Epoch 12/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3443 - accuracy: 0.8576 - recall_57: 0.8119 - precision_57: 0.7885\n",
            "Epoch 13/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3414 - accuracy: 0.8576 - recall_57: 0.8119 - precision_57: 0.7885\n",
            "Epoch 14/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3398 - accuracy: 0.8542 - recall_57: 0.8119 - precision_57: 0.7810\n",
            "Epoch 15/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3384 - accuracy: 0.8576 - recall_57: 0.8119 - precision_57: 0.7885\n",
            "Epoch 16/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3374 - accuracy: 0.8576 - recall_57: 0.8119 - precision_57: 0.7885\n",
            "Epoch 17/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3359 - accuracy: 0.8576 - recall_57: 0.8119 - precision_57: 0.7885\n",
            "Epoch 18/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3338 - accuracy: 0.8576 - recall_57: 0.8119 - precision_57: 0.7885\n",
            "Epoch 19/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3328 - accuracy: 0.8542 - recall_57: 0.8119 - precision_57: 0.7810\n",
            "Epoch 20/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3311 - accuracy: 0.8611 - recall_57: 0.8218 - precision_57: 0.7905\n",
            "Epoch 21/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3296 - accuracy: 0.8576 - recall_57: 0.8218 - precision_57: 0.7830\n",
            "Epoch 22/30\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3281 - accuracy: 0.8646 - recall_57: 0.8218 - precision_57: 0.7981\n",
            "Epoch 23/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3268 - accuracy: 0.8646 - recall_57: 0.8218 - precision_57: 0.7981\n",
            "Epoch 24/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3257 - accuracy: 0.8646 - recall_57: 0.8218 - precision_57: 0.7981\n",
            "Epoch 25/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3240 - accuracy: 0.8681 - recall_57: 0.8317 - precision_57: 0.8000\n",
            "Epoch 26/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3229 - accuracy: 0.8681 - recall_57: 0.8317 - precision_57: 0.8000\n",
            "Epoch 27/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3212 - accuracy: 0.8681 - recall_57: 0.8317 - precision_57: 0.8000\n",
            "Epoch 28/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3196 - accuracy: 0.8715 - recall_57: 0.8416 - precision_57: 0.8019\n",
            "Epoch 29/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3178 - accuracy: 0.8750 - recall_57: 0.8416 - precision_57: 0.8095\n",
            "Epoch 30/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3171 - accuracy: 0.8681 - recall_57: 0.8218 - precision_57: 0.8058\n",
            "4/4 - 0s - loss: 0.4624 - accuracy: 0.7984 - recall_57: 0.7209 - precision_57: 0.7045 - 20ms/epoch - 5ms/step\n",
            "Epoch 1/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3154 - accuracy: 0.8750 - recall_57: 0.8317 - precision_57: 0.8155\n",
            "Epoch 2/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3146 - accuracy: 0.8715 - recall_57: 0.8317 - precision_57: 0.8077\n",
            "Epoch 3/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3134 - accuracy: 0.8750 - recall_57: 0.8416 - precision_57: 0.8095\n",
            "Epoch 4/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3114 - accuracy: 0.8785 - recall_57: 0.8416 - precision_57: 0.8173\n",
            "Epoch 5/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3108 - accuracy: 0.8715 - recall_57: 0.8218 - precision_57: 0.8137\n",
            "Epoch 6/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3095 - accuracy: 0.8750 - recall_57: 0.8317 - precision_57: 0.8155\n",
            "Epoch 7/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3076 - accuracy: 0.8750 - recall_57: 0.8416 - precision_57: 0.8095\n",
            "Epoch 8/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3060 - accuracy: 0.8750 - recall_57: 0.8317 - precision_57: 0.8155\n",
            "Epoch 9/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3052 - accuracy: 0.8750 - recall_57: 0.8317 - precision_57: 0.8155\n",
            "Epoch 10/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3038 - accuracy: 0.8750 - recall_57: 0.8317 - precision_57: 0.8155\n",
            "Epoch 11/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3016 - accuracy: 0.8750 - recall_57: 0.8317 - precision_57: 0.8155\n",
            "Epoch 12/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3002 - accuracy: 0.8750 - recall_57: 0.8317 - precision_57: 0.8155\n",
            "Epoch 13/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2991 - accuracy: 0.8750 - recall_57: 0.8317 - precision_57: 0.8155\n",
            "Epoch 14/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2976 - accuracy: 0.8750 - recall_57: 0.8317 - precision_57: 0.8155\n",
            "Epoch 15/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2967 - accuracy: 0.8785 - recall_57: 0.8416 - precision_57: 0.8173\n",
            "Epoch 16/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2955 - accuracy: 0.8715 - recall_57: 0.8218 - precision_57: 0.8137\n",
            "Epoch 17/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2935 - accuracy: 0.8750 - recall_57: 0.8317 - precision_57: 0.8155\n",
            "Epoch 18/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2931 - accuracy: 0.8750 - recall_57: 0.8317 - precision_57: 0.8155\n",
            "Epoch 19/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.2918 - accuracy: 0.8785 - recall_57: 0.8416 - precision_57: 0.8173\n",
            "Epoch 20/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2906 - accuracy: 0.8750 - recall_57: 0.8317 - precision_57: 0.8155\n",
            "Epoch 21/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.2913 - accuracy: 0.8819 - recall_57: 0.8515 - precision_57: 0.8190\n",
            "Epoch 22/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2896 - accuracy: 0.8819 - recall_57: 0.8515 - precision_57: 0.8190\n",
            "Epoch 23/40\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.2874 - accuracy: 0.8785 - recall_57: 0.8218 - precision_57: 0.8300\n",
            "Epoch 24/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2850 - accuracy: 0.8785 - recall_57: 0.8416 - precision_57: 0.8173\n",
            "Epoch 25/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2839 - accuracy: 0.8785 - recall_57: 0.8317 - precision_57: 0.8235\n",
            "Epoch 26/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2832 - accuracy: 0.8785 - recall_57: 0.8317 - precision_57: 0.8235\n",
            "Epoch 27/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2838 - accuracy: 0.8785 - recall_57: 0.8416 - precision_57: 0.8173\n",
            "Epoch 28/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2792 - accuracy: 0.8819 - recall_57: 0.8515 - precision_57: 0.8190\n",
            "Epoch 29/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2792 - accuracy: 0.8819 - recall_57: 0.8317 - precision_57: 0.8317\n",
            "Epoch 30/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2781 - accuracy: 0.8854 - recall_57: 0.8416 - precision_57: 0.8333\n",
            "Epoch 31/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2767 - accuracy: 0.8854 - recall_57: 0.8416 - precision_57: 0.8333\n",
            "Epoch 32/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2758 - accuracy: 0.8854 - recall_57: 0.8416 - precision_57: 0.8333\n",
            "Epoch 33/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2745 - accuracy: 0.8854 - recall_57: 0.8416 - precision_57: 0.8333\n",
            "Epoch 34/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2726 - accuracy: 0.8854 - recall_57: 0.8416 - precision_57: 0.8333\n",
            "Epoch 35/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2716 - accuracy: 0.8854 - recall_57: 0.8416 - precision_57: 0.8333\n",
            "Epoch 36/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2712 - accuracy: 0.8854 - recall_57: 0.8416 - precision_57: 0.8333\n",
            "Epoch 37/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2709 - accuracy: 0.8854 - recall_57: 0.8416 - precision_57: 0.8333\n",
            "Epoch 38/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2695 - accuracy: 0.8854 - recall_57: 0.8416 - precision_57: 0.8333\n",
            "Epoch 39/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2671 - accuracy: 0.8854 - recall_57: 0.8416 - precision_57: 0.8333\n",
            "Epoch 40/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2668 - accuracy: 0.8924 - recall_57: 0.8416 - precision_57: 0.8500\n",
            "4/4 - 0s - loss: 0.4839 - accuracy: 0.8226 - recall_57: 0.7442 - precision_57: 0.7442 - 26ms/epoch - 6ms/step\n",
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 2ms/step - loss: 0.6668 - accuracy: 0.6493 - recall_58: 0.6733 - precision_58: 0.5000\n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.6337 - accuracy: 0.7118 - recall_58: 0.6832 - precision_58: 0.5750\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.6069 - accuracy: 0.7257 - recall_58: 0.7030 - precision_58: 0.5917\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5831 - accuracy: 0.7569 - recall_58: 0.7129 - precision_58: 0.6372\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5613 - accuracy: 0.7604 - recall_58: 0.7030 - precision_58: 0.6455\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5398 - accuracy: 0.7743 - recall_58: 0.7129 - precision_58: 0.6667\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5210 - accuracy: 0.7743 - recall_58: 0.7030 - precision_58: 0.6698\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5031 - accuracy: 0.7986 - recall_58: 0.7129 - precision_58: 0.7129\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4862 - accuracy: 0.8056 - recall_58: 0.7129 - precision_58: 0.7273\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4710 - accuracy: 0.8090 - recall_58: 0.7228 - precision_58: 0.7300\n",
            "4/4 - 0s - loss: 0.4952 - accuracy: 0.7823 - recall_58: 0.6744 - precision_58: 0.6905 - 361ms/epoch - 90ms/step\n",
            "Epoch 1/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4572 - accuracy: 0.8125 - recall_58: 0.7228 - precision_58: 0.7374\n",
            "Epoch 2/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4433 - accuracy: 0.8194 - recall_58: 0.7327 - precision_58: 0.7475\n",
            "Epoch 3/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4320 - accuracy: 0.8194 - recall_58: 0.7327 - precision_58: 0.7475\n",
            "Epoch 4/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4217 - accuracy: 0.8194 - recall_58: 0.7426 - precision_58: 0.7426\n",
            "Epoch 5/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4134 - accuracy: 0.8299 - recall_58: 0.7525 - precision_58: 0.7600\n",
            "Epoch 6/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4061 - accuracy: 0.8333 - recall_58: 0.7525 - precision_58: 0.7677\n",
            "Epoch 7/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3990 - accuracy: 0.8299 - recall_58: 0.7525 - precision_58: 0.7600\n",
            "Epoch 8/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3931 - accuracy: 0.8299 - recall_58: 0.7525 - precision_58: 0.7600\n",
            "Epoch 9/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3883 - accuracy: 0.8229 - recall_58: 0.7426 - precision_58: 0.7500\n",
            "Epoch 10/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3841 - accuracy: 0.8229 - recall_58: 0.7426 - precision_58: 0.7500\n",
            "Epoch 11/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3810 - accuracy: 0.8264 - recall_58: 0.7525 - precision_58: 0.7525\n",
            "Epoch 12/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3766 - accuracy: 0.8264 - recall_58: 0.7624 - precision_58: 0.7476\n",
            "Epoch 13/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3744 - accuracy: 0.8333 - recall_58: 0.7723 - precision_58: 0.7573\n",
            "Epoch 14/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3714 - accuracy: 0.8333 - recall_58: 0.7723 - precision_58: 0.7573\n",
            "Epoch 15/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3695 - accuracy: 0.8333 - recall_58: 0.7723 - precision_58: 0.7573\n",
            "Epoch 16/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3688 - accuracy: 0.8333 - recall_58: 0.7822 - precision_58: 0.7524\n",
            "Epoch 17/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3665 - accuracy: 0.8299 - recall_58: 0.7822 - precision_58: 0.7453\n",
            "Epoch 18/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3645 - accuracy: 0.8368 - recall_58: 0.7822 - precision_58: 0.7596\n",
            "Epoch 19/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3623 - accuracy: 0.8368 - recall_58: 0.7822 - precision_58: 0.7596\n",
            "Epoch 20/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3610 - accuracy: 0.8368 - recall_58: 0.7822 - precision_58: 0.7596\n",
            "4/4 - 0s - loss: 0.4405 - accuracy: 0.8145 - recall_58: 0.7674 - precision_58: 0.7174 - 24ms/epoch - 6ms/step\n",
            "Epoch 1/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3596 - accuracy: 0.8368 - recall_58: 0.7822 - precision_58: 0.7596\n",
            "Epoch 2/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3582 - accuracy: 0.8368 - recall_58: 0.7822 - precision_58: 0.7596\n",
            "Epoch 3/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3567 - accuracy: 0.8403 - recall_58: 0.7822 - precision_58: 0.7670\n",
            "Epoch 4/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3559 - accuracy: 0.8403 - recall_58: 0.7822 - precision_58: 0.7670\n",
            "Epoch 5/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3548 - accuracy: 0.8403 - recall_58: 0.7822 - precision_58: 0.7670\n",
            "Epoch 6/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3534 - accuracy: 0.8438 - recall_58: 0.7921 - precision_58: 0.7692\n",
            "Epoch 7/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3539 - accuracy: 0.8368 - recall_58: 0.7921 - precision_58: 0.7547\n",
            "Epoch 8/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3509 - accuracy: 0.8403 - recall_58: 0.7921 - precision_58: 0.7619\n",
            "Epoch 9/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3521 - accuracy: 0.8472 - recall_58: 0.7921 - precision_58: 0.7767\n",
            "Epoch 10/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3497 - accuracy: 0.8472 - recall_58: 0.7921 - precision_58: 0.7767\n",
            "Epoch 11/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3482 - accuracy: 0.8438 - recall_58: 0.7921 - precision_58: 0.7692\n",
            "Epoch 12/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3467 - accuracy: 0.8438 - recall_58: 0.7921 - precision_58: 0.7692\n",
            "Epoch 13/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3460 - accuracy: 0.8438 - recall_58: 0.7921 - precision_58: 0.7692\n",
            "Epoch 14/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3461 - accuracy: 0.8438 - recall_58: 0.7921 - precision_58: 0.7692\n",
            "Epoch 15/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3469 - accuracy: 0.8507 - recall_58: 0.7921 - precision_58: 0.7843\n",
            "Epoch 16/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3432 - accuracy: 0.8542 - recall_58: 0.7921 - precision_58: 0.7921\n",
            "Epoch 17/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3429 - accuracy: 0.8438 - recall_58: 0.7921 - precision_58: 0.7692\n",
            "Epoch 18/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3425 - accuracy: 0.8403 - recall_58: 0.7921 - precision_58: 0.7619\n",
            "Epoch 19/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3417 - accuracy: 0.8507 - recall_58: 0.7921 - precision_58: 0.7843\n",
            "Epoch 20/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3413 - accuracy: 0.8542 - recall_58: 0.7921 - precision_58: 0.7921\n",
            "Epoch 21/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3395 - accuracy: 0.8507 - recall_58: 0.7921 - precision_58: 0.7843\n",
            "Epoch 22/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3385 - accuracy: 0.8542 - recall_58: 0.7921 - precision_58: 0.7921\n",
            "Epoch 23/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3374 - accuracy: 0.8438 - recall_58: 0.7921 - precision_58: 0.7692\n",
            "Epoch 24/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3382 - accuracy: 0.8403 - recall_58: 0.7921 - precision_58: 0.7619\n",
            "Epoch 25/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3360 - accuracy: 0.8472 - recall_58: 0.7921 - precision_58: 0.7767\n",
            "Epoch 26/30\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3371 - accuracy: 0.8507 - recall_58: 0.7822 - precision_58: 0.7900\n",
            "Epoch 27/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3353 - accuracy: 0.8576 - recall_58: 0.7921 - precision_58: 0.8000\n",
            "Epoch 28/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3346 - accuracy: 0.8438 - recall_58: 0.7921 - precision_58: 0.7692\n",
            "Epoch 29/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3330 - accuracy: 0.8542 - recall_58: 0.7921 - precision_58: 0.7921\n",
            "Epoch 30/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3321 - accuracy: 0.8576 - recall_58: 0.7921 - precision_58: 0.8000\n",
            "4/4 - 0s - loss: 0.4549 - accuracy: 0.8226 - recall_58: 0.7674 - precision_58: 0.7333 - 20ms/epoch - 5ms/step\n",
            "Epoch 1/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3313 - accuracy: 0.8611 - recall_58: 0.8020 - precision_58: 0.8020\n",
            "Epoch 2/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3306 - accuracy: 0.8646 - recall_58: 0.8119 - precision_58: 0.8039\n",
            "Epoch 3/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3308 - accuracy: 0.8611 - recall_58: 0.8020 - precision_58: 0.8020\n",
            "Epoch 4/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3288 - accuracy: 0.8611 - recall_58: 0.8020 - precision_58: 0.8020\n",
            "Epoch 5/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3307 - accuracy: 0.8472 - recall_58: 0.8218 - precision_58: 0.7615\n",
            "Epoch 6/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3291 - accuracy: 0.8611 - recall_58: 0.8020 - precision_58: 0.8020\n",
            "Epoch 7/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3279 - accuracy: 0.8542 - recall_58: 0.7822 - precision_58: 0.7980\n",
            "Epoch 8/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3262 - accuracy: 0.8576 - recall_58: 0.7921 - precision_58: 0.8000\n",
            "Epoch 9/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3262 - accuracy: 0.8611 - recall_58: 0.8218 - precision_58: 0.7905\n",
            "Epoch 10/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3258 - accuracy: 0.8576 - recall_58: 0.8020 - precision_58: 0.7941\n",
            "Epoch 11/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3250 - accuracy: 0.8611 - recall_58: 0.8020 - precision_58: 0.8020\n",
            "Epoch 12/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3238 - accuracy: 0.8646 - recall_58: 0.8119 - precision_58: 0.8039\n",
            "Epoch 13/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3233 - accuracy: 0.8681 - recall_58: 0.8218 - precision_58: 0.8058\n",
            "Epoch 14/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3235 - accuracy: 0.8611 - recall_58: 0.8020 - precision_58: 0.8020\n",
            "Epoch 15/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3226 - accuracy: 0.8611 - recall_58: 0.8020 - precision_58: 0.8020\n",
            "Epoch 16/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3215 - accuracy: 0.8681 - recall_58: 0.8218 - precision_58: 0.8058\n",
            "Epoch 17/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3207 - accuracy: 0.8681 - recall_58: 0.8218 - precision_58: 0.8058\n",
            "Epoch 18/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3197 - accuracy: 0.8646 - recall_58: 0.8119 - precision_58: 0.8039\n",
            "Epoch 19/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3203 - accuracy: 0.8576 - recall_58: 0.8119 - precision_58: 0.7885\n",
            "Epoch 20/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3187 - accuracy: 0.8646 - recall_58: 0.8119 - precision_58: 0.8039\n",
            "Epoch 21/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3224 - accuracy: 0.8611 - recall_58: 0.8020 - precision_58: 0.8020\n",
            "Epoch 22/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3163 - accuracy: 0.8611 - recall_58: 0.8020 - precision_58: 0.8020\n",
            "Epoch 23/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3193 - accuracy: 0.8576 - recall_58: 0.8317 - precision_58: 0.7778\n",
            "Epoch 24/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3177 - accuracy: 0.8611 - recall_58: 0.8119 - precision_58: 0.7961\n",
            "Epoch 25/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3153 - accuracy: 0.8646 - recall_58: 0.8119 - precision_58: 0.8039\n",
            "Epoch 26/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3159 - accuracy: 0.8611 - recall_58: 0.8020 - precision_58: 0.8020\n",
            "Epoch 27/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3140 - accuracy: 0.8576 - recall_58: 0.7921 - precision_58: 0.8000\n",
            "Epoch 28/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3141 - accuracy: 0.8646 - recall_58: 0.8119 - precision_58: 0.8039\n",
            "Epoch 29/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3133 - accuracy: 0.8646 - recall_58: 0.8119 - precision_58: 0.8039\n",
            "Epoch 30/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3118 - accuracy: 0.8611 - recall_58: 0.8020 - precision_58: 0.8020\n",
            "Epoch 31/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3112 - accuracy: 0.8611 - recall_58: 0.8020 - precision_58: 0.8020\n",
            "Epoch 32/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3113 - accuracy: 0.8611 - recall_58: 0.8119 - precision_58: 0.7961\n",
            "Epoch 33/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3093 - accuracy: 0.8646 - recall_58: 0.8119 - precision_58: 0.8039\n",
            "Epoch 34/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3089 - accuracy: 0.8646 - recall_58: 0.8119 - precision_58: 0.8039\n",
            "Epoch 35/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3088 - accuracy: 0.8611 - recall_58: 0.8020 - precision_58: 0.8020\n",
            "Epoch 36/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3076 - accuracy: 0.8611 - recall_58: 0.8020 - precision_58: 0.8020\n",
            "Epoch 37/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3074 - accuracy: 0.8611 - recall_58: 0.8119 - precision_58: 0.7961\n",
            "Epoch 38/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3061 - accuracy: 0.8646 - recall_58: 0.8119 - precision_58: 0.8039\n",
            "Epoch 39/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3055 - accuracy: 0.8646 - recall_58: 0.8119 - precision_58: 0.8039\n",
            "Epoch 40/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3072 - accuracy: 0.8611 - recall_58: 0.8020 - precision_58: 0.8020\n",
            "4/4 - 0s - loss: 0.4699 - accuracy: 0.8226 - recall_58: 0.7442 - precision_58: 0.7442 - 21ms/epoch - 5ms/step\n",
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 3ms/step - loss: 0.6738 - accuracy: 0.6007 - recall_59: 0.3168 - precision_59: 0.4103\n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6438 - accuracy: 0.6701 - recall_59: 0.4653 - precision_59: 0.5341\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.6153 - accuracy: 0.7118 - recall_59: 0.5446 - precision_59: 0.5978\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5906 - accuracy: 0.7257 - recall_59: 0.5941 - precision_59: 0.6122\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5708 - accuracy: 0.7396 - recall_59: 0.6337 - precision_59: 0.6275\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5518 - accuracy: 0.7604 - recall_59: 0.6634 - precision_59: 0.6569\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5355 - accuracy: 0.7639 - recall_59: 0.6436 - precision_59: 0.6701\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.5209 - accuracy: 0.7674 - recall_59: 0.6436 - precision_59: 0.6771\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.5078 - accuracy: 0.7674 - recall_59: 0.6436 - precision_59: 0.6771\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4976 - accuracy: 0.7778 - recall_59: 0.6436 - precision_59: 0.6989\n",
            "4/4 - 0s - loss: 0.5219 - accuracy: 0.7339 - recall_59: 0.5349 - precision_59: 0.6389 - 390ms/epoch - 97ms/step\n",
            "Epoch 1/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4865 - accuracy: 0.7847 - recall_59: 0.6535 - precision_59: 0.7097\n",
            "Epoch 2/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4780 - accuracy: 0.7882 - recall_59: 0.6634 - precision_59: 0.7128\n",
            "Epoch 3/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4702 - accuracy: 0.7847 - recall_59: 0.6832 - precision_59: 0.6970\n",
            "Epoch 4/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4629 - accuracy: 0.7847 - recall_59: 0.6832 - precision_59: 0.6970\n",
            "Epoch 5/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4554 - accuracy: 0.7917 - recall_59: 0.6832 - precision_59: 0.7113\n",
            "Epoch 6/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4492 - accuracy: 0.7986 - recall_59: 0.6931 - precision_59: 0.7216\n",
            "Epoch 7/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4437 - accuracy: 0.7917 - recall_59: 0.6931 - precision_59: 0.7071\n",
            "Epoch 8/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4380 - accuracy: 0.7951 - recall_59: 0.6931 - precision_59: 0.7143\n",
            "Epoch 9/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4332 - accuracy: 0.8056 - recall_59: 0.7030 - precision_59: 0.7320\n",
            "Epoch 10/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4288 - accuracy: 0.8056 - recall_59: 0.7030 - precision_59: 0.7320\n",
            "Epoch 11/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4248 - accuracy: 0.8056 - recall_59: 0.7129 - precision_59: 0.7273\n",
            "Epoch 12/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4208 - accuracy: 0.8160 - recall_59: 0.7327 - precision_59: 0.7400\n",
            "Epoch 13/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4173 - accuracy: 0.8160 - recall_59: 0.7426 - precision_59: 0.7353\n",
            "Epoch 14/20\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.4144 - accuracy: 0.8194 - recall_59: 0.7525 - precision_59: 0.7379\n",
            "Epoch 15/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4114 - accuracy: 0.8229 - recall_59: 0.7525 - precision_59: 0.7451\n",
            "Epoch 16/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4080 - accuracy: 0.8229 - recall_59: 0.7525 - precision_59: 0.7451\n",
            "Epoch 17/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4056 - accuracy: 0.8229 - recall_59: 0.7624 - precision_59: 0.7404\n",
            "Epoch 18/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4028 - accuracy: 0.8299 - recall_59: 0.7624 - precision_59: 0.7549\n",
            "Epoch 19/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.4001 - accuracy: 0.8299 - recall_59: 0.7624 - precision_59: 0.7549\n",
            "Epoch 20/20\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3967 - accuracy: 0.8299 - recall_59: 0.7624 - precision_59: 0.7549\n",
            "4/4 - 0s - loss: 0.4492 - accuracy: 0.7661 - recall_59: 0.6977 - precision_59: 0.6522 - 22ms/epoch - 6ms/step\n",
            "Epoch 1/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3944 - accuracy: 0.8299 - recall_59: 0.7624 - precision_59: 0.7549\n",
            "Epoch 2/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3916 - accuracy: 0.8333 - recall_59: 0.7723 - precision_59: 0.7573\n",
            "Epoch 3/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3902 - accuracy: 0.8333 - recall_59: 0.7822 - precision_59: 0.7524\n",
            "Epoch 4/30\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.3863 - accuracy: 0.8368 - recall_59: 0.7822 - precision_59: 0.7596\n",
            "Epoch 5/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3840 - accuracy: 0.8368 - recall_59: 0.7822 - precision_59: 0.7596\n",
            "Epoch 6/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3823 - accuracy: 0.8368 - recall_59: 0.7822 - precision_59: 0.7596\n",
            "Epoch 7/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3815 - accuracy: 0.8403 - recall_59: 0.7822 - precision_59: 0.7670\n",
            "Epoch 8/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3794 - accuracy: 0.8403 - recall_59: 0.7822 - precision_59: 0.7670\n",
            "Epoch 9/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3767 - accuracy: 0.8403 - recall_59: 0.7921 - precision_59: 0.7619\n",
            "Epoch 10/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3752 - accuracy: 0.8438 - recall_59: 0.8020 - precision_59: 0.7642\n",
            "Epoch 11/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3734 - accuracy: 0.8472 - recall_59: 0.8020 - precision_59: 0.7714\n",
            "Epoch 12/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3727 - accuracy: 0.8507 - recall_59: 0.8020 - precision_59: 0.7788\n",
            "Epoch 13/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3710 - accuracy: 0.8472 - recall_59: 0.8020 - precision_59: 0.7714\n",
            "Epoch 14/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3696 - accuracy: 0.8472 - recall_59: 0.8020 - precision_59: 0.7714\n",
            "Epoch 15/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3684 - accuracy: 0.8472 - recall_59: 0.8020 - precision_59: 0.7714\n",
            "Epoch 16/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3667 - accuracy: 0.8472 - recall_59: 0.8020 - precision_59: 0.7714\n",
            "Epoch 17/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3658 - accuracy: 0.8507 - recall_59: 0.8020 - precision_59: 0.7788\n",
            "Epoch 18/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3644 - accuracy: 0.8472 - recall_59: 0.7921 - precision_59: 0.7767\n",
            "Epoch 19/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3638 - accuracy: 0.8472 - recall_59: 0.7921 - precision_59: 0.7767\n",
            "Epoch 20/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3625 - accuracy: 0.8472 - recall_59: 0.8119 - precision_59: 0.7664\n",
            "Epoch 21/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3613 - accuracy: 0.8472 - recall_59: 0.8119 - precision_59: 0.7664\n",
            "Epoch 22/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3602 - accuracy: 0.8403 - recall_59: 0.7921 - precision_59: 0.7619\n",
            "Epoch 23/30\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3594 - accuracy: 0.8403 - recall_59: 0.7921 - precision_59: 0.7619\n",
            "Epoch 24/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3580 - accuracy: 0.8472 - recall_59: 0.7921 - precision_59: 0.7767\n",
            "Epoch 25/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3569 - accuracy: 0.8472 - recall_59: 0.8020 - precision_59: 0.7714\n",
            "Epoch 26/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3565 - accuracy: 0.8472 - recall_59: 0.8020 - precision_59: 0.7714\n",
            "Epoch 27/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3561 - accuracy: 0.8438 - recall_59: 0.8020 - precision_59: 0.7642\n",
            "Epoch 28/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3541 - accuracy: 0.8438 - recall_59: 0.8020 - precision_59: 0.7642\n",
            "Epoch 29/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3533 - accuracy: 0.8472 - recall_59: 0.8020 - precision_59: 0.7714\n",
            "Epoch 30/30\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3524 - accuracy: 0.8472 - recall_59: 0.8020 - precision_59: 0.7714\n",
            "4/4 - 0s - loss: 0.4504 - accuracy: 0.7742 - recall_59: 0.6977 - precision_59: 0.6667 - 22ms/epoch - 6ms/step\n",
            "Epoch 1/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3516 - accuracy: 0.8507 - recall_59: 0.8020 - precision_59: 0.7788\n",
            "Epoch 2/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3508 - accuracy: 0.8472 - recall_59: 0.8020 - precision_59: 0.7714\n",
            "Epoch 3/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3498 - accuracy: 0.8542 - recall_59: 0.8020 - precision_59: 0.7864\n",
            "Epoch 4/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3492 - accuracy: 0.8542 - recall_59: 0.8020 - precision_59: 0.7864\n",
            "Epoch 5/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3485 - accuracy: 0.8542 - recall_59: 0.8020 - precision_59: 0.7864\n",
            "Epoch 6/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3475 - accuracy: 0.8507 - recall_59: 0.8020 - precision_59: 0.7788\n",
            "Epoch 7/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3471 - accuracy: 0.8542 - recall_59: 0.8020 - precision_59: 0.7864\n",
            "Epoch 8/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3458 - accuracy: 0.8542 - recall_59: 0.8020 - precision_59: 0.7864\n",
            "Epoch 9/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3444 - accuracy: 0.8576 - recall_59: 0.8119 - precision_59: 0.7885\n",
            "Epoch 10/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3436 - accuracy: 0.8576 - recall_59: 0.8119 - precision_59: 0.7885\n",
            "Epoch 11/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3430 - accuracy: 0.8542 - recall_59: 0.8020 - precision_59: 0.7864\n",
            "Epoch 12/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3420 - accuracy: 0.8542 - recall_59: 0.8020 - precision_59: 0.7864\n",
            "Epoch 13/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3416 - accuracy: 0.8576 - recall_59: 0.8119 - precision_59: 0.7885\n",
            "Epoch 14/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3405 - accuracy: 0.8542 - recall_59: 0.8020 - precision_59: 0.7864\n",
            "Epoch 15/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3396 - accuracy: 0.8542 - recall_59: 0.8020 - precision_59: 0.7864\n",
            "Epoch 16/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3383 - accuracy: 0.8576 - recall_59: 0.8119 - precision_59: 0.7885\n",
            "Epoch 17/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3392 - accuracy: 0.8611 - recall_59: 0.8218 - precision_59: 0.7905\n",
            "Epoch 18/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3373 - accuracy: 0.8681 - recall_59: 0.8416 - precision_59: 0.7944\n",
            "Epoch 19/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3360 - accuracy: 0.8646 - recall_59: 0.8317 - precision_59: 0.7925\n",
            "Epoch 20/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3352 - accuracy: 0.8576 - recall_59: 0.8119 - precision_59: 0.7885\n",
            "Epoch 21/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3340 - accuracy: 0.8576 - recall_59: 0.8119 - precision_59: 0.7885\n",
            "Epoch 22/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3330 - accuracy: 0.8611 - recall_59: 0.8218 - precision_59: 0.7905\n",
            "Epoch 23/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3321 - accuracy: 0.8646 - recall_59: 0.8416 - precision_59: 0.7870\n",
            "Epoch 24/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3307 - accuracy: 0.8681 - recall_59: 0.8416 - precision_59: 0.7944\n",
            "Epoch 25/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3301 - accuracy: 0.8611 - recall_59: 0.8218 - precision_59: 0.7905\n",
            "Epoch 26/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3293 - accuracy: 0.8646 - recall_59: 0.8218 - precision_59: 0.7981\n",
            "Epoch 27/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3287 - accuracy: 0.8611 - recall_59: 0.8218 - precision_59: 0.7905\n",
            "Epoch 28/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3270 - accuracy: 0.8646 - recall_59: 0.8317 - precision_59: 0.7925\n",
            "Epoch 29/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3261 - accuracy: 0.8681 - recall_59: 0.8317 - precision_59: 0.8000\n",
            "Epoch 30/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3261 - accuracy: 0.8681 - recall_59: 0.8218 - precision_59: 0.8058\n",
            "Epoch 31/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3244 - accuracy: 0.8681 - recall_59: 0.8218 - precision_59: 0.8058\n",
            "Epoch 32/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3240 - accuracy: 0.8715 - recall_59: 0.8317 - precision_59: 0.8077\n",
            "Epoch 33/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3227 - accuracy: 0.8750 - recall_59: 0.8416 - precision_59: 0.8095\n",
            "Epoch 34/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3229 - accuracy: 0.8681 - recall_59: 0.8218 - precision_59: 0.8058\n",
            "Epoch 35/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3211 - accuracy: 0.8750 - recall_59: 0.8416 - precision_59: 0.8095\n",
            "Epoch 36/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3200 - accuracy: 0.8785 - recall_59: 0.8515 - precision_59: 0.8113\n",
            "Epoch 37/40\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.3196 - accuracy: 0.8785 - recall_59: 0.8515 - precision_59: 0.8113\n",
            "Epoch 38/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3190 - accuracy: 0.8715 - recall_59: 0.8317 - precision_59: 0.8077\n",
            "Epoch 39/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3177 - accuracy: 0.8715 - recall_59: 0.8218 - precision_59: 0.8137\n",
            "Epoch 40/40\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.3167 - accuracy: 0.8785 - recall_59: 0.8515 - precision_59: 0.8113\n",
            "4/4 - 0s - loss: 0.4631 - accuracy: 0.7903 - recall_59: 0.7442 - precision_59: 0.6809 - 22ms/epoch - 5ms/step\n"
          ]
        }
      ],
      "source": [
        "# Create a good model\n",
        "\n",
        "testsets=[]\n",
        "for m in ['relu','elu','tanh','LeakyReLU']:\n",
        "  for i in [64,32]:\n",
        "    for j in [16,8,4]:\n",
        "      nn_test = tf.keras.models.Sequential()\n",
        "      # Input layer\n",
        "      nn_test.add(tf.keras.layers.Dense(units = i, activation = m, input_dim = 15))\n",
        "      # First hidden layer\n",
        "      nn_test.add(tf.keras.layers.Dense(units = j, activation = m))\n",
        "      # Output layer\n",
        "      nn_test.add(tf.keras.layers.Dense(units = 1, activation = 'sigmoid'))\n",
        "\n",
        "      # Compile the model\n",
        "      nn_test.compile(loss = 'binary_crossentropy', optimizer = 'Adam',metrics=[\"accuracy\",tf.keras.metrics.Recall(),tf.keras.metrics.Precision()])\n",
        "    \n",
        "      for u in [10,20,30,40]:\n",
        "        nn_test.fit(X_train_scaled,y_train,epochs =u)\n",
        "        model_loss, model_accuracy, recall,precision= nn_test.evaluate(X_test_scaled,y_test,verbose=2)\n",
        "        F1 = 2*(recall*precision)/(recall+precision)\n",
        "        testset = {'activation':m, 'match':[i,j,u],'Loss':model_loss,'Accuracy':model_accuracy,'Recall':recall,'Precision':precision,'F1':F1}\n",
        "        testsets.append(testset)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Nv3Xbi_Wkb8m",
        "outputId": "14227503-9e80-42fa-fdc7-887ba19abeda"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-1d5f4cb5-807a-4f14-a604-520d385858bb\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>activation</th>\n",
              "      <th>match</th>\n",
              "      <th>Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Precision</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>LeakyReLU</td>\n",
              "      <td>[64, 8, 40]</td>\n",
              "      <td>0.445607</td>\n",
              "      <td>0.854839</td>\n",
              "      <td>0.767442</td>\n",
              "      <td>0.804878</td>\n",
              "      <td>0.785714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>relu</td>\n",
              "      <td>[64, 16, 30]</td>\n",
              "      <td>0.471842</td>\n",
              "      <td>0.854839</td>\n",
              "      <td>0.744186</td>\n",
              "      <td>0.820513</td>\n",
              "      <td>0.780488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>relu</td>\n",
              "      <td>[32, 16, 20]</td>\n",
              "      <td>0.422710</td>\n",
              "      <td>0.846774</td>\n",
              "      <td>0.767442</td>\n",
              "      <td>0.785714</td>\n",
              "      <td>0.776471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>tanh</td>\n",
              "      <td>[64, 16, 40]</td>\n",
              "      <td>0.423855</td>\n",
              "      <td>0.846774</td>\n",
              "      <td>0.813953</td>\n",
              "      <td>0.760870</td>\n",
              "      <td>0.786517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>LeakyReLU</td>\n",
              "      <td>[64, 8, 20]</td>\n",
              "      <td>0.414322</td>\n",
              "      <td>0.838710</td>\n",
              "      <td>0.744186</td>\n",
              "      <td>0.780488</td>\n",
              "      <td>0.761905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>LeakyReLU</td>\n",
              "      <td>[32, 16, 10]</td>\n",
              "      <td>0.553315</td>\n",
              "      <td>0.733871</td>\n",
              "      <td>0.581395</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.602410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>tanh</td>\n",
              "      <td>[64, 16, 10]</td>\n",
              "      <td>0.480124</td>\n",
              "      <td>0.733871</td>\n",
              "      <td>0.697674</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.645161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>tanh</td>\n",
              "      <td>[32, 4, 10]</td>\n",
              "      <td>0.523262</td>\n",
              "      <td>0.725806</td>\n",
              "      <td>0.697674</td>\n",
              "      <td>0.588235</td>\n",
              "      <td>0.638298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>tanh</td>\n",
              "      <td>[32, 8, 10]</td>\n",
              "      <td>0.526373</td>\n",
              "      <td>0.709677</td>\n",
              "      <td>0.720930</td>\n",
              "      <td>0.563636</td>\n",
              "      <td>0.632653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>tanh</td>\n",
              "      <td>[32, 16, 10]</td>\n",
              "      <td>0.495612</td>\n",
              "      <td>0.709677</td>\n",
              "      <td>0.697674</td>\n",
              "      <td>0.566038</td>\n",
              "      <td>0.625000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>96 rows × 7 columns</p>\n",
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              "          const element = document.querySelector('#df-1d5f4cb5-807a-4f14-a604-520d385858bb');\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ],
            "text/plain": [
              "   activation         match      Loss  Accuracy    Recall  Precision        F1\n",
              "79  LeakyReLU   [64, 8, 40]  0.445607  0.854839  0.767442   0.804878  0.785714\n",
              "2        relu  [64, 16, 30]  0.471842  0.854839  0.744186   0.820513  0.780488\n",
              "13       relu  [32, 16, 20]  0.422710  0.846774  0.767442   0.785714  0.776471\n",
              "51       tanh  [64, 16, 40]  0.423855  0.846774  0.813953   0.760870  0.786517\n",
              "77  LeakyReLU   [64, 8, 20]  0.414322  0.838710  0.744186   0.780488  0.761905\n",
              "..        ...           ...       ...       ...       ...        ...       ...\n",
              "84  LeakyReLU  [32, 16, 10]  0.553315  0.733871  0.581395   0.625000  0.602410\n",
              "48       tanh  [64, 16, 10]  0.480124  0.733871  0.697674   0.600000  0.645161\n",
              "68       tanh   [32, 4, 10]  0.523262  0.725806  0.697674   0.588235  0.638298\n",
              "64       tanh   [32, 8, 10]  0.526373  0.709677  0.720930   0.563636  0.632653\n",
              "60       tanh  [32, 16, 10]  0.495612  0.709677  0.697674   0.566038  0.625000\n",
              "\n",
              "[96 rows x 7 columns]"
            ]
          },
          "execution_count": 93,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "NNResult_df = pd.DataFrame(testsets)\n",
        "NNResult_df = NNResult_df.sort_values('Accuracy',ascending=False)\n",
        "NNResult_df\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "JTo2R3d8dOu_",
        "outputId": "a57fc1df-2bf9-4c73-b302-eda61b6a53bf"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": "download(\"download_e8fa805e-df57-4ece-9581-5fe2a9aba72d\", \"NNResult.csv\", 11250)",
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              "<IPython.core.display.Javascript object>"
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          "metadata": {},
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      ],
      "source": [
        "NNResult_df.to_csv('NNResult.csv') \n",
        "files.download('NNResult.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R_ugzqbOgOWR"
      },
      "source": [
        "### <span style='font-family:Helvetica;color:darkorange'> Final Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "metadata": {
        "id": "m6hwtehtgNXx"
      },
      "outputs": [],
      "source": [
        "Scaler = StandardScaler().fit(X_train)\n",
        "X_train_scaled = Scaler.transform(X_train)\n",
        "X_test_scaled = Scaler.transform(X_test)\n",
        "predmodel=GradientBoostingClassifier(n_estimators = 100, max_depth =1)\n",
        "model = predmodel.fit(X_train_scaled, y_train) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "O3dYVBDii6Mb",
        "outputId": "5eb99874-2b4d-4384-debb-81984e34bfc4"
      },
      "outputs": [
        {
          "data": {
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          "metadata": {},
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        {
          "data": {
            "application/javascript": "download(\"download_68c1a19b-a59c-47f8-ac8d-7f3a0fc26984\", \"scaler.pkl\", 1071)",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": "download(\"download_6dd38a33-d80b-4414-abbd-bee481913409\", \"model.pkl\", 46628)",
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              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import pickle\n",
        "\n",
        "pickle.dump(Scaler, open('scaler.pkl', 'wb'))\n",
        "pickle.dump(model, open('model.pkl', 'wb'))\n",
        "\n",
        "# save model\n",
        "files.download('scaler.pkl')\n",
        "files.download('model.pkl')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.7.15 ('PythonData')",
      "language": "python",
      "name": "python3"
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    "language_info": {
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        "name": "ipython",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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    "vscode": {
      "interpreter": {
        "hash": "c9b0bf6a235527dee47d8aa1349c2e3aa22b2d4d85c71094e9bb0f838d643ae3"
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  "nbformat_minor": 0
}