Exploracion.ipynb

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      "source": [
        "# Reto: Predicción de BD de Titanic de Kaggle\n",
        "\n",
        "A través del siguiente programa buscamos predecir si un pasajero a bordo del Titanic tendría posibilidades de sobrevivir dadas ciertas condiciones.\n",
        "\n",
        "Estaremos utilizando un dataset de Kaggle que se puede encontrar en el siguiente link: https://www.kaggle.com/competitions/titanic\n",
        "\n",
        "Para empezar, haremos una limpieza de los datos con la finalidad de que el modelo que planteemos pueda aprender de mejor forma y haremos ciertas gráficas y cálculos estadísticos para familiarizarnos de mejor forma y representar los datos de la base de datos.\n",
        "\n",
        "Procederemos a analizar los datos para eliminar algunas variables que no tendran uso para el modelo que propondremos, asi como crear otras que consideramos importantes y que obtuvimos de la misma base de datos. Aplicamos unas tranformaciones y con eso tendremos mejores datos para entrenar modelos de predicción."
      ],
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      "source": [
        "## Extraer"
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          "text": [
            "Mounted at /content/gdrive/\n",
            "/content/gdrive/Shareddrives/Concentracion_AI/Titanic\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn import tree, preprocessing\n",
        "\n",
        "from google.colab import files,drive\n",
        "\n",
        "drive.mount(\"/content/gdrive/\")  \n",
        "# !pwd\n",
        "# !ls\n",
        "\n",
        "%cd \"/content/gdrive/Shareddrives/Concentracion_AI/Titanic\"\n",
        "# !ls"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lectura de datos y almacenamiento en un DataFrame."
      ],
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        "id": "4_cS8bhrAJH0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "titanic = pd.read_csv(\"train.csv\")\n",
        "titanic.head()"
      ],
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              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
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              "3            4         1       1   \n",
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              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
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      "source": [
        "Se usa un describe para conocer información general acerca de los datos."
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              "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
              "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
              "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
              "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
              "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
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              "\n",
              "            Parch        Fare  \n",
              "count  891.000000  891.000000  \n",
              "mean     0.381594   32.204208  \n",
              "std      0.806057   49.693429  \n",
              "min      0.000000    0.000000  \n",
              "25%      0.000000    7.910400  \n",
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              "      <td>1.000000</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>2.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <th>75%</th>\n",
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              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>max</th>\n",
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              "      <td>1.000000</td>\n",
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              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-01f6b6f5-b64b-423e-af47-6b357206bbb0');\n",
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              "            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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Con el objetivo de verificar si existen registros duplicados en la base de datos, eliminamos los duplicados y observamos los datos que quedan posterior a ello."
      ],
      "metadata": {
        "id": "f4G3hzyg7EHj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def drop_duplicates(df):\n",
        "  return df.drop_duplicates()\n",
        "titanic = drop_duplicates(titanic)\n",
        "titanic.describe()"
      ],
      "metadata": {
        "id": "ZdfWfpWYF1tb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "c1d5010d-fbf1-467f-f18c-e00c2898a246"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
              "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
              "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
              "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
              "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
              "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
              "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
              "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
              "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
              "\n",
              "            Parch        Fare  \n",
              "count  891.000000  891.000000  \n",
              "mean     0.381594   32.204208  \n",
              "std      0.806057   49.693429  \n",
              "min      0.000000    0.000000  \n",
              "25%      0.000000    7.910400  \n",
              "50%      0.000000   14.454200  \n",
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              "      <th>Age</th>\n",
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              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
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              "      <th>std</th>\n",
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              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>14.526497</td>\n",
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              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
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              "      <td>2.000000</td>\n",
              "      <td>20.125000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>28.000000</td>\n",
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              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
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              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
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              "            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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Como se puede observar no existen datos duplicados por lo que la base de datos se mantuvo igual.\n",
        "\n",
        "Otra parte importante para conocer los datos, es saber si existen valores nulos en algún registro. Para esto consultamos la información del dataframe y obtenemos el porcentaje de valores nulos para cada uno de los datos."
      ],
      "metadata": {
        "id": "R0kG3UJ6F1VM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "titanic.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zAcwKfTd0GGT",
        "outputId": "762ec03a-e158-4567-a744-125b2b7bf5e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 90.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Porcentaje de valores faltantes en el dataframe\n",
        "titanic.isnull().sum()/len(titanic)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GpbpXpJCjWXr",
        "outputId": "9f3f286a-607f-40a0-863b-52d5fea7d90f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    0.000000\n",
              "Survived       0.000000\n",
              "Pclass         0.000000\n",
              "Name           0.000000\n",
              "Sex            0.000000\n",
              "Age            0.198653\n",
              "SibSp          0.000000\n",
              "Parch          0.000000\n",
              "Ticket         0.000000\n",
              "Fare           0.000000\n",
              "Cabin          0.771044\n",
              "Embarked       0.002245\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Como se puede observar, la columna \"Cabin\" tiene un 77% de datos faltantes, el siguiente registro con mayor número de datos faltantes es \"Age\" con 19%, y por último \"Embarked\" que tiene un muy pequeños porcentaje de datos faltantes de 0.2%"
      ],
      "metadata": {
        "id": "0LoUXfMV7fJ5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Transformaciones"
      ],
      "metadata": {
        "id": "7rlPHzbYzCc1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Después de analizar los datos con los que contamos, así como la información que nos provee cada columna. Pudimos identificar los datos que requerimos para resolver el problema y poder aplicar modelos analíticos y visualizaciones posteriormente."
      ],
      "metadata": {
        "id": "db5fZByBzWcG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Limpieza de datos"
      ],
      "metadata": {
        "id": "Ka0TiH6VsZiH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Los datos incluidos deben ser correctos, inequívocos, consistentes y completos. Es por eso que es importante descartar la información que no sea relevante.\n",
        "\n",
        "\n",
        "**Eliminar Cabin**\n",
        "\n",
        "Esta columna cuenta con 77% de datos faltantes para los registros. Por la gran cantidad de datos faltantes la información no sería relevante, por lo que lo mejor es eliminar esta columna ya que no provee demasiada información. \n"
      ],
      "metadata": {
        "id": "uqV6q7D01RFK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def drop_cabin_and_id(df):\n",
        "  return df.drop(columns=['Cabin', 'PassengerId'])\n",
        "titanic = drop_cabin_and_id(titanic)\n",
        "titanic.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "NHeMDQ2_DiC_",
        "outputId": "7062ce7e-e1ac-42b3-ce14-ed2f2303eab1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Survived  Pclass                     Name   Sex   Age  SibSp  Parch  \\\n",
              "0         0       3  Braund, Mr. Owen Harris  male  22.0      1      0   \n",
              "\n",
              "      Ticket  Fare Embarked  \n",
              "0  A/5 21171  7.25        S  "
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d9f1227f-8a4d-46d9-aa07-7e545e11542b 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-d9f1227f-8a4d-46d9-aa07-7e545e11542b');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Eliminar Registros sin Puerta de Embarque Registrada**\n",
        "\n",
        "La puerta de embarque nos puede ser útil para sacar conclusiones de si hubieron más sobrevivientes dependiendo de la puerta en la que subieron. Esta columna cuenta con 0.22% de datos vacíos; al ser un número muy pequeño lo mejor es eliminar los registros que no tengan esta información. \n",
        "\n",
        "Los registros eliminados fueron solamemte 2, por lo que esta limpieza no tiene mayor afectación al conjunto de datos."
      ],
      "metadata": {
        "id": "tClXU9XDBMPe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def drop_na_embarked(df):\n",
        "  return df.dropna(subset=['Embarked'])\n",
        "titanic = drop_na_embarked(titanic)\n",
        "titanic.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Lv78ZtM6GKy8",
        "outputId": "f6772a6d-e857-448a-d411-13e5b9f9c94a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Survived      Pclass         Age       SibSp       Parch        Fare\n",
              "count  889.000000  889.000000  712.000000  889.000000  889.000000  889.000000\n",
              "mean     0.382452    2.311586   29.642093    0.524184    0.382452   32.096681\n",
              "std      0.486260    0.834700   14.492933    1.103705    0.806761   49.697504\n",
              "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
              "25%      0.000000    2.000000   20.000000    0.000000    0.000000    7.895800\n",
              "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
              "75%      1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
              "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>889.000000</td>\n",
              "      <td>889.000000</td>\n",
              "      <td>712.000000</td>\n",
              "      <td>889.000000</td>\n",
              "      <td>889.000000</td>\n",
              "      <td>889.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.382452</td>\n",
              "      <td>2.311586</td>\n",
              "      <td>29.642093</td>\n",
              "      <td>0.524184</td>\n",
              "      <td>0.382452</td>\n",
              "      <td>32.096681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.486260</td>\n",
              "      <td>0.834700</td>\n",
              "      <td>14.492933</td>\n",
              "      <td>1.103705</td>\n",
              "      <td>0.806761</td>\n",
              "      <td>49.697504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.895800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a22b74e2-0256-456c-ad4f-e4fded762a8d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\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",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a22b74e2-0256-456c-ad4f-e4fded762a8d 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-a22b74e2-0256-456c-ad4f-e4fded762a8d');\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",
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              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Datos Atípicos**\n"
      ],
      "metadata": {
        "id": "VUBccTX42Uj-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data=titanic, x=\"Fare\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "GPNxgu_dszIq",
        "outputId": "b998d7cd-2d42-4f3f-bb10-cb941a9d0ca2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f50731b4fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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sk8q/jWK33n4bwZdCCE/Fx6vjx7NxOal8kAkw5gQCjPlos6QLJC0PIbwR383rnPjcf0u2M0lfDyF8r8nnB0wJ94AxH52n/PvYvmFmKyS9b4LtHpf0ufhexjKz7vhesMCcwAwY89EeSfvM7JikrKThWhuFEH5pZu+X9Ov4BvJjkm7V2+/5Crjix9AAwAm3IADACQEGACcEGACcEGAAcEKAAcAJAQYAJwQYAJz8H5DGZGPRDykRAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "La tarifa es el único campo que tiene un gran rango de datos, como se puede observar hay un dato que esta muy alejado del resto; pues hay una tarifa de $500. \n",
        "\n",
        "Podemos eliminar los registros que contienen una tarifa mayor a "
      ],
      "metadata": {
        "id": "Fr6XpOr44Keo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def drop_outliers_fare(df):\n",
        "  delete = df[df['Fare'] > 400].index\n",
        "  return df.drop(delete)\n",
        "\n",
        "titanic = drop_outliers_fare(titanic)\n",
        "sns.boxplot(data=titanic, x=\"Fare\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "yxI4nwlfwSTL",
        "outputId": "dbb5d2ab-b00d-4043-bbea-cfe54150e7de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f50730b6ad0>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Transformación de datos\n"
      ],
      "metadata": {
        "id": "aGSEa5KDr7Ew"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Cambiar la columna del puerto de embarque por 3 columnas dummys**\n",
        "\n",
        "Utilizamos una función dentro de la libreria de pandas para crear variables dummys en lugar de la variable categorica \"Embarked\" que ya teniamos, con el fin de poder trabajar de mejor manera en los modelos predictivos."
      ],
      "metadata": {
        "id": "vMjTg__E3pTI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(titanic['Embarked'], titanic['Survived']).plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "ERRGWAF7sjHw",
        "outputId": "91f4fc91-1d28-4ac4-9d9f-f6ea3266a7b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5072bc8450>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se puede observar cómo el puerto con mayor número de embarques fue Southampton, siendo el puerto con menor tasa de supervivencia.\n",
        "\n",
        "El único puerto en el que la tasa de supervivencia fue mayor al 50% fue Cherbourg, mientras que en Queenstown fue más parejo que en Sothampton, sin embargo, sigue siendo menor la superivencia."
      ],
      "metadata": {
        "id": "KTSssPGKs0o9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def embarked_dummies(df):\n",
        "  df = pd.concat([df, pd.get_dummies(df[\"Embarked\"])], axis = 1)\n",
        "  return df.drop('Embarked', axis=1)\n",
        "titanic = embarked_dummies(titanic)\n",
        "titanic.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "XgMy3JrK3e5A",
        "outputId": "f89a7ba9-4c22-4dae-ce4a-ff33213a6f61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Survived  Pclass                     Name   Sex   Age  SibSp  Parch  \\\n",
              "0         0       3  Braund, Mr. Owen Harris  male  22.0      1      0   \n",
              "\n",
              "      Ticket  Fare  C  Q  S  \n",
              "0  A/5 21171  7.25  0  0  1  "
            ],
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              "      <th>Survived</th>\n",
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              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
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              "      <td>A/5 21171</td>\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-35bd457d-ebfc-483c-97dc-f6ac9d297783 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-35bd457d-ebfc-483c-97dc-f6ac9d297783');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Crear columna para saber si pasajero abordó acompañado de alguien**\n",
        "\n",
        "Las columnas de \"Parch\" y \"Sibp\" son relevantes para saber si el pasajero viajaba acompañado, sin embargo por si solas no ofrecen información relevante. Por lo que creamos esta nueva columna que nos indica si un pasajero abordó acompañado o no.\n",
        "\n",
        "Se asigna un valor de 1 en caso de que se logre identificar que el pasajero abordó  acompañado. Se puede determinar esto si forma parte de una misma compra al tener el mismo ticket o si se sabe por la base de datos que asistió con algún familiar. Se elimina la columna que relaciona al pasajero con padres o hijos y la columna que lo relaciona con hermanos o su matrimonio."
      ],
      "metadata": {
        "id": "qIn7FJW2qdYX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def boarded_in_group(df):\n",
        "  is_group = df.duplicated(subset=['Ticket'], keep=False)\n",
        "  df.loc[is_group == True, 'Group'] = 1\n",
        "  df.loc[is_group == False, 'Group'] = 0\n",
        "  df.loc[df['SibSp'] > 0, 'Group'] = 1\n",
        "  df.loc[df['Parch'] > 0, 'Group'] = 1\n",
        "  df.drop('SibSp', axis=1, inplace=True)\n",
        "  df.drop('Parch', axis=1, inplace=True)\n",
        "  df.drop('Ticket', axis=1, inplace=True)\n",
        "  return df\n",
        "titanic = boarded_in_group(titanic)\n",
        "titanic.head(200)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "fPvicc7lPktc",
        "outputId": "5b33f699-9dde-43fd-e4a4-344ee83e02d2"
      },
      "execution_count": null,
      "outputs": [
        {
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              "     Survived  Pclass                                               Name  \\\n",
              "0           0       3                            Braund, Mr. Owen Harris   \n",
              "1           1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
              "2           1       3                             Heikkinen, Miss. Laina   \n",
              "3           1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
              "4           0       3                           Allen, Mr. William Henry   \n",
              "..        ...     ...                                                ...   \n",
              "196         0       3                                Mernagh, Mr. Robert   \n",
              "197         0       3                   Olsen, Mr. Karl Siegwart Andreas   \n",
              "198         1       3                   Madigan, Miss. Margaret \"Maggie\"   \n",
              "199         0       2             Yrois, Miss. Henriette (\"Mrs Harbeck\")   \n",
              "200         0       3                     Vande Walle, Mr. Nestor Cyriel   \n",
              "\n",
              "        Sex   Age     Fare  C  Q  S  Group  \n",
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              "1    female  38.0  71.2833  1  0  0    1.0  \n",
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              "3    female  35.0  53.1000  0  0  1    1.0  \n",
              "4      male  35.0   8.0500  0  0  1    0.0  \n",
              "..      ...   ...      ... .. .. ..    ...  \n",
              "196    male   NaN   7.7500  0  1  0    0.0  \n",
              "197    male  42.0   8.4042  0  0  1    1.0  \n",
              "198  female   NaN   7.7500  0  1  0    0.0  \n",
              "199  female  24.0  13.0000  0  0  1    0.0  \n",
              "200    male  28.0   9.5000  0  0  1    0.0  \n",
              "\n",
              "[200 rows x 10 columns]"
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              "      <td>7.7500</td>\n",
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              "      <td>Yrois, Miss. Henriette (\"Mrs Harbeck\")</td>\n",
              "      <td>female</td>\n",
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              "<p>200 rows × 10 columns</p>\n",
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              "          const element = document.querySelector('#df-db067dcf-98fe-4e43-b8fa-c50c8a93ff58');\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Cambiar columna de clases (Clase 1 por clase 3, y viceversa)**\n",
        "\n",
        "Se cambiaron las clases para facilitar la interpretación del modelo, tomando el número de clase como variable directamente relacionada la probabilidad de sobrevivencia. Esto permite facilitar la asignación de pesos a la variable."
      ],
      "metadata": {
        "id": "s_TwwxsJB4fK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def switch_class(df):\n",
        "  df[\"Pclass\"] = df[\"Pclass\"].replace(1, \"x\")\n",
        "  df[\"Pclass\"] = df[\"Pclass\"].replace(3, 1)\n",
        "  df[\"Pclass\"] = df[\"Pclass\"].replace(\"x\", 3)\n",
        "  return df"
      ],
      "metadata": {
        "id": "D0MXr9idB5Qq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "titanic = switch_class(titanic)\n",
        "titanic.head()"
      ],
      "metadata": {
        "id": "dXC1IdjfCE-4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "0fc12ba6-3310-4bbd-fd25-f88e74a72e84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Survived  Pclass                                               Name  \\\n",
              "0         0       1                            Braund, Mr. Owen Harris   \n",
              "1         1       3  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
              "2         1       1                             Heikkinen, Miss. Laina   \n",
              "3         1       3       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
              "4         0       1                           Allen, Mr. William Henry   \n",
              "\n",
              "      Sex   Age     Fare  C  Q  S  Group  \n",
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              "3  female  35.0  53.1000  0  0  1    1.0  \n",
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              "      <td>26.0</td>\n",
              "      <td>7.9250</td>\n",
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              "      <th>3</th>\n",
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              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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              "      <td>35.0</td>\n",
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              "      <td>Allen, Mr. William Henry</td>\n",
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              "                                                     [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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Cambiar columna de sexo (cambiar cadena por valores binarios)**\n",
        "\n",
        "Cambiaremos los valores en cadena por valores numericos para que el modelo pueda utilizarlos y entenderlos de mejor forma. Se asigna un valor de 0 en caso de que sea hombre y un valor de 1 en caso de ser mujer."
      ],
      "metadata": {
        "id": "A3wrURA4N99X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def change_sex(df):\n",
        "  return df['Sex'].replace(['male', 'female'],[0.0, 1.0], inplace=True)\n",
        "change_sex(titanic)\n",
        "titanic.head()"
      ],
      "metadata": {
        "id": "2UTInQiTxAty",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "cb67c83e-9b71-42fa-d0a3-42ca1a3340a9"
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      "execution_count": null,
      "outputs": [
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            "text/plain": [
              "   Survived  Pclass                                               Name  Sex  \\\n",
              "0         0       1                            Braund, Mr. Owen Harris  0.0   \n",
              "1         1       3  Cumings, Mrs. John Bradley (Florence Briggs Th...  1.0   \n",
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              "4         0       1                           Allen, Mr. William Henry  0.0   \n",
              "\n",
              "    Age     Fare  C  Q  S  Group  \n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Obtener el título de las personas**\n",
        "\n",
        "El nombre es un dato irrelevante para el análisis de datos; sin embargo, en esta variable también se incluye el título de la persona. Esto nos puede ser relevante para saber si sobrevivieron más personas dependiendo de su título."
      ],
      "metadata": {
        "id": "m7vX4AsRElnL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Existen muchos títulos que solo corresponden a una o a un par de personas, lo que no sería muy útil. Investigando acerca de estos títulos se puede obtener información para mapearlos con títulos con mayor número de registros; ya que algunos de estos títulos representan lo mismo tan solo que expresado en un lenguaje diferente."
      ],
      "metadata": {
        "id": "pIZGuvdHE5jx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def create_title(df):\n",
        "  for name_string in df['Name']:\n",
        "    df['Title']=df['Name'].str.extract('([A-Za-z]+)\\.',expand=True)\n",
        "\n",
        "  mapping = {'Capt': 'Mr', 'Col': 'Mr', 'Countess': 'Mrs', 'Don': 'Mr',\n",
        "           'Jonkheer': 'Mr', 'Lady': 'Mrs', 'Major': 'Mr', 'Mlle': 'Miss',\n",
        "           'Mme': 'Miss', 'Ms': 'Miss', 'Rev': 'Mr', 'Sir': 'Mr', 'Dr': 'Mr', \n",
        "           'Dona': 'Mrs'}\n",
        "  return df.replace({'Title': mapping}, inplace=True)\n",
        "\n",
        "create_title(titanic)\n",
        "titanic.groupby(['Title'])['Name'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bTZWz-TTE2mG",
        "outputId": "bc28a92d-9487-4836-8dc7-f09e34edb1da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Title\n",
              "Master     40\n",
              "Miss      184\n",
              "Mr        536\n",
              "Mrs       126\n",
              "Name: Name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Con esto obtenemos 4 categorías de títulos, lo cual puede ser valioso al momento de realizar el análisis. Y se elimina la columna de nombre ya que no nos da información relevante para el modelo. La información que pudo resultar útil acaba de ser extraída para clasificar al pasajero según su título."
      ],
      "metadata": {
        "id": "HIBIACi1FYJo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def dummy_title(df):\n",
        "  df = pd.concat([df, pd.get_dummies(df[\"Title\"])], axis = 1)\n",
        "  df.drop(['Title'], axis=1, inplace=True)\n",
        "  df.drop(['Name'], axis=1, inplace=True)\n",
        "  return df\n",
        "\n",
        "titanic = dummy_title(titanic)\n",
        "titanic.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "DTU3VJBDH22k",
        "outputId": "b92ba27c-ca6f-4b8e-8bdd-4850413d4517"
      },
      "execution_count": null,
      "outputs": [
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            "text/plain": [
              "   Survived  Pclass  Sex   Age     Fare  C  Q  S  Group  Master  Miss  Mr  Mrs\n",
              "0         0       1  0.0  22.0   7.2500  0  0  1    1.0       0     0   1    0\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Predecir las edades faltantes con una regresión de Árboles de Decisiones**"
      ],
      "metadata": {
        "id": "0slai3GVIexn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trained_age_model = False\n",
        "age_model = None\n",
        "\n",
        "def predict_ages(df):\n",
        "  predictors = df[[\"Pclass\", \"Sex\", \"Group\", \"Master\", \"Miss\", \"Mr\", \"Mrs\", \"Age\"]]\n",
        "  predict_age = predictors.loc[predictors['Age'].isnull()]\n",
        "  \n",
        "  global trained_age_model\n",
        "  # Fit model from train, test will use model fitted from train\n",
        "  if not trained_age_model:\n",
        "    train_age = predictors.loc[predictors['Age'].notnull()]\n",
        "    train_age_X = train_age.drop(['Age'], axis = 1)\n",
        "    global age_model\n",
        "\n",
        "    # from sklearn import tree\n",
        "    # age_model = tree.DecisionTreeRegressor().fit(train_age_X, train_age[\"Age\"])\n",
        "\n",
        "    # from sklearn.neighbors import KNeighborsRegressor\n",
        "    # age_model = KNeighborsRegressor(n_neighbors=5).fit(train_age_X, train_age[\"Age\"])\n",
        "  \n",
        "    from sklearn.ensemble import RandomForestRegressor\n",
        "    age_model = RandomForestRegressor().fit(train_age_X, train_age[\"Age\"])\n",
        "    trained_age_model = True\n",
        "\n",
        "  predicted_ages = age_model.predict(predict_age.drop(['Age'], axis = 1))\n",
        "  df.loc[df['Age'].isnull(), \"Age\"] = predicted_ages\n",
        "  return df\n",
        "\n",
        "titanic = predict_ages(titanic)\n",
        "titanic.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BxeiYI2dIdq6",
        "outputId": "91aac050-719b-4e00-ccf1-4e6c056b2ebc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 886 entries, 0 to 890\n",
            "Data columns (total 13 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Survived  886 non-null    int64  \n",
            " 1   Pclass    886 non-null    int64  \n",
            " 2   Sex       886 non-null    float64\n",
            " 3   Age       886 non-null    float64\n",
            " 4   Fare      886 non-null    float64\n",
            " 5   C         886 non-null    uint8  \n",
            " 6   Q         886 non-null    uint8  \n",
            " 7   S         886 non-null    uint8  \n",
            " 8   Group     886 non-null    float64\n",
            " 9   Master    886 non-null    uint8  \n",
            " 10  Miss      886 non-null    uint8  \n",
            " 11  Mr        886 non-null    uint8  \n",
            " 12  Mrs       886 non-null    uint8  \n",
            "dtypes: float64(4), int64(2), uint8(7)\n",
            "memory usage: 54.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Como se puede observar, ya no existen valores nulos en la columna de edad, pues se lograron predecir edades según el modelo generado para todos los registros que no contaban con este dato."
      ],
      "metadata": {
        "id": "A_1FcMc14typ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Agregar columna con grupos de edad**\n",
        "\n",
        "Agruparemos a las personas por grupo de edad para poder trabajar mas fácilmente con las categorias. De esta forma se podrá comparar la relación que tienen distintos grupos etarios con el resto de variables con la finalidad de saber si tienen algo que ver con la supervivencia a bordo del Titanic."
      ],
      "metadata": {
        "id": "wF2wy-rBL7Mg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bins=[0, 10, 20, 30, 40, 50, 60, 70, 80]\n",
        "titanic[\"AgeGroup\"] = pd.cut(x = titanic['Age'],bins = bins,labels = [1,10,20,30,40,50,60,70])\n",
        "titanic.head()"
      ],
      "metadata": {
        "id": "q5fRC9bkL3ct",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "9259683d-c613-4b1c-9ae9-ebbb3360d67c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Survived  Pclass  Sex   Age     Fare  C  Q  S  Group  Master  Miss  Mr  \\\n",
              "0         0       1  0.0  22.0   7.2500  0  0  1    1.0       0     0   1   \n",
              "1         1       3  1.0  38.0  71.2833  1  0  0    1.0       0     0   0   \n",
              "2         1       1  1.0  26.0   7.9250  0  0  1    0.0       0     1   0   \n",
              "3         1       3  1.0  35.0  53.1000  0  0  1    1.0       0     0   0   \n",
              "4         0       1  0.0  35.0   8.0500  0  0  1    0.0       0     0   1   \n",
              "\n",
              "   Mrs AgeGroup  \n",
              "0    0       20  \n",
              "1    1       30  \n",
              "2    0       20  \n",
              "3    1       30  \n",
              "4    0       30  "
            ],
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Función para tratar los datos"
      ],
      "metadata": {
        "id": "kO7ygxT6wCRW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def limpieza_datos(df, train):\n",
        "  if train:\n",
        "    df = drop_duplicates(df)\n",
        "    df = drop_na_embarked(df)\n",
        "    df = drop_outliers_fare(df)\n",
        "  df = drop_cabin_and_id(df)\n",
        "  df = embarked_dummies(df)\n",
        "  df = boarded_in_group(df)\n",
        "  df = switch_class(df)\n",
        "  change_sex(df)\n",
        "  create_title(df)\n",
        "  df = dummy_title(df)\n",
        "  df = predict_ages(df)\n",
        "  return df.fillna(0)"
      ],
      "metadata": {
        "id": "S_hu8O0HwJli"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Análisis de datos"
      ],
      "metadata": {
        "id": "0cJ0NfW-slnw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14,14))\n",
        "sns.heatmap(titanic.corr(), cmap=\"RdYlBu\", \n",
        "    annot=True, square=True,\n",
        "    vmin=-1, vmax=1, fmt=\"+.3f\")\n",
        "plt.title(\"Correlation matrix for the titanic dataset\")"
      ],
      "metadata": {
        "id": "G3_Ehp8Wwu27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "outputId": "e975d324-92a9-43d8-950d-3c9482ec38ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Correlation matrix for the titanic dataset')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x1008 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Como se puede observar en el mapa de correlaciones anterior, ciertas variables guardan relación entre sí. Es interesante poder analizar el color que se grafica entre las columnas agregadas tras la limpieza y transformación de los datos dado que se puede ver cómo se genera correlación entre la supervivencia y variables como el puerto de embarque en Cherbourg o si los pasajeros abordaron acompañados al Titanic.\n",
        "\n",
        "De igual forma se puede observar relaciones obvias como el título para nombrar al pasajero y su edad, como otras no tan obvias como el costo de los boletos con el hecho de que un pasajero llegó acompañado o no.\n",
        "\n",
        "Al haber generado transformaciones en los datos, se obtuvieron nuevas columnas de datos procesados que muestran tener correlación entre sí y con la supervivencia a bordo del Titanic por lo cuál se espera que el modelo que se vaya a utilizar pueda dar predicciones más acertadas al contar con mayor información."
      ],
      "metadata": {
        "id": "xsDu_K9SHoKg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Porcentaje de Supervivencia"
      ],
      "metadata": {
        "id": "JFn-Iuv5nOhv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(data=titanic, x = \"Survived\")\n",
        "plt.title('Pasajeros');\n",
        "plt.xlabel('Supervivencia');\n",
        "plt.ylabel('# de personas');"
      ],
      "metadata": {
        "id": "17RftuqkpMXE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "1eab1a59-bb5d-43a4-dd23-ea2ab5025952"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic['Survived'].value_counts(normalize=True).plot(kind='pie', autopct='%1.0f%%', explode=(0.05, 0.05))"
      ],
      "metadata": {
        "id": "SqIe8aT9gsSM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "8606585f-57a9-496e-e468-962f18433807"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f506fd501d0>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Como se puede observar en las gráficas superiores, el porcentaje de sobrevivientes es menor al de fallecidos. El 38% de los pasajeros logró sobrevivir a la trajedia por la que pasó el Titanic."
      ],
      "metadata": {
        "id": "bGr_0AtsioIx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Sobrevivientes por Sexo y Clase"
      ],
      "metadata": {
        "id": "_bfQ9uoIjyNB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "did_survive = titanic[titanic['Survived'].isin([1])]\n",
        "pd.crosstab(did_survive['Pclass'], did_survive['Sex']).plot(kind='bar')"
      ],
      "metadata": {
        "id": "mN1BrDUg2IT7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "outputId": "bee2c62a-78cd-4a88-ca3d-4375b417cf4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f506e489b50>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "En la gráfica anterior se puede observar a todos los sobrevivientes del Titanic registrados en la base de datos divididos según su clase de ticket y sexo. \n",
        "\n",
        "Se puede observar cómo el número de supervivientes es semejante entre las diferentes clases, parece que primera clase tiene el mayor número de sobrevivientes, seguido de tercera y por último de segunda. Esto es relevante dado que más de la mitad de los pasajeros viajaban en tercera clase y el número de sobrevivientes es semejante con el resto de clases.\n",
        "\n",
        "En las tres clases sobrevivieron mayor número de mujeres frente al número de hombres."
      ],
      "metadata": {
        "id": "jKu3AdJ4KBWR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### No sobrevivientes por Sexo y Clase"
      ],
      "metadata": {
        "id": "o6oSAjO5kZlK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "not_survived = titanic[titanic['Survived'].isin([0])]\n",
        "pd.crosstab(not_survived['Pclass'], not_survived['Sex']).plot(kind='bar')"
      ],
      "metadata": {
        "id": "fwa_omnYW4Hp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "outputId": "2be73522-e01c-4659-9931-ff04789e8282"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f506e484e50>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "En la gráfica se observa a los pasajeros que no lograron sobrevivir divididos según la clase de ticket adquirido y su sexo. Es notable que el número de hombres es mayor frente al de mujeres. En este caso se puede observar cómo la tercera clase fue la más afectada."
      ],
      "metadata": {
        "id": "9wqi4rFIano4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Sobrevivientes por clase"
      ],
      "metadata": {
        "id": "Hi4AUH0zlB6H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(titanic['Pclass'], titanic['Survived']).plot(kind='bar')"
      ],
      "metadata": {
        "id": "CaM9mJ2obVxH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "outputId": "ce5e6047-cfbc-421d-ce5a-77e5c95d255a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f506e37ea10>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "En esta última gráfica se puede observar cómo la única clase con mayor número de supervivientes que de fallecidos fue en primera clase, seguido de segunda clase con un número de sobrevivientes y fallecidos similar a pesar de que este último resultó mayor.\n",
        "\n",
        "En tercera clase se puede observar cómo el número de sobrevivientes fue mucho más pequeño en comparación al número de fallecidos de la misma clase. Pasa la contrario a primera clase, pues el número de fallecidos supera en gran medida al de sobrevivientes."
      ],
      "metadata": {
        "id": "mdn5aL59bgt4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Sobrevivientes y no sobrevivientes por género"
      ],
      "metadata": {
        "id": "OI-tGNnpnzJQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(titanic['Sex'], titanic['Survived']).plot.bar()\n",
        "plt.title('Pasajeros por género');\n",
        "plt.xlabel('Sexo');\n",
        "plt.ylabel('# de personas');"
      ],
      "metadata": {
        "id": "VVQKtrTRpZcl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "60262fad-2ba2-4b5d-d9a1-986496772fa2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "La gráfica muestra cómo de la cantidad total de hombres tan solo pocos lograron sobrevivir a diferencia de las mujeres que lograron sobrevivir más de las que no lo hicieron."
      ],
      "metadata": {
        "id": "j885FMFUjm9F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x = 'AgeGroup', y = 'Fare', data  = titanic, hue = 'Survived')\n",
        "plt.title('Supervivencia por costo de ticket y edad');\n",
        "plt.xlabel('Edad');\n",
        "plt.ylabel('Costo de ticket');"
      ],
      "metadata": {
        "id": "qWSqiHnvphCR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "888afcdd-231d-4c5f-a4e3-6751e3c96891"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(titanic['AgeGroup'], titanic['Pclass']).plot.bar()\n",
        "plt.title('Pasajeros por género');\n",
        "plt.xlabel('Sexo');\n",
        "plt.ylabel('# de personas');"
      ],
      "metadata": {
        "id": "aPi0FQkIo6WN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "ee620c4f-6226-4cde-b2b5-e6cdad7552c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x = 'Age', y = 'Pclass', data  = titanic, hue = 'Survived')\n",
        "plt.title('Supervivencia por clase y edad');\n",
        "plt.xlabel('Edad');\n",
        "plt.ylabel('Clase');"
      ],
      "metadata": {
        "id": "zaILzxQjoNZT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "08708dac-5f0b-4582-823f-b7d05e4d37b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "La gráfica utiliza los distintos grupos etarios creados anteriormente mostrando de color naranja aquellos que sobrevivieron y de azul aquellos que no. En el eje y se evalúa qué tanto pagaron por su boleto."
      ],
      "metadata": {
        "id": "ehGJAmibk_jR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.violinplot(x = 'AgeGroup', y = 'Survived',hue = \"Pclass\", data  = titanic)\n",
        "plt.title('Supervivencia por edad y clase');\n",
        "plt.xlabel('Edad');\n",
        "plt.ylabel('Supervivencia');"
      ],
      "metadata": {
        "id": "9FI3JAlHpvky",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "d093a45c-5597-447e-813d-1da975a3e6e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "age_surv = pd.crosstab(titanic['AgeGroup'], titanic['Survived'])\n",
        "age_surv.plot(kind='bar')\n",
        "plt.title('Supervivientes por grupo de edad');\n",
        "plt.xlabel('Frecuencia');\n",
        "plt.ylabel('Edad');"
      ],
      "metadata": {
        "id": "ZKKv0s0XMpdS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "3af4165b-80b5-4999-b728-22d26f65cf9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "En la gráfica se observa cómo el único grupo de edad con mayor tasa de supervivencia que de muerte es el de los bebés. El resto de grupos de edad muestra una proporción semejante respecto a tasa de supervivencia. El grupo de 30 años y el de 50 muestran una relación más pareja entre supervivencia y fallecimiento, pues en el resto de grupos es más alejada."
      ],
      "metadata": {
        "id": "9LgR6dcRmGg3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "titanic['Group'].value_counts(normalize=True).plot(kind='pie', autopct='%1.0f%%')"
      ],
      "metadata": {
        "id": "tIRqPwbRAxy6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "6772b14b-a052-46fc-a479-2fe233223b0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f506e130750>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Como se puede observar, 48% de los pasajeros abordaron acompañados de otro pasajero al Titanic."
      ],
      "metadata": {
        "id": "249f_MasoeC8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "not_group = titanic[titanic['Group'].isin([0])]\n",
        "in_group = titanic[titanic['Group'].isin([1])]\n",
        "not_group = not_group['Survived'].value_counts(normalize=True)\n",
        "in_group = in_group['Survived'].value_counts(normalize=True)"
      ],
      "metadata": {
        "id": "zjPOHm8rryYh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "not_group.plot(kind='pie', autopct='%1.0f%%')"
      ],
      "metadata": {
        "id": "OkgySAnbA2yU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "8af2e7ce-9c42-48c1-ebd7-bb4b6fc1d1e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f506e143510>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "73% de los pasajeros que abordaron solos el Titanic no lograron sobrevivir. Es decir, el 73% del 52% del total de pasajeros no logró sobrevivir y justo coincide que toda esta proporción abordó sola el Titanic."
      ],
      "metadata": {
        "id": "fPi7yj4Tp8Bm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "in_group.plot(kind='pie', autopct='%1.0f%%')"
      ],
      "metadata": {
        "id": "FVl_dLFvqkv0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "eb77a190-1a5d-401c-f530-ad652d24f4f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f506e27a350>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Por otra parte, la mitad de los pasajeros que abordaron acompañados de alguien más el Titanic, lograron sobrevivir. Una cifra que indica que de las personas que abordaron acompañadas, la supervivencia fue el doble comparado a las personas que abordaron solas."
      ],
      "metadata": {
        "id": "sn3DFlxaqxNw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "Ts3tAwkdxxNR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_alone = titanic[titanic['Group'].isin([0])]\n",
        "titanic_with_friends = titanic[titanic['Group'].isin([1])]\n",
        "\n",
        "from scipy.stats import ttest_ind\n",
        "\n",
        "alone_mean = titanic_alone[\"Survived\"].mean()\n",
        "friends_mean = titanic_with_friends[\"Survived\"].mean()\n",
        "\n",
        "print(\"Alone survivement mean value:\",alone_mean)\n",
        "print(\"Friends survivement mean value:\",friends_mean)\n",
        "\n",
        "alone_std = titanic_alone[\"Survived\"].std()\n",
        "friends_std = titanic_with_friends[\"Survived\"].std()\n",
        "print(\"Alone survivement std value:\",alone_std)\n",
        "print(\"Friends survivement std value:\",friends_std)\n",
        "\n",
        "ttest,pval = ttest_ind(titanic_alone[\"Survived\"],titanic_with_friends[\"Survived\"])\n",
        "\n",
        "print(\"p-value\",pval)\n",
        "if pval <0.05:\n",
        "  print(\"Se rechaza la hipótesis nula (las medias son diferentes estadisticamente con un 95% de confianza)\")\n",
        "else:\n",
        "  print(\"No se rechaza la hipótesis nula (las medias son iguales estadisticamente con un 95% de confianza)\")"
      ],
      "metadata": {
        "id": "GsGTLpD0xiFi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0b215de6-b46e-4aa3-bae9-1651738d2daa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Alone survivement mean value: 0.27056277056277056\n",
            "Friends survivement mean value: 0.5\n",
            "Alone survivement std value: 0.4447321299590394\n",
            "Friends survivement std value: 0.5005906676601786\n",
            "p-value 1.093814468879732e-12\n",
            "Se rechaza la hipótesis nula (las medias son diferentes estadisticamente con un 95% de confianza)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic.info()"
      ],
      "metadata": {
        "id": "62IweW3JNsEU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4bde191b-522c-458e-cf04-c3bdb4d828a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 886 entries, 0 to 890\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype   \n",
            "---  ------    --------------  -----   \n",
            " 0   Survived  886 non-null    int64   \n",
            " 1   Pclass    886 non-null    int64   \n",
            " 2   Sex       886 non-null    float64 \n",
            " 3   Age       886 non-null    float64 \n",
            " 4   Fare      886 non-null    float64 \n",
            " 5   C         886 non-null    uint8   \n",
            " 6   Q         886 non-null    uint8   \n",
            " 7   S         886 non-null    uint8   \n",
            " 8   Group     886 non-null    float64 \n",
            " 9   Master    886 non-null    uint8   \n",
            " 10  Miss      886 non-null    uint8   \n",
            " 11  Mr        886 non-null    uint8   \n",
            " 12  Mrs       886 non-null    uint8   \n",
            " 13  AgeGroup  886 non-null    category\n",
            "dtypes: category(1), float64(4), int64(2), uint8(7)\n",
            "memory usage: 88.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Creación y evaluación de modelos"
      ],
      "metadata": {
        "id": "U9mlS6l446eV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import tree\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn import preprocessing\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.svm import SVC\n",
        "from xgboost import XGBClassifier"
      ],
      "metadata": {
        "id": "bNEuEEL0FDgB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train = limpieza_datos(pd.read_csv(\"train.csv\"), train=True)\n",
        "train_X = train.drop(['Survived'], axis=1)\n",
        "train_Y = train['Survived']\n",
        "\n",
        "test_X = limpieza_datos(pd.read_csv(\"test.csv\"), train=False)\n",
        "test_Y = pd.read_csv(\"test_perfecto.csv\")[\"Survived\"]"
      ],
      "metadata": {
        "id": "5he2rt9B42em"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def concat_test_pred(test_Y, y_pred):\n",
        "    \n",
        "    df_test = test_Y.to_frame(name='Titanic').reset_index().drop(columns='index')\n",
        "    df_pred = pd.Series(y_pred).to_frame(name='Prediction')\n",
        "    results = pd.concat([df_test, df_pred], axis=1)\n",
        "    \n",
        "    return results\n",
        "\n",
        "def performance_metrics(results):\n",
        "    \n",
        "    positives = results[['Titanic', 'Prediction']][results['Titanic'] == 1]\n",
        "    negatives = results[['Titanic', 'Prediction']][results['Titanic'] == 0]\n",
        "    \n",
        "    true_negatives = negatives[negatives['Titanic'] == negatives['Prediction']].shape[0]\n",
        "    false_positives = negatives[negatives['Titanic'] != negatives['Prediction']].shape[0]\n",
        "    true_positives = positives[positives['Titanic'] == positives['Prediction']].shape[0]\n",
        "    false_negatives = positives[positives['Titanic'] != positives['Prediction']].shape[0]\n",
        "    \n",
        "    confusion_matrix = {'actual positives' : [true_positives, false_negatives], \n",
        "                        'actual negatives' : [false_positives, true_negatives]}\n",
        "    \n",
        "    confusion_matrix_df = pd.DataFrame.from_dict(confusion_matrix, orient='index', \n",
        "                                                 columns=['predicted positives', 'predicted negatives'])\n",
        "    \n",
        "    accuracy = (true_positives + true_negatives) / (true_positives + false_positives +  true_negatives + false_negatives)\n",
        "    precission = true_positives / (true_positives + false_positives)\n",
        "    recall = true_positives / (true_positives + false_negatives)\n",
        "    f1_score = 2 * (precission * recall) / (precission + recall)\n",
        "    \n",
        "    metrics = {'Accuracy' : accuracy, 'Precission' : precission, 'Recall' : recall, 'F1 Score' : f1_score}\n",
        "    \n",
        "    metrics_df = pd.DataFrame.from_dict(metrics, orient='index', columns=['Metrics'])\n",
        "    \n",
        "    return confusion_matrix_df, metrics_df  \n",
        "\n",
        "def modelos(test_X, train_X):\n",
        "\n",
        "  # modelos\n",
        "  tree_model = tree.DecisionTreeClassifier().fit(train_X, train_Y)\n",
        "  y_pred_tree = tree_model.predict(test_X)\n",
        "\n",
        "  random_model = RandomForestClassifier().fit(train_X, train_Y)\n",
        "  y_pred_rand = random_model.predict(test_X)\n",
        "\n",
        "  xgb_model = XGBClassifier(use_label_encoder=False, eval_metric='error').fit(train_X, train_Y)\n",
        "  y_pred_xgb = xgb_model.predict(test_X)\n",
        "\n",
        "  svm_model = make_pipeline(StandardScaler(), SVC(gamma='auto')).fit(train_X, train_Y)\n",
        "  y_pred_svm = svm_model.predict(test_X)\n",
        "\n",
        "  logistic_model = LogisticRegression(max_iter = 1000).fit(train_X, train_Y)\n",
        "  y_pred_log = logistic_model.predict(test_X)\n",
        "\n",
        "  # generación de archivos\n",
        "  results_tree = [pd.read_csv(\"gender_submission.csv\")['PassengerId'], y_pred_tree]\n",
        "  submission_tree = pd.DataFrame(results_tree).transpose()\n",
        "  submission_tree = submission_tree.astype(int)\n",
        "  submission_tree.columns = ['PassengerId', 'Survived']\n",
        "  submission_tree.to_csv(\"Results/results_tree.csv\",index = False)\n",
        "  \n",
        "  results_randomforest = [pd.read_csv(\"gender_submission.csv\")['PassengerId'], y_pred_rand]\n",
        "  submission_randomforest = pd.DataFrame(results_randomforest).transpose()\n",
        "  submission_randomforest = submission_randomforest.astype(int)\n",
        "  submission_randomforest.columns = ['PassengerId', 'Survived']\n",
        "  submission_randomforest.to_csv(\"Results/results_randomforest.csv\",index = False)\n",
        "\n",
        "  results_XGB = [pd.read_csv(\"gender_submission.csv\")['PassengerId'], y_pred_xgb]\n",
        "  submission_xgb = pd.DataFrame(results_XGB).transpose()\n",
        "  submission_xgb = submission_xgb.astype(int)\n",
        "  submission_xgb.columns = ['PassengerId', 'Survived']\n",
        "  submission_xgb.to_csv(\"Results/results_XGB.csv\",index = False)\n",
        "\n",
        "  results_svm = [pd.read_csv(\"gender_submission.csv\")['PassengerId'], y_pred_svm]\n",
        "  submission_svm = pd.DataFrame(results_svm).transpose()\n",
        "  submission_svm = submission_svm.astype(int)\n",
        "  submission_svm.columns = ['PassengerId', 'Survived']\n",
        "  submission_svm.to_csv(\"Results/results_svm.csv\",index = False)\n",
        "\n",
        "  results_log = [pd.read_csv(\"gender_submission.csv\")['PassengerId'], y_pred_log]\n",
        "  submission_log = pd.DataFrame(results_log).transpose()\n",
        "  submission_log = submission_log.astype(int)\n",
        "  submission_log.columns = ['PassengerId', 'Survived']\n",
        "  submission_log.to_csv(\"Results/results_log.csv\",index = False)\n",
        "\n",
        "  return y_pred_tree, y_pred_rand, y_pred_xgb, y_pred_svm, y_pred_log\n",
        "\n",
        "def evaluate(y_pred, test_Y, name):\n",
        "  results = concat_test_pred(test_Y, y_pred)\n",
        "  confusion_matrix, metrics = performance_metrics(results)\n",
        "  print(name+\":\")\n",
        "  print(metrics,\"\\n\")\n",
        "  return"
      ],
      "metadata": {
        "id": "yge_ioAEdwSQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_X.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MLtIdN0UVkHy",
        "outputId": "e366b823-6b08-4fc4-a063-8e9a821bf733"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 886 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Pclass  886 non-null    int64  \n",
            " 1   Sex     886 non-null    float64\n",
            " 2   Age     886 non-null    float64\n",
            " 3   Fare    886 non-null    float64\n",
            " 4   C       886 non-null    uint8  \n",
            " 5   Q       886 non-null    uint8  \n",
            " 6   S       886 non-null    uint8  \n",
            " 7   Group   886 non-null    float64\n",
            " 8   Master  886 non-null    uint8  \n",
            " 9   Miss    886 non-null    uint8  \n",
            " 10  Mr      886 non-null    uint8  \n",
            " 11  Mrs     886 non-null    uint8  \n",
            "dtypes: float64(4), int64(1), uint8(7)\n",
            "memory usage: 47.6 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_tree, y_pred_rand, y_pred_xgb, y_pred_svm, y_pred_log = modelos(test_X, train_X)\n",
        "evaluate(y_pred_tree, test_Y, \"Decision tree\")\n",
        "evaluate(y_pred_rand, test_Y, \"Random forest\")\n",
        "evaluate(y_pred_xgb, test_Y, \"XGB\")\n",
        "evaluate(y_pred_svm, test_Y, \"SVM\")\n",
        "evaluate(y_pred_log, test_Y, \"Logistic regression\")"
      ],
      "metadata": {
        "id": "wittoJBZE_0E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8996ded-c525-43a5-e47b-49c4c1c89bf2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision tree:\n",
            "             Metrics\n",
            "Accuracy    0.739234\n",
            "Precission  0.643275\n",
            "Recall      0.696203\n",
            "F1 Score    0.668693 \n",
            "\n",
            "Random forest:\n",
            "             Metrics\n",
            "Accuracy    0.753589\n",
            "Precission  0.677419\n",
            "Recall      0.664557\n",
            "F1 Score    0.670927 \n",
            "\n",
            "XGB:\n",
            "             Metrics\n",
            "Accuracy    0.760766\n",
            "Precission  0.693333\n",
            "Recall      0.658228\n",
            "F1 Score    0.675325 \n",
            "\n",
            "SVM:\n",
            "             Metrics\n",
            "Accuracy    0.777512\n",
            "Precission  0.748092\n",
            "Recall      0.620253\n",
            "F1 Score    0.678201 \n",
            "\n",
            "Logistic regression:\n",
            "             Metrics\n",
            "Accuracy    0.782297\n",
            "Precission  0.708075\n",
            "Recall      0.721519\n",
            "F1 Score    0.714734 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler_train = preprocessing.MinMaxScaler().fit(train_X)\n",
        "\n",
        "train_X_scaled = pd.DataFrame(scaler_train.transform(train_X))\n",
        "test_X_scaled = pd.DataFrame(scaler_train.transform(test_X))"
      ],
      "metadata": {
        "id": "lbSmDD43KEX-"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "test_X_scaled"
      ],
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          "height": 424
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        "id": "18-747CIgRdT",
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      "execution_count": null,
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              "            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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_X_scaled[test_X_scaled.select_dtypes(np.float64).columns] = test_X_scaled.select_dtypes(np.float64).astype(np.float16)\n",
        "train_X_scaled[train_X_scaled.select_dtypes(np.float64).columns] = train_X_scaled.select_dtypes(np.float64).astype(np.float16)"
      ],
      "metadata": {
        "id": "v7BTcmu5fAun"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_X.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "SyAmunnXnwOt",
        "outputId": "c201488b-41f4-4207-c76a-0cf6aba3640f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Pclass         Sex         Age        Fare           C           Q  \\\n",
              "count  886.000000  886.000000  886.000000  886.000000  886.000000  886.000000   \n",
              "mean     1.683973    0.351016   29.522330   30.470611    0.186230    0.086907   \n",
              "std      0.832607    0.477557   13.679015   41.155721    0.389512    0.281858   \n",
              "min      1.000000    0.000000    0.420000    0.000000    0.000000    0.000000   \n",
              "25%      1.000000    0.000000   21.000000    7.895800    0.000000    0.000000   \n",
              "50%      1.000000    0.000000   29.361283   14.454200    0.000000    0.000000   \n",
              "75%      2.000000    1.000000   37.000000   30.646850    0.000000    0.000000   \n",
              "max      3.000000    1.000000   80.000000  263.000000    1.000000    1.000000   \n",
              "\n",
              "                S       Group      Master        Miss          Mr         Mrs  \n",
              "count  886.000000  886.000000  886.000000  886.000000  886.000000  886.000000  \n",
              "mean     0.726862    0.478555    0.045147    0.207675    0.604966    0.142212  \n",
              "std      0.445823    0.499822    0.207743    0.405872    0.489134    0.349465  \n",
              "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000  \n",
              "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000  \n",
              "50%      1.000000    0.000000    0.000000    0.000000    1.000000    0.000000  \n",
              "75%      1.000000    1.000000    0.000000    0.000000    1.000000    0.000000  \n",
              "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000  "
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>C</th>\n",
              "      <th>Q</th>\n",
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              "      <th>Group</th>\n",
              "      <th>Master</th>\n",
              "      <th>Miss</th>\n",
              "      <th>Mr</th>\n",
              "      <th>Mrs</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>886.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>886.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.683973</td>\n",
              "      <td>0.351016</td>\n",
              "      <td>29.522330</td>\n",
              "      <td>30.470611</td>\n",
              "      <td>0.186230</td>\n",
              "      <td>0.086907</td>\n",
              "      <td>0.726862</td>\n",
              "      <td>0.478555</td>\n",
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              "      <td>0.207675</td>\n",
              "      <td>0.604966</td>\n",
              "      <td>0.142212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.832607</td>\n",
              "      <td>0.477557</td>\n",
              "      <td>13.679015</td>\n",
              "      <td>41.155721</td>\n",
              "      <td>0.389512</td>\n",
              "      <td>0.281858</td>\n",
              "      <td>0.445823</td>\n",
              "      <td>0.499822</td>\n",
              "      <td>0.207743</td>\n",
              "      <td>0.405872</td>\n",
              "      <td>0.489134</td>\n",
              "      <td>0.349465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>7.895800</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>29.361283</td>\n",
              "      <td>14.454200</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>30.646850</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>263.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f51ed271-af54-4612-a9eb-b911e52621ab')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-container {\n",
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              "\n",
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              "\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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f51ed271-af54-4612-a9eb-b911e52621ab');\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",
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            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_X_scaled.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "P3G2It4gktvm",
        "outputId": "e8f8ffa3-30c0-4390-d0d1-f3e84b5c181b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               0           1           2           3           4           5   \\\n",
              "count  886.000000  886.000000  886.000000  886.000000  886.000000  886.000000   \n",
              "mean     0.342041    0.351074    0.365723    0.115845    0.186279    0.086914   \n",
              "std      0.416504    0.477539    0.171875    0.156494    0.389404    0.281738   \n",
              "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "25%      0.000000    0.000000    0.258545    0.030029    0.000000    0.000000   \n",
              "50%      0.000000    0.000000    0.363770    0.054962    0.000000    0.000000   \n",
              "75%      0.500000    1.000000    0.459717    0.116516    0.000000    0.000000   \n",
              "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
              "\n",
              "               6           7           8           9           10          11  \n",
              "count  886.000000  886.000000  886.000000  886.000000  886.000000  886.000000  \n",
              "mean     0.727051    0.478516    0.045135    0.207642    0.604980    0.142212  \n",
              "std      0.446045    0.499756    0.207764    0.406006    0.489258    0.349365  \n",
              "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000  \n",
              "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000  \n",
              "50%      1.000000    0.000000    0.000000    0.000000    1.000000    0.000000  \n",
              "75%      1.000000    1.000000    0.000000    0.000000    1.000000    0.000000  \n",
              "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000  "
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>886.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>886.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.342041</td>\n",
              "      <td>0.351074</td>\n",
              "      <td>0.365723</td>\n",
              "      <td>0.115845</td>\n",
              "      <td>0.186279</td>\n",
              "      <td>0.086914</td>\n",
              "      <td>0.727051</td>\n",
              "      <td>0.478516</td>\n",
              "      <td>0.045135</td>\n",
              "      <td>0.207642</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.416504</td>\n",
              "      <td>0.477539</td>\n",
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              "      <td>0.389404</td>\n",
              "      <td>0.281738</td>\n",
              "      <td>0.446045</td>\n",
              "      <td>0.499756</td>\n",
              "      <td>0.207764</td>\n",
              "      <td>0.406006</td>\n",
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              "      <td>0.349365</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.258545</td>\n",
              "      <td>0.030029</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.363770</td>\n",
              "      <td>0.054962</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.459717</td>\n",
              "      <td>0.116516</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-384038b8-883d-45b2-b90b-96f79a4e9300')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-384038b8-883d-45b2-b90b-96f79a4e9300 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-384038b8-883d-45b2-b90b-96f79a4e9300');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_X_scaled = train_X_scaled.fillna(0)\n",
        "train_X_scaled.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rwc06lggdso1",
        "outputId": "d8cfc77d-285f-46ce-a088-b123a58da26c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 886 entries, 0 to 885\n",
            "Data columns (total 12 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   0       886 non-null    float16\n",
            " 1   1       886 non-null    float16\n",
            " 2   2       886 non-null    float16\n",
            " 3   3       886 non-null    float16\n",
            " 4   4       886 non-null    float16\n",
            " 5   5       886 non-null    float16\n",
            " 6   6       886 non-null    float16\n",
            " 7   7       886 non-null    float16\n",
            " 8   8       886 non-null    float16\n",
            " 9   9       886 non-null    float16\n",
            " 10  10      886 non-null    float16\n",
            " 11  11      886 non-null    float16\n",
            "dtypes: float16(12)\n",
            "memory usage: 20.9 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_X_scaled.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jlbYQ61eY_BH",
        "outputId": "514ab487-1e38-41cb-e5bd-9b1a0cd5a7a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 12 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   0       418 non-null    float16\n",
            " 1   1       418 non-null    float16\n",
            " 2   2       418 non-null    float16\n",
            " 3   3       418 non-null    float16\n",
            " 4   4       418 non-null    float16\n",
            " 5   5       418 non-null    float16\n",
            " 6   6       418 non-null    float16\n",
            " 7   7       418 non-null    float16\n",
            " 8   8       418 non-null    float16\n",
            " 9   9       418 non-null    float16\n",
            " 10  10      418 non-null    float16\n",
            " 11  11      418 non-null    float16\n",
            "dtypes: float16(12)\n",
            "memory usage: 9.9 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_tree_scaled, y_pred_rand_scaled, y_pred_xgb_scaled, y_pred_svm_scaled, y_pred_log_scaled = modelos(test_X_scaled, train_X_scaled)\n",
        "\n",
        "evaluate(y_pred_tree_scaled, test_Y, \"Decision tree (scaled)\")\n",
        "evaluate(y_pred_rand_scaled, test_Y, \"Random forest (scaled)\")\n",
        "evaluate(y_pred_xgb_scaled, test_Y, \"XGB (scaled)\")\n",
        "evaluate(y_pred_svm_scaled, test_Y, \"SVM (scaled)\")\n",
        "evaluate(y_pred_log_scaled, test_Y, \"Logistic regression (scaled)\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8OCLDRKmKZFy",
        "outputId": "ed2b8c69-5ba8-4b33-dcec-b1526c8631b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision tree (scaled):\n",
            "             Metrics\n",
            "Accuracy    0.736842\n",
            "Precission  0.644578\n",
            "Recall      0.677215\n",
            "F1 Score    0.660494 \n",
            "\n",
            "Random forest (scaled):\n",
            "             Metrics\n",
            "Accuracy    0.755981\n",
            "Precission  0.684211\n",
            "Recall      0.658228\n",
            "F1 Score    0.670968 \n",
            "\n",
            "XGB (scaled):\n",
            "             Metrics\n",
            "Accuracy    0.763158\n",
            "Precission  0.697987\n",
            "Recall      0.658228\n",
            "F1 Score    0.677524 \n",
            "\n",
            "SVM (scaled):\n",
            "             Metrics\n",
            "Accuracy    0.753589\n",
            "Precission  0.658960\n",
            "Recall      0.721519\n",
            "F1 Score    0.688822 \n",
            "\n",
            "Logistic regression (scaled):\n",
            "             Metrics\n",
            "Accuracy    0.782297\n",
            "Precission  0.705521\n",
            "Recall      0.727848\n",
            "F1 Score    0.716511 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Resultados\n",
        "Como se puede observar, probamos 5 diferentes modelos usando Scikit-Learn y obtuvimos 4 importantes métricas para cada uno:\n",
        "\n",
        "\n",
        "*   Accuracy: Porcentaje de predicciones que obtuvo correctamente el modelo.\n",
        "*   Precission: Proporción de verdaderos positivos en el número total de predicciones positivas. \n",
        "*   Recall: Proporción de positivos identificados correctamente. \n",
        "*   F1 Score: Combina medidas de precission y recall.\n",
        "\n",
        "De los diferentes modelos, tuvimos mejores resultados con Logistic Regression. Obteniendo un Accuracy de 78.22%, un buen valor y mayor a la predicción solo basándonos en si son mujer u hombres. \n",
        "\n",
        "Este es un buen resultado, sin embargo podríamos buscar mejorar los resultados usando redes neuronales.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "r4hCVZab2rwa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Modelo con Tensorflow"
      ],
      "metadata": {
        "id": "Y7P-CaBw_JyD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "-7yp5Shw0xDb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_X_scaled.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_pTqOOAIBMnW",
        "outputId": "3b4d756d-f561-4e96-d558-bb36cd67074f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(886, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Crear el modelo\n",
        "model_tf = tf.keras.Sequential()\n",
        "model_tf.add(tf.keras.layers.Dense(units = 5, activation = \"relu\", input_dim = train_X_scaled.shape[1]))\n",
        "model_tf.add(tf.keras.layers.Dropout(0.25))\n",
        "model_tf.add(tf.keras.layers.Dense(units = 1, activation = \"sigmoid\"))"
      ],
      "metadata": {
        "id": "cmn_nGGX_ZrC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Compilar el modelo\n",
        "model_tf.compile(\n",
        "    optimizer = tf.keras.optimizers.SGD(\n",
        "        learning_rate=0.001,\n",
        "        momentum=0.9,\n",
        "        nesterov=False,\n",
        "        name='SGD',\n",
        "    ),\n",
        "    loss = \"binary_crossentropy\",\n",
        "    metrics = [tf.keras.metrics.BinaryAccuracy()]\n",
        ")"
      ],
      "metadata": {
        "id": "x7DBhvvu_yZS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 32\n",
        "# Entrenar el modelo\n",
        "print('Inicio del entrenamiento')\n",
        "historia = model_tf.fit(\n",
        "    train_X_scaled, \n",
        "    train_Y, \n",
        "    epochs = 1000, \n",
        "    verbose = 2, \n",
        "    steps_per_epoch = len(train_X)/batch_size,\n",
        "    validation_data= (test_X_scaled, test_Y)\n",
        ")\n",
        "print(\"Modelo entrenado\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3XWlHEfb_3sb",
        "outputId": "ecbe29b0-f757-41b6-e498-76a02e3d5515"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Inicio del entrenamiento\n",
            "Epoch 1/1000\n",
            "27/27 - 1s - loss: 0.6665 - binary_accuracy: 0.5982 - val_loss: 0.6315 - val_binary_accuracy: 0.6818 - 757ms/epoch - 27ms/step\n",
            "Epoch 2/1000\n",
            "27/27 - 0s - loss: 0.6531 - binary_accuracy: 0.6275 - val_loss: 0.6170 - val_binary_accuracy: 0.6938 - 61ms/epoch - 2ms/step\n",
            "Epoch 3/1000\n",
            "27/27 - 0s - loss: 0.6213 - binary_accuracy: 0.6670 - val_loss: 0.6048 - val_binary_accuracy: 0.7010 - 57ms/epoch - 2ms/step\n",
            "Epoch 4/1000\n",
            "27/27 - 0s - loss: 0.6015 - binary_accuracy: 0.6885 - val_loss: 0.5956 - val_binary_accuracy: 0.7153 - 56ms/epoch - 2ms/step\n",
            "Epoch 5/1000\n",
            "27/27 - 0s - loss: 0.6053 - binary_accuracy: 0.6727 - val_loss: 0.5868 - val_binary_accuracy: 0.7201 - 47ms/epoch - 2ms/step\n",
            "Epoch 6/1000\n",
            "27/27 - 0s - loss: 0.5843 - binary_accuracy: 0.6874 - val_loss: 0.5794 - val_binary_accuracy: 0.7297 - 59ms/epoch - 2ms/step\n",
            "Epoch 7/1000\n",
            "27/27 - 0s - loss: 0.5830 - binary_accuracy: 0.6975 - val_loss: 0.5732 - val_binary_accuracy: 0.7416 - 45ms/epoch - 2ms/step\n",
            "Epoch 8/1000\n",
            "27/27 - 0s - loss: 0.5697 - binary_accuracy: 0.7088 - val_loss: 0.5670 - val_binary_accuracy: 0.7560 - 66ms/epoch - 2ms/step\n",
            "Epoch 9/1000\n",
            "27/27 - 0s - loss: 0.5757 - binary_accuracy: 0.6964 - val_loss: 0.5619 - val_binary_accuracy: 0.7584 - 51ms/epoch - 2ms/step\n",
            "Epoch 10/1000\n",
            "27/27 - 0s - loss: 0.5670 - binary_accuracy: 0.7009 - val_loss: 0.5574 - val_binary_accuracy: 0.7632 - 56ms/epoch - 2ms/step\n",
            "Epoch 11/1000\n",
            "27/27 - 0s - loss: 0.5734 - binary_accuracy: 0.6907 - val_loss: 0.5537 - val_binary_accuracy: 0.7656 - 60ms/epoch - 2ms/step\n",
            "Epoch 12/1000\n",
            "27/27 - 0s - loss: 0.5555 - binary_accuracy: 0.7235 - val_loss: 0.5504 - val_binary_accuracy: 0.7656 - 49ms/epoch - 2ms/step\n",
            "Epoch 13/1000\n",
            "27/27 - 0s - loss: 0.5566 - binary_accuracy: 0.7122 - val_loss: 0.5472 - val_binary_accuracy: 0.7656 - 50ms/epoch - 2ms/step\n",
            "Epoch 14/1000\n",
            "27/27 - 0s - loss: 0.5598 - binary_accuracy: 0.6964 - val_loss: 0.5444 - val_binary_accuracy: 0.7656 - 52ms/epoch - 2ms/step\n",
            "Epoch 15/1000\n",
            "27/27 - 0s - loss: 0.5433 - binary_accuracy: 0.7156 - val_loss: 0.5419 - val_binary_accuracy: 0.7656 - 52ms/epoch - 2ms/step\n",
            "Epoch 16/1000\n",
            "27/27 - 0s - loss: 0.5508 - binary_accuracy: 0.7020 - val_loss: 0.5397 - val_binary_accuracy: 0.7679 - 56ms/epoch - 2ms/step\n",
            "Epoch 17/1000\n",
            "27/27 - 0s - loss: 0.5373 - binary_accuracy: 0.7302 - val_loss: 0.5379 - val_binary_accuracy: 0.7703 - 47ms/epoch - 2ms/step\n",
            "Epoch 18/1000\n",
            "27/27 - 0s - loss: 0.5330 - binary_accuracy: 0.7348 - val_loss: 0.5358 - val_binary_accuracy: 0.7751 - 58ms/epoch - 2ms/step\n",
            "Epoch 19/1000\n",
            "27/27 - 0s - loss: 0.5394 - binary_accuracy: 0.7257 - val_loss: 0.5340 - val_binary_accuracy: 0.7751 - 68ms/epoch - 2ms/step\n",
            "Epoch 20/1000\n",
            "27/27 - 0s - loss: 0.5401 - binary_accuracy: 0.7302 - val_loss: 0.5326 - val_binary_accuracy: 0.7751 - 47ms/epoch - 2ms/step\n",
            "Epoch 21/1000\n",
            "27/27 - 0s - loss: 0.5306 - binary_accuracy: 0.7438 - val_loss: 0.5309 - val_binary_accuracy: 0.7727 - 44ms/epoch - 2ms/step\n",
            "Epoch 22/1000\n",
            "27/27 - 0s - loss: 0.5401 - binary_accuracy: 0.7201 - val_loss: 0.5296 - val_binary_accuracy: 0.7703 - 47ms/epoch - 2ms/step\n",
            "Epoch 23/1000\n",
            "27/27 - 0s - loss: 0.5338 - binary_accuracy: 0.7223 - val_loss: 0.5286 - val_binary_accuracy: 0.7679 - 52ms/epoch - 2ms/step\n",
            "Epoch 24/1000\n",
            "27/27 - 0s - loss: 0.5201 - binary_accuracy: 0.7573 - val_loss: 0.5275 - val_binary_accuracy: 0.7632 - 53ms/epoch - 2ms/step\n",
            "Epoch 25/1000\n",
            "27/27 - 0s - loss: 0.5426 - binary_accuracy: 0.7325 - val_loss: 0.5267 - val_binary_accuracy: 0.7703 - 60ms/epoch - 2ms/step\n",
            "Epoch 26/1000\n",
            "27/27 - 0s - loss: 0.5249 - binary_accuracy: 0.7438 - val_loss: 0.5259 - val_binary_accuracy: 0.7679 - 63ms/epoch - 2ms/step\n",
            "Epoch 27/1000\n",
            "27/27 - 0s - loss: 0.5214 - binary_accuracy: 0.7506 - val_loss: 0.5249 - val_binary_accuracy: 0.7656 - 49ms/epoch - 2ms/step\n",
            "Epoch 28/1000\n",
            "27/27 - 0s - loss: 0.5399 - binary_accuracy: 0.7302 - val_loss: 0.5244 - val_binary_accuracy: 0.7703 - 52ms/epoch - 2ms/step\n",
            "Epoch 29/1000\n",
            "27/27 - 0s - loss: 0.5260 - binary_accuracy: 0.7460 - val_loss: 0.5235 - val_binary_accuracy: 0.7727 - 59ms/epoch - 2ms/step\n",
            "Epoch 30/1000\n",
            "27/27 - 0s - loss: 0.5208 - binary_accuracy: 0.7472 - val_loss: 0.5229 - val_binary_accuracy: 0.7727 - 51ms/epoch - 2ms/step\n",
            "Epoch 31/1000\n",
            "27/27 - 0s - loss: 0.5169 - binary_accuracy: 0.7483 - val_loss: 0.5220 - val_binary_accuracy: 0.7727 - 47ms/epoch - 2ms/step\n",
            "Epoch 32/1000\n",
            "27/27 - 0s - loss: 0.5203 - binary_accuracy: 0.7460 - val_loss: 0.5212 - val_binary_accuracy: 0.7727 - 46ms/epoch - 2ms/step\n",
            "Epoch 33/1000\n",
            "27/27 - 0s - loss: 0.5167 - binary_accuracy: 0.7415 - val_loss: 0.5206 - val_binary_accuracy: 0.7727 - 54ms/epoch - 2ms/step\n",
            "Epoch 34/1000\n",
            "27/27 - 0s - loss: 0.5310 - binary_accuracy: 0.7325 - val_loss: 0.5204 - val_binary_accuracy: 0.7727 - 44ms/epoch - 2ms/step\n",
            "Epoch 35/1000\n",
            "27/27 - 0s - loss: 0.5127 - binary_accuracy: 0.7607 - val_loss: 0.5198 - val_binary_accuracy: 0.7727 - 59ms/epoch - 2ms/step\n",
            "Epoch 36/1000\n",
            "27/27 - 0s - loss: 0.5143 - binary_accuracy: 0.7517 - val_loss: 0.5193 - val_binary_accuracy: 0.7727 - 44ms/epoch - 2ms/step\n",
            "Epoch 37/1000\n",
            "27/27 - 0s - loss: 0.5150 - binary_accuracy: 0.7551 - val_loss: 0.5188 - val_binary_accuracy: 0.7727 - 46ms/epoch - 2ms/step\n",
            "Epoch 38/1000\n",
            "27/27 - 0s - loss: 0.5066 - binary_accuracy: 0.7698 - val_loss: 0.5183 - val_binary_accuracy: 0.7703 - 61ms/epoch - 2ms/step\n",
            "Epoch 39/1000\n",
            "27/27 - 0s - loss: 0.5290 - binary_accuracy: 0.7506 - val_loss: 0.5180 - val_binary_accuracy: 0.7703 - 50ms/epoch - 2ms/step\n",
            "Epoch 40/1000\n",
            "27/27 - 0s - loss: 0.5034 - binary_accuracy: 0.7822 - val_loss: 0.5175 - val_binary_accuracy: 0.7703 - 49ms/epoch - 2ms/step\n",
            "Epoch 41/1000\n",
            "27/27 - 0s - loss: 0.5376 - binary_accuracy: 0.7359 - val_loss: 0.5175 - val_binary_accuracy: 0.7703 - 52ms/epoch - 2ms/step\n",
            "Epoch 42/1000\n",
            "27/27 - 0s - loss: 0.5182 - binary_accuracy: 0.7494 - val_loss: 0.5172 - val_binary_accuracy: 0.7703 - 47ms/epoch - 2ms/step\n",
            "Epoch 43/1000\n",
            "27/27 - 0s - loss: 0.5212 - binary_accuracy: 0.7483 - val_loss: 0.5170 - val_binary_accuracy: 0.7703 - 48ms/epoch - 2ms/step\n",
            "Epoch 44/1000\n",
            "27/27 - 0s - loss: 0.5133 - binary_accuracy: 0.7641 - val_loss: 0.5169 - val_binary_accuracy: 0.7703 - 59ms/epoch - 2ms/step\n",
            "Epoch 45/1000\n",
            "27/27 - 0s - loss: 0.5230 - binary_accuracy: 0.7483 - val_loss: 0.5167 - val_binary_accuracy: 0.7703 - 63ms/epoch - 2ms/step\n",
            "Epoch 46/1000\n",
            "27/27 - 0s - loss: 0.5181 - binary_accuracy: 0.7506 - val_loss: 0.5164 - val_binary_accuracy: 0.7703 - 63ms/epoch - 2ms/step\n",
            "Epoch 47/1000\n",
            "27/27 - 0s - loss: 0.5176 - binary_accuracy: 0.7619 - val_loss: 0.5163 - val_binary_accuracy: 0.7703 - 61ms/epoch - 2ms/step\n",
            "Epoch 48/1000\n",
            "27/27 - 0s - loss: 0.5098 - binary_accuracy: 0.7562 - val_loss: 0.5159 - val_binary_accuracy: 0.7703 - 49ms/epoch - 2ms/step\n",
            "Epoch 49/1000\n",
            "27/27 - 0s - loss: 0.5077 - binary_accuracy: 0.7562 - val_loss: 0.5158 - val_binary_accuracy: 0.7703 - 50ms/epoch - 2ms/step\n",
            "Epoch 50/1000\n",
            "27/27 - 0s - loss: 0.5148 - binary_accuracy: 0.7540 - val_loss: 0.5156 - val_binary_accuracy: 0.7703 - 50ms/epoch - 2ms/step\n",
            "Epoch 51/1000\n",
            "27/27 - 0s - loss: 0.5072 - binary_accuracy: 0.7573 - val_loss: 0.5151 - val_binary_accuracy: 0.7703 - 55ms/epoch - 2ms/step\n",
            "Epoch 52/1000\n",
            "27/27 - 0s - loss: 0.5058 - binary_accuracy: 0.7630 - val_loss: 0.5147 - val_binary_accuracy: 0.7703 - 47ms/epoch - 2ms/step\n",
            "Epoch 53/1000\n",
            "27/27 - 0s - loss: 0.4974 - binary_accuracy: 0.7743 - val_loss: 0.5145 - val_binary_accuracy: 0.7703 - 62ms/epoch - 2ms/step\n",
            "Epoch 54/1000\n",
            "27/27 - 0s - loss: 0.5188 - binary_accuracy: 0.7573 - val_loss: 0.5144 - val_binary_accuracy: 0.7703 - 61ms/epoch - 2ms/step\n",
            "Epoch 55/1000\n",
            "27/27 - 0s - loss: 0.5217 - binary_accuracy: 0.7404 - val_loss: 0.5141 - val_binary_accuracy: 0.7703 - 60ms/epoch - 2ms/step\n",
            "Epoch 56/1000\n",
            "27/27 - 0s - loss: 0.5112 - binary_accuracy: 0.7619 - val_loss: 0.5139 - val_binary_accuracy: 0.7703 - 47ms/epoch - 2ms/step\n",
            "Epoch 57/1000\n",
            "27/27 - 0s - loss: 0.5047 - binary_accuracy: 0.7562 - val_loss: 0.5135 - val_binary_accuracy: 0.7703 - 53ms/epoch - 2ms/step\n",
            "Epoch 58/1000\n",
            "27/27 - 0s - loss: 0.5093 - binary_accuracy: 0.7619 - val_loss: 0.5132 - val_binary_accuracy: 0.7703 - 54ms/epoch - 2ms/step\n",
            "Epoch 59/1000\n",
            "27/27 - 0s - loss: 0.5204 - binary_accuracy: 0.7336 - val_loss: 0.5131 - val_binary_accuracy: 0.7703 - 48ms/epoch - 2ms/step\n",
            "Epoch 60/1000\n",
            "27/27 - 0s - loss: 0.5150 - binary_accuracy: 0.7483 - val_loss: 0.5129 - val_binary_accuracy: 0.7703 - 58ms/epoch - 2ms/step\n",
            "Epoch 61/1000\n",
            "27/27 - 0s - loss: 0.5177 - binary_accuracy: 0.7415 - val_loss: 0.5129 - val_binary_accuracy: 0.7703 - 53ms/epoch - 2ms/step\n",
            "Epoch 62/1000\n",
            "27/27 - 0s - loss: 0.5111 - binary_accuracy: 0.7517 - val_loss: 0.5128 - val_binary_accuracy: 0.7703 - 61ms/epoch - 2ms/step\n",
            "Epoch 63/1000\n",
            "27/27 - 0s - loss: 0.5229 - binary_accuracy: 0.7472 - val_loss: 0.5129 - val_binary_accuracy: 0.7703 - 49ms/epoch - 2ms/step\n",
            "Epoch 64/1000\n",
            "27/27 - 0s - loss: 0.5066 - binary_accuracy: 0.7562 - val_loss: 0.5128 - val_binary_accuracy: 0.7703 - 61ms/epoch - 2ms/step\n",
            "Epoch 65/1000\n",
            "27/27 - 0s - loss: 0.4970 - binary_accuracy: 0.7698 - val_loss: 0.5121 - val_binary_accuracy: 0.7703 - 47ms/epoch - 2ms/step\n",
            "Epoch 66/1000\n",
            "27/27 - 0s - loss: 0.5153 - binary_accuracy: 0.7506 - val_loss: 0.5118 - val_binary_accuracy: 0.7703 - 47ms/epoch - 2ms/step\n",
            "Epoch 67/1000\n",
            "27/27 - 0s - loss: 0.5000 - binary_accuracy: 0.7596 - val_loss: 0.5118 - val_binary_accuracy: 0.7703 - 57ms/epoch - 2ms/step\n",
            "Epoch 68/1000\n",
            "27/27 - 0s - loss: 0.4959 - binary_accuracy: 0.7607 - val_loss: 0.5116 - val_binary_accuracy: 0.7703 - 58ms/epoch - 2ms/step\n",
            "Epoch 69/1000\n",
            "27/27 - 0s - loss: 0.4983 - binary_accuracy: 0.7664 - val_loss: 0.5113 - val_binary_accuracy: 0.7703 - 50ms/epoch - 2ms/step\n",
            "Epoch 70/1000\n",
            "27/27 - 0s - loss: 0.5015 - binary_accuracy: 0.7449 - val_loss: 0.5110 - val_binary_accuracy: 0.7703 - 59ms/epoch - 2ms/step\n",
            "Epoch 71/1000\n",
            "27/27 - 0s - loss: 0.5065 - binary_accuracy: 0.7494 - val_loss: 0.5108 - val_binary_accuracy: 0.7703 - 60ms/epoch - 2ms/step\n",
            "Epoch 72/1000\n",
            "27/27 - 0s - loss: 0.5085 - binary_accuracy: 0.7528 - val_loss: 0.5108 - val_binary_accuracy: 0.7703 - 54ms/epoch - 2ms/step\n",
            "Epoch 73/1000\n",
            "27/27 - 0s - loss: 0.5019 - binary_accuracy: 0.7585 - val_loss: 0.5107 - val_binary_accuracy: 0.7703 - 66ms/epoch - 2ms/step\n",
            "Epoch 74/1000\n",
            "27/27 - 0s - loss: 0.5006 - binary_accuracy: 0.7641 - val_loss: 0.5109 - val_binary_accuracy: 0.7703 - 59ms/epoch - 2ms/step\n",
            "Epoch 75/1000\n",
            "27/27 - 0s - loss: 0.5154 - binary_accuracy: 0.7630 - val_loss: 0.5106 - val_binary_accuracy: 0.7703 - 49ms/epoch - 2ms/step\n",
            "Epoch 76/1000\n",
            "27/27 - 0s - loss: 0.5138 - binary_accuracy: 0.7393 - val_loss: 0.5104 - val_binary_accuracy: 0.7703 - 52ms/epoch - 2ms/step\n",
            "Epoch 77/1000\n",
            "27/27 - 0s - loss: 0.5030 - binary_accuracy: 0.7619 - val_loss: 0.5098 - val_binary_accuracy: 0.7703 - 62ms/epoch - 2ms/step\n",
            "Epoch 78/1000\n",
            "27/27 - 0s - loss: 0.5062 - binary_accuracy: 0.7517 - val_loss: 0.5099 - val_binary_accuracy: 0.7703 - 50ms/epoch - 2ms/step\n",
            "Epoch 79/1000\n",
            "27/27 - 0s - loss: 0.5143 - binary_accuracy: 0.7494 - val_loss: 0.5101 - val_binary_accuracy: 0.7703 - 61ms/epoch - 2ms/step\n",
            "Epoch 80/1000\n",
            "27/27 - 0s - loss: 0.5054 - binary_accuracy: 0.7607 - val_loss: 0.5099 - val_binary_accuracy: 0.7703 - 54ms/epoch - 2ms/step\n",
            "Epoch 81/1000\n",
            "27/27 - 0s - loss: 0.5018 - binary_accuracy: 0.7562 - val_loss: 0.5097 - val_binary_accuracy: 0.7703 - 63ms/epoch - 2ms/step\n",
            "Epoch 82/1000\n",
            "27/27 - 0s - loss: 0.5131 - binary_accuracy: 0.7562 - val_loss: 0.5096 - val_binary_accuracy: 0.7703 - 49ms/epoch - 2ms/step\n",
            "Epoch 83/1000\n",
            "27/27 - 0s - loss: 0.5087 - binary_accuracy: 0.7460 - val_loss: 0.5095 - val_binary_accuracy: 0.7703 - 44ms/epoch - 2ms/step\n",
            "Epoch 84/1000\n",
            "27/27 - 0s - loss: 0.5128 - binary_accuracy: 0.7404 - val_loss: 0.5092 - val_binary_accuracy: 0.7703 - 49ms/epoch - 2ms/step\n",
            "Epoch 85/1000\n",
            "27/27 - 0s - loss: 0.4978 - binary_accuracy: 0.7709 - val_loss: 0.5094 - val_binary_accuracy: 0.7703 - 59ms/epoch - 2ms/step\n",
            "Epoch 86/1000\n",
            "27/27 - 0s - loss: 0.4960 - binary_accuracy: 0.7743 - val_loss: 0.5094 - val_binary_accuracy: 0.7703 - 60ms/epoch - 2ms/step\n",
            "Epoch 87/1000\n",
            "27/27 - 0s - loss: 0.5070 - binary_accuracy: 0.7472 - val_loss: 0.5091 - val_binary_accuracy: 0.7703 - 44ms/epoch - 2ms/step\n",
            "Epoch 88/1000\n",
            "27/27 - 0s - loss: 0.5090 - binary_accuracy: 0.7585 - val_loss: 0.5088 - val_binary_accuracy: 0.7703 - 55ms/epoch - 2ms/step\n",
            "Epoch 89/1000\n",
            "27/27 - 0s - loss: 0.4974 - binary_accuracy: 0.7630 - val_loss: 0.5088 - val_binary_accuracy: 0.7727 - 65ms/epoch - 2ms/step\n",
            "Epoch 90/1000\n",
            "27/27 - 0s - loss: 0.5043 - binary_accuracy: 0.7494 - val_loss: 0.5082 - val_binary_accuracy: 0.7703 - 60ms/epoch - 2ms/step\n",
            "Epoch 91/1000\n",
            "27/27 - 0s - loss: 0.5164 - binary_accuracy: 0.7494 - val_loss: 0.5085 - val_binary_accuracy: 0.7727 - 52ms/epoch - 2ms/step\n",
            "Epoch 92/1000\n",
            "27/27 - 0s - loss: 0.4890 - binary_accuracy: 0.7709 - val_loss: 0.5078 - val_binary_accuracy: 0.7727 - 50ms/epoch - 2ms/step\n",
            "Epoch 93/1000\n",
            "27/27 - 0s - loss: 0.5026 - binary_accuracy: 0.7664 - val_loss: 0.5079 - val_binary_accuracy: 0.7727 - 53ms/epoch - 2ms/step\n",
            "Epoch 94/1000\n",
            "27/27 - 0s - loss: 0.5004 - binary_accuracy: 0.7619 - val_loss: 0.5078 - val_binary_accuracy: 0.7727 - 55ms/epoch - 2ms/step\n",
            "Epoch 95/1000\n",
            "27/27 - 0s - loss: 0.4931 - binary_accuracy: 0.7664 - val_loss: 0.5076 - val_binary_accuracy: 0.7727 - 52ms/epoch - 2ms/step\n",
            "Epoch 96/1000\n",
            "27/27 - 0s - loss: 0.4994 - binary_accuracy: 0.7709 - val_loss: 0.5073 - val_binary_accuracy: 0.7727 - 59ms/epoch - 2ms/step\n",
            "Epoch 97/1000\n",
            "27/27 - 0s - loss: 0.5067 - binary_accuracy: 0.7540 - val_loss: 0.5070 - val_binary_accuracy: 0.7727 - 45ms/epoch - 2ms/step\n",
            "Epoch 98/1000\n",
            "27/27 - 0s - loss: 0.4844 - binary_accuracy: 0.7664 - val_loss: 0.5068 - val_binary_accuracy: 0.7727 - 61ms/epoch - 2ms/step\n",
            "Epoch 99/1000\n",
            "27/27 - 0s - loss: 0.4809 - binary_accuracy: 0.7788 - val_loss: 0.5066 - val_binary_accuracy: 0.7727 - 163ms/epoch - 6ms/step\n",
            "Epoch 100/1000\n",
            "27/27 - 0s - loss: 0.5017 - binary_accuracy: 0.7698 - val_loss: 0.5065 - val_binary_accuracy: 0.7727 - 175ms/epoch - 6ms/step\n",
            "Epoch 101/1000\n",
            "27/27 - 0s - loss: 0.4932 - binary_accuracy: 0.7573 - val_loss: 0.5064 - val_binary_accuracy: 0.7727 - 164ms/epoch - 6ms/step\n",
            "Epoch 102/1000\n",
            "27/27 - 0s - loss: 0.5073 - binary_accuracy: 0.7528 - val_loss: 0.5064 - val_binary_accuracy: 0.7727 - 137ms/epoch - 5ms/step\n",
            "Epoch 103/1000\n",
            "27/27 - 0s - loss: 0.4954 - binary_accuracy: 0.7596 - val_loss: 0.5063 - val_binary_accuracy: 0.7727 - 78ms/epoch - 3ms/step\n",
            "Epoch 104/1000\n",
            "27/27 - 0s - loss: 0.5108 - binary_accuracy: 0.7404 - val_loss: 0.5063 - val_binary_accuracy: 0.7751 - 61ms/epoch - 2ms/step\n",
            "Epoch 105/1000\n",
            "27/27 - 0s - loss: 0.5001 - binary_accuracy: 0.7562 - val_loss: 0.5065 - val_binary_accuracy: 0.7751 - 53ms/epoch - 2ms/step\n",
            "Epoch 106/1000\n",
            "27/27 - 0s - loss: 0.4924 - binary_accuracy: 0.7460 - val_loss: 0.5061 - val_binary_accuracy: 0.7751 - 49ms/epoch - 2ms/step\n",
            "Epoch 107/1000\n",
            "27/27 - 0s - loss: 0.4798 - binary_accuracy: 0.7833 - val_loss: 0.5060 - val_binary_accuracy: 0.7751 - 67ms/epoch - 2ms/step\n",
            "Epoch 108/1000\n",
            "27/27 - 0s - loss: 0.4889 - binary_accuracy: 0.7698 - val_loss: 0.5059 - val_binary_accuracy: 0.7751 - 56ms/epoch - 2ms/step\n",
            "Epoch 109/1000\n",
            "27/27 - 0s - loss: 0.4964 - binary_accuracy: 0.7641 - val_loss: 0.5056 - val_binary_accuracy: 0.7751 - 47ms/epoch - 2ms/step\n",
            "Epoch 110/1000\n",
            "27/27 - 0s - loss: 0.4999 - binary_accuracy: 0.7585 - val_loss: 0.5053 - val_binary_accuracy: 0.7751 - 59ms/epoch - 2ms/step\n",
            "Epoch 111/1000\n",
            "27/27 - 0s - loss: 0.4889 - binary_accuracy: 0.7686 - val_loss: 0.5053 - val_binary_accuracy: 0.7751 - 83ms/epoch - 3ms/step\n",
            "Epoch 112/1000\n",
            "27/27 - 0s - loss: 0.4833 - binary_accuracy: 0.7731 - val_loss: 0.5048 - val_binary_accuracy: 0.7751 - 212ms/epoch - 8ms/step\n",
            "Epoch 113/1000\n",
            "27/27 - 0s - loss: 0.4917 - binary_accuracy: 0.7596 - val_loss: 0.5051 - val_binary_accuracy: 0.7751 - 179ms/epoch - 6ms/step\n",
            "Epoch 114/1000\n",
            "27/27 - 0s - loss: 0.4861 - binary_accuracy: 0.7709 - val_loss: 0.5046 - val_binary_accuracy: 0.7751 - 187ms/epoch - 7ms/step\n",
            "Epoch 115/1000\n",
            "27/27 - 0s - loss: 0.4845 - binary_accuracy: 0.7833 - val_loss: 0.5044 - val_binary_accuracy: 0.7751 - 84ms/epoch - 3ms/step\n",
            "Epoch 116/1000\n",
            "27/27 - 0s - loss: 0.4979 - binary_accuracy: 0.7664 - val_loss: 0.5043 - val_binary_accuracy: 0.7751 - 59ms/epoch - 2ms/step\n",
            "Epoch 117/1000\n",
            "27/27 - 0s - loss: 0.4825 - binary_accuracy: 0.7833 - val_loss: 0.5041 - val_binary_accuracy: 0.7751 - 52ms/epoch - 2ms/step\n",
            "Epoch 118/1000\n",
            "27/27 - 0s - loss: 0.4864 - binary_accuracy: 0.7709 - val_loss: 0.5040 - val_binary_accuracy: 0.7751 - 47ms/epoch - 2ms/step\n",
            "Epoch 119/1000\n",
            "27/27 - 0s - loss: 0.4906 - binary_accuracy: 0.7562 - val_loss: 0.5039 - val_binary_accuracy: 0.7751 - 51ms/epoch - 2ms/step\n",
            "Epoch 120/1000\n",
            "27/27 - 0s - loss: 0.4772 - binary_accuracy: 0.7822 - val_loss: 0.5037 - val_binary_accuracy: 0.7751 - 48ms/epoch - 2ms/step\n",
            "Epoch 121/1000\n",
            "27/27 - 0s - loss: 0.4818 - binary_accuracy: 0.7585 - val_loss: 0.5038 - val_binary_accuracy: 0.7751 - 48ms/epoch - 2ms/step\n",
            "Epoch 122/1000\n",
            "27/27 - 0s - loss: 0.4929 - binary_accuracy: 0.7686 - val_loss: 0.5039 - val_binary_accuracy: 0.7751 - 65ms/epoch - 2ms/step\n",
            "Epoch 123/1000\n",
            "27/27 - 0s - loss: 0.4957 - binary_accuracy: 0.7517 - val_loss: 0.5039 - val_binary_accuracy: 0.7751 - 52ms/epoch - 2ms/step\n",
            "Epoch 124/1000\n",
            "27/27 - 0s - loss: 0.4761 - binary_accuracy: 0.7833 - val_loss: 0.5035 - val_binary_accuracy: 0.7751 - 143ms/epoch - 5ms/step\n",
            "Epoch 125/1000\n",
            "27/27 - 0s - loss: 0.4755 - binary_accuracy: 0.7810 - val_loss: 0.5030 - val_binary_accuracy: 0.7751 - 176ms/epoch - 6ms/step\n",
            "Epoch 126/1000\n",
            "27/27 - 0s - loss: 0.4886 - binary_accuracy: 0.7607 - val_loss: 0.5031 - val_binary_accuracy: 0.7751 - 160ms/epoch - 6ms/step\n",
            "Epoch 127/1000\n",
            "27/27 - 0s - loss: 0.4877 - binary_accuracy: 0.7573 - val_loss: 0.5031 - val_binary_accuracy: 0.7751 - 148ms/epoch - 5ms/step\n",
            "Epoch 128/1000\n",
            "27/27 - 0s - loss: 0.4891 - binary_accuracy: 0.7754 - val_loss: 0.5031 - val_binary_accuracy: 0.7751 - 67ms/epoch - 2ms/step\n",
            "Epoch 129/1000\n",
            "27/27 - 0s - loss: 0.4881 - binary_accuracy: 0.7619 - val_loss: 0.5030 - val_binary_accuracy: 0.7751 - 58ms/epoch - 2ms/step\n",
            "Epoch 130/1000\n",
            "27/27 - 0s - loss: 0.4871 - binary_accuracy: 0.7641 - val_loss: 0.5030 - val_binary_accuracy: 0.7751 - 48ms/epoch - 2ms/step\n",
            "Epoch 131/1000\n",
            "27/27 - 0s - loss: 0.4866 - binary_accuracy: 0.7720 - val_loss: 0.5028 - val_binary_accuracy: 0.7751 - 50ms/epoch - 2ms/step\n",
            "Epoch 132/1000\n",
            "27/27 - 0s - loss: 0.4835 - binary_accuracy: 0.7765 - val_loss: 0.5027 - val_binary_accuracy: 0.7751 - 45ms/epoch - 2ms/step\n",
            "Epoch 133/1000\n",
            "27/27 - 0s - loss: 0.4974 - binary_accuracy: 0.7596 - val_loss: 0.5027 - val_binary_accuracy: 0.7751 - 51ms/epoch - 2ms/step\n",
            "Epoch 134/1000\n",
            "27/27 - 0s - loss: 0.4792 - binary_accuracy: 0.7878 - val_loss: 0.5026 - val_binary_accuracy: 0.7751 - 49ms/epoch - 2ms/step\n",
            "Epoch 135/1000\n",
            "27/27 - 0s - loss: 0.4917 - binary_accuracy: 0.7799 - val_loss: 0.5026 - val_binary_accuracy: 0.7751 - 56ms/epoch - 2ms/step\n",
            "Epoch 136/1000\n",
            "27/27 - 0s - loss: 0.4896 - binary_accuracy: 0.7720 - val_loss: 0.5026 - val_binary_accuracy: 0.7751 - 64ms/epoch - 2ms/step\n",
            "Epoch 137/1000\n",
            "27/27 - 0s - loss: 0.4839 - binary_accuracy: 0.7743 - val_loss: 0.5026 - val_binary_accuracy: 0.7751 - 53ms/epoch - 2ms/step\n",
            "Epoch 138/1000\n",
            "27/27 - 0s - loss: 0.4748 - binary_accuracy: 0.7777 - val_loss: 0.5025 - val_binary_accuracy: 0.7751 - 53ms/epoch - 2ms/step\n",
            "Epoch 139/1000\n",
            "27/27 - 0s - loss: 0.4856 - binary_accuracy: 0.7844 - val_loss: 0.5025 - val_binary_accuracy: 0.7751 - 51ms/epoch - 2ms/step\n",
            "Epoch 140/1000\n",
            "27/27 - 0s - loss: 0.4973 - binary_accuracy: 0.7731 - val_loss: 0.5024 - val_binary_accuracy: 0.7751 - 64ms/epoch - 2ms/step\n",
            "Epoch 141/1000\n",
            "27/27 - 0s - loss: 0.4885 - binary_accuracy: 0.7754 - val_loss: 0.5023 - val_binary_accuracy: 0.7751 - 60ms/epoch - 2ms/step\n",
            "Epoch 142/1000\n",
            "27/27 - 0s - loss: 0.4884 - binary_accuracy: 0.7777 - val_loss: 0.5022 - val_binary_accuracy: 0.7751 - 75ms/epoch - 3ms/step\n",
            "Epoch 143/1000\n",
            "27/27 - 0s - loss: 0.4800 - binary_accuracy: 0.7844 - val_loss: 0.5023 - val_binary_accuracy: 0.7751 - 61ms/epoch - 2ms/step\n",
            "Epoch 144/1000\n",
            "27/27 - 0s - loss: 0.5055 - binary_accuracy: 0.7483 - val_loss: 0.5020 - val_binary_accuracy: 0.7751 - 59ms/epoch - 2ms/step\n",
            "Epoch 145/1000\n",
            "27/27 - 0s - loss: 0.4832 - binary_accuracy: 0.7743 - val_loss: 0.5022 - val_binary_accuracy: 0.7751 - 45ms/epoch - 2ms/step\n",
            "Epoch 146/1000\n",
            "27/27 - 0s - loss: 0.4718 - binary_accuracy: 0.7743 - val_loss: 0.5020 - val_binary_accuracy: 0.7751 - 57ms/epoch - 2ms/step\n",
            "Epoch 147/1000\n",
            "27/27 - 0s - loss: 0.4881 - binary_accuracy: 0.7698 - val_loss: 0.5016 - val_binary_accuracy: 0.7775 - 50ms/epoch - 2ms/step\n",
            "Epoch 148/1000\n",
            "27/27 - 0s - loss: 0.4879 - binary_accuracy: 0.7709 - val_loss: 0.5016 - val_binary_accuracy: 0.7775 - 56ms/epoch - 2ms/step\n",
            "Epoch 149/1000\n",
            "27/27 - 0s - loss: 0.4981 - binary_accuracy: 0.7641 - val_loss: 0.5013 - val_binary_accuracy: 0.7775 - 58ms/epoch - 2ms/step\n",
            "Epoch 150/1000\n",
            "27/27 - 0s - loss: 0.4992 - binary_accuracy: 0.7573 - val_loss: 0.5016 - val_binary_accuracy: 0.7775 - 53ms/epoch - 2ms/step\n",
            "Epoch 151/1000\n",
            "27/27 - 0s - loss: 0.4894 - binary_accuracy: 0.7607 - val_loss: 0.5017 - val_binary_accuracy: 0.7775 - 63ms/epoch - 2ms/step\n",
            "Epoch 152/1000\n",
            "27/27 - 0s - loss: 0.4852 - binary_accuracy: 0.7822 - val_loss: 0.5014 - val_binary_accuracy: 0.7775 - 47ms/epoch - 2ms/step\n",
            "Epoch 153/1000\n",
            "27/27 - 0s - loss: 0.4814 - binary_accuracy: 0.7799 - val_loss: 0.5014 - val_binary_accuracy: 0.7775 - 56ms/epoch - 2ms/step\n",
            "Epoch 154/1000\n",
            "27/27 - 0s - loss: 0.4903 - binary_accuracy: 0.7833 - val_loss: 0.5012 - val_binary_accuracy: 0.7775 - 59ms/epoch - 2ms/step\n",
            "Epoch 155/1000\n",
            "27/27 - 0s - loss: 0.4849 - binary_accuracy: 0.7822 - val_loss: 0.5010 - val_binary_accuracy: 0.7775 - 52ms/epoch - 2ms/step\n",
            "Epoch 156/1000\n",
            "27/27 - 0s - loss: 0.4960 - binary_accuracy: 0.7596 - val_loss: 0.5012 - val_binary_accuracy: 0.7775 - 49ms/epoch - 2ms/step\n",
            "Epoch 157/1000\n",
            "27/27 - 0s - loss: 0.4862 - binary_accuracy: 0.7698 - val_loss: 0.5009 - val_binary_accuracy: 0.7775 - 59ms/epoch - 2ms/step\n",
            "Epoch 158/1000\n",
            "27/27 - 0s - loss: 0.4773 - binary_accuracy: 0.7788 - val_loss: 0.5007 - val_binary_accuracy: 0.7775 - 60ms/epoch - 2ms/step\n",
            "Epoch 159/1000\n",
            "27/27 - 0s - loss: 0.4956 - binary_accuracy: 0.7799 - val_loss: 0.5012 - val_binary_accuracy: 0.7775 - 61ms/epoch - 2ms/step\n",
            "Epoch 160/1000\n",
            "27/27 - 0s - loss: 0.4830 - binary_accuracy: 0.7720 - val_loss: 0.5012 - val_binary_accuracy: 0.7775 - 51ms/epoch - 2ms/step\n",
            "Epoch 161/1000\n",
            "27/27 - 0s - loss: 0.4888 - binary_accuracy: 0.7641 - val_loss: 0.5014 - val_binary_accuracy: 0.7775 - 63ms/epoch - 2ms/step\n",
            "Epoch 162/1000\n",
            "27/27 - 0s - loss: 0.4784 - binary_accuracy: 0.7799 - val_loss: 0.5011 - val_binary_accuracy: 0.7775 - 60ms/epoch - 2ms/step\n",
            "Epoch 163/1000\n",
            "27/27 - 0s - loss: 0.4828 - binary_accuracy: 0.7641 - val_loss: 0.5009 - val_binary_accuracy: 0.7775 - 48ms/epoch - 2ms/step\n",
            "Epoch 164/1000\n",
            "27/27 - 0s - loss: 0.4984 - binary_accuracy: 0.7652 - val_loss: 0.5012 - val_binary_accuracy: 0.7775 - 46ms/epoch - 2ms/step\n",
            "Epoch 165/1000\n",
            "27/27 - 0s - loss: 0.4999 - binary_accuracy: 0.7675 - val_loss: 0.5008 - val_binary_accuracy: 0.7775 - 49ms/epoch - 2ms/step\n",
            "Epoch 166/1000\n",
            "27/27 - 0s - loss: 0.4815 - binary_accuracy: 0.7822 - val_loss: 0.5004 - val_binary_accuracy: 0.7799 - 46ms/epoch - 2ms/step\n",
            "Epoch 167/1000\n",
            "27/27 - 0s - loss: 0.4988 - binary_accuracy: 0.7607 - val_loss: 0.5008 - val_binary_accuracy: 0.7775 - 57ms/epoch - 2ms/step\n",
            "Epoch 168/1000\n",
            "27/27 - 0s - loss: 0.4844 - binary_accuracy: 0.7607 - val_loss: 0.5006 - val_binary_accuracy: 0.7775 - 47ms/epoch - 2ms/step\n",
            "Epoch 169/1000\n",
            "27/27 - 0s - loss: 0.4881 - binary_accuracy: 0.7765 - val_loss: 0.5004 - val_binary_accuracy: 0.7775 - 49ms/epoch - 2ms/step\n",
            "Epoch 170/1000\n",
            "27/27 - 0s - loss: 0.4876 - binary_accuracy: 0.7743 - val_loss: 0.5003 - val_binary_accuracy: 0.7775 - 47ms/epoch - 2ms/step\n",
            "Epoch 171/1000\n",
            "27/27 - 0s - loss: 0.4854 - binary_accuracy: 0.7675 - val_loss: 0.5004 - val_binary_accuracy: 0.7775 - 60ms/epoch - 2ms/step\n",
            "Epoch 172/1000\n",
            "27/27 - 0s - loss: 0.4820 - binary_accuracy: 0.7777 - val_loss: 0.5003 - val_binary_accuracy: 0.7775 - 44ms/epoch - 2ms/step\n",
            "Epoch 173/1000\n",
            "27/27 - 0s - loss: 0.4771 - binary_accuracy: 0.7867 - val_loss: 0.5002 - val_binary_accuracy: 0.7775 - 45ms/epoch - 2ms/step\n",
            "Epoch 174/1000\n",
            "27/27 - 0s - loss: 0.4881 - binary_accuracy: 0.7641 - val_loss: 0.5002 - val_binary_accuracy: 0.7775 - 45ms/epoch - 2ms/step\n",
            "Epoch 175/1000\n",
            "27/27 - 0s - loss: 0.4890 - binary_accuracy: 0.7652 - val_loss: 0.4999 - val_binary_accuracy: 0.7823 - 46ms/epoch - 2ms/step\n",
            "Epoch 176/1000\n",
            "27/27 - 0s - loss: 0.4864 - binary_accuracy: 0.7698 - val_loss: 0.4997 - val_binary_accuracy: 0.7799 - 46ms/epoch - 2ms/step\n",
            "Epoch 177/1000\n",
            "27/27 - 0s - loss: 0.4707 - binary_accuracy: 0.7788 - val_loss: 0.4998 - val_binary_accuracy: 0.7823 - 58ms/epoch - 2ms/step\n",
            "Epoch 178/1000\n",
            "27/27 - 0s - loss: 0.4882 - binary_accuracy: 0.7754 - val_loss: 0.5000 - val_binary_accuracy: 0.7775 - 60ms/epoch - 2ms/step\n",
            "Epoch 179/1000\n",
            "27/27 - 0s - loss: 0.4926 - binary_accuracy: 0.7709 - val_loss: 0.5000 - val_binary_accuracy: 0.7775 - 52ms/epoch - 2ms/step\n",
            "Epoch 180/1000\n",
            "27/27 - 0s - loss: 0.4875 - binary_accuracy: 0.7709 - val_loss: 0.5000 - val_binary_accuracy: 0.7775 - 58ms/epoch - 2ms/step\n",
            "Epoch 181/1000\n",
            "27/27 - 0s - loss: 0.4862 - binary_accuracy: 0.7777 - val_loss: 0.4997 - val_binary_accuracy: 0.7823 - 46ms/epoch - 2ms/step\n",
            "Epoch 182/1000\n",
            "27/27 - 0s - loss: 0.4666 - binary_accuracy: 0.7810 - val_loss: 0.4992 - val_binary_accuracy: 0.7799 - 46ms/epoch - 2ms/step\n",
            "Epoch 183/1000\n",
            "27/27 - 0s - loss: 0.4993 - binary_accuracy: 0.7675 - val_loss: 0.4992 - val_binary_accuracy: 0.7799 - 65ms/epoch - 2ms/step\n",
            "Epoch 184/1000\n",
            "27/27 - 0s - loss: 0.4734 - binary_accuracy: 0.7799 - val_loss: 0.4991 - val_binary_accuracy: 0.7799 - 49ms/epoch - 2ms/step\n",
            "Epoch 185/1000\n",
            "27/27 - 0s - loss: 0.4791 - binary_accuracy: 0.7810 - val_loss: 0.4988 - val_binary_accuracy: 0.7799 - 54ms/epoch - 2ms/step\n",
            "Epoch 186/1000\n",
            "27/27 - 0s - loss: 0.4735 - binary_accuracy: 0.7675 - val_loss: 0.4989 - val_binary_accuracy: 0.7799 - 58ms/epoch - 2ms/step\n",
            "Epoch 187/1000\n",
            "27/27 - 0s - loss: 0.4795 - binary_accuracy: 0.7731 - val_loss: 0.4988 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 188/1000\n",
            "27/27 - 0s - loss: 0.4800 - binary_accuracy: 0.7799 - val_loss: 0.4985 - val_binary_accuracy: 0.7799 - 51ms/epoch - 2ms/step\n",
            "Epoch 189/1000\n",
            "27/27 - 0s - loss: 0.4838 - binary_accuracy: 0.7731 - val_loss: 0.4985 - val_binary_accuracy: 0.7799 - 58ms/epoch - 2ms/step\n",
            "Epoch 190/1000\n",
            "27/27 - 0s - loss: 0.4768 - binary_accuracy: 0.7607 - val_loss: 0.4982 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 191/1000\n",
            "27/27 - 0s - loss: 0.4824 - binary_accuracy: 0.7822 - val_loss: 0.4983 - val_binary_accuracy: 0.7823 - 65ms/epoch - 2ms/step\n",
            "Epoch 192/1000\n",
            "27/27 - 0s - loss: 0.4932 - binary_accuracy: 0.7630 - val_loss: 0.4984 - val_binary_accuracy: 0.7799 - 62ms/epoch - 2ms/step\n",
            "Epoch 193/1000\n",
            "27/27 - 0s - loss: 0.4724 - binary_accuracy: 0.7856 - val_loss: 0.4985 - val_binary_accuracy: 0.7799 - 65ms/epoch - 2ms/step\n",
            "Epoch 194/1000\n",
            "27/27 - 0s - loss: 0.4833 - binary_accuracy: 0.7833 - val_loss: 0.4983 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 195/1000\n",
            "27/27 - 0s - loss: 0.4865 - binary_accuracy: 0.7822 - val_loss: 0.4984 - val_binary_accuracy: 0.7823 - 62ms/epoch - 2ms/step\n",
            "Epoch 196/1000\n",
            "27/27 - 0s - loss: 0.4802 - binary_accuracy: 0.7822 - val_loss: 0.4981 - val_binary_accuracy: 0.7847 - 65ms/epoch - 2ms/step\n",
            "Epoch 197/1000\n",
            "27/27 - 0s - loss: 0.4709 - binary_accuracy: 0.7878 - val_loss: 0.4981 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 198/1000\n",
            "27/27 - 0s - loss: 0.4687 - binary_accuracy: 0.7720 - val_loss: 0.4981 - val_binary_accuracy: 0.7847 - 49ms/epoch - 2ms/step\n",
            "Epoch 199/1000\n",
            "27/27 - 0s - loss: 0.4764 - binary_accuracy: 0.7743 - val_loss: 0.4977 - val_binary_accuracy: 0.7847 - 50ms/epoch - 2ms/step\n",
            "Epoch 200/1000\n",
            "27/27 - 0s - loss: 0.4890 - binary_accuracy: 0.7585 - val_loss: 0.4977 - val_binary_accuracy: 0.7847 - 63ms/epoch - 2ms/step\n",
            "Epoch 201/1000\n",
            "27/27 - 0s - loss: 0.4814 - binary_accuracy: 0.7968 - val_loss: 0.4976 - val_binary_accuracy: 0.7847 - 48ms/epoch - 2ms/step\n",
            "Epoch 202/1000\n",
            "27/27 - 0s - loss: 0.4852 - binary_accuracy: 0.7833 - val_loss: 0.4975 - val_binary_accuracy: 0.7847 - 58ms/epoch - 2ms/step\n",
            "Epoch 203/1000\n",
            "27/27 - 0s - loss: 0.4883 - binary_accuracy: 0.7664 - val_loss: 0.4976 - val_binary_accuracy: 0.7847 - 57ms/epoch - 2ms/step\n",
            "Epoch 204/1000\n",
            "27/27 - 0s - loss: 0.4760 - binary_accuracy: 0.7822 - val_loss: 0.4976 - val_binary_accuracy: 0.7847 - 48ms/epoch - 2ms/step\n",
            "Epoch 205/1000\n",
            "27/27 - 0s - loss: 0.4717 - binary_accuracy: 0.7810 - val_loss: 0.4976 - val_binary_accuracy: 0.7847 - 49ms/epoch - 2ms/step\n",
            "Epoch 206/1000\n",
            "27/27 - 0s - loss: 0.4824 - binary_accuracy: 0.7743 - val_loss: 0.4974 - val_binary_accuracy: 0.7871 - 47ms/epoch - 2ms/step\n",
            "Epoch 207/1000\n",
            "27/27 - 0s - loss: 0.4896 - binary_accuracy: 0.7551 - val_loss: 0.4974 - val_binary_accuracy: 0.7871 - 62ms/epoch - 2ms/step\n",
            "Epoch 208/1000\n",
            "27/27 - 0s - loss: 0.4721 - binary_accuracy: 0.7743 - val_loss: 0.4971 - val_binary_accuracy: 0.7871 - 59ms/epoch - 2ms/step\n",
            "Epoch 209/1000\n",
            "27/27 - 0s - loss: 0.4946 - binary_accuracy: 0.7720 - val_loss: 0.4973 - val_binary_accuracy: 0.7847 - 64ms/epoch - 2ms/step\n",
            "Epoch 210/1000\n",
            "27/27 - 0s - loss: 0.4776 - binary_accuracy: 0.7765 - val_loss: 0.4975 - val_binary_accuracy: 0.7847 - 59ms/epoch - 2ms/step\n",
            "Epoch 211/1000\n",
            "27/27 - 0s - loss: 0.4828 - binary_accuracy: 0.7777 - val_loss: 0.4973 - val_binary_accuracy: 0.7871 - 46ms/epoch - 2ms/step\n",
            "Epoch 212/1000\n",
            "27/27 - 0s - loss: 0.4772 - binary_accuracy: 0.7912 - val_loss: 0.4971 - val_binary_accuracy: 0.7871 - 56ms/epoch - 2ms/step\n",
            "Epoch 213/1000\n",
            "27/27 - 0s - loss: 0.4775 - binary_accuracy: 0.7822 - val_loss: 0.4969 - val_binary_accuracy: 0.7871 - 58ms/epoch - 2ms/step\n",
            "Epoch 214/1000\n",
            "27/27 - 0s - loss: 0.4753 - binary_accuracy: 0.7810 - val_loss: 0.4971 - val_binary_accuracy: 0.7847 - 47ms/epoch - 2ms/step\n",
            "Epoch 215/1000\n",
            "27/27 - 0s - loss: 0.4640 - binary_accuracy: 0.7991 - val_loss: 0.4969 - val_binary_accuracy: 0.7847 - 48ms/epoch - 2ms/step\n",
            "Epoch 216/1000\n",
            "27/27 - 0s - loss: 0.4924 - binary_accuracy: 0.7731 - val_loss: 0.4970 - val_binary_accuracy: 0.7847 - 64ms/epoch - 2ms/step\n",
            "Epoch 217/1000\n",
            "27/27 - 0s - loss: 0.4648 - binary_accuracy: 0.7946 - val_loss: 0.4968 - val_binary_accuracy: 0.7847 - 48ms/epoch - 2ms/step\n",
            "Epoch 218/1000\n",
            "27/27 - 0s - loss: 0.4740 - binary_accuracy: 0.7878 - val_loss: 0.4969 - val_binary_accuracy: 0.7847 - 49ms/epoch - 2ms/step\n",
            "Epoch 219/1000\n",
            "27/27 - 0s - loss: 0.4767 - binary_accuracy: 0.7652 - val_loss: 0.4968 - val_binary_accuracy: 0.7847 - 50ms/epoch - 2ms/step\n",
            "Epoch 220/1000\n",
            "27/27 - 0s - loss: 0.4791 - binary_accuracy: 0.7788 - val_loss: 0.4968 - val_binary_accuracy: 0.7871 - 64ms/epoch - 2ms/step\n",
            "Epoch 221/1000\n",
            "27/27 - 0s - loss: 0.4792 - binary_accuracy: 0.7777 - val_loss: 0.4971 - val_binary_accuracy: 0.7847 - 48ms/epoch - 2ms/step\n",
            "Epoch 222/1000\n",
            "27/27 - 0s - loss: 0.4644 - binary_accuracy: 0.7799 - val_loss: 0.4970 - val_binary_accuracy: 0.7871 - 47ms/epoch - 2ms/step\n",
            "Epoch 223/1000\n",
            "27/27 - 0s - loss: 0.4713 - binary_accuracy: 0.7810 - val_loss: 0.4967 - val_binary_accuracy: 0.7871 - 50ms/epoch - 2ms/step\n",
            "Epoch 224/1000\n",
            "27/27 - 0s - loss: 0.4761 - binary_accuracy: 0.7743 - val_loss: 0.4967 - val_binary_accuracy: 0.7871 - 58ms/epoch - 2ms/step\n",
            "Epoch 225/1000\n",
            "27/27 - 0s - loss: 0.4694 - binary_accuracy: 0.7991 - val_loss: 0.4969 - val_binary_accuracy: 0.7847 - 56ms/epoch - 2ms/step\n",
            "Epoch 226/1000\n",
            "27/27 - 0s - loss: 0.4733 - binary_accuracy: 0.7923 - val_loss: 0.4967 - val_binary_accuracy: 0.7871 - 60ms/epoch - 2ms/step\n",
            "Epoch 227/1000\n",
            "27/27 - 0s - loss: 0.4828 - binary_accuracy: 0.7957 - val_loss: 0.4967 - val_binary_accuracy: 0.7871 - 52ms/epoch - 2ms/step\n",
            "Epoch 228/1000\n",
            "27/27 - 0s - loss: 0.4807 - binary_accuracy: 0.7709 - val_loss: 0.4967 - val_binary_accuracy: 0.7871 - 51ms/epoch - 2ms/step\n",
            "Epoch 229/1000\n",
            "27/27 - 0s - loss: 0.4800 - binary_accuracy: 0.7788 - val_loss: 0.4965 - val_binary_accuracy: 0.7871 - 63ms/epoch - 2ms/step\n",
            "Epoch 230/1000\n",
            "27/27 - 0s - loss: 0.4874 - binary_accuracy: 0.7810 - val_loss: 0.4959 - val_binary_accuracy: 0.7871 - 44ms/epoch - 2ms/step\n",
            "Epoch 231/1000\n",
            "27/27 - 0s - loss: 0.4674 - binary_accuracy: 0.7912 - val_loss: 0.4963 - val_binary_accuracy: 0.7871 - 45ms/epoch - 2ms/step\n",
            "Epoch 232/1000\n",
            "27/27 - 0s - loss: 0.4765 - binary_accuracy: 0.7889 - val_loss: 0.4960 - val_binary_accuracy: 0.7871 - 58ms/epoch - 2ms/step\n",
            "Epoch 233/1000\n",
            "27/27 - 0s - loss: 0.4742 - binary_accuracy: 0.7788 - val_loss: 0.4956 - val_binary_accuracy: 0.7895 - 58ms/epoch - 2ms/step\n",
            "Epoch 234/1000\n",
            "27/27 - 0s - loss: 0.4649 - binary_accuracy: 0.7957 - val_loss: 0.4957 - val_binary_accuracy: 0.7871 - 48ms/epoch - 2ms/step\n",
            "Epoch 235/1000\n",
            "27/27 - 0s - loss: 0.4703 - binary_accuracy: 0.8014 - val_loss: 0.4963 - val_binary_accuracy: 0.7871 - 51ms/epoch - 2ms/step\n",
            "Epoch 236/1000\n",
            "27/27 - 0s - loss: 0.4808 - binary_accuracy: 0.7810 - val_loss: 0.4961 - val_binary_accuracy: 0.7871 - 59ms/epoch - 2ms/step\n",
            "Epoch 237/1000\n",
            "27/27 - 0s - loss: 0.4865 - binary_accuracy: 0.7833 - val_loss: 0.4965 - val_binary_accuracy: 0.7871 - 47ms/epoch - 2ms/step\n",
            "Epoch 238/1000\n",
            "27/27 - 0s - loss: 0.4733 - binary_accuracy: 0.7901 - val_loss: 0.4962 - val_binary_accuracy: 0.7871 - 60ms/epoch - 2ms/step\n",
            "Epoch 239/1000\n",
            "27/27 - 0s - loss: 0.4629 - binary_accuracy: 0.8025 - val_loss: 0.4959 - val_binary_accuracy: 0.7871 - 53ms/epoch - 2ms/step\n",
            "Epoch 240/1000\n",
            "27/27 - 0s - loss: 0.4702 - binary_accuracy: 0.7935 - val_loss: 0.4959 - val_binary_accuracy: 0.7871 - 53ms/epoch - 2ms/step\n",
            "Epoch 241/1000\n",
            "27/27 - 0s - loss: 0.4787 - binary_accuracy: 0.7889 - val_loss: 0.4960 - val_binary_accuracy: 0.7871 - 48ms/epoch - 2ms/step\n",
            "Epoch 242/1000\n",
            "27/27 - 0s - loss: 0.4635 - binary_accuracy: 0.7901 - val_loss: 0.4958 - val_binary_accuracy: 0.7871 - 59ms/epoch - 2ms/step\n",
            "Epoch 243/1000\n",
            "27/27 - 0s - loss: 0.4723 - binary_accuracy: 0.7799 - val_loss: 0.4955 - val_binary_accuracy: 0.7871 - 60ms/epoch - 2ms/step\n",
            "Epoch 244/1000\n",
            "27/27 - 0s - loss: 0.4799 - binary_accuracy: 0.7822 - val_loss: 0.4955 - val_binary_accuracy: 0.7871 - 48ms/epoch - 2ms/step\n",
            "Epoch 245/1000\n",
            "27/27 - 0s - loss: 0.4644 - binary_accuracy: 0.7856 - val_loss: 0.4953 - val_binary_accuracy: 0.7871 - 56ms/epoch - 2ms/step\n",
            "Epoch 246/1000\n",
            "27/27 - 0s - loss: 0.4790 - binary_accuracy: 0.7935 - val_loss: 0.4952 - val_binary_accuracy: 0.7871 - 56ms/epoch - 2ms/step\n",
            "Epoch 247/1000\n",
            "27/27 - 0s - loss: 0.4609 - binary_accuracy: 0.8014 - val_loss: 0.4953 - val_binary_accuracy: 0.7871 - 57ms/epoch - 2ms/step\n",
            "Epoch 248/1000\n",
            "27/27 - 0s - loss: 0.4593 - binary_accuracy: 0.7923 - val_loss: 0.4955 - val_binary_accuracy: 0.7871 - 57ms/epoch - 2ms/step\n",
            "Epoch 249/1000\n",
            "27/27 - 0s - loss: 0.4739 - binary_accuracy: 0.7788 - val_loss: 0.4952 - val_binary_accuracy: 0.7871 - 57ms/epoch - 2ms/step\n",
            "Epoch 250/1000\n",
            "27/27 - 0s - loss: 0.4755 - binary_accuracy: 0.7980 - val_loss: 0.4949 - val_binary_accuracy: 0.7871 - 57ms/epoch - 2ms/step\n",
            "Epoch 251/1000\n",
            "27/27 - 0s - loss: 0.4705 - binary_accuracy: 0.7946 - val_loss: 0.4952 - val_binary_accuracy: 0.7871 - 49ms/epoch - 2ms/step\n",
            "Epoch 252/1000\n",
            "27/27 - 0s - loss: 0.4705 - binary_accuracy: 0.7810 - val_loss: 0.4950 - val_binary_accuracy: 0.7871 - 57ms/epoch - 2ms/step\n",
            "Epoch 253/1000\n",
            "27/27 - 0s - loss: 0.4924 - binary_accuracy: 0.7686 - val_loss: 0.4950 - val_binary_accuracy: 0.7871 - 56ms/epoch - 2ms/step\n",
            "Epoch 254/1000\n",
            "27/27 - 0s - loss: 0.4655 - binary_accuracy: 0.7957 - val_loss: 0.4947 - val_binary_accuracy: 0.7871 - 55ms/epoch - 2ms/step\n",
            "Epoch 255/1000\n",
            "27/27 - 0s - loss: 0.4786 - binary_accuracy: 0.8025 - val_loss: 0.4947 - val_binary_accuracy: 0.7871 - 60ms/epoch - 2ms/step\n",
            "Epoch 256/1000\n",
            "27/27 - 0s - loss: 0.4769 - binary_accuracy: 0.7777 - val_loss: 0.4950 - val_binary_accuracy: 0.7871 - 55ms/epoch - 2ms/step\n",
            "Epoch 257/1000\n",
            "27/27 - 0s - loss: 0.4749 - binary_accuracy: 0.7935 - val_loss: 0.4951 - val_binary_accuracy: 0.7871 - 50ms/epoch - 2ms/step\n",
            "Epoch 258/1000\n",
            "27/27 - 0s - loss: 0.4729 - binary_accuracy: 0.7957 - val_loss: 0.4945 - val_binary_accuracy: 0.7871 - 66ms/epoch - 2ms/step\n",
            "Epoch 259/1000\n",
            "27/27 - 0s - loss: 0.4643 - binary_accuracy: 0.7856 - val_loss: 0.4943 - val_binary_accuracy: 0.7871 - 51ms/epoch - 2ms/step\n",
            "Epoch 260/1000\n",
            "27/27 - 0s - loss: 0.4624 - binary_accuracy: 0.7844 - val_loss: 0.4942 - val_binary_accuracy: 0.7823 - 55ms/epoch - 2ms/step\n",
            "Epoch 261/1000\n",
            "27/27 - 0s - loss: 0.4615 - binary_accuracy: 0.7889 - val_loss: 0.4942 - val_binary_accuracy: 0.7799 - 63ms/epoch - 2ms/step\n",
            "Epoch 262/1000\n",
            "27/27 - 0s - loss: 0.4684 - binary_accuracy: 0.7833 - val_loss: 0.4943 - val_binary_accuracy: 0.7871 - 47ms/epoch - 2ms/step\n",
            "Epoch 263/1000\n",
            "27/27 - 0s - loss: 0.4752 - binary_accuracy: 0.7867 - val_loss: 0.4941 - val_binary_accuracy: 0.7775 - 53ms/epoch - 2ms/step\n",
            "Epoch 264/1000\n",
            "27/27 - 0s - loss: 0.4667 - binary_accuracy: 0.7935 - val_loss: 0.4942 - val_binary_accuracy: 0.7871 - 64ms/epoch - 2ms/step\n",
            "Epoch 265/1000\n",
            "27/27 - 0s - loss: 0.4742 - binary_accuracy: 0.7856 - val_loss: 0.4935 - val_binary_accuracy: 0.7775 - 55ms/epoch - 2ms/step\n",
            "Epoch 266/1000\n",
            "27/27 - 0s - loss: 0.4805 - binary_accuracy: 0.7878 - val_loss: 0.4935 - val_binary_accuracy: 0.7799 - 49ms/epoch - 2ms/step\n",
            "Epoch 267/1000\n",
            "27/27 - 0s - loss: 0.4704 - binary_accuracy: 0.7923 - val_loss: 0.4935 - val_binary_accuracy: 0.7823 - 54ms/epoch - 2ms/step\n",
            "Epoch 268/1000\n",
            "27/27 - 0s - loss: 0.4615 - binary_accuracy: 0.7912 - val_loss: 0.4937 - val_binary_accuracy: 0.7847 - 48ms/epoch - 2ms/step\n",
            "Epoch 269/1000\n",
            "27/27 - 0s - loss: 0.4728 - binary_accuracy: 0.7867 - val_loss: 0.4935 - val_binary_accuracy: 0.7751 - 53ms/epoch - 2ms/step\n",
            "Epoch 270/1000\n",
            "27/27 - 0s - loss: 0.4702 - binary_accuracy: 0.8014 - val_loss: 0.4937 - val_binary_accuracy: 0.7871 - 53ms/epoch - 2ms/step\n",
            "Epoch 271/1000\n",
            "27/27 - 0s - loss: 0.4665 - binary_accuracy: 0.7935 - val_loss: 0.4933 - val_binary_accuracy: 0.7751 - 60ms/epoch - 2ms/step\n",
            "Epoch 272/1000\n",
            "27/27 - 0s - loss: 0.4752 - binary_accuracy: 0.7912 - val_loss: 0.4934 - val_binary_accuracy: 0.7823 - 58ms/epoch - 2ms/step\n",
            "Epoch 273/1000\n",
            "27/27 - 0s - loss: 0.4636 - binary_accuracy: 0.7946 - val_loss: 0.4934 - val_binary_accuracy: 0.7871 - 49ms/epoch - 2ms/step\n",
            "Epoch 274/1000\n",
            "27/27 - 0s - loss: 0.4537 - binary_accuracy: 0.7878 - val_loss: 0.4934 - val_binary_accuracy: 0.7895 - 50ms/epoch - 2ms/step\n",
            "Epoch 275/1000\n",
            "27/27 - 0s - loss: 0.4733 - binary_accuracy: 0.7788 - val_loss: 0.4935 - val_binary_accuracy: 0.7895 - 63ms/epoch - 2ms/step\n",
            "Epoch 276/1000\n",
            "27/27 - 0s - loss: 0.4660 - binary_accuracy: 0.7754 - val_loss: 0.4935 - val_binary_accuracy: 0.7847 - 45ms/epoch - 2ms/step\n",
            "Epoch 277/1000\n",
            "27/27 - 0s - loss: 0.4651 - binary_accuracy: 0.8070 - val_loss: 0.4934 - val_binary_accuracy: 0.7775 - 46ms/epoch - 2ms/step\n",
            "Epoch 278/1000\n",
            "27/27 - 0s - loss: 0.4626 - binary_accuracy: 0.7957 - val_loss: 0.4939 - val_binary_accuracy: 0.7871 - 47ms/epoch - 2ms/step\n",
            "Epoch 279/1000\n",
            "27/27 - 0s - loss: 0.4628 - binary_accuracy: 0.7912 - val_loss: 0.4937 - val_binary_accuracy: 0.7871 - 45ms/epoch - 2ms/step\n",
            "Epoch 280/1000\n",
            "27/27 - 0s - loss: 0.4665 - binary_accuracy: 0.7935 - val_loss: 0.4933 - val_binary_accuracy: 0.7823 - 62ms/epoch - 2ms/step\n",
            "Epoch 281/1000\n",
            "27/27 - 0s - loss: 0.4651 - binary_accuracy: 0.7980 - val_loss: 0.4933 - val_binary_accuracy: 0.7847 - 46ms/epoch - 2ms/step\n",
            "Epoch 282/1000\n",
            "27/27 - 0s - loss: 0.4684 - binary_accuracy: 0.7935 - val_loss: 0.4931 - val_binary_accuracy: 0.7871 - 51ms/epoch - 2ms/step\n",
            "Epoch 283/1000\n",
            "27/27 - 0s - loss: 0.4688 - binary_accuracy: 0.7957 - val_loss: 0.4934 - val_binary_accuracy: 0.7871 - 52ms/epoch - 2ms/step\n",
            "Epoch 284/1000\n",
            "27/27 - 0s - loss: 0.4763 - binary_accuracy: 0.7867 - val_loss: 0.4933 - val_binary_accuracy: 0.7871 - 58ms/epoch - 2ms/step\n",
            "Epoch 285/1000\n",
            "27/27 - 0s - loss: 0.4681 - binary_accuracy: 0.7889 - val_loss: 0.4932 - val_binary_accuracy: 0.7871 - 49ms/epoch - 2ms/step\n",
            "Epoch 286/1000\n",
            "27/27 - 0s - loss: 0.4729 - binary_accuracy: 0.7822 - val_loss: 0.4930 - val_binary_accuracy: 0.7847 - 63ms/epoch - 2ms/step\n",
            "Epoch 287/1000\n",
            "27/27 - 0s - loss: 0.4598 - binary_accuracy: 0.8025 - val_loss: 0.4929 - val_binary_accuracy: 0.7847 - 61ms/epoch - 2ms/step\n",
            "Epoch 288/1000\n",
            "27/27 - 0s - loss: 0.4713 - binary_accuracy: 0.7799 - val_loss: 0.4929 - val_binary_accuracy: 0.7847 - 54ms/epoch - 2ms/step\n",
            "Epoch 289/1000\n",
            "27/27 - 0s - loss: 0.4662 - binary_accuracy: 0.7889 - val_loss: 0.4927 - val_binary_accuracy: 0.7871 - 51ms/epoch - 2ms/step\n",
            "Epoch 290/1000\n",
            "27/27 - 0s - loss: 0.4566 - binary_accuracy: 0.7923 - val_loss: 0.4926 - val_binary_accuracy: 0.7847 - 59ms/epoch - 2ms/step\n",
            "Epoch 291/1000\n",
            "27/27 - 0s - loss: 0.4605 - binary_accuracy: 0.7968 - val_loss: 0.4925 - val_binary_accuracy: 0.7895 - 53ms/epoch - 2ms/step\n",
            "Epoch 292/1000\n",
            "27/27 - 0s - loss: 0.4676 - binary_accuracy: 0.7833 - val_loss: 0.4922 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 293/1000\n",
            "27/27 - 0s - loss: 0.4646 - binary_accuracy: 0.7901 - val_loss: 0.4926 - val_binary_accuracy: 0.7895 - 51ms/epoch - 2ms/step\n",
            "Epoch 294/1000\n",
            "27/27 - 0s - loss: 0.4640 - binary_accuracy: 0.8002 - val_loss: 0.4922 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 295/1000\n",
            "27/27 - 0s - loss: 0.4587 - binary_accuracy: 0.7957 - val_loss: 0.4925 - val_binary_accuracy: 0.7895 - 65ms/epoch - 2ms/step\n",
            "Epoch 296/1000\n",
            "27/27 - 0s - loss: 0.4652 - binary_accuracy: 0.7991 - val_loss: 0.4922 - val_binary_accuracy: 0.7799 - 57ms/epoch - 2ms/step\n",
            "Epoch 297/1000\n",
            "27/27 - 0s - loss: 0.4681 - binary_accuracy: 0.7923 - val_loss: 0.4920 - val_binary_accuracy: 0.7871 - 64ms/epoch - 2ms/step\n",
            "Epoch 298/1000\n",
            "27/27 - 0s - loss: 0.4572 - binary_accuracy: 0.8104 - val_loss: 0.4918 - val_binary_accuracy: 0.7823 - 62ms/epoch - 2ms/step\n",
            "Epoch 299/1000\n",
            "27/27 - 0s - loss: 0.4645 - binary_accuracy: 0.8014 - val_loss: 0.4916 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 300/1000\n",
            "27/27 - 0s - loss: 0.4558 - binary_accuracy: 0.8115 - val_loss: 0.4910 - val_binary_accuracy: 0.7823 - 61ms/epoch - 2ms/step\n",
            "Epoch 301/1000\n",
            "27/27 - 0s - loss: 0.4663 - binary_accuracy: 0.8047 - val_loss: 0.4913 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 302/1000\n",
            "27/27 - 0s - loss: 0.4629 - binary_accuracy: 0.8025 - val_loss: 0.4917 - val_binary_accuracy: 0.7871 - 52ms/epoch - 2ms/step\n",
            "Epoch 303/1000\n",
            "27/27 - 0s - loss: 0.4749 - binary_accuracy: 0.7844 - val_loss: 0.4917 - val_binary_accuracy: 0.7871 - 57ms/epoch - 2ms/step\n",
            "Epoch 304/1000\n",
            "27/27 - 0s - loss: 0.4576 - binary_accuracy: 0.7901 - val_loss: 0.4913 - val_binary_accuracy: 0.7823 - 47ms/epoch - 2ms/step\n",
            "Epoch 305/1000\n",
            "27/27 - 0s - loss: 0.4637 - binary_accuracy: 0.7788 - val_loss: 0.4914 - val_binary_accuracy: 0.7871 - 52ms/epoch - 2ms/step\n",
            "Epoch 306/1000\n",
            "27/27 - 0s - loss: 0.4738 - binary_accuracy: 0.7980 - val_loss: 0.4912 - val_binary_accuracy: 0.7847 - 54ms/epoch - 2ms/step\n",
            "Epoch 307/1000\n",
            "27/27 - 0s - loss: 0.4715 - binary_accuracy: 0.7878 - val_loss: 0.4912 - val_binary_accuracy: 0.7919 - 55ms/epoch - 2ms/step\n",
            "Epoch 308/1000\n",
            "27/27 - 0s - loss: 0.4711 - binary_accuracy: 0.8002 - val_loss: 0.4911 - val_binary_accuracy: 0.7943 - 62ms/epoch - 2ms/step\n",
            "Epoch 309/1000\n",
            "27/27 - 0s - loss: 0.4772 - binary_accuracy: 0.7946 - val_loss: 0.4912 - val_binary_accuracy: 0.7847 - 45ms/epoch - 2ms/step\n",
            "Epoch 310/1000\n",
            "27/27 - 0s - loss: 0.4711 - binary_accuracy: 0.7856 - val_loss: 0.4909 - val_binary_accuracy: 0.7823 - 55ms/epoch - 2ms/step\n",
            "Epoch 311/1000\n",
            "27/27 - 0s - loss: 0.4580 - binary_accuracy: 0.7878 - val_loss: 0.4908 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 312/1000\n",
            "27/27 - 0s - loss: 0.4706 - binary_accuracy: 0.7968 - val_loss: 0.4909 - val_binary_accuracy: 0.7823 - 64ms/epoch - 2ms/step\n",
            "Epoch 313/1000\n",
            "27/27 - 0s - loss: 0.4621 - binary_accuracy: 0.7867 - val_loss: 0.4909 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 314/1000\n",
            "27/27 - 0s - loss: 0.4721 - binary_accuracy: 0.7856 - val_loss: 0.4906 - val_binary_accuracy: 0.7823 - 65ms/epoch - 2ms/step\n",
            "Epoch 315/1000\n",
            "27/27 - 0s - loss: 0.4758 - binary_accuracy: 0.7878 - val_loss: 0.4905 - val_binary_accuracy: 0.7823 - 61ms/epoch - 2ms/step\n",
            "Epoch 316/1000\n",
            "27/27 - 0s - loss: 0.4650 - binary_accuracy: 0.7935 - val_loss: 0.4905 - val_binary_accuracy: 0.7823 - 67ms/epoch - 2ms/step\n",
            "Epoch 317/1000\n",
            "27/27 - 0s - loss: 0.4734 - binary_accuracy: 0.7889 - val_loss: 0.4902 - val_binary_accuracy: 0.7823 - 54ms/epoch - 2ms/step\n",
            "Epoch 318/1000\n",
            "27/27 - 0s - loss: 0.4577 - binary_accuracy: 0.7935 - val_loss: 0.4903 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 319/1000\n",
            "27/27 - 0s - loss: 0.4719 - binary_accuracy: 0.7957 - val_loss: 0.4900 - val_binary_accuracy: 0.7823 - 59ms/epoch - 2ms/step\n",
            "Epoch 320/1000\n",
            "27/27 - 0s - loss: 0.4541 - binary_accuracy: 0.8093 - val_loss: 0.4900 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 321/1000\n",
            "27/27 - 0s - loss: 0.4726 - binary_accuracy: 0.7991 - val_loss: 0.4901 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 322/1000\n",
            "27/27 - 0s - loss: 0.4681 - binary_accuracy: 0.7935 - val_loss: 0.4900 - val_binary_accuracy: 0.7823 - 47ms/epoch - 2ms/step\n",
            "Epoch 323/1000\n",
            "27/27 - 0s - loss: 0.4748 - binary_accuracy: 0.7901 - val_loss: 0.4900 - val_binary_accuracy: 0.7823 - 57ms/epoch - 2ms/step\n",
            "Epoch 324/1000\n",
            "27/27 - 0s - loss: 0.4642 - binary_accuracy: 0.7856 - val_loss: 0.4899 - val_binary_accuracy: 0.7823 - 54ms/epoch - 2ms/step\n",
            "Epoch 325/1000\n",
            "27/27 - 0s - loss: 0.4673 - binary_accuracy: 0.7957 - val_loss: 0.4897 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 326/1000\n",
            "27/27 - 0s - loss: 0.4712 - binary_accuracy: 0.8002 - val_loss: 0.4896 - val_binary_accuracy: 0.7823 - 64ms/epoch - 2ms/step\n",
            "Epoch 327/1000\n",
            "27/27 - 0s - loss: 0.4587 - binary_accuracy: 0.7901 - val_loss: 0.4896 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 328/1000\n",
            "27/27 - 0s - loss: 0.4561 - binary_accuracy: 0.7991 - val_loss: 0.4891 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 329/1000\n",
            "27/27 - 0s - loss: 0.4723 - binary_accuracy: 0.7878 - val_loss: 0.4888 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 330/1000\n",
            "27/27 - 0s - loss: 0.4584 - binary_accuracy: 0.7935 - val_loss: 0.4891 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 331/1000\n",
            "27/27 - 0s - loss: 0.4466 - binary_accuracy: 0.8093 - val_loss: 0.4890 - val_binary_accuracy: 0.7823 - 63ms/epoch - 2ms/step\n",
            "Epoch 332/1000\n",
            "27/27 - 0s - loss: 0.4581 - binary_accuracy: 0.8104 - val_loss: 0.4891 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 333/1000\n",
            "27/27 - 0s - loss: 0.4631 - binary_accuracy: 0.7946 - val_loss: 0.4891 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 334/1000\n",
            "27/27 - 0s - loss: 0.4630 - binary_accuracy: 0.7957 - val_loss: 0.4890 - val_binary_accuracy: 0.7823 - 55ms/epoch - 2ms/step\n",
            "Epoch 335/1000\n",
            "27/27 - 0s - loss: 0.4533 - binary_accuracy: 0.7878 - val_loss: 0.4891 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 336/1000\n",
            "27/27 - 0s - loss: 0.4653 - binary_accuracy: 0.7912 - val_loss: 0.4891 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 337/1000\n",
            "27/27 - 0s - loss: 0.4630 - binary_accuracy: 0.7968 - val_loss: 0.4894 - val_binary_accuracy: 0.7823 - 61ms/epoch - 2ms/step\n",
            "Epoch 338/1000\n",
            "27/27 - 0s - loss: 0.4592 - binary_accuracy: 0.7878 - val_loss: 0.4891 - val_binary_accuracy: 0.7799 - 55ms/epoch - 2ms/step\n",
            "Epoch 339/1000\n",
            "27/27 - 0s - loss: 0.4552 - binary_accuracy: 0.7957 - val_loss: 0.4893 - val_binary_accuracy: 0.7799 - 53ms/epoch - 2ms/step\n",
            "Epoch 340/1000\n",
            "27/27 - 0s - loss: 0.4611 - binary_accuracy: 0.7901 - val_loss: 0.4892 - val_binary_accuracy: 0.7823 - 45ms/epoch - 2ms/step\n",
            "Epoch 341/1000\n",
            "27/27 - 0s - loss: 0.4769 - binary_accuracy: 0.7901 - val_loss: 0.4886 - val_binary_accuracy: 0.7799 - 53ms/epoch - 2ms/step\n",
            "Epoch 342/1000\n",
            "27/27 - 0s - loss: 0.4630 - binary_accuracy: 0.7878 - val_loss: 0.4886 - val_binary_accuracy: 0.7799 - 47ms/epoch - 2ms/step\n",
            "Epoch 343/1000\n",
            "27/27 - 0s - loss: 0.4637 - binary_accuracy: 0.7957 - val_loss: 0.4883 - val_binary_accuracy: 0.7823 - 45ms/epoch - 2ms/step\n",
            "Epoch 344/1000\n",
            "27/27 - 0s - loss: 0.4702 - binary_accuracy: 0.7912 - val_loss: 0.4886 - val_binary_accuracy: 0.7799 - 55ms/epoch - 2ms/step\n",
            "Epoch 345/1000\n",
            "27/27 - 0s - loss: 0.4640 - binary_accuracy: 0.8070 - val_loss: 0.4881 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 346/1000\n",
            "27/27 - 0s - loss: 0.4644 - binary_accuracy: 0.8081 - val_loss: 0.4885 - val_binary_accuracy: 0.7799 - 54ms/epoch - 2ms/step\n",
            "Epoch 347/1000\n",
            "27/27 - 0s - loss: 0.4437 - binary_accuracy: 0.7923 - val_loss: 0.4881 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 348/1000\n",
            "27/27 - 0s - loss: 0.4698 - binary_accuracy: 0.7901 - val_loss: 0.4880 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 349/1000\n",
            "27/27 - 0s - loss: 0.4592 - binary_accuracy: 0.8104 - val_loss: 0.4879 - val_binary_accuracy: 0.7823 - 61ms/epoch - 2ms/step\n",
            "Epoch 350/1000\n",
            "27/27 - 0s - loss: 0.4587 - binary_accuracy: 0.8025 - val_loss: 0.4879 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 351/1000\n",
            "27/27 - 0s - loss: 0.4586 - binary_accuracy: 0.8014 - val_loss: 0.4877 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 352/1000\n",
            "27/27 - 0s - loss: 0.4690 - binary_accuracy: 0.8081 - val_loss: 0.4878 - val_binary_accuracy: 0.7823 - 63ms/epoch - 2ms/step\n",
            "Epoch 353/1000\n",
            "27/27 - 0s - loss: 0.4875 - binary_accuracy: 0.7946 - val_loss: 0.4882 - val_binary_accuracy: 0.7799 - 52ms/epoch - 2ms/step\n",
            "Epoch 354/1000\n",
            "27/27 - 0s - loss: 0.4438 - binary_accuracy: 0.7968 - val_loss: 0.4878 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 355/1000\n",
            "27/27 - 0s - loss: 0.4600 - binary_accuracy: 0.7991 - val_loss: 0.4877 - val_binary_accuracy: 0.7823 - 60ms/epoch - 2ms/step\n",
            "Epoch 356/1000\n",
            "27/27 - 0s - loss: 0.4647 - binary_accuracy: 0.7935 - val_loss: 0.4878 - val_binary_accuracy: 0.7799 - 55ms/epoch - 2ms/step\n",
            "Epoch 357/1000\n",
            "27/27 - 0s - loss: 0.4554 - binary_accuracy: 0.8047 - val_loss: 0.4871 - val_binary_accuracy: 0.7823 - 67ms/epoch - 2ms/step\n",
            "Epoch 358/1000\n",
            "27/27 - 0s - loss: 0.4693 - binary_accuracy: 0.7810 - val_loss: 0.4873 - val_binary_accuracy: 0.7823 - 46ms/epoch - 2ms/step\n",
            "Epoch 359/1000\n",
            "27/27 - 0s - loss: 0.4583 - binary_accuracy: 0.8002 - val_loss: 0.4873 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 360/1000\n",
            "27/27 - 0s - loss: 0.4590 - binary_accuracy: 0.8025 - val_loss: 0.4870 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 361/1000\n",
            "27/27 - 0s - loss: 0.4448 - binary_accuracy: 0.8036 - val_loss: 0.4866 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 362/1000\n",
            "27/27 - 0s - loss: 0.4729 - binary_accuracy: 0.7765 - val_loss: 0.4867 - val_binary_accuracy: 0.7823 - 46ms/epoch - 2ms/step\n",
            "Epoch 363/1000\n",
            "27/27 - 0s - loss: 0.4392 - binary_accuracy: 0.8047 - val_loss: 0.4867 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 364/1000\n",
            "27/27 - 0s - loss: 0.4509 - binary_accuracy: 0.8138 - val_loss: 0.4869 - val_binary_accuracy: 0.7823 - 46ms/epoch - 2ms/step\n",
            "Epoch 365/1000\n",
            "27/27 - 0s - loss: 0.4720 - binary_accuracy: 0.7980 - val_loss: 0.4868 - val_binary_accuracy: 0.7823 - 47ms/epoch - 2ms/step\n",
            "Epoch 366/1000\n",
            "27/27 - 0s - loss: 0.4681 - binary_accuracy: 0.7912 - val_loss: 0.4865 - val_binary_accuracy: 0.7823 - 63ms/epoch - 2ms/step\n",
            "Epoch 367/1000\n",
            "27/27 - 0s - loss: 0.4593 - binary_accuracy: 0.7991 - val_loss: 0.4865 - val_binary_accuracy: 0.7823 - 59ms/epoch - 2ms/step\n",
            "Epoch 368/1000\n",
            "27/27 - 0s - loss: 0.4611 - binary_accuracy: 0.8014 - val_loss: 0.4864 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 369/1000\n",
            "27/27 - 0s - loss: 0.4487 - binary_accuracy: 0.8002 - val_loss: 0.4861 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 370/1000\n",
            "27/27 - 0s - loss: 0.4605 - binary_accuracy: 0.8047 - val_loss: 0.4862 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 371/1000\n",
            "27/27 - 0s - loss: 0.4455 - binary_accuracy: 0.8115 - val_loss: 0.4861 - val_binary_accuracy: 0.7823 - 44ms/epoch - 2ms/step\n",
            "Epoch 372/1000\n",
            "27/27 - 0s - loss: 0.4623 - binary_accuracy: 0.8002 - val_loss: 0.4861 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 373/1000\n",
            "27/27 - 0s - loss: 0.4502 - binary_accuracy: 0.7912 - val_loss: 0.4865 - val_binary_accuracy: 0.7823 - 70ms/epoch - 3ms/step\n",
            "Epoch 374/1000\n",
            "27/27 - 0s - loss: 0.4536 - binary_accuracy: 0.7878 - val_loss: 0.4864 - val_binary_accuracy: 0.7799 - 65ms/epoch - 2ms/step\n",
            "Epoch 375/1000\n",
            "27/27 - 0s - loss: 0.4696 - binary_accuracy: 0.7878 - val_loss: 0.4860 - val_binary_accuracy: 0.7799 - 60ms/epoch - 2ms/step\n",
            "Epoch 376/1000\n",
            "27/27 - 0s - loss: 0.4621 - binary_accuracy: 0.7923 - val_loss: 0.4861 - val_binary_accuracy: 0.7799 - 52ms/epoch - 2ms/step\n",
            "Epoch 377/1000\n",
            "27/27 - 0s - loss: 0.4650 - binary_accuracy: 0.7935 - val_loss: 0.4859 - val_binary_accuracy: 0.7823 - 60ms/epoch - 2ms/step\n",
            "Epoch 378/1000\n",
            "27/27 - 0s - loss: 0.4805 - binary_accuracy: 0.7856 - val_loss: 0.4858 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 379/1000\n",
            "27/27 - 0s - loss: 0.4519 - binary_accuracy: 0.8002 - val_loss: 0.4854 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 380/1000\n",
            "27/27 - 0s - loss: 0.4509 - binary_accuracy: 0.8070 - val_loss: 0.4854 - val_binary_accuracy: 0.7823 - 62ms/epoch - 2ms/step\n",
            "Epoch 381/1000\n",
            "27/27 - 0s - loss: 0.4472 - binary_accuracy: 0.7957 - val_loss: 0.4855 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 382/1000\n",
            "27/27 - 0s - loss: 0.4642 - binary_accuracy: 0.7923 - val_loss: 0.4858 - val_binary_accuracy: 0.7823 - 45ms/epoch - 2ms/step\n",
            "Epoch 383/1000\n",
            "27/27 - 0s - loss: 0.4518 - binary_accuracy: 0.7968 - val_loss: 0.4853 - val_binary_accuracy: 0.7823 - 46ms/epoch - 2ms/step\n",
            "Epoch 384/1000\n",
            "27/27 - 0s - loss: 0.4578 - binary_accuracy: 0.8093 - val_loss: 0.4853 - val_binary_accuracy: 0.7823 - 47ms/epoch - 2ms/step\n",
            "Epoch 385/1000\n",
            "27/27 - 0s - loss: 0.4562 - binary_accuracy: 0.7912 - val_loss: 0.4852 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 386/1000\n",
            "27/27 - 0s - loss: 0.4571 - binary_accuracy: 0.7980 - val_loss: 0.4852 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 387/1000\n",
            "27/27 - 0s - loss: 0.4597 - binary_accuracy: 0.8126 - val_loss: 0.4850 - val_binary_accuracy: 0.7823 - 54ms/epoch - 2ms/step\n",
            "Epoch 388/1000\n",
            "27/27 - 0s - loss: 0.4657 - binary_accuracy: 0.7980 - val_loss: 0.4853 - val_binary_accuracy: 0.7823 - 46ms/epoch - 2ms/step\n",
            "Epoch 389/1000\n",
            "27/27 - 0s - loss: 0.4536 - binary_accuracy: 0.8036 - val_loss: 0.4855 - val_binary_accuracy: 0.7823 - 47ms/epoch - 2ms/step\n",
            "Epoch 390/1000\n",
            "27/27 - 0s - loss: 0.4566 - binary_accuracy: 0.7923 - val_loss: 0.4855 - val_binary_accuracy: 0.7799 - 61ms/epoch - 2ms/step\n",
            "Epoch 391/1000\n",
            "27/27 - 0s - loss: 0.4660 - binary_accuracy: 0.8025 - val_loss: 0.4850 - val_binary_accuracy: 0.7823 - 62ms/epoch - 2ms/step\n",
            "Epoch 392/1000\n",
            "27/27 - 0s - loss: 0.4594 - binary_accuracy: 0.7935 - val_loss: 0.4853 - val_binary_accuracy: 0.7799 - 49ms/epoch - 2ms/step\n",
            "Epoch 393/1000\n",
            "27/27 - 0s - loss: 0.4611 - binary_accuracy: 0.7867 - val_loss: 0.4848 - val_binary_accuracy: 0.7823 - 47ms/epoch - 2ms/step\n",
            "Epoch 394/1000\n",
            "27/27 - 0s - loss: 0.4515 - binary_accuracy: 0.8104 - val_loss: 0.4853 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 395/1000\n",
            "27/27 - 0s - loss: 0.4561 - binary_accuracy: 0.8081 - val_loss: 0.4854 - val_binary_accuracy: 0.7823 - 55ms/epoch - 2ms/step\n",
            "Epoch 396/1000\n",
            "27/27 - 0s - loss: 0.4373 - binary_accuracy: 0.8036 - val_loss: 0.4852 - val_binary_accuracy: 0.7823 - 57ms/epoch - 2ms/step\n",
            "Epoch 397/1000\n",
            "27/27 - 0s - loss: 0.4475 - binary_accuracy: 0.8070 - val_loss: 0.4851 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 398/1000\n",
            "27/27 - 0s - loss: 0.4487 - binary_accuracy: 0.8002 - val_loss: 0.4851 - val_binary_accuracy: 0.7823 - 57ms/epoch - 2ms/step\n",
            "Epoch 399/1000\n",
            "27/27 - 0s - loss: 0.4558 - binary_accuracy: 0.8014 - val_loss: 0.4853 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 400/1000\n",
            "27/27 - 0s - loss: 0.4632 - binary_accuracy: 0.8025 - val_loss: 0.4852 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 401/1000\n",
            "27/27 - 0s - loss: 0.4469 - binary_accuracy: 0.7878 - val_loss: 0.4854 - val_binary_accuracy: 0.7799 - 47ms/epoch - 2ms/step\n",
            "Epoch 402/1000\n",
            "27/27 - 0s - loss: 0.4639 - binary_accuracy: 0.7867 - val_loss: 0.4853 - val_binary_accuracy: 0.7823 - 59ms/epoch - 2ms/step\n",
            "Epoch 403/1000\n",
            "27/27 - 0s - loss: 0.4375 - binary_accuracy: 0.8217 - val_loss: 0.4853 - val_binary_accuracy: 0.7823 - 61ms/epoch - 2ms/step\n",
            "Epoch 404/1000\n",
            "27/27 - 0s - loss: 0.4554 - binary_accuracy: 0.7957 - val_loss: 0.4853 - val_binary_accuracy: 0.7823 - 59ms/epoch - 2ms/step\n",
            "Epoch 405/1000\n",
            "27/27 - 0s - loss: 0.4404 - binary_accuracy: 0.8115 - val_loss: 0.4851 - val_binary_accuracy: 0.7823 - 46ms/epoch - 2ms/step\n",
            "Epoch 406/1000\n",
            "27/27 - 0s - loss: 0.4520 - binary_accuracy: 0.8002 - val_loss: 0.4847 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 407/1000\n",
            "27/27 - 0s - loss: 0.4467 - binary_accuracy: 0.8047 - val_loss: 0.4850 - val_binary_accuracy: 0.7823 - 57ms/epoch - 2ms/step\n",
            "Epoch 408/1000\n",
            "27/27 - 0s - loss: 0.4366 - binary_accuracy: 0.8172 - val_loss: 0.4853 - val_binary_accuracy: 0.7823 - 59ms/epoch - 2ms/step\n",
            "Epoch 409/1000\n",
            "27/27 - 0s - loss: 0.4496 - binary_accuracy: 0.8183 - val_loss: 0.4852 - val_binary_accuracy: 0.7823 - 59ms/epoch - 2ms/step\n",
            "Epoch 410/1000\n",
            "27/27 - 0s - loss: 0.4524 - binary_accuracy: 0.8036 - val_loss: 0.4854 - val_binary_accuracy: 0.7799 - 59ms/epoch - 2ms/step\n",
            "Epoch 411/1000\n",
            "27/27 - 0s - loss: 0.4435 - binary_accuracy: 0.8025 - val_loss: 0.4853 - val_binary_accuracy: 0.7823 - 67ms/epoch - 2ms/step\n",
            "Epoch 412/1000\n",
            "27/27 - 0s - loss: 0.4574 - binary_accuracy: 0.8081 - val_loss: 0.4852 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 413/1000\n",
            "27/27 - 0s - loss: 0.4535 - binary_accuracy: 0.8025 - val_loss: 0.4850 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 414/1000\n",
            "27/27 - 0s - loss: 0.4695 - binary_accuracy: 0.7754 - val_loss: 0.4848 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 415/1000\n",
            "27/27 - 0s - loss: 0.4472 - binary_accuracy: 0.8025 - val_loss: 0.4848 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 416/1000\n",
            "27/27 - 0s - loss: 0.4499 - binary_accuracy: 0.8081 - val_loss: 0.4847 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 417/1000\n",
            "27/27 - 0s - loss: 0.4488 - binary_accuracy: 0.8036 - val_loss: 0.4846 - val_binary_accuracy: 0.7823 - 46ms/epoch - 2ms/step\n",
            "Epoch 418/1000\n",
            "27/27 - 0s - loss: 0.4708 - binary_accuracy: 0.7935 - val_loss: 0.4847 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 419/1000\n",
            "27/27 - 0s - loss: 0.4581 - binary_accuracy: 0.8047 - val_loss: 0.4845 - val_binary_accuracy: 0.7823 - 62ms/epoch - 2ms/step\n",
            "Epoch 420/1000\n",
            "27/27 - 0s - loss: 0.4530 - binary_accuracy: 0.8036 - val_loss: 0.4847 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 421/1000\n",
            "27/27 - 0s - loss: 0.4551 - binary_accuracy: 0.8070 - val_loss: 0.4845 - val_binary_accuracy: 0.7823 - 45ms/epoch - 2ms/step\n",
            "Epoch 422/1000\n",
            "27/27 - 0s - loss: 0.4541 - binary_accuracy: 0.7923 - val_loss: 0.4849 - val_binary_accuracy: 0.7823 - 63ms/epoch - 2ms/step\n",
            "Epoch 423/1000\n",
            "27/27 - 0s - loss: 0.4465 - binary_accuracy: 0.8036 - val_loss: 0.4848 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 424/1000\n",
            "27/27 - 0s - loss: 0.4576 - binary_accuracy: 0.8149 - val_loss: 0.4849 - val_binary_accuracy: 0.7823 - 58ms/epoch - 2ms/step\n",
            "Epoch 425/1000\n",
            "27/27 - 0s - loss: 0.4543 - binary_accuracy: 0.8047 - val_loss: 0.4846 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 426/1000\n",
            "27/27 - 0s - loss: 0.4665 - binary_accuracy: 0.8047 - val_loss: 0.4849 - val_binary_accuracy: 0.7823 - 59ms/epoch - 2ms/step\n",
            "Epoch 427/1000\n",
            "27/27 - 0s - loss: 0.4483 - binary_accuracy: 0.8160 - val_loss: 0.4847 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 428/1000\n",
            "27/27 - 0s - loss: 0.4446 - binary_accuracy: 0.8002 - val_loss: 0.4845 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 429/1000\n",
            "27/27 - 0s - loss: 0.4380 - binary_accuracy: 0.8228 - val_loss: 0.4849 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 430/1000\n",
            "27/27 - 0s - loss: 0.4325 - binary_accuracy: 0.8149 - val_loss: 0.4847 - val_binary_accuracy: 0.7823 - 68ms/epoch - 2ms/step\n",
            "Epoch 431/1000\n",
            "27/27 - 0s - loss: 0.4411 - binary_accuracy: 0.8160 - val_loss: 0.4843 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 432/1000\n",
            "27/27 - 0s - loss: 0.4508 - binary_accuracy: 0.8014 - val_loss: 0.4841 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 433/1000\n",
            "27/27 - 0s - loss: 0.4463 - binary_accuracy: 0.8014 - val_loss: 0.4841 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 434/1000\n",
            "27/27 - 0s - loss: 0.4696 - binary_accuracy: 0.8014 - val_loss: 0.4844 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 435/1000\n",
            "27/27 - 0s - loss: 0.4487 - binary_accuracy: 0.8126 - val_loss: 0.4844 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 436/1000\n",
            "27/27 - 0s - loss: 0.4504 - binary_accuracy: 0.8104 - val_loss: 0.4844 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 437/1000\n",
            "27/27 - 0s - loss: 0.4516 - binary_accuracy: 0.8059 - val_loss: 0.4843 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 438/1000\n",
            "27/27 - 0s - loss: 0.4497 - binary_accuracy: 0.8217 - val_loss: 0.4845 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 439/1000\n",
            "27/27 - 0s - loss: 0.4551 - binary_accuracy: 0.8047 - val_loss: 0.4841 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 440/1000\n",
            "27/27 - 0s - loss: 0.4466 - binary_accuracy: 0.8081 - val_loss: 0.4837 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 441/1000\n",
            "27/27 - 0s - loss: 0.4333 - binary_accuracy: 0.8149 - val_loss: 0.4838 - val_binary_accuracy: 0.7823 - 66ms/epoch - 2ms/step\n",
            "Epoch 442/1000\n",
            "27/27 - 0s - loss: 0.4661 - binary_accuracy: 0.7957 - val_loss: 0.4840 - val_binary_accuracy: 0.7823 - 59ms/epoch - 2ms/step\n",
            "Epoch 443/1000\n",
            "27/27 - 0s - loss: 0.4488 - binary_accuracy: 0.8014 - val_loss: 0.4835 - val_binary_accuracy: 0.7823 - 55ms/epoch - 2ms/step\n",
            "Epoch 444/1000\n",
            "27/27 - 0s - loss: 0.4584 - binary_accuracy: 0.8070 - val_loss: 0.4840 - val_binary_accuracy: 0.7823 - 63ms/epoch - 2ms/step\n",
            "Epoch 445/1000\n",
            "27/27 - 0s - loss: 0.4449 - binary_accuracy: 0.7980 - val_loss: 0.4838 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 446/1000\n",
            "27/27 - 0s - loss: 0.4517 - binary_accuracy: 0.8002 - val_loss: 0.4835 - val_binary_accuracy: 0.7823 - 63ms/epoch - 2ms/step\n",
            "Epoch 447/1000\n",
            "27/27 - 0s - loss: 0.4419 - binary_accuracy: 0.8104 - val_loss: 0.4833 - val_binary_accuracy: 0.7823 - 57ms/epoch - 2ms/step\n",
            "Epoch 448/1000\n",
            "27/27 - 0s - loss: 0.4463 - binary_accuracy: 0.7980 - val_loss: 0.4834 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 449/1000\n",
            "27/27 - 0s - loss: 0.4629 - binary_accuracy: 0.8014 - val_loss: 0.4835 - val_binary_accuracy: 0.7823 - 55ms/epoch - 2ms/step\n",
            "Epoch 450/1000\n",
            "27/27 - 0s - loss: 0.4651 - binary_accuracy: 0.7968 - val_loss: 0.4833 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 451/1000\n",
            "27/27 - 0s - loss: 0.4538 - binary_accuracy: 0.8036 - val_loss: 0.4834 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 452/1000\n",
            "27/27 - 0s - loss: 0.4550 - binary_accuracy: 0.7980 - val_loss: 0.4834 - val_binary_accuracy: 0.7823 - 54ms/epoch - 2ms/step\n",
            "Epoch 453/1000\n",
            "27/27 - 0s - loss: 0.4560 - binary_accuracy: 0.7946 - val_loss: 0.4832 - val_binary_accuracy: 0.7823 - 55ms/epoch - 2ms/step\n",
            "Epoch 454/1000\n",
            "27/27 - 0s - loss: 0.4480 - binary_accuracy: 0.8002 - val_loss: 0.4828 - val_binary_accuracy: 0.7823 - 60ms/epoch - 2ms/step\n",
            "Epoch 455/1000\n",
            "27/27 - 0s - loss: 0.4398 - binary_accuracy: 0.8205 - val_loss: 0.4831 - val_binary_accuracy: 0.7823 - 62ms/epoch - 2ms/step\n",
            "Epoch 456/1000\n",
            "27/27 - 0s - loss: 0.4455 - binary_accuracy: 0.8014 - val_loss: 0.4834 - val_binary_accuracy: 0.7823 - 64ms/epoch - 2ms/step\n",
            "Epoch 457/1000\n",
            "27/27 - 0s - loss: 0.4542 - binary_accuracy: 0.8059 - val_loss: 0.4831 - val_binary_accuracy: 0.7823 - 61ms/epoch - 2ms/step\n",
            "Epoch 458/1000\n",
            "27/27 - 0s - loss: 0.4452 - binary_accuracy: 0.8002 - val_loss: 0.4828 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 459/1000\n",
            "27/27 - 0s - loss: 0.4540 - binary_accuracy: 0.8036 - val_loss: 0.4829 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 460/1000\n",
            "27/27 - 0s - loss: 0.4398 - binary_accuracy: 0.8183 - val_loss: 0.4829 - val_binary_accuracy: 0.7823 - 54ms/epoch - 2ms/step\n",
            "Epoch 461/1000\n",
            "27/27 - 0s - loss: 0.4345 - binary_accuracy: 0.8172 - val_loss: 0.4831 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 462/1000\n",
            "27/27 - 0s - loss: 0.4578 - binary_accuracy: 0.7991 - val_loss: 0.4832 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 463/1000\n",
            "27/27 - 0s - loss: 0.4358 - binary_accuracy: 0.8115 - val_loss: 0.4830 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 464/1000\n",
            "27/27 - 0s - loss: 0.4421 - binary_accuracy: 0.8104 - val_loss: 0.4832 - val_binary_accuracy: 0.7823 - 61ms/epoch - 2ms/step\n",
            "Epoch 465/1000\n",
            "27/27 - 0s - loss: 0.4452 - binary_accuracy: 0.8172 - val_loss: 0.4832 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 466/1000\n",
            "27/27 - 0s - loss: 0.4423 - binary_accuracy: 0.8160 - val_loss: 0.4830 - val_binary_accuracy: 0.7823 - 62ms/epoch - 2ms/step\n",
            "Epoch 467/1000\n",
            "27/27 - 0s - loss: 0.4511 - binary_accuracy: 0.8014 - val_loss: 0.4829 - val_binary_accuracy: 0.7823 - 66ms/epoch - 2ms/step\n",
            "Epoch 468/1000\n",
            "27/27 - 0s - loss: 0.4432 - binary_accuracy: 0.8025 - val_loss: 0.4829 - val_binary_accuracy: 0.7823 - 54ms/epoch - 2ms/step\n",
            "Epoch 469/1000\n",
            "27/27 - 0s - loss: 0.4394 - binary_accuracy: 0.8093 - val_loss: 0.4825 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 470/1000\n",
            "27/27 - 0s - loss: 0.4424 - binary_accuracy: 0.8172 - val_loss: 0.4825 - val_binary_accuracy: 0.7823 - 62ms/epoch - 2ms/step\n",
            "Epoch 471/1000\n",
            "27/27 - 0s - loss: 0.4527 - binary_accuracy: 0.8047 - val_loss: 0.4824 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 472/1000\n",
            "27/27 - 0s - loss: 0.4386 - binary_accuracy: 0.8251 - val_loss: 0.4825 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 473/1000\n",
            "27/27 - 0s - loss: 0.4421 - binary_accuracy: 0.8138 - val_loss: 0.4827 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 474/1000\n",
            "27/27 - 0s - loss: 0.4497 - binary_accuracy: 0.8059 - val_loss: 0.4826 - val_binary_accuracy: 0.7823 - 64ms/epoch - 2ms/step\n",
            "Epoch 475/1000\n",
            "27/27 - 0s - loss: 0.4469 - binary_accuracy: 0.8104 - val_loss: 0.4824 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 476/1000\n",
            "27/27 - 0s - loss: 0.4292 - binary_accuracy: 0.8115 - val_loss: 0.4825 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 477/1000\n",
            "27/27 - 0s - loss: 0.4364 - binary_accuracy: 0.8115 - val_loss: 0.4827 - val_binary_accuracy: 0.7823 - 55ms/epoch - 2ms/step\n",
            "Epoch 478/1000\n",
            "27/27 - 0s - loss: 0.4438 - binary_accuracy: 0.8070 - val_loss: 0.4826 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 479/1000\n",
            "27/27 - 0s - loss: 0.4403 - binary_accuracy: 0.8194 - val_loss: 0.4825 - val_binary_accuracy: 0.7823 - 61ms/epoch - 2ms/step\n",
            "Epoch 480/1000\n",
            "27/27 - 0s - loss: 0.4673 - binary_accuracy: 0.8025 - val_loss: 0.4826 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 481/1000\n",
            "27/27 - 0s - loss: 0.4439 - binary_accuracy: 0.8183 - val_loss: 0.4826 - val_binary_accuracy: 0.7823 - 60ms/epoch - 2ms/step\n",
            "Epoch 482/1000\n",
            "27/27 - 0s - loss: 0.4382 - binary_accuracy: 0.8059 - val_loss: 0.4820 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 483/1000\n",
            "27/27 - 0s - loss: 0.4371 - binary_accuracy: 0.8126 - val_loss: 0.4818 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 484/1000\n",
            "27/27 - 0s - loss: 0.4467 - binary_accuracy: 0.8036 - val_loss: 0.4820 - val_binary_accuracy: 0.7823 - 62ms/epoch - 2ms/step\n",
            "Epoch 485/1000\n",
            "27/27 - 0s - loss: 0.4398 - binary_accuracy: 0.8047 - val_loss: 0.4817 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 486/1000\n",
            "27/27 - 0s - loss: 0.4330 - binary_accuracy: 0.8115 - val_loss: 0.4821 - val_binary_accuracy: 0.7823 - 58ms/epoch - 2ms/step\n",
            "Epoch 487/1000\n",
            "27/27 - 0s - loss: 0.4439 - binary_accuracy: 0.8036 - val_loss: 0.4821 - val_binary_accuracy: 0.7823 - 57ms/epoch - 2ms/step\n",
            "Epoch 488/1000\n",
            "27/27 - 0s - loss: 0.4468 - binary_accuracy: 0.8070 - val_loss: 0.4819 - val_binary_accuracy: 0.7823 - 59ms/epoch - 2ms/step\n",
            "Epoch 489/1000\n",
            "27/27 - 0s - loss: 0.4528 - binary_accuracy: 0.8115 - val_loss: 0.4815 - val_binary_accuracy: 0.7823 - 64ms/epoch - 2ms/step\n",
            "Epoch 490/1000\n",
            "27/27 - 0s - loss: 0.4522 - binary_accuracy: 0.8104 - val_loss: 0.4816 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 491/1000\n",
            "27/27 - 0s - loss: 0.4446 - binary_accuracy: 0.8047 - val_loss: 0.4814 - val_binary_accuracy: 0.7823 - 76ms/epoch - 3ms/step\n",
            "Epoch 492/1000\n",
            "27/27 - 0s - loss: 0.4475 - binary_accuracy: 0.8183 - val_loss: 0.4819 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 493/1000\n",
            "27/27 - 0s - loss: 0.4659 - binary_accuracy: 0.8070 - val_loss: 0.4820 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 494/1000\n",
            "27/27 - 0s - loss: 0.4447 - binary_accuracy: 0.8126 - val_loss: 0.4820 - val_binary_accuracy: 0.7823 - 59ms/epoch - 2ms/step\n",
            "Epoch 495/1000\n",
            "27/27 - 0s - loss: 0.4474 - binary_accuracy: 0.7968 - val_loss: 0.4817 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 496/1000\n",
            "27/27 - 0s - loss: 0.4450 - binary_accuracy: 0.8104 - val_loss: 0.4819 - val_binary_accuracy: 0.7823 - 47ms/epoch - 2ms/step\n",
            "Epoch 497/1000\n",
            "27/27 - 0s - loss: 0.4506 - binary_accuracy: 0.8160 - val_loss: 0.4821 - val_binary_accuracy: 0.7823 - 58ms/epoch - 2ms/step\n",
            "Epoch 498/1000\n",
            "27/27 - 0s - loss: 0.4468 - binary_accuracy: 0.8183 - val_loss: 0.4821 - val_binary_accuracy: 0.7823 - 57ms/epoch - 2ms/step\n",
            "Epoch 499/1000\n",
            "27/27 - 0s - loss: 0.4427 - binary_accuracy: 0.8002 - val_loss: 0.4822 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 500/1000\n",
            "27/27 - 0s - loss: 0.4414 - binary_accuracy: 0.8138 - val_loss: 0.4820 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 501/1000\n",
            "27/27 - 0s - loss: 0.4379 - binary_accuracy: 0.8093 - val_loss: 0.4818 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 502/1000\n",
            "27/27 - 0s - loss: 0.4544 - binary_accuracy: 0.8047 - val_loss: 0.4821 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 503/1000\n",
            "27/27 - 0s - loss: 0.4516 - binary_accuracy: 0.8059 - val_loss: 0.4819 - val_binary_accuracy: 0.7823 - 54ms/epoch - 2ms/step\n",
            "Epoch 504/1000\n",
            "27/27 - 0s - loss: 0.4485 - binary_accuracy: 0.7991 - val_loss: 0.4820 - val_binary_accuracy: 0.7823 - 57ms/epoch - 2ms/step\n",
            "Epoch 505/1000\n",
            "27/27 - 0s - loss: 0.4521 - binary_accuracy: 0.8059 - val_loss: 0.4819 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 506/1000\n",
            "27/27 - 0s - loss: 0.4492 - binary_accuracy: 0.8025 - val_loss: 0.4818 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 507/1000\n",
            "27/27 - 0s - loss: 0.4405 - binary_accuracy: 0.8093 - val_loss: 0.4816 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 508/1000\n",
            "27/27 - 0s - loss: 0.4489 - binary_accuracy: 0.7980 - val_loss: 0.4813 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 509/1000\n",
            "27/27 - 0s - loss: 0.4400 - binary_accuracy: 0.8149 - val_loss: 0.4816 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 510/1000\n",
            "27/27 - 0s - loss: 0.4421 - binary_accuracy: 0.8160 - val_loss: 0.4817 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 511/1000\n",
            "27/27 - 0s - loss: 0.4397 - binary_accuracy: 0.8126 - val_loss: 0.4815 - val_binary_accuracy: 0.7823 - 59ms/epoch - 2ms/step\n",
            "Epoch 512/1000\n",
            "27/27 - 0s - loss: 0.4390 - binary_accuracy: 0.8002 - val_loss: 0.4816 - val_binary_accuracy: 0.7823 - 64ms/epoch - 2ms/step\n",
            "Epoch 513/1000\n",
            "27/27 - 0s - loss: 0.4310 - binary_accuracy: 0.8160 - val_loss: 0.4815 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 514/1000\n",
            "27/27 - 0s - loss: 0.4584 - binary_accuracy: 0.8047 - val_loss: 0.4813 - val_binary_accuracy: 0.7823 - 59ms/epoch - 2ms/step\n",
            "Epoch 515/1000\n",
            "27/27 - 0s - loss: 0.4346 - binary_accuracy: 0.8081 - val_loss: 0.4811 - val_binary_accuracy: 0.7823 - 61ms/epoch - 2ms/step\n",
            "Epoch 516/1000\n",
            "27/27 - 0s - loss: 0.4398 - binary_accuracy: 0.8070 - val_loss: 0.4810 - val_binary_accuracy: 0.7823 - 62ms/epoch - 2ms/step\n",
            "Epoch 517/1000\n",
            "27/27 - 0s - loss: 0.4544 - binary_accuracy: 0.8149 - val_loss: 0.4812 - val_binary_accuracy: 0.7823 - 67ms/epoch - 2ms/step\n",
            "Epoch 518/1000\n",
            "27/27 - 0s - loss: 0.4592 - binary_accuracy: 0.7901 - val_loss: 0.4813 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 519/1000\n",
            "27/27 - 0s - loss: 0.4447 - binary_accuracy: 0.8205 - val_loss: 0.4812 - val_binary_accuracy: 0.7823 - 46ms/epoch - 2ms/step\n",
            "Epoch 520/1000\n",
            "27/27 - 0s - loss: 0.4298 - binary_accuracy: 0.8149 - val_loss: 0.4816 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 521/1000\n",
            "27/27 - 0s - loss: 0.4281 - binary_accuracy: 0.8205 - val_loss: 0.4817 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 522/1000\n",
            "27/27 - 0s - loss: 0.4319 - binary_accuracy: 0.8217 - val_loss: 0.4812 - val_binary_accuracy: 0.7823 - 46ms/epoch - 2ms/step\n",
            "Epoch 523/1000\n",
            "27/27 - 0s - loss: 0.4430 - binary_accuracy: 0.8093 - val_loss: 0.4813 - val_binary_accuracy: 0.7823 - 64ms/epoch - 2ms/step\n",
            "Epoch 524/1000\n",
            "27/27 - 0s - loss: 0.4456 - binary_accuracy: 0.8059 - val_loss: 0.4814 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 525/1000\n",
            "27/27 - 0s - loss: 0.4421 - binary_accuracy: 0.8059 - val_loss: 0.4812 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 526/1000\n",
            "27/27 - 0s - loss: 0.4442 - binary_accuracy: 0.8002 - val_loss: 0.4812 - val_binary_accuracy: 0.7823 - 61ms/epoch - 2ms/step\n",
            "Epoch 527/1000\n",
            "27/27 - 0s - loss: 0.4367 - binary_accuracy: 0.8126 - val_loss: 0.4812 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 528/1000\n",
            "27/27 - 0s - loss: 0.4283 - binary_accuracy: 0.8149 - val_loss: 0.4812 - val_binary_accuracy: 0.7823 - 55ms/epoch - 2ms/step\n",
            "Epoch 529/1000\n",
            "27/27 - 0s - loss: 0.4465 - binary_accuracy: 0.8070 - val_loss: 0.4814 - val_binary_accuracy: 0.7823 - 59ms/epoch - 2ms/step\n",
            "Epoch 530/1000\n",
            "27/27 - 0s - loss: 0.4417 - binary_accuracy: 0.8138 - val_loss: 0.4810 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 531/1000\n",
            "27/27 - 0s - loss: 0.4339 - binary_accuracy: 0.8093 - val_loss: 0.4811 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 532/1000\n",
            "27/27 - 0s - loss: 0.4380 - binary_accuracy: 0.8115 - val_loss: 0.4813 - val_binary_accuracy: 0.7823 - 55ms/epoch - 2ms/step\n",
            "Epoch 533/1000\n",
            "27/27 - 0s - loss: 0.4335 - binary_accuracy: 0.8194 - val_loss: 0.4814 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 534/1000\n",
            "27/27 - 0s - loss: 0.4448 - binary_accuracy: 0.8093 - val_loss: 0.4815 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 535/1000\n",
            "27/27 - 0s - loss: 0.4346 - binary_accuracy: 0.8115 - val_loss: 0.4810 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 536/1000\n",
            "27/27 - 0s - loss: 0.4389 - binary_accuracy: 0.8149 - val_loss: 0.4809 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 537/1000\n",
            "27/27 - 0s - loss: 0.4331 - binary_accuracy: 0.8115 - val_loss: 0.4809 - val_binary_accuracy: 0.7823 - 64ms/epoch - 2ms/step\n",
            "Epoch 538/1000\n",
            "27/27 - 0s - loss: 0.4471 - binary_accuracy: 0.8138 - val_loss: 0.4810 - val_binary_accuracy: 0.7823 - 67ms/epoch - 2ms/step\n",
            "Epoch 539/1000\n",
            "27/27 - 0s - loss: 0.4408 - binary_accuracy: 0.8047 - val_loss: 0.4809 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 540/1000\n",
            "27/27 - 0s - loss: 0.4297 - binary_accuracy: 0.8239 - val_loss: 0.4808 - val_binary_accuracy: 0.7823 - 61ms/epoch - 2ms/step\n",
            "Epoch 541/1000\n",
            "27/27 - 0s - loss: 0.4493 - binary_accuracy: 0.8183 - val_loss: 0.4805 - val_binary_accuracy: 0.7823 - 63ms/epoch - 2ms/step\n",
            "Epoch 542/1000\n",
            "27/27 - 0s - loss: 0.4492 - binary_accuracy: 0.8002 - val_loss: 0.4809 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 543/1000\n",
            "27/27 - 0s - loss: 0.4440 - binary_accuracy: 0.8093 - val_loss: 0.4803 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 544/1000\n",
            "27/27 - 0s - loss: 0.4419 - binary_accuracy: 0.8183 - val_loss: 0.4805 - val_binary_accuracy: 0.7823 - 65ms/epoch - 2ms/step\n",
            "Epoch 545/1000\n",
            "27/27 - 0s - loss: 0.4313 - binary_accuracy: 0.8104 - val_loss: 0.4808 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 546/1000\n",
            "27/27 - 0s - loss: 0.4442 - binary_accuracy: 0.8081 - val_loss: 0.4810 - val_binary_accuracy: 0.7823 - 63ms/epoch - 2ms/step\n",
            "Epoch 547/1000\n",
            "27/27 - 0s - loss: 0.4340 - binary_accuracy: 0.8002 - val_loss: 0.4814 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 548/1000\n",
            "27/27 - 0s - loss: 0.4333 - binary_accuracy: 0.8036 - val_loss: 0.4809 - val_binary_accuracy: 0.7823 - 57ms/epoch - 2ms/step\n",
            "Epoch 549/1000\n",
            "27/27 - 0s - loss: 0.4341 - binary_accuracy: 0.8217 - val_loss: 0.4809 - val_binary_accuracy: 0.7823 - 64ms/epoch - 2ms/step\n",
            "Epoch 550/1000\n",
            "27/27 - 0s - loss: 0.4312 - binary_accuracy: 0.8104 - val_loss: 0.4810 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 551/1000\n",
            "27/27 - 0s - loss: 0.4347 - binary_accuracy: 0.8138 - val_loss: 0.4809 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 552/1000\n",
            "27/27 - 0s - loss: 0.4406 - binary_accuracy: 0.8081 - val_loss: 0.4810 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 553/1000\n",
            "27/27 - 0s - loss: 0.4421 - binary_accuracy: 0.8047 - val_loss: 0.4809 - val_binary_accuracy: 0.7823 - 58ms/epoch - 2ms/step\n",
            "Epoch 554/1000\n",
            "27/27 - 0s - loss: 0.4496 - binary_accuracy: 0.8047 - val_loss: 0.4804 - val_binary_accuracy: 0.7823 - 63ms/epoch - 2ms/step\n",
            "Epoch 555/1000\n",
            "27/27 - 0s - loss: 0.4403 - binary_accuracy: 0.8160 - val_loss: 0.4803 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 556/1000\n",
            "27/27 - 0s - loss: 0.4319 - binary_accuracy: 0.8205 - val_loss: 0.4805 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 557/1000\n",
            "27/27 - 0s - loss: 0.4398 - binary_accuracy: 0.8093 - val_loss: 0.4807 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 558/1000\n",
            "27/27 - 0s - loss: 0.4404 - binary_accuracy: 0.8183 - val_loss: 0.4807 - val_binary_accuracy: 0.7823 - 47ms/epoch - 2ms/step\n",
            "Epoch 559/1000\n",
            "27/27 - 0s - loss: 0.4484 - binary_accuracy: 0.8081 - val_loss: 0.4804 - val_binary_accuracy: 0.7823 - 45ms/epoch - 2ms/step\n",
            "Epoch 560/1000\n",
            "27/27 - 0s - loss: 0.4395 - binary_accuracy: 0.8126 - val_loss: 0.4802 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 561/1000\n",
            "27/27 - 0s - loss: 0.4482 - binary_accuracy: 0.8160 - val_loss: 0.4808 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 562/1000\n",
            "27/27 - 0s - loss: 0.4464 - binary_accuracy: 0.8014 - val_loss: 0.4805 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 563/1000\n",
            "27/27 - 0s - loss: 0.4473 - binary_accuracy: 0.8025 - val_loss: 0.4806 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 564/1000\n",
            "27/27 - 0s - loss: 0.4520 - binary_accuracy: 0.8081 - val_loss: 0.4804 - val_binary_accuracy: 0.7823 - 58ms/epoch - 2ms/step\n",
            "Epoch 565/1000\n",
            "27/27 - 0s - loss: 0.4444 - binary_accuracy: 0.8081 - val_loss: 0.4805 - val_binary_accuracy: 0.7823 - 59ms/epoch - 2ms/step\n",
            "Epoch 566/1000\n",
            "27/27 - 0s - loss: 0.4573 - binary_accuracy: 0.8014 - val_loss: 0.4809 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 567/1000\n",
            "27/27 - 0s - loss: 0.4367 - binary_accuracy: 0.7980 - val_loss: 0.4803 - val_binary_accuracy: 0.7823 - 65ms/epoch - 2ms/step\n",
            "Epoch 568/1000\n",
            "27/27 - 0s - loss: 0.4297 - binary_accuracy: 0.8115 - val_loss: 0.4805 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 569/1000\n",
            "27/27 - 0s - loss: 0.4445 - binary_accuracy: 0.8081 - val_loss: 0.4802 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 570/1000\n",
            "27/27 - 0s - loss: 0.4402 - binary_accuracy: 0.8183 - val_loss: 0.4801 - val_binary_accuracy: 0.7823 - 46ms/epoch - 2ms/step\n",
            "Epoch 571/1000\n",
            "27/27 - 0s - loss: 0.4284 - binary_accuracy: 0.8217 - val_loss: 0.4803 - val_binary_accuracy: 0.7823 - 64ms/epoch - 2ms/step\n",
            "Epoch 572/1000\n",
            "27/27 - 0s - loss: 0.4452 - binary_accuracy: 0.8149 - val_loss: 0.4799 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 573/1000\n",
            "27/27 - 0s - loss: 0.4387 - binary_accuracy: 0.8070 - val_loss: 0.4798 - val_binary_accuracy: 0.7823 - 54ms/epoch - 2ms/step\n",
            "Epoch 574/1000\n",
            "27/27 - 0s - loss: 0.4366 - binary_accuracy: 0.8036 - val_loss: 0.4798 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 575/1000\n",
            "27/27 - 0s - loss: 0.4259 - binary_accuracy: 0.8104 - val_loss: 0.4800 - val_binary_accuracy: 0.7823 - 66ms/epoch - 2ms/step\n",
            "Epoch 576/1000\n",
            "27/27 - 0s - loss: 0.4417 - binary_accuracy: 0.8149 - val_loss: 0.4804 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 577/1000\n",
            "27/27 - 0s - loss: 0.4270 - binary_accuracy: 0.8149 - val_loss: 0.4799 - val_binary_accuracy: 0.7823 - 60ms/epoch - 2ms/step\n",
            "Epoch 578/1000\n",
            "27/27 - 0s - loss: 0.4445 - binary_accuracy: 0.8081 - val_loss: 0.4802 - val_binary_accuracy: 0.7823 - 55ms/epoch - 2ms/step\n",
            "Epoch 579/1000\n",
            "27/27 - 0s - loss: 0.4392 - binary_accuracy: 0.8115 - val_loss: 0.4800 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 580/1000\n",
            "27/27 - 0s - loss: 0.4237 - binary_accuracy: 0.8205 - val_loss: 0.4803 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 581/1000\n",
            "27/27 - 0s - loss: 0.4308 - binary_accuracy: 0.8149 - val_loss: 0.4799 - val_binary_accuracy: 0.7823 - 61ms/epoch - 2ms/step\n",
            "Epoch 582/1000\n",
            "27/27 - 0s - loss: 0.4352 - binary_accuracy: 0.8093 - val_loss: 0.4804 - val_binary_accuracy: 0.7847 - 61ms/epoch - 2ms/step\n",
            "Epoch 583/1000\n",
            "27/27 - 0s - loss: 0.4419 - binary_accuracy: 0.8126 - val_loss: 0.4802 - val_binary_accuracy: 0.7847 - 50ms/epoch - 2ms/step\n",
            "Epoch 584/1000\n",
            "27/27 - 0s - loss: 0.4402 - binary_accuracy: 0.8104 - val_loss: 0.4801 - val_binary_accuracy: 0.7847 - 54ms/epoch - 2ms/step\n",
            "Epoch 585/1000\n",
            "27/27 - 0s - loss: 0.4297 - binary_accuracy: 0.8093 - val_loss: 0.4797 - val_binary_accuracy: 0.7823 - 54ms/epoch - 2ms/step\n",
            "Epoch 586/1000\n",
            "27/27 - 0s - loss: 0.4271 - binary_accuracy: 0.8138 - val_loss: 0.4797 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 587/1000\n",
            "27/27 - 0s - loss: 0.4470 - binary_accuracy: 0.8093 - val_loss: 0.4799 - val_binary_accuracy: 0.7823 - 45ms/epoch - 2ms/step\n",
            "Epoch 588/1000\n",
            "27/27 - 0s - loss: 0.4309 - binary_accuracy: 0.8183 - val_loss: 0.4799 - val_binary_accuracy: 0.7823 - 45ms/epoch - 2ms/step\n",
            "Epoch 589/1000\n",
            "27/27 - 0s - loss: 0.4404 - binary_accuracy: 0.8160 - val_loss: 0.4797 - val_binary_accuracy: 0.7823 - 45ms/epoch - 2ms/step\n",
            "Epoch 590/1000\n",
            "27/27 - 0s - loss: 0.4468 - binary_accuracy: 0.8126 - val_loss: 0.4794 - val_binary_accuracy: 0.7823 - 44ms/epoch - 2ms/step\n",
            "Epoch 591/1000\n",
            "27/27 - 0s - loss: 0.4298 - binary_accuracy: 0.8172 - val_loss: 0.4794 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 592/1000\n",
            "27/27 - 0s - loss: 0.4474 - binary_accuracy: 0.8172 - val_loss: 0.4793 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 593/1000\n",
            "27/27 - 0s - loss: 0.4355 - binary_accuracy: 0.8205 - val_loss: 0.4793 - val_binary_accuracy: 0.7823 - 58ms/epoch - 2ms/step\n",
            "Epoch 594/1000\n",
            "27/27 - 0s - loss: 0.4356 - binary_accuracy: 0.8172 - val_loss: 0.4794 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 595/1000\n",
            "27/27 - 0s - loss: 0.4505 - binary_accuracy: 0.7968 - val_loss: 0.4795 - val_binary_accuracy: 0.7823 - 57ms/epoch - 2ms/step\n",
            "Epoch 596/1000\n",
            "27/27 - 0s - loss: 0.4383 - binary_accuracy: 0.8002 - val_loss: 0.4794 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 597/1000\n",
            "27/27 - 0s - loss: 0.4386 - binary_accuracy: 0.8093 - val_loss: 0.4793 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 598/1000\n",
            "27/27 - 0s - loss: 0.4262 - binary_accuracy: 0.8239 - val_loss: 0.4794 - val_binary_accuracy: 0.7823 - 54ms/epoch - 2ms/step\n",
            "Epoch 599/1000\n",
            "27/27 - 0s - loss: 0.4354 - binary_accuracy: 0.8160 - val_loss: 0.4793 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 600/1000\n",
            "27/27 - 0s - loss: 0.4533 - binary_accuracy: 0.8025 - val_loss: 0.4791 - val_binary_accuracy: 0.7823 - 62ms/epoch - 2ms/step\n",
            "Epoch 601/1000\n",
            "27/27 - 0s - loss: 0.4325 - binary_accuracy: 0.8194 - val_loss: 0.4791 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 602/1000\n",
            "27/27 - 0s - loss: 0.4325 - binary_accuracy: 0.8172 - val_loss: 0.4792 - val_binary_accuracy: 0.7823 - 62ms/epoch - 2ms/step\n",
            "Epoch 603/1000\n",
            "27/27 - 0s - loss: 0.4396 - binary_accuracy: 0.8160 - val_loss: 0.4792 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 604/1000\n",
            "27/27 - 0s - loss: 0.4399 - binary_accuracy: 0.8160 - val_loss: 0.4791 - val_binary_accuracy: 0.7823 - 55ms/epoch - 2ms/step\n",
            "Epoch 605/1000\n",
            "27/27 - 0s - loss: 0.4418 - binary_accuracy: 0.8081 - val_loss: 0.4792 - val_binary_accuracy: 0.7823 - 64ms/epoch - 2ms/step\n",
            "Epoch 606/1000\n",
            "27/27 - 0s - loss: 0.4237 - binary_accuracy: 0.8296 - val_loss: 0.4794 - val_binary_accuracy: 0.7847 - 48ms/epoch - 2ms/step\n",
            "Epoch 607/1000\n",
            "27/27 - 0s - loss: 0.4345 - binary_accuracy: 0.8126 - val_loss: 0.4795 - val_binary_accuracy: 0.7847 - 54ms/epoch - 2ms/step\n",
            "Epoch 608/1000\n",
            "27/27 - 0s - loss: 0.4404 - binary_accuracy: 0.8205 - val_loss: 0.4795 - val_binary_accuracy: 0.7847 - 53ms/epoch - 2ms/step\n",
            "Epoch 609/1000\n",
            "27/27 - 0s - loss: 0.4518 - binary_accuracy: 0.8138 - val_loss: 0.4790 - val_binary_accuracy: 0.7823 - 58ms/epoch - 2ms/step\n",
            "Epoch 610/1000\n",
            "27/27 - 0s - loss: 0.4264 - binary_accuracy: 0.8115 - val_loss: 0.4795 - val_binary_accuracy: 0.7847 - 62ms/epoch - 2ms/step\n",
            "Epoch 611/1000\n",
            "27/27 - 0s - loss: 0.4387 - binary_accuracy: 0.8070 - val_loss: 0.4788 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 612/1000\n",
            "27/27 - 0s - loss: 0.4298 - binary_accuracy: 0.8149 - val_loss: 0.4785 - val_binary_accuracy: 0.7823 - 54ms/epoch - 2ms/step\n",
            "Epoch 613/1000\n",
            "27/27 - 0s - loss: 0.4196 - binary_accuracy: 0.8228 - val_loss: 0.4785 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 614/1000\n",
            "27/27 - 0s - loss: 0.4333 - binary_accuracy: 0.8194 - val_loss: 0.4789 - val_binary_accuracy: 0.7823 - 61ms/epoch - 2ms/step\n",
            "Epoch 615/1000\n",
            "27/27 - 0s - loss: 0.4319 - binary_accuracy: 0.8194 - val_loss: 0.4787 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 616/1000\n",
            "27/27 - 0s - loss: 0.4326 - binary_accuracy: 0.8093 - val_loss: 0.4788 - val_binary_accuracy: 0.7823 - 46ms/epoch - 2ms/step\n",
            "Epoch 617/1000\n",
            "27/27 - 0s - loss: 0.4257 - binary_accuracy: 0.8172 - val_loss: 0.4790 - val_binary_accuracy: 0.7823 - 66ms/epoch - 2ms/step\n",
            "Epoch 618/1000\n",
            "27/27 - 0s - loss: 0.4479 - binary_accuracy: 0.8160 - val_loss: 0.4788 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 619/1000\n",
            "27/27 - 0s - loss: 0.4268 - binary_accuracy: 0.8126 - val_loss: 0.4787 - val_binary_accuracy: 0.7823 - 47ms/epoch - 2ms/step\n",
            "Epoch 620/1000\n",
            "27/27 - 0s - loss: 0.4375 - binary_accuracy: 0.8183 - val_loss: 0.4785 - val_binary_accuracy: 0.7823 - 68ms/epoch - 2ms/step\n",
            "Epoch 621/1000\n",
            "27/27 - 0s - loss: 0.4485 - binary_accuracy: 0.8115 - val_loss: 0.4784 - val_binary_accuracy: 0.7823 - 54ms/epoch - 2ms/step\n",
            "Epoch 622/1000\n",
            "27/27 - 0s - loss: 0.4374 - binary_accuracy: 0.8104 - val_loss: 0.4784 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 623/1000\n",
            "27/27 - 0s - loss: 0.4332 - binary_accuracy: 0.8194 - val_loss: 0.4781 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 624/1000\n",
            "27/27 - 0s - loss: 0.4364 - binary_accuracy: 0.8115 - val_loss: 0.4782 - val_binary_accuracy: 0.7823 - 67ms/epoch - 2ms/step\n",
            "Epoch 625/1000\n",
            "27/27 - 0s - loss: 0.4358 - binary_accuracy: 0.8059 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 626/1000\n",
            "27/27 - 0s - loss: 0.4379 - binary_accuracy: 0.8149 - val_loss: 0.4784 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 627/1000\n",
            "27/27 - 0s - loss: 0.4302 - binary_accuracy: 0.8239 - val_loss: 0.4788 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 628/1000\n",
            "27/27 - 0s - loss: 0.4272 - binary_accuracy: 0.8217 - val_loss: 0.4793 - val_binary_accuracy: 0.7847 - 49ms/epoch - 2ms/step\n",
            "Epoch 629/1000\n",
            "27/27 - 0s - loss: 0.4299 - binary_accuracy: 0.8081 - val_loss: 0.4789 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 630/1000\n",
            "27/27 - 0s - loss: 0.4328 - binary_accuracy: 0.8115 - val_loss: 0.4787 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 631/1000\n",
            "27/27 - 0s - loss: 0.4446 - binary_accuracy: 0.8149 - val_loss: 0.4785 - val_binary_accuracy: 0.7823 - 61ms/epoch - 2ms/step\n",
            "Epoch 632/1000\n",
            "27/27 - 0s - loss: 0.4396 - binary_accuracy: 0.8160 - val_loss: 0.4789 - val_binary_accuracy: 0.7823 - 61ms/epoch - 2ms/step\n",
            "Epoch 633/1000\n",
            "27/27 - 0s - loss: 0.4240 - binary_accuracy: 0.8239 - val_loss: 0.4789 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 634/1000\n",
            "27/27 - 0s - loss: 0.4443 - binary_accuracy: 0.8002 - val_loss: 0.4787 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 635/1000\n",
            "27/27 - 0s - loss: 0.4345 - binary_accuracy: 0.8149 - val_loss: 0.4788 - val_binary_accuracy: 0.7823 - 62ms/epoch - 2ms/step\n",
            "Epoch 636/1000\n",
            "27/27 - 0s - loss: 0.4367 - binary_accuracy: 0.8126 - val_loss: 0.4785 - val_binary_accuracy: 0.7823 - 61ms/epoch - 2ms/step\n",
            "Epoch 637/1000\n",
            "27/27 - 0s - loss: 0.4364 - binary_accuracy: 0.8251 - val_loss: 0.4778 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 638/1000\n",
            "27/27 - 0s - loss: 0.4319 - binary_accuracy: 0.8149 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 639/1000\n",
            "27/27 - 0s - loss: 0.4478 - binary_accuracy: 0.8149 - val_loss: 0.4784 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 640/1000\n",
            "27/27 - 0s - loss: 0.4385 - binary_accuracy: 0.8160 - val_loss: 0.4786 - val_binary_accuracy: 0.7847 - 70ms/epoch - 3ms/step\n",
            "Epoch 641/1000\n",
            "27/27 - 0s - loss: 0.4348 - binary_accuracy: 0.8081 - val_loss: 0.4787 - val_binary_accuracy: 0.7823 - 67ms/epoch - 2ms/step\n",
            "Epoch 642/1000\n",
            "27/27 - 0s - loss: 0.4303 - binary_accuracy: 0.8070 - val_loss: 0.4787 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 643/1000\n",
            "27/27 - 0s - loss: 0.4327 - binary_accuracy: 0.8025 - val_loss: 0.4782 - val_binary_accuracy: 0.7823 - 61ms/epoch - 2ms/step\n",
            "Epoch 644/1000\n",
            "27/27 - 0s - loss: 0.4365 - binary_accuracy: 0.8149 - val_loss: 0.4791 - val_binary_accuracy: 0.7847 - 56ms/epoch - 2ms/step\n",
            "Epoch 645/1000\n",
            "27/27 - 0s - loss: 0.4283 - binary_accuracy: 0.8217 - val_loss: 0.4791 - val_binary_accuracy: 0.7847 - 50ms/epoch - 2ms/step\n",
            "Epoch 646/1000\n",
            "27/27 - 0s - loss: 0.4428 - binary_accuracy: 0.8160 - val_loss: 0.4792 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 647/1000\n",
            "27/27 - 0s - loss: 0.4415 - binary_accuracy: 0.8059 - val_loss: 0.4786 - val_binary_accuracy: 0.7847 - 57ms/epoch - 2ms/step\n",
            "Epoch 648/1000\n",
            "27/27 - 0s - loss: 0.4468 - binary_accuracy: 0.8059 - val_loss: 0.4788 - val_binary_accuracy: 0.7847 - 47ms/epoch - 2ms/step\n",
            "Epoch 649/1000\n",
            "27/27 - 0s - loss: 0.4420 - binary_accuracy: 0.8149 - val_loss: 0.4787 - val_binary_accuracy: 0.7847 - 50ms/epoch - 2ms/step\n",
            "Epoch 650/1000\n",
            "27/27 - 0s - loss: 0.4337 - binary_accuracy: 0.8138 - val_loss: 0.4787 - val_binary_accuracy: 0.7871 - 51ms/epoch - 2ms/step\n",
            "Epoch 651/1000\n",
            "27/27 - 0s - loss: 0.4454 - binary_accuracy: 0.8149 - val_loss: 0.4785 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 652/1000\n",
            "27/27 - 0s - loss: 0.4322 - binary_accuracy: 0.8149 - val_loss: 0.4787 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 653/1000\n",
            "27/27 - 0s - loss: 0.4384 - binary_accuracy: 0.8194 - val_loss: 0.4784 - val_binary_accuracy: 0.7823 - 60ms/epoch - 2ms/step\n",
            "Epoch 654/1000\n",
            "27/27 - 0s - loss: 0.4469 - binary_accuracy: 0.8149 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 655/1000\n",
            "27/27 - 0s - loss: 0.4351 - binary_accuracy: 0.8194 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 656/1000\n",
            "27/27 - 0s - loss: 0.4239 - binary_accuracy: 0.8296 - val_loss: 0.4785 - val_binary_accuracy: 0.7823 - 54ms/epoch - 2ms/step\n",
            "Epoch 657/1000\n",
            "27/27 - 0s - loss: 0.4318 - binary_accuracy: 0.8081 - val_loss: 0.4782 - val_binary_accuracy: 0.7823 - 59ms/epoch - 2ms/step\n",
            "Epoch 658/1000\n",
            "27/27 - 0s - loss: 0.4315 - binary_accuracy: 0.8251 - val_loss: 0.4785 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 659/1000\n",
            "27/27 - 0s - loss: 0.4292 - binary_accuracy: 0.8149 - val_loss: 0.4784 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 660/1000\n",
            "27/27 - 0s - loss: 0.4366 - binary_accuracy: 0.8160 - val_loss: 0.4785 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 661/1000\n",
            "27/27 - 0s - loss: 0.4328 - binary_accuracy: 0.8138 - val_loss: 0.4782 - val_binary_accuracy: 0.7847 - 48ms/epoch - 2ms/step\n",
            "Epoch 662/1000\n",
            "27/27 - 0s - loss: 0.4408 - binary_accuracy: 0.8059 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 54ms/epoch - 2ms/step\n",
            "Epoch 663/1000\n",
            "27/27 - 0s - loss: 0.4357 - binary_accuracy: 0.8217 - val_loss: 0.4784 - val_binary_accuracy: 0.7823 - 57ms/epoch - 2ms/step\n",
            "Epoch 664/1000\n",
            "27/27 - 0s - loss: 0.4297 - binary_accuracy: 0.8239 - val_loss: 0.4789 - val_binary_accuracy: 0.7823 - 47ms/epoch - 2ms/step\n",
            "Epoch 665/1000\n",
            "27/27 - 0s - loss: 0.4273 - binary_accuracy: 0.8239 - val_loss: 0.4788 - val_binary_accuracy: 0.7823 - 59ms/epoch - 2ms/step\n",
            "Epoch 666/1000\n",
            "27/27 - 0s - loss: 0.4353 - binary_accuracy: 0.8059 - val_loss: 0.4783 - val_binary_accuracy: 0.7799 - 50ms/epoch - 2ms/step\n",
            "Epoch 667/1000\n",
            "27/27 - 0s - loss: 0.4308 - binary_accuracy: 0.8059 - val_loss: 0.4788 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 668/1000\n",
            "27/27 - 0s - loss: 0.4337 - binary_accuracy: 0.8194 - val_loss: 0.4787 - val_binary_accuracy: 0.7823 - 60ms/epoch - 2ms/step\n",
            "Epoch 669/1000\n",
            "27/27 - 0s - loss: 0.4407 - binary_accuracy: 0.8059 - val_loss: 0.4789 - val_binary_accuracy: 0.7823 - 62ms/epoch - 2ms/step\n",
            "Epoch 670/1000\n",
            "27/27 - 0s - loss: 0.4305 - binary_accuracy: 0.8307 - val_loss: 0.4788 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 671/1000\n",
            "27/27 - 0s - loss: 0.4284 - binary_accuracy: 0.8149 - val_loss: 0.4787 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 672/1000\n",
            "27/27 - 0s - loss: 0.4325 - binary_accuracy: 0.8183 - val_loss: 0.4786 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 673/1000\n",
            "27/27 - 0s - loss: 0.4239 - binary_accuracy: 0.8081 - val_loss: 0.4786 - val_binary_accuracy: 0.7823 - 59ms/epoch - 2ms/step\n",
            "Epoch 674/1000\n",
            "27/27 - 0s - loss: 0.4412 - binary_accuracy: 0.8149 - val_loss: 0.4784 - val_binary_accuracy: 0.7823 - 62ms/epoch - 2ms/step\n",
            "Epoch 675/1000\n",
            "27/27 - 0s - loss: 0.4362 - binary_accuracy: 0.8059 - val_loss: 0.4786 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 676/1000\n",
            "27/27 - 0s - loss: 0.4390 - binary_accuracy: 0.8160 - val_loss: 0.4782 - val_binary_accuracy: 0.7823 - 47ms/epoch - 2ms/step\n",
            "Epoch 677/1000\n",
            "27/27 - 0s - loss: 0.4412 - binary_accuracy: 0.8160 - val_loss: 0.4778 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 678/1000\n",
            "27/27 - 0s - loss: 0.4441 - binary_accuracy: 0.8093 - val_loss: 0.4780 - val_binary_accuracy: 0.7799 - 54ms/epoch - 2ms/step\n",
            "Epoch 679/1000\n",
            "27/27 - 0s - loss: 0.4339 - binary_accuracy: 0.8194 - val_loss: 0.4780 - val_binary_accuracy: 0.7799 - 53ms/epoch - 2ms/step\n",
            "Epoch 680/1000\n",
            "27/27 - 0s - loss: 0.4264 - binary_accuracy: 0.8115 - val_loss: 0.4781 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 681/1000\n",
            "27/27 - 0s - loss: 0.4300 - binary_accuracy: 0.8070 - val_loss: 0.4784 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 682/1000\n",
            "27/27 - 0s - loss: 0.4334 - binary_accuracy: 0.8126 - val_loss: 0.4778 - val_binary_accuracy: 0.7799 - 63ms/epoch - 2ms/step\n",
            "Epoch 683/1000\n",
            "27/27 - 0s - loss: 0.4238 - binary_accuracy: 0.8002 - val_loss: 0.4781 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 684/1000\n",
            "27/27 - 0s - loss: 0.4508 - binary_accuracy: 0.8093 - val_loss: 0.4781 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 685/1000\n",
            "27/27 - 0s - loss: 0.4399 - binary_accuracy: 0.8138 - val_loss: 0.4782 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 686/1000\n",
            "27/27 - 0s - loss: 0.4315 - binary_accuracy: 0.8036 - val_loss: 0.4781 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 687/1000\n",
            "27/27 - 0s - loss: 0.4410 - binary_accuracy: 0.8070 - val_loss: 0.4780 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 688/1000\n",
            "27/27 - 0s - loss: 0.4451 - binary_accuracy: 0.8093 - val_loss: 0.4781 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 689/1000\n",
            "27/27 - 0s - loss: 0.4250 - binary_accuracy: 0.8194 - val_loss: 0.4780 - val_binary_accuracy: 0.7823 - 58ms/epoch - 2ms/step\n",
            "Epoch 690/1000\n",
            "27/27 - 0s - loss: 0.4282 - binary_accuracy: 0.8059 - val_loss: 0.4781 - val_binary_accuracy: 0.7823 - 55ms/epoch - 2ms/step\n",
            "Epoch 691/1000\n",
            "27/27 - 0s - loss: 0.4438 - binary_accuracy: 0.8059 - val_loss: 0.4776 - val_binary_accuracy: 0.7799 - 55ms/epoch - 2ms/step\n",
            "Epoch 692/1000\n",
            "27/27 - 0s - loss: 0.4345 - binary_accuracy: 0.8138 - val_loss: 0.4776 - val_binary_accuracy: 0.7799 - 49ms/epoch - 2ms/step\n",
            "Epoch 693/1000\n",
            "27/27 - 0s - loss: 0.4421 - binary_accuracy: 0.8194 - val_loss: 0.4785 - val_binary_accuracy: 0.7799 - 61ms/epoch - 2ms/step\n",
            "Epoch 694/1000\n",
            "27/27 - 0s - loss: 0.4427 - binary_accuracy: 0.8160 - val_loss: 0.4786 - val_binary_accuracy: 0.7799 - 48ms/epoch - 2ms/step\n",
            "Epoch 695/1000\n",
            "27/27 - 0s - loss: 0.4266 - binary_accuracy: 0.8228 - val_loss: 0.4785 - val_binary_accuracy: 0.7823 - 70ms/epoch - 3ms/step\n",
            "Epoch 696/1000\n",
            "27/27 - 0s - loss: 0.4270 - binary_accuracy: 0.8284 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 697/1000\n",
            "27/27 - 0s - loss: 0.4353 - binary_accuracy: 0.8239 - val_loss: 0.4786 - val_binary_accuracy: 0.7775 - 65ms/epoch - 2ms/step\n",
            "Epoch 698/1000\n",
            "27/27 - 0s - loss: 0.4402 - binary_accuracy: 0.8104 - val_loss: 0.4789 - val_binary_accuracy: 0.7775 - 68ms/epoch - 2ms/step\n",
            "Epoch 699/1000\n",
            "27/27 - 0s - loss: 0.4403 - binary_accuracy: 0.8047 - val_loss: 0.4788 - val_binary_accuracy: 0.7799 - 64ms/epoch - 2ms/step\n",
            "Epoch 700/1000\n",
            "27/27 - 0s - loss: 0.4283 - binary_accuracy: 0.8217 - val_loss: 0.4784 - val_binary_accuracy: 0.7799 - 63ms/epoch - 2ms/step\n",
            "Epoch 701/1000\n",
            "27/27 - 0s - loss: 0.4222 - binary_accuracy: 0.8262 - val_loss: 0.4783 - val_binary_accuracy: 0.7799 - 49ms/epoch - 2ms/step\n",
            "Epoch 702/1000\n",
            "27/27 - 0s - loss: 0.4357 - binary_accuracy: 0.8070 - val_loss: 0.4781 - val_binary_accuracy: 0.7775 - 54ms/epoch - 2ms/step\n",
            "Epoch 703/1000\n",
            "27/27 - 0s - loss: 0.4390 - binary_accuracy: 0.8126 - val_loss: 0.4783 - val_binary_accuracy: 0.7799 - 54ms/epoch - 2ms/step\n",
            "Epoch 704/1000\n",
            "27/27 - 0s - loss: 0.4412 - binary_accuracy: 0.8183 - val_loss: 0.4779 - val_binary_accuracy: 0.7775 - 61ms/epoch - 2ms/step\n",
            "Epoch 705/1000\n",
            "27/27 - 0s - loss: 0.4430 - binary_accuracy: 0.8059 - val_loss: 0.4779 - val_binary_accuracy: 0.7775 - 51ms/epoch - 2ms/step\n",
            "Epoch 706/1000\n",
            "27/27 - 0s - loss: 0.4324 - binary_accuracy: 0.8138 - val_loss: 0.4781 - val_binary_accuracy: 0.7775 - 55ms/epoch - 2ms/step\n",
            "Epoch 707/1000\n",
            "27/27 - 0s - loss: 0.4196 - binary_accuracy: 0.8273 - val_loss: 0.4780 - val_binary_accuracy: 0.7775 - 58ms/epoch - 2ms/step\n",
            "Epoch 708/1000\n",
            "27/27 - 0s - loss: 0.4266 - binary_accuracy: 0.8307 - val_loss: 0.4779 - val_binary_accuracy: 0.7775 - 59ms/epoch - 2ms/step\n",
            "Epoch 709/1000\n",
            "27/27 - 0s - loss: 0.4436 - binary_accuracy: 0.8138 - val_loss: 0.4784 - val_binary_accuracy: 0.7775 - 56ms/epoch - 2ms/step\n",
            "Epoch 710/1000\n",
            "27/27 - 0s - loss: 0.4461 - binary_accuracy: 0.8172 - val_loss: 0.4778 - val_binary_accuracy: 0.7775 - 50ms/epoch - 2ms/step\n",
            "Epoch 711/1000\n",
            "27/27 - 0s - loss: 0.4417 - binary_accuracy: 0.8228 - val_loss: 0.4782 - val_binary_accuracy: 0.7799 - 59ms/epoch - 2ms/step\n",
            "Epoch 712/1000\n",
            "27/27 - 0s - loss: 0.4344 - binary_accuracy: 0.8183 - val_loss: 0.4785 - val_binary_accuracy: 0.7823 - 61ms/epoch - 2ms/step\n",
            "Epoch 713/1000\n",
            "27/27 - 0s - loss: 0.4331 - binary_accuracy: 0.8047 - val_loss: 0.4792 - val_binary_accuracy: 0.7823 - 59ms/epoch - 2ms/step\n",
            "Epoch 714/1000\n",
            "27/27 - 0s - loss: 0.4352 - binary_accuracy: 0.8160 - val_loss: 0.4788 - val_binary_accuracy: 0.7799 - 49ms/epoch - 2ms/step\n",
            "Epoch 715/1000\n",
            "27/27 - 0s - loss: 0.4359 - binary_accuracy: 0.8194 - val_loss: 0.4791 - val_binary_accuracy: 0.7799 - 52ms/epoch - 2ms/step\n",
            "Epoch 716/1000\n",
            "27/27 - 0s - loss: 0.4395 - binary_accuracy: 0.8138 - val_loss: 0.4782 - val_binary_accuracy: 0.7775 - 47ms/epoch - 2ms/step\n",
            "Epoch 717/1000\n",
            "27/27 - 0s - loss: 0.4284 - binary_accuracy: 0.8194 - val_loss: 0.4787 - val_binary_accuracy: 0.7775 - 49ms/epoch - 2ms/step\n",
            "Epoch 718/1000\n",
            "27/27 - 0s - loss: 0.4352 - binary_accuracy: 0.8160 - val_loss: 0.4784 - val_binary_accuracy: 0.7799 - 49ms/epoch - 2ms/step\n",
            "Epoch 719/1000\n",
            "27/27 - 0s - loss: 0.4341 - binary_accuracy: 0.8081 - val_loss: 0.4785 - val_binary_accuracy: 0.7799 - 46ms/epoch - 2ms/step\n",
            "Epoch 720/1000\n",
            "27/27 - 0s - loss: 0.4404 - binary_accuracy: 0.8126 - val_loss: 0.4787 - val_binary_accuracy: 0.7823 - 60ms/epoch - 2ms/step\n",
            "Epoch 721/1000\n",
            "27/27 - 0s - loss: 0.4353 - binary_accuracy: 0.8262 - val_loss: 0.4787 - val_binary_accuracy: 0.7799 - 69ms/epoch - 2ms/step\n",
            "Epoch 722/1000\n",
            "27/27 - 0s - loss: 0.4390 - binary_accuracy: 0.8194 - val_loss: 0.4789 - val_binary_accuracy: 0.7823 - 62ms/epoch - 2ms/step\n",
            "Epoch 723/1000\n",
            "27/27 - 0s - loss: 0.4448 - binary_accuracy: 0.8104 - val_loss: 0.4784 - val_binary_accuracy: 0.7799 - 55ms/epoch - 2ms/step\n",
            "Epoch 724/1000\n",
            "27/27 - 0s - loss: 0.4320 - binary_accuracy: 0.8138 - val_loss: 0.4786 - val_binary_accuracy: 0.7775 - 61ms/epoch - 2ms/step\n",
            "Epoch 725/1000\n",
            "27/27 - 0s - loss: 0.4349 - binary_accuracy: 0.8081 - val_loss: 0.4778 - val_binary_accuracy: 0.7775 - 56ms/epoch - 2ms/step\n",
            "Epoch 726/1000\n",
            "27/27 - 0s - loss: 0.4463 - binary_accuracy: 0.8149 - val_loss: 0.4777 - val_binary_accuracy: 0.7775 - 59ms/epoch - 2ms/step\n",
            "Epoch 727/1000\n",
            "27/27 - 0s - loss: 0.4362 - binary_accuracy: 0.8149 - val_loss: 0.4777 - val_binary_accuracy: 0.7775 - 48ms/epoch - 2ms/step\n",
            "Epoch 728/1000\n",
            "27/27 - 0s - loss: 0.4373 - binary_accuracy: 0.8081 - val_loss: 0.4779 - val_binary_accuracy: 0.7799 - 65ms/epoch - 2ms/step\n",
            "Epoch 729/1000\n",
            "27/27 - 0s - loss: 0.4369 - binary_accuracy: 0.8126 - val_loss: 0.4779 - val_binary_accuracy: 0.7799 - 55ms/epoch - 2ms/step\n",
            "Epoch 730/1000\n",
            "27/27 - 0s - loss: 0.4300 - binary_accuracy: 0.8228 - val_loss: 0.4781 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 731/1000\n",
            "27/27 - 0s - loss: 0.4237 - binary_accuracy: 0.8284 - val_loss: 0.4780 - val_binary_accuracy: 0.7799 - 50ms/epoch - 2ms/step\n",
            "Epoch 732/1000\n",
            "27/27 - 0s - loss: 0.4300 - binary_accuracy: 0.8239 - val_loss: 0.4781 - val_binary_accuracy: 0.7799 - 54ms/epoch - 2ms/step\n",
            "Epoch 733/1000\n",
            "27/27 - 0s - loss: 0.4369 - binary_accuracy: 0.8251 - val_loss: 0.4781 - val_binary_accuracy: 0.7799 - 48ms/epoch - 2ms/step\n",
            "Epoch 734/1000\n",
            "27/27 - 0s - loss: 0.4284 - binary_accuracy: 0.8126 - val_loss: 0.4781 - val_binary_accuracy: 0.7799 - 49ms/epoch - 2ms/step\n",
            "Epoch 735/1000\n",
            "27/27 - 0s - loss: 0.4266 - binary_accuracy: 0.8228 - val_loss: 0.4780 - val_binary_accuracy: 0.7799 - 45ms/epoch - 2ms/step\n",
            "Epoch 736/1000\n",
            "27/27 - 0s - loss: 0.4379 - binary_accuracy: 0.8160 - val_loss: 0.4777 - val_binary_accuracy: 0.7799 - 62ms/epoch - 2ms/step\n",
            "Epoch 737/1000\n",
            "27/27 - 0s - loss: 0.4228 - binary_accuracy: 0.8183 - val_loss: 0.4778 - val_binary_accuracy: 0.7799 - 47ms/epoch - 2ms/step\n",
            "Epoch 738/1000\n",
            "27/27 - 0s - loss: 0.4286 - binary_accuracy: 0.8217 - val_loss: 0.4781 - val_binary_accuracy: 0.7823 - 62ms/epoch - 2ms/step\n",
            "Epoch 739/1000\n",
            "27/27 - 0s - loss: 0.4257 - binary_accuracy: 0.8183 - val_loss: 0.4781 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 740/1000\n",
            "27/27 - 0s - loss: 0.4253 - binary_accuracy: 0.8172 - val_loss: 0.4783 - val_binary_accuracy: 0.7799 - 49ms/epoch - 2ms/step\n",
            "Epoch 741/1000\n",
            "27/27 - 0s - loss: 0.4415 - binary_accuracy: 0.8104 - val_loss: 0.4780 - val_binary_accuracy: 0.7823 - 59ms/epoch - 2ms/step\n",
            "Epoch 742/1000\n",
            "27/27 - 0s - loss: 0.4382 - binary_accuracy: 0.8149 - val_loss: 0.4778 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 743/1000\n",
            "27/27 - 0s - loss: 0.4347 - binary_accuracy: 0.8126 - val_loss: 0.4778 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 744/1000\n",
            "27/27 - 0s - loss: 0.4227 - binary_accuracy: 0.8138 - val_loss: 0.4779 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 745/1000\n",
            "27/27 - 0s - loss: 0.4402 - binary_accuracy: 0.8160 - val_loss: 0.4775 - val_binary_accuracy: 0.7775 - 64ms/epoch - 2ms/step\n",
            "Epoch 746/1000\n",
            "27/27 - 0s - loss: 0.4407 - binary_accuracy: 0.8093 - val_loss: 0.4780 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 747/1000\n",
            "27/27 - 0s - loss: 0.4264 - binary_accuracy: 0.8160 - val_loss: 0.4782 - val_binary_accuracy: 0.7823 - 64ms/epoch - 2ms/step\n",
            "Epoch 748/1000\n",
            "27/27 - 0s - loss: 0.4287 - binary_accuracy: 0.8183 - val_loss: 0.4776 - val_binary_accuracy: 0.7799 - 51ms/epoch - 2ms/step\n",
            "Epoch 749/1000\n",
            "27/27 - 0s - loss: 0.4227 - binary_accuracy: 0.8183 - val_loss: 0.4780 - val_binary_accuracy: 0.7823 - 75ms/epoch - 3ms/step\n",
            "Epoch 750/1000\n",
            "27/27 - 0s - loss: 0.4387 - binary_accuracy: 0.8160 - val_loss: 0.4780 - val_binary_accuracy: 0.7823 - 58ms/epoch - 2ms/step\n",
            "Epoch 751/1000\n",
            "27/27 - 0s - loss: 0.4251 - binary_accuracy: 0.8093 - val_loss: 0.4781 - val_binary_accuracy: 0.7823 - 54ms/epoch - 2ms/step\n",
            "Epoch 752/1000\n",
            "27/27 - 0s - loss: 0.4317 - binary_accuracy: 0.8104 - val_loss: 0.4782 - val_binary_accuracy: 0.7823 - 67ms/epoch - 2ms/step\n",
            "Epoch 753/1000\n",
            "27/27 - 0s - loss: 0.4325 - binary_accuracy: 0.8126 - val_loss: 0.4778 - val_binary_accuracy: 0.7799 - 54ms/epoch - 2ms/step\n",
            "Epoch 754/1000\n",
            "27/27 - 0s - loss: 0.4256 - binary_accuracy: 0.8183 - val_loss: 0.4779 - val_binary_accuracy: 0.7799 - 56ms/epoch - 2ms/step\n",
            "Epoch 755/1000\n",
            "27/27 - 0s - loss: 0.4400 - binary_accuracy: 0.8126 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 64ms/epoch - 2ms/step\n",
            "Epoch 756/1000\n",
            "27/27 - 0s - loss: 0.4207 - binary_accuracy: 0.8183 - val_loss: 0.4779 - val_binary_accuracy: 0.7799 - 53ms/epoch - 2ms/step\n",
            "Epoch 757/1000\n",
            "27/27 - 0s - loss: 0.4262 - binary_accuracy: 0.8059 - val_loss: 0.4781 - val_binary_accuracy: 0.7823 - 62ms/epoch - 2ms/step\n",
            "Epoch 758/1000\n",
            "27/27 - 0s - loss: 0.4382 - binary_accuracy: 0.8149 - val_loss: 0.4780 - val_binary_accuracy: 0.7799 - 64ms/epoch - 2ms/step\n",
            "Epoch 759/1000\n",
            "27/27 - 0s - loss: 0.4382 - binary_accuracy: 0.8160 - val_loss: 0.4782 - val_binary_accuracy: 0.7823 - 65ms/epoch - 2ms/step\n",
            "Epoch 760/1000\n",
            "27/27 - 0s - loss: 0.4371 - binary_accuracy: 0.8115 - val_loss: 0.4779 - val_binary_accuracy: 0.7799 - 53ms/epoch - 2ms/step\n",
            "Epoch 761/1000\n",
            "27/27 - 0s - loss: 0.4269 - binary_accuracy: 0.8273 - val_loss: 0.4780 - val_binary_accuracy: 0.7799 - 56ms/epoch - 2ms/step\n",
            "Epoch 762/1000\n",
            "27/27 - 0s - loss: 0.4230 - binary_accuracy: 0.8205 - val_loss: 0.4784 - val_binary_accuracy: 0.7847 - 50ms/epoch - 2ms/step\n",
            "Epoch 763/1000\n",
            "27/27 - 0s - loss: 0.4274 - binary_accuracy: 0.8138 - val_loss: 0.4789 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 764/1000\n",
            "27/27 - 0s - loss: 0.4221 - binary_accuracy: 0.8205 - val_loss: 0.4782 - val_binary_accuracy: 0.7799 - 58ms/epoch - 2ms/step\n",
            "Epoch 765/1000\n",
            "27/27 - 0s - loss: 0.4301 - binary_accuracy: 0.8093 - val_loss: 0.4779 - val_binary_accuracy: 0.7799 - 65ms/epoch - 2ms/step\n",
            "Epoch 766/1000\n",
            "27/27 - 0s - loss: 0.4264 - binary_accuracy: 0.8126 - val_loss: 0.4782 - val_binary_accuracy: 0.7799 - 49ms/epoch - 2ms/step\n",
            "Epoch 767/1000\n",
            "27/27 - 0s - loss: 0.4304 - binary_accuracy: 0.8160 - val_loss: 0.4776 - val_binary_accuracy: 0.7799 - 48ms/epoch - 2ms/step\n",
            "Epoch 768/1000\n",
            "27/27 - 0s - loss: 0.4351 - binary_accuracy: 0.8115 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 57ms/epoch - 2ms/step\n",
            "Epoch 769/1000\n",
            "27/27 - 0s - loss: 0.4333 - binary_accuracy: 0.8228 - val_loss: 0.4781 - val_binary_accuracy: 0.7799 - 59ms/epoch - 2ms/step\n",
            "Epoch 770/1000\n",
            "27/27 - 0s - loss: 0.4176 - binary_accuracy: 0.8194 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 60ms/epoch - 2ms/step\n",
            "Epoch 771/1000\n",
            "27/27 - 0s - loss: 0.4271 - binary_accuracy: 0.8160 - val_loss: 0.4781 - val_binary_accuracy: 0.7799 - 49ms/epoch - 2ms/step\n",
            "Epoch 772/1000\n",
            "27/27 - 0s - loss: 0.4289 - binary_accuracy: 0.8160 - val_loss: 0.4778 - val_binary_accuracy: 0.7799 - 58ms/epoch - 2ms/step\n",
            "Epoch 773/1000\n",
            "27/27 - 0s - loss: 0.4295 - binary_accuracy: 0.8126 - val_loss: 0.4777 - val_binary_accuracy: 0.7799 - 49ms/epoch - 2ms/step\n",
            "Epoch 774/1000\n",
            "27/27 - 0s - loss: 0.4304 - binary_accuracy: 0.8284 - val_loss: 0.4782 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 775/1000\n",
            "27/27 - 0s - loss: 0.4194 - binary_accuracy: 0.8126 - val_loss: 0.4784 - val_binary_accuracy: 0.7823 - 59ms/epoch - 2ms/step\n",
            "Epoch 776/1000\n",
            "27/27 - 0s - loss: 0.4380 - binary_accuracy: 0.8126 - val_loss: 0.4784 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 777/1000\n",
            "27/27 - 0s - loss: 0.4261 - binary_accuracy: 0.8217 - val_loss: 0.4780 - val_binary_accuracy: 0.7799 - 51ms/epoch - 2ms/step\n",
            "Epoch 778/1000\n",
            "27/27 - 0s - loss: 0.4144 - binary_accuracy: 0.8352 - val_loss: 0.4779 - val_binary_accuracy: 0.7799 - 63ms/epoch - 2ms/step\n",
            "Epoch 779/1000\n",
            "27/27 - 0s - loss: 0.4351 - binary_accuracy: 0.8047 - val_loss: 0.4782 - val_binary_accuracy: 0.7799 - 58ms/epoch - 2ms/step\n",
            "Epoch 780/1000\n",
            "27/27 - 0s - loss: 0.4144 - binary_accuracy: 0.8194 - val_loss: 0.4782 - val_binary_accuracy: 0.7799 - 50ms/epoch - 2ms/step\n",
            "Epoch 781/1000\n",
            "27/27 - 0s - loss: 0.4287 - binary_accuracy: 0.8318 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 61ms/epoch - 2ms/step\n",
            "Epoch 782/1000\n",
            "27/27 - 0s - loss: 0.4301 - binary_accuracy: 0.8160 - val_loss: 0.4785 - val_binary_accuracy: 0.7823 - 62ms/epoch - 2ms/step\n",
            "Epoch 783/1000\n",
            "27/27 - 0s - loss: 0.4342 - binary_accuracy: 0.8296 - val_loss: 0.4782 - val_binary_accuracy: 0.7799 - 48ms/epoch - 2ms/step\n",
            "Epoch 784/1000\n",
            "27/27 - 0s - loss: 0.4332 - binary_accuracy: 0.8149 - val_loss: 0.4785 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 785/1000\n",
            "27/27 - 0s - loss: 0.4458 - binary_accuracy: 0.8081 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 786/1000\n",
            "27/27 - 0s - loss: 0.4316 - binary_accuracy: 0.8059 - val_loss: 0.4783 - val_binary_accuracy: 0.7799 - 50ms/epoch - 2ms/step\n",
            "Epoch 787/1000\n",
            "27/27 - 0s - loss: 0.4255 - binary_accuracy: 0.8228 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 54ms/epoch - 2ms/step\n",
            "Epoch 788/1000\n",
            "27/27 - 0s - loss: 0.4304 - binary_accuracy: 0.8093 - val_loss: 0.4783 - val_binary_accuracy: 0.7799 - 55ms/epoch - 2ms/step\n",
            "Epoch 789/1000\n",
            "27/27 - 0s - loss: 0.4418 - binary_accuracy: 0.8149 - val_loss: 0.4779 - val_binary_accuracy: 0.7799 - 52ms/epoch - 2ms/step\n",
            "Epoch 790/1000\n",
            "27/27 - 0s - loss: 0.4322 - binary_accuracy: 0.8205 - val_loss: 0.4786 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 791/1000\n",
            "27/27 - 0s - loss: 0.4274 - binary_accuracy: 0.8194 - val_loss: 0.4787 - val_binary_accuracy: 0.7823 - 66ms/epoch - 2ms/step\n",
            "Epoch 792/1000\n",
            "27/27 - 0s - loss: 0.4297 - binary_accuracy: 0.8228 - val_loss: 0.4785 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 793/1000\n",
            "27/27 - 0s - loss: 0.4428 - binary_accuracy: 0.8149 - val_loss: 0.4787 - val_binary_accuracy: 0.7823 - 65ms/epoch - 2ms/step\n",
            "Epoch 794/1000\n",
            "27/27 - 0s - loss: 0.4207 - binary_accuracy: 0.8251 - val_loss: 0.4784 - val_binary_accuracy: 0.7823 - 60ms/epoch - 2ms/step\n",
            "Epoch 795/1000\n",
            "27/27 - 0s - loss: 0.4192 - binary_accuracy: 0.8172 - val_loss: 0.4786 - val_binary_accuracy: 0.7823 - 68ms/epoch - 2ms/step\n",
            "Epoch 796/1000\n",
            "27/27 - 0s - loss: 0.4473 - binary_accuracy: 0.8036 - val_loss: 0.4781 - val_binary_accuracy: 0.7799 - 54ms/epoch - 2ms/step\n",
            "Epoch 797/1000\n",
            "27/27 - 0s - loss: 0.4436 - binary_accuracy: 0.8149 - val_loss: 0.4780 - val_binary_accuracy: 0.7799 - 52ms/epoch - 2ms/step\n",
            "Epoch 798/1000\n",
            "27/27 - 0s - loss: 0.4352 - binary_accuracy: 0.8194 - val_loss: 0.4781 - val_binary_accuracy: 0.7799 - 51ms/epoch - 2ms/step\n",
            "Epoch 799/1000\n",
            "27/27 - 0s - loss: 0.4360 - binary_accuracy: 0.8217 - val_loss: 0.4777 - val_binary_accuracy: 0.7799 - 55ms/epoch - 2ms/step\n",
            "Epoch 800/1000\n",
            "27/27 - 0s - loss: 0.4404 - binary_accuracy: 0.8160 - val_loss: 0.4780 - val_binary_accuracy: 0.7799 - 66ms/epoch - 2ms/step\n",
            "Epoch 801/1000\n",
            "27/27 - 0s - loss: 0.4326 - binary_accuracy: 0.8104 - val_loss: 0.4777 - val_binary_accuracy: 0.7799 - 61ms/epoch - 2ms/step\n",
            "Epoch 802/1000\n",
            "27/27 - 0s - loss: 0.4294 - binary_accuracy: 0.8115 - val_loss: 0.4778 - val_binary_accuracy: 0.7799 - 52ms/epoch - 2ms/step\n",
            "Epoch 803/1000\n",
            "27/27 - 0s - loss: 0.4258 - binary_accuracy: 0.8160 - val_loss: 0.4778 - val_binary_accuracy: 0.7799 - 56ms/epoch - 2ms/step\n",
            "Epoch 804/1000\n",
            "27/27 - 0s - loss: 0.4326 - binary_accuracy: 0.8115 - val_loss: 0.4784 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 805/1000\n",
            "27/27 - 0s - loss: 0.4362 - binary_accuracy: 0.8217 - val_loss: 0.4780 - val_binary_accuracy: 0.7799 - 61ms/epoch - 2ms/step\n",
            "Epoch 806/1000\n",
            "27/27 - 0s - loss: 0.4448 - binary_accuracy: 0.8115 - val_loss: 0.4782 - val_binary_accuracy: 0.7823 - 62ms/epoch - 2ms/step\n",
            "Epoch 807/1000\n",
            "27/27 - 0s - loss: 0.4266 - binary_accuracy: 0.8183 - val_loss: 0.4785 - val_binary_accuracy: 0.7847 - 51ms/epoch - 2ms/step\n",
            "Epoch 808/1000\n",
            "27/27 - 0s - loss: 0.4391 - binary_accuracy: 0.8194 - val_loss: 0.4782 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 809/1000\n",
            "27/27 - 0s - loss: 0.4394 - binary_accuracy: 0.8160 - val_loss: 0.4776 - val_binary_accuracy: 0.7799 - 59ms/epoch - 2ms/step\n",
            "Epoch 810/1000\n",
            "27/27 - 0s - loss: 0.4314 - binary_accuracy: 0.8160 - val_loss: 0.4773 - val_binary_accuracy: 0.7799 - 69ms/epoch - 2ms/step\n",
            "Epoch 811/1000\n",
            "27/27 - 0s - loss: 0.4274 - binary_accuracy: 0.8149 - val_loss: 0.4776 - val_binary_accuracy: 0.7799 - 62ms/epoch - 2ms/step\n",
            "Epoch 812/1000\n",
            "27/27 - 0s - loss: 0.4496 - binary_accuracy: 0.8115 - val_loss: 0.4778 - val_binary_accuracy: 0.7799 - 56ms/epoch - 2ms/step\n",
            "Epoch 813/1000\n",
            "27/27 - 0s - loss: 0.4442 - binary_accuracy: 0.8138 - val_loss: 0.4782 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 814/1000\n",
            "27/27 - 0s - loss: 0.4328 - binary_accuracy: 0.8160 - val_loss: 0.4780 - val_binary_accuracy: 0.7799 - 51ms/epoch - 2ms/step\n",
            "Epoch 815/1000\n",
            "27/27 - 0s - loss: 0.4298 - binary_accuracy: 0.8081 - val_loss: 0.4776 - val_binary_accuracy: 0.7799 - 54ms/epoch - 2ms/step\n",
            "Epoch 816/1000\n",
            "27/27 - 0s - loss: 0.4363 - binary_accuracy: 0.8205 - val_loss: 0.4777 - val_binary_accuracy: 0.7799 - 54ms/epoch - 2ms/step\n",
            "Epoch 817/1000\n",
            "27/27 - 0s - loss: 0.4173 - binary_accuracy: 0.8205 - val_loss: 0.4786 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 818/1000\n",
            "27/27 - 0s - loss: 0.4273 - binary_accuracy: 0.8104 - val_loss: 0.4781 - val_binary_accuracy: 0.7823 - 64ms/epoch - 2ms/step\n",
            "Epoch 819/1000\n",
            "27/27 - 0s - loss: 0.4206 - binary_accuracy: 0.8239 - val_loss: 0.4785 - val_binary_accuracy: 0.7823 - 63ms/epoch - 2ms/step\n",
            "Epoch 820/1000\n",
            "27/27 - 0s - loss: 0.4308 - binary_accuracy: 0.8126 - val_loss: 0.4785 - val_binary_accuracy: 0.7799 - 61ms/epoch - 2ms/step\n",
            "Epoch 821/1000\n",
            "27/27 - 0s - loss: 0.4310 - binary_accuracy: 0.8138 - val_loss: 0.4780 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 822/1000\n",
            "27/27 - 0s - loss: 0.4236 - binary_accuracy: 0.8251 - val_loss: 0.4778 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 823/1000\n",
            "27/27 - 0s - loss: 0.4303 - binary_accuracy: 0.8172 - val_loss: 0.4779 - val_binary_accuracy: 0.7799 - 62ms/epoch - 2ms/step\n",
            "Epoch 824/1000\n",
            "27/27 - 0s - loss: 0.4415 - binary_accuracy: 0.8194 - val_loss: 0.4781 - val_binary_accuracy: 0.7823 - 54ms/epoch - 2ms/step\n",
            "Epoch 825/1000\n",
            "27/27 - 0s - loss: 0.4334 - binary_accuracy: 0.8194 - val_loss: 0.4781 - val_binary_accuracy: 0.7823 - 60ms/epoch - 2ms/step\n",
            "Epoch 826/1000\n",
            "27/27 - 0s - loss: 0.4357 - binary_accuracy: 0.8093 - val_loss: 0.4779 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 827/1000\n",
            "27/27 - 0s - loss: 0.4393 - binary_accuracy: 0.8172 - val_loss: 0.4781 - val_binary_accuracy: 0.7823 - 45ms/epoch - 2ms/step\n",
            "Epoch 828/1000\n",
            "27/27 - 0s - loss: 0.4239 - binary_accuracy: 0.8262 - val_loss: 0.4786 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 829/1000\n",
            "27/27 - 0s - loss: 0.4392 - binary_accuracy: 0.8036 - val_loss: 0.4784 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 830/1000\n",
            "27/27 - 0s - loss: 0.4246 - binary_accuracy: 0.8262 - val_loss: 0.4788 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 831/1000\n",
            "27/27 - 0s - loss: 0.4312 - binary_accuracy: 0.8138 - val_loss: 0.4788 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 832/1000\n",
            "27/27 - 0s - loss: 0.4348 - binary_accuracy: 0.8149 - val_loss: 0.4781 - val_binary_accuracy: 0.7847 - 49ms/epoch - 2ms/step\n",
            "Epoch 833/1000\n",
            "27/27 - 0s - loss: 0.4358 - binary_accuracy: 0.8183 - val_loss: 0.4779 - val_binary_accuracy: 0.7823 - 66ms/epoch - 2ms/step\n",
            "Epoch 834/1000\n",
            "27/27 - 0s - loss: 0.4455 - binary_accuracy: 0.8149 - val_loss: 0.4780 - val_binary_accuracy: 0.7823 - 65ms/epoch - 2ms/step\n",
            "Epoch 835/1000\n",
            "27/27 - 0s - loss: 0.4358 - binary_accuracy: 0.8205 - val_loss: 0.4777 - val_binary_accuracy: 0.7799 - 63ms/epoch - 2ms/step\n",
            "Epoch 836/1000\n",
            "27/27 - 0s - loss: 0.4306 - binary_accuracy: 0.8194 - val_loss: 0.4777 - val_binary_accuracy: 0.7799 - 59ms/epoch - 2ms/step\n",
            "Epoch 837/1000\n",
            "27/27 - 0s - loss: 0.4370 - binary_accuracy: 0.8059 - val_loss: 0.4777 - val_binary_accuracy: 0.7799 - 53ms/epoch - 2ms/step\n",
            "Epoch 838/1000\n",
            "27/27 - 0s - loss: 0.4287 - binary_accuracy: 0.8205 - val_loss: 0.4783 - val_binary_accuracy: 0.7847 - 67ms/epoch - 2ms/step\n",
            "Epoch 839/1000\n",
            "27/27 - 0s - loss: 0.4318 - binary_accuracy: 0.8228 - val_loss: 0.4773 - val_binary_accuracy: 0.7799 - 58ms/epoch - 2ms/step\n",
            "Epoch 840/1000\n",
            "27/27 - 0s - loss: 0.4283 - binary_accuracy: 0.8205 - val_loss: 0.4774 - val_binary_accuracy: 0.7799 - 45ms/epoch - 2ms/step\n",
            "Epoch 841/1000\n",
            "27/27 - 0s - loss: 0.4350 - binary_accuracy: 0.8126 - val_loss: 0.4780 - val_binary_accuracy: 0.7799 - 57ms/epoch - 2ms/step\n",
            "Epoch 842/1000\n",
            "27/27 - 0s - loss: 0.4367 - binary_accuracy: 0.8138 - val_loss: 0.4771 - val_binary_accuracy: 0.7799 - 46ms/epoch - 2ms/step\n",
            "Epoch 843/1000\n",
            "27/27 - 0s - loss: 0.4319 - binary_accuracy: 0.8104 - val_loss: 0.4777 - val_binary_accuracy: 0.7799 - 69ms/epoch - 2ms/step\n",
            "Epoch 844/1000\n",
            "27/27 - 0s - loss: 0.4272 - binary_accuracy: 0.8239 - val_loss: 0.4778 - val_binary_accuracy: 0.7799 - 57ms/epoch - 2ms/step\n",
            "Epoch 845/1000\n",
            "27/27 - 0s - loss: 0.4433 - binary_accuracy: 0.8036 - val_loss: 0.4775 - val_binary_accuracy: 0.7799 - 62ms/epoch - 2ms/step\n",
            "Epoch 846/1000\n",
            "27/27 - 0s - loss: 0.4390 - binary_accuracy: 0.8093 - val_loss: 0.4777 - val_binary_accuracy: 0.7799 - 50ms/epoch - 2ms/step\n",
            "Epoch 847/1000\n",
            "27/27 - 0s - loss: 0.4231 - binary_accuracy: 0.8251 - val_loss: 0.4780 - val_binary_accuracy: 0.7799 - 46ms/epoch - 2ms/step\n",
            "Epoch 848/1000\n",
            "27/27 - 0s - loss: 0.4285 - binary_accuracy: 0.8014 - val_loss: 0.4778 - val_binary_accuracy: 0.7799 - 59ms/epoch - 2ms/step\n",
            "Epoch 849/1000\n",
            "27/27 - 0s - loss: 0.4358 - binary_accuracy: 0.8138 - val_loss: 0.4786 - val_binary_accuracy: 0.7847 - 47ms/epoch - 2ms/step\n",
            "Epoch 850/1000\n",
            "27/27 - 0s - loss: 0.4289 - binary_accuracy: 0.8172 - val_loss: 0.4783 - val_binary_accuracy: 0.7847 - 57ms/epoch - 2ms/step\n",
            "Epoch 851/1000\n",
            "27/27 - 0s - loss: 0.4313 - binary_accuracy: 0.8138 - val_loss: 0.4787 - val_binary_accuracy: 0.7823 - 59ms/epoch - 2ms/step\n",
            "Epoch 852/1000\n",
            "27/27 - 0s - loss: 0.4341 - binary_accuracy: 0.8205 - val_loss: 0.4790 - val_binary_accuracy: 0.7823 - 65ms/epoch - 2ms/step\n",
            "Epoch 853/1000\n",
            "27/27 - 0s - loss: 0.4301 - binary_accuracy: 0.8093 - val_loss: 0.4781 - val_binary_accuracy: 0.7799 - 64ms/epoch - 2ms/step\n",
            "Epoch 854/1000\n",
            "27/27 - 0s - loss: 0.4356 - binary_accuracy: 0.8115 - val_loss: 0.4781 - val_binary_accuracy: 0.7823 - 71ms/epoch - 3ms/step\n",
            "Epoch 855/1000\n",
            "27/27 - 0s - loss: 0.4295 - binary_accuracy: 0.8126 - val_loss: 0.4779 - val_binary_accuracy: 0.7799 - 61ms/epoch - 2ms/step\n",
            "Epoch 856/1000\n",
            "27/27 - 0s - loss: 0.4415 - binary_accuracy: 0.8217 - val_loss: 0.4789 - val_binary_accuracy: 0.7823 - 55ms/epoch - 2ms/step\n",
            "Epoch 857/1000\n",
            "27/27 - 0s - loss: 0.4255 - binary_accuracy: 0.8104 - val_loss: 0.4790 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 858/1000\n",
            "27/27 - 0s - loss: 0.4201 - binary_accuracy: 0.8172 - val_loss: 0.4793 - val_binary_accuracy: 0.7823 - 57ms/epoch - 2ms/step\n",
            "Epoch 859/1000\n",
            "27/27 - 0s - loss: 0.4331 - binary_accuracy: 0.8149 - val_loss: 0.4793 - val_binary_accuracy: 0.7823 - 67ms/epoch - 2ms/step\n",
            "Epoch 860/1000\n",
            "27/27 - 0s - loss: 0.4372 - binary_accuracy: 0.8160 - val_loss: 0.4789 - val_binary_accuracy: 0.7823 - 63ms/epoch - 2ms/step\n",
            "Epoch 861/1000\n",
            "27/27 - 0s - loss: 0.4331 - binary_accuracy: 0.8172 - val_loss: 0.4782 - val_binary_accuracy: 0.7823 - 64ms/epoch - 2ms/step\n",
            "Epoch 862/1000\n",
            "27/27 - 0s - loss: 0.4352 - binary_accuracy: 0.8138 - val_loss: 0.4784 - val_binary_accuracy: 0.7823 - 61ms/epoch - 2ms/step\n",
            "Epoch 863/1000\n",
            "27/27 - 0s - loss: 0.4291 - binary_accuracy: 0.8160 - val_loss: 0.4785 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 864/1000\n",
            "27/27 - 0s - loss: 0.4228 - binary_accuracy: 0.8126 - val_loss: 0.4782 - val_binary_accuracy: 0.7823 - 64ms/epoch - 2ms/step\n",
            "Epoch 865/1000\n",
            "27/27 - 0s - loss: 0.4223 - binary_accuracy: 0.8239 - val_loss: 0.4780 - val_binary_accuracy: 0.7823 - 62ms/epoch - 2ms/step\n",
            "Epoch 866/1000\n",
            "27/27 - 0s - loss: 0.4246 - binary_accuracy: 0.8205 - val_loss: 0.4785 - val_binary_accuracy: 0.7823 - 63ms/epoch - 2ms/step\n",
            "Epoch 867/1000\n",
            "27/27 - 0s - loss: 0.4328 - binary_accuracy: 0.8093 - val_loss: 0.4783 - val_binary_accuracy: 0.7799 - 63ms/epoch - 2ms/step\n",
            "Epoch 868/1000\n",
            "27/27 - 0s - loss: 0.4235 - binary_accuracy: 0.8217 - val_loss: 0.4775 - val_binary_accuracy: 0.7799 - 53ms/epoch - 2ms/step\n",
            "Epoch 869/1000\n",
            "27/27 - 0s - loss: 0.4331 - binary_accuracy: 0.8115 - val_loss: 0.4777 - val_binary_accuracy: 0.7823 - 51ms/epoch - 2ms/step\n",
            "Epoch 870/1000\n",
            "27/27 - 0s - loss: 0.4295 - binary_accuracy: 0.8126 - val_loss: 0.4776 - val_binary_accuracy: 0.7799 - 53ms/epoch - 2ms/step\n",
            "Epoch 871/1000\n",
            "27/27 - 0s - loss: 0.4256 - binary_accuracy: 0.8172 - val_loss: 0.4775 - val_binary_accuracy: 0.7799 - 57ms/epoch - 2ms/step\n",
            "Epoch 872/1000\n",
            "27/27 - 0s - loss: 0.4300 - binary_accuracy: 0.8228 - val_loss: 0.4775 - val_binary_accuracy: 0.7799 - 63ms/epoch - 2ms/step\n",
            "Epoch 873/1000\n",
            "27/27 - 0s - loss: 0.4407 - binary_accuracy: 0.8126 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 55ms/epoch - 2ms/step\n",
            "Epoch 874/1000\n",
            "27/27 - 0s - loss: 0.4287 - binary_accuracy: 0.8160 - val_loss: 0.4783 - val_binary_accuracy: 0.7847 - 64ms/epoch - 2ms/step\n",
            "Epoch 875/1000\n",
            "27/27 - 0s - loss: 0.4268 - binary_accuracy: 0.8149 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 876/1000\n",
            "27/27 - 0s - loss: 0.4236 - binary_accuracy: 0.8104 - val_loss: 0.4781 - val_binary_accuracy: 0.7799 - 67ms/epoch - 2ms/step\n",
            "Epoch 877/1000\n",
            "27/27 - 0s - loss: 0.4331 - binary_accuracy: 0.8138 - val_loss: 0.4783 - val_binary_accuracy: 0.7799 - 53ms/epoch - 2ms/step\n",
            "Epoch 878/1000\n",
            "27/27 - 0s - loss: 0.4265 - binary_accuracy: 0.8172 - val_loss: 0.4784 - val_binary_accuracy: 0.7799 - 67ms/epoch - 2ms/step\n",
            "Epoch 879/1000\n",
            "27/27 - 0s - loss: 0.4366 - binary_accuracy: 0.8115 - val_loss: 0.4787 - val_binary_accuracy: 0.7823 - 54ms/epoch - 2ms/step\n",
            "Epoch 880/1000\n",
            "27/27 - 0s - loss: 0.4433 - binary_accuracy: 0.8194 - val_loss: 0.4780 - val_binary_accuracy: 0.7847 - 54ms/epoch - 2ms/step\n",
            "Epoch 881/1000\n",
            "27/27 - 0s - loss: 0.4342 - binary_accuracy: 0.8002 - val_loss: 0.4775 - val_binary_accuracy: 0.7799 - 57ms/epoch - 2ms/step\n",
            "Epoch 882/1000\n",
            "27/27 - 0s - loss: 0.4251 - binary_accuracy: 0.8160 - val_loss: 0.4774 - val_binary_accuracy: 0.7799 - 47ms/epoch - 2ms/step\n",
            "Epoch 883/1000\n",
            "27/27 - 0s - loss: 0.4309 - binary_accuracy: 0.8172 - val_loss: 0.4772 - val_binary_accuracy: 0.7799 - 63ms/epoch - 2ms/step\n",
            "Epoch 884/1000\n",
            "27/27 - 0s - loss: 0.4350 - binary_accuracy: 0.8104 - val_loss: 0.4781 - val_binary_accuracy: 0.7847 - 47ms/epoch - 2ms/step\n",
            "Epoch 885/1000\n",
            "27/27 - 0s - loss: 0.4306 - binary_accuracy: 0.8160 - val_loss: 0.4777 - val_binary_accuracy: 0.7847 - 54ms/epoch - 2ms/step\n",
            "Epoch 886/1000\n",
            "27/27 - 0s - loss: 0.4260 - binary_accuracy: 0.8160 - val_loss: 0.4779 - val_binary_accuracy: 0.7847 - 54ms/epoch - 2ms/step\n",
            "Epoch 887/1000\n",
            "27/27 - 0s - loss: 0.4407 - binary_accuracy: 0.8115 - val_loss: 0.4784 - val_binary_accuracy: 0.7823 - 46ms/epoch - 2ms/step\n",
            "Epoch 888/1000\n",
            "27/27 - 0s - loss: 0.4441 - binary_accuracy: 0.8059 - val_loss: 0.4779 - val_binary_accuracy: 0.7847 - 61ms/epoch - 2ms/step\n",
            "Epoch 889/1000\n",
            "27/27 - 0s - loss: 0.4262 - binary_accuracy: 0.8273 - val_loss: 0.4772 - val_binary_accuracy: 0.7799 - 52ms/epoch - 2ms/step\n",
            "Epoch 890/1000\n",
            "27/27 - 0s - loss: 0.4356 - binary_accuracy: 0.8160 - val_loss: 0.4779 - val_binary_accuracy: 0.7847 - 61ms/epoch - 2ms/step\n",
            "Epoch 891/1000\n",
            "27/27 - 0s - loss: 0.4279 - binary_accuracy: 0.8160 - val_loss: 0.4780 - val_binary_accuracy: 0.7847 - 50ms/epoch - 2ms/step\n",
            "Epoch 892/1000\n",
            "27/27 - 0s - loss: 0.4344 - binary_accuracy: 0.8149 - val_loss: 0.4776 - val_binary_accuracy: 0.7823 - 60ms/epoch - 2ms/step\n",
            "Epoch 893/1000\n",
            "27/27 - 0s - loss: 0.4274 - binary_accuracy: 0.8194 - val_loss: 0.4780 - val_binary_accuracy: 0.7847 - 61ms/epoch - 2ms/step\n",
            "Epoch 894/1000\n",
            "27/27 - 0s - loss: 0.4338 - binary_accuracy: 0.8239 - val_loss: 0.4778 - val_binary_accuracy: 0.7823 - 59ms/epoch - 2ms/step\n",
            "Epoch 895/1000\n",
            "27/27 - 0s - loss: 0.4240 - binary_accuracy: 0.8251 - val_loss: 0.4784 - val_binary_accuracy: 0.7799 - 63ms/epoch - 2ms/step\n",
            "Epoch 896/1000\n",
            "27/27 - 0s - loss: 0.4352 - binary_accuracy: 0.8126 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 897/1000\n",
            "27/27 - 0s - loss: 0.4325 - binary_accuracy: 0.8149 - val_loss: 0.4785 - val_binary_accuracy: 0.7823 - 57ms/epoch - 2ms/step\n",
            "Epoch 898/1000\n",
            "27/27 - 0s - loss: 0.4299 - binary_accuracy: 0.8228 - val_loss: 0.4782 - val_binary_accuracy: 0.7799 - 52ms/epoch - 2ms/step\n",
            "Epoch 899/1000\n",
            "27/27 - 0s - loss: 0.4261 - binary_accuracy: 0.8183 - val_loss: 0.4778 - val_binary_accuracy: 0.7799 - 48ms/epoch - 2ms/step\n",
            "Epoch 900/1000\n",
            "27/27 - 0s - loss: 0.4364 - binary_accuracy: 0.8138 - val_loss: 0.4778 - val_binary_accuracy: 0.7799 - 52ms/epoch - 2ms/step\n",
            "Epoch 901/1000\n",
            "27/27 - 0s - loss: 0.4327 - binary_accuracy: 0.8160 - val_loss: 0.4781 - val_binary_accuracy: 0.7823 - 47ms/epoch - 2ms/step\n",
            "Epoch 902/1000\n",
            "27/27 - 0s - loss: 0.4383 - binary_accuracy: 0.8070 - val_loss: 0.4785 - val_binary_accuracy: 0.7823 - 47ms/epoch - 2ms/step\n",
            "Epoch 903/1000\n",
            "27/27 - 0s - loss: 0.4294 - binary_accuracy: 0.8228 - val_loss: 0.4779 - val_binary_accuracy: 0.7847 - 46ms/epoch - 2ms/step\n",
            "Epoch 904/1000\n",
            "27/27 - 0s - loss: 0.4271 - binary_accuracy: 0.8228 - val_loss: 0.4782 - val_binary_accuracy: 0.7823 - 54ms/epoch - 2ms/step\n",
            "Epoch 905/1000\n",
            "27/27 - 0s - loss: 0.4425 - binary_accuracy: 0.8160 - val_loss: 0.4777 - val_binary_accuracy: 0.7847 - 64ms/epoch - 2ms/step\n",
            "Epoch 906/1000\n",
            "27/27 - 0s - loss: 0.4302 - binary_accuracy: 0.8149 - val_loss: 0.4777 - val_binary_accuracy: 0.7847 - 53ms/epoch - 2ms/step\n",
            "Epoch 907/1000\n",
            "27/27 - 0s - loss: 0.4371 - binary_accuracy: 0.8205 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 55ms/epoch - 2ms/step\n",
            "Epoch 908/1000\n",
            "27/27 - 0s - loss: 0.4318 - binary_accuracy: 0.8228 - val_loss: 0.4778 - val_binary_accuracy: 0.7823 - 64ms/epoch - 2ms/step\n",
            "Epoch 909/1000\n",
            "27/27 - 0s - loss: 0.4186 - binary_accuracy: 0.8239 - val_loss: 0.4779 - val_binary_accuracy: 0.7823 - 54ms/epoch - 2ms/step\n",
            "Epoch 910/1000\n",
            "27/27 - 0s - loss: 0.4233 - binary_accuracy: 0.8205 - val_loss: 0.4786 - val_binary_accuracy: 0.7823 - 69ms/epoch - 2ms/step\n",
            "Epoch 911/1000\n",
            "27/27 - 0s - loss: 0.4317 - binary_accuracy: 0.8126 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 912/1000\n",
            "27/27 - 0s - loss: 0.4289 - binary_accuracy: 0.8284 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 70ms/epoch - 3ms/step\n",
            "Epoch 913/1000\n",
            "27/27 - 0s - loss: 0.4191 - binary_accuracy: 0.8138 - val_loss: 0.4785 - val_binary_accuracy: 0.7823 - 62ms/epoch - 2ms/step\n",
            "Epoch 914/1000\n",
            "27/27 - 0s - loss: 0.4393 - binary_accuracy: 0.8183 - val_loss: 0.4786 - val_binary_accuracy: 0.7823 - 64ms/epoch - 2ms/step\n",
            "Epoch 915/1000\n",
            "27/27 - 0s - loss: 0.4322 - binary_accuracy: 0.8217 - val_loss: 0.4787 - val_binary_accuracy: 0.7823 - 59ms/epoch - 2ms/step\n",
            "Epoch 916/1000\n",
            "27/27 - 0s - loss: 0.4363 - binary_accuracy: 0.8138 - val_loss: 0.4784 - val_binary_accuracy: 0.7823 - 65ms/epoch - 2ms/step\n",
            "Epoch 917/1000\n",
            "27/27 - 0s - loss: 0.4244 - binary_accuracy: 0.8205 - val_loss: 0.4785 - val_binary_accuracy: 0.7823 - 46ms/epoch - 2ms/step\n",
            "Epoch 918/1000\n",
            "27/27 - 0s - loss: 0.4249 - binary_accuracy: 0.8126 - val_loss: 0.4786 - val_binary_accuracy: 0.7871 - 50ms/epoch - 2ms/step\n",
            "Epoch 919/1000\n",
            "27/27 - 0s - loss: 0.4299 - binary_accuracy: 0.8093 - val_loss: 0.4787 - val_binary_accuracy: 0.7871 - 61ms/epoch - 2ms/step\n",
            "Epoch 920/1000\n",
            "27/27 - 0s - loss: 0.4160 - binary_accuracy: 0.8205 - val_loss: 0.4783 - val_binary_accuracy: 0.7799 - 51ms/epoch - 2ms/step\n",
            "Epoch 921/1000\n",
            "27/27 - 0s - loss: 0.4284 - binary_accuracy: 0.8217 - val_loss: 0.4784 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 922/1000\n",
            "27/27 - 0s - loss: 0.4365 - binary_accuracy: 0.8228 - val_loss: 0.4781 - val_binary_accuracy: 0.7799 - 49ms/epoch - 2ms/step\n",
            "Epoch 923/1000\n",
            "27/27 - 0s - loss: 0.4331 - binary_accuracy: 0.8138 - val_loss: 0.4787 - val_binary_accuracy: 0.7871 - 51ms/epoch - 2ms/step\n",
            "Epoch 924/1000\n",
            "27/27 - 0s - loss: 0.4413 - binary_accuracy: 0.8115 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 925/1000\n",
            "27/27 - 0s - loss: 0.4240 - binary_accuracy: 0.8262 - val_loss: 0.4786 - val_binary_accuracy: 0.7871 - 53ms/epoch - 2ms/step\n",
            "Epoch 926/1000\n",
            "27/27 - 0s - loss: 0.4233 - binary_accuracy: 0.8307 - val_loss: 0.4784 - val_binary_accuracy: 0.7823 - 61ms/epoch - 2ms/step\n",
            "Epoch 927/1000\n",
            "27/27 - 0s - loss: 0.4301 - binary_accuracy: 0.8138 - val_loss: 0.4787 - val_binary_accuracy: 0.7871 - 60ms/epoch - 2ms/step\n",
            "Epoch 928/1000\n",
            "27/27 - 0s - loss: 0.4415 - binary_accuracy: 0.8047 - val_loss: 0.4789 - val_binary_accuracy: 0.7871 - 61ms/epoch - 2ms/step\n",
            "Epoch 929/1000\n",
            "27/27 - 0s - loss: 0.4269 - binary_accuracy: 0.8205 - val_loss: 0.4784 - val_binary_accuracy: 0.7823 - 58ms/epoch - 2ms/step\n",
            "Epoch 930/1000\n",
            "27/27 - 0s - loss: 0.4447 - binary_accuracy: 0.8036 - val_loss: 0.4777 - val_binary_accuracy: 0.7799 - 51ms/epoch - 2ms/step\n",
            "Epoch 931/1000\n",
            "27/27 - 0s - loss: 0.4208 - binary_accuracy: 0.8183 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 932/1000\n",
            "27/27 - 0s - loss: 0.4290 - binary_accuracy: 0.8104 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 933/1000\n",
            "27/27 - 0s - loss: 0.4364 - binary_accuracy: 0.8183 - val_loss: 0.4777 - val_binary_accuracy: 0.7775 - 65ms/epoch - 2ms/step\n",
            "Epoch 934/1000\n",
            "27/27 - 0s - loss: 0.4223 - binary_accuracy: 0.8251 - val_loss: 0.4780 - val_binary_accuracy: 0.7823 - 48ms/epoch - 2ms/step\n",
            "Epoch 935/1000\n",
            "27/27 - 0s - loss: 0.4350 - binary_accuracy: 0.8115 - val_loss: 0.4782 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 936/1000\n",
            "27/27 - 0s - loss: 0.4418 - binary_accuracy: 0.8126 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 61ms/epoch - 2ms/step\n",
            "Epoch 937/1000\n",
            "27/27 - 0s - loss: 0.4230 - binary_accuracy: 0.8194 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 938/1000\n",
            "27/27 - 0s - loss: 0.4110 - binary_accuracy: 0.8330 - val_loss: 0.4791 - val_binary_accuracy: 0.7871 - 47ms/epoch - 2ms/step\n",
            "Epoch 939/1000\n",
            "27/27 - 0s - loss: 0.4297 - binary_accuracy: 0.8183 - val_loss: 0.4794 - val_binary_accuracy: 0.7919 - 51ms/epoch - 2ms/step\n",
            "Epoch 940/1000\n",
            "27/27 - 0s - loss: 0.4305 - binary_accuracy: 0.8160 - val_loss: 0.4782 - val_binary_accuracy: 0.7799 - 49ms/epoch - 2ms/step\n",
            "Epoch 941/1000\n",
            "27/27 - 0s - loss: 0.4356 - binary_accuracy: 0.8160 - val_loss: 0.4777 - val_binary_accuracy: 0.7799 - 61ms/epoch - 2ms/step\n",
            "Epoch 942/1000\n",
            "27/27 - 0s - loss: 0.4333 - binary_accuracy: 0.8183 - val_loss: 0.4779 - val_binary_accuracy: 0.7799 - 62ms/epoch - 2ms/step\n",
            "Epoch 943/1000\n",
            "27/27 - 0s - loss: 0.4308 - binary_accuracy: 0.8160 - val_loss: 0.4779 - val_binary_accuracy: 0.7799 - 65ms/epoch - 2ms/step\n",
            "Epoch 944/1000\n",
            "27/27 - 0s - loss: 0.4434 - binary_accuracy: 0.8149 - val_loss: 0.4784 - val_binary_accuracy: 0.7823 - 56ms/epoch - 2ms/step\n",
            "Epoch 945/1000\n",
            "27/27 - 0s - loss: 0.4351 - binary_accuracy: 0.8183 - val_loss: 0.4781 - val_binary_accuracy: 0.7799 - 62ms/epoch - 2ms/step\n",
            "Epoch 946/1000\n",
            "27/27 - 0s - loss: 0.4314 - binary_accuracy: 0.8104 - val_loss: 0.4784 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 947/1000\n",
            "27/27 - 0s - loss: 0.4342 - binary_accuracy: 0.8115 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 948/1000\n",
            "27/27 - 0s - loss: 0.4297 - binary_accuracy: 0.8149 - val_loss: 0.4784 - val_binary_accuracy: 0.7823 - 64ms/epoch - 2ms/step\n",
            "Epoch 949/1000\n",
            "27/27 - 0s - loss: 0.4302 - binary_accuracy: 0.8172 - val_loss: 0.4786 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 950/1000\n",
            "27/27 - 0s - loss: 0.4368 - binary_accuracy: 0.8160 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 951/1000\n",
            "27/27 - 0s - loss: 0.4414 - binary_accuracy: 0.8104 - val_loss: 0.4788 - val_binary_accuracy: 0.7871 - 47ms/epoch - 2ms/step\n",
            "Epoch 952/1000\n",
            "27/27 - 0s - loss: 0.4421 - binary_accuracy: 0.8115 - val_loss: 0.4774 - val_binary_accuracy: 0.7775 - 53ms/epoch - 2ms/step\n",
            "Epoch 953/1000\n",
            "27/27 - 0s - loss: 0.4388 - binary_accuracy: 0.8036 - val_loss: 0.4779 - val_binary_accuracy: 0.7823 - 50ms/epoch - 2ms/step\n",
            "Epoch 954/1000\n",
            "27/27 - 0s - loss: 0.4358 - binary_accuracy: 0.8172 - val_loss: 0.4777 - val_binary_accuracy: 0.7799 - 62ms/epoch - 2ms/step\n",
            "Epoch 955/1000\n",
            "27/27 - 0s - loss: 0.4322 - binary_accuracy: 0.8228 - val_loss: 0.4779 - val_binary_accuracy: 0.7823 - 67ms/epoch - 2ms/step\n",
            "Epoch 956/1000\n",
            "27/27 - 0s - loss: 0.4230 - binary_accuracy: 0.8205 - val_loss: 0.4782 - val_binary_accuracy: 0.7847 - 46ms/epoch - 2ms/step\n",
            "Epoch 957/1000\n",
            "27/27 - 0s - loss: 0.4380 - binary_accuracy: 0.8104 - val_loss: 0.4786 - val_binary_accuracy: 0.7871 - 58ms/epoch - 2ms/step\n",
            "Epoch 958/1000\n",
            "27/27 - 0s - loss: 0.4284 - binary_accuracy: 0.8228 - val_loss: 0.4781 - val_binary_accuracy: 0.7847 - 49ms/epoch - 2ms/step\n",
            "Epoch 959/1000\n",
            "27/27 - 0s - loss: 0.4309 - binary_accuracy: 0.8138 - val_loss: 0.4780 - val_binary_accuracy: 0.7775 - 60ms/epoch - 2ms/step\n",
            "Epoch 960/1000\n",
            "27/27 - 0s - loss: 0.4242 - binary_accuracy: 0.8228 - val_loss: 0.4787 - val_binary_accuracy: 0.7871 - 48ms/epoch - 2ms/step\n",
            "Epoch 961/1000\n",
            "27/27 - 0s - loss: 0.4276 - binary_accuracy: 0.8183 - val_loss: 0.4786 - val_binary_accuracy: 0.7847 - 54ms/epoch - 2ms/step\n",
            "Epoch 962/1000\n",
            "27/27 - 0s - loss: 0.4308 - binary_accuracy: 0.8149 - val_loss: 0.4790 - val_binary_accuracy: 0.7871 - 61ms/epoch - 2ms/step\n",
            "Epoch 963/1000\n",
            "27/27 - 0s - loss: 0.4366 - binary_accuracy: 0.8194 - val_loss: 0.4791 - val_binary_accuracy: 0.7871 - 53ms/epoch - 2ms/step\n",
            "Epoch 964/1000\n",
            "27/27 - 0s - loss: 0.4317 - binary_accuracy: 0.8149 - val_loss: 0.4786 - val_binary_accuracy: 0.7871 - 49ms/epoch - 2ms/step\n",
            "Epoch 965/1000\n",
            "27/27 - 0s - loss: 0.4247 - binary_accuracy: 0.8047 - val_loss: 0.4789 - val_binary_accuracy: 0.7871 - 55ms/epoch - 2ms/step\n",
            "Epoch 966/1000\n",
            "27/27 - 0s - loss: 0.4334 - binary_accuracy: 0.8149 - val_loss: 0.4788 - val_binary_accuracy: 0.7871 - 63ms/epoch - 2ms/step\n",
            "Epoch 967/1000\n",
            "27/27 - 0s - loss: 0.4348 - binary_accuracy: 0.8115 - val_loss: 0.4780 - val_binary_accuracy: 0.7799 - 47ms/epoch - 2ms/step\n",
            "Epoch 968/1000\n",
            "27/27 - 0s - loss: 0.4329 - binary_accuracy: 0.8115 - val_loss: 0.4779 - val_binary_accuracy: 0.7799 - 49ms/epoch - 2ms/step\n",
            "Epoch 969/1000\n",
            "27/27 - 0s - loss: 0.4322 - binary_accuracy: 0.8228 - val_loss: 0.4775 - val_binary_accuracy: 0.7799 - 51ms/epoch - 2ms/step\n",
            "Epoch 970/1000\n",
            "27/27 - 0s - loss: 0.4436 - binary_accuracy: 0.8115 - val_loss: 0.4780 - val_binary_accuracy: 0.7799 - 52ms/epoch - 2ms/step\n",
            "Epoch 971/1000\n",
            "27/27 - 0s - loss: 0.4279 - binary_accuracy: 0.8251 - val_loss: 0.4783 - val_binary_accuracy: 0.7871 - 61ms/epoch - 2ms/step\n",
            "Epoch 972/1000\n",
            "27/27 - 0s - loss: 0.4288 - binary_accuracy: 0.8262 - val_loss: 0.4783 - val_binary_accuracy: 0.7847 - 63ms/epoch - 2ms/step\n",
            "Epoch 973/1000\n",
            "27/27 - 0s - loss: 0.4358 - binary_accuracy: 0.8081 - val_loss: 0.4783 - val_binary_accuracy: 0.7823 - 57ms/epoch - 2ms/step\n",
            "Epoch 974/1000\n",
            "27/27 - 0s - loss: 0.4416 - binary_accuracy: 0.8183 - val_loss: 0.4773 - val_binary_accuracy: 0.7799 - 48ms/epoch - 2ms/step\n",
            "Epoch 975/1000\n",
            "27/27 - 0s - loss: 0.4286 - binary_accuracy: 0.8217 - val_loss: 0.4777 - val_binary_accuracy: 0.7799 - 49ms/epoch - 2ms/step\n",
            "Epoch 976/1000\n",
            "27/27 - 0s - loss: 0.4290 - binary_accuracy: 0.8138 - val_loss: 0.4777 - val_binary_accuracy: 0.7799 - 61ms/epoch - 2ms/step\n",
            "Epoch 977/1000\n",
            "27/27 - 0s - loss: 0.4407 - binary_accuracy: 0.8183 - val_loss: 0.4776 - val_binary_accuracy: 0.7799 - 58ms/epoch - 2ms/step\n",
            "Epoch 978/1000\n",
            "27/27 - 0s - loss: 0.4266 - binary_accuracy: 0.8104 - val_loss: 0.4772 - val_binary_accuracy: 0.7799 - 59ms/epoch - 2ms/step\n",
            "Epoch 979/1000\n",
            "27/27 - 0s - loss: 0.4470 - binary_accuracy: 0.8160 - val_loss: 0.4778 - val_binary_accuracy: 0.7799 - 61ms/epoch - 2ms/step\n",
            "Epoch 980/1000\n",
            "27/27 - 0s - loss: 0.4341 - binary_accuracy: 0.8205 - val_loss: 0.4782 - val_binary_accuracy: 0.7823 - 49ms/epoch - 2ms/step\n",
            "Epoch 981/1000\n",
            "27/27 - 0s - loss: 0.4391 - binary_accuracy: 0.8217 - val_loss: 0.4785 - val_binary_accuracy: 0.7823 - 61ms/epoch - 2ms/step\n",
            "Epoch 982/1000\n",
            "27/27 - 0s - loss: 0.4309 - binary_accuracy: 0.8160 - val_loss: 0.4780 - val_binary_accuracy: 0.7823 - 53ms/epoch - 2ms/step\n",
            "Epoch 983/1000\n",
            "27/27 - 0s - loss: 0.4290 - binary_accuracy: 0.8194 - val_loss: 0.4776 - val_binary_accuracy: 0.7799 - 58ms/epoch - 2ms/step\n",
            "Epoch 984/1000\n",
            "27/27 - 0s - loss: 0.4187 - binary_accuracy: 0.8183 - val_loss: 0.4787 - val_binary_accuracy: 0.7919 - 50ms/epoch - 2ms/step\n",
            "Epoch 985/1000\n",
            "27/27 - 0s - loss: 0.4300 - binary_accuracy: 0.8183 - val_loss: 0.4778 - val_binary_accuracy: 0.7799 - 51ms/epoch - 2ms/step\n",
            "Epoch 986/1000\n",
            "27/27 - 0s - loss: 0.4379 - binary_accuracy: 0.8070 - val_loss: 0.4785 - val_binary_accuracy: 0.7895 - 51ms/epoch - 2ms/step\n",
            "Epoch 987/1000\n",
            "27/27 - 0s - loss: 0.4259 - binary_accuracy: 0.8296 - val_loss: 0.4780 - val_binary_accuracy: 0.7823 - 52ms/epoch - 2ms/step\n",
            "Epoch 988/1000\n",
            "27/27 - 0s - loss: 0.4386 - binary_accuracy: 0.8205 - val_loss: 0.4780 - val_binary_accuracy: 0.7799 - 59ms/epoch - 2ms/step\n",
            "Epoch 989/1000\n",
            "27/27 - 0s - loss: 0.4309 - binary_accuracy: 0.8126 - val_loss: 0.4779 - val_binary_accuracy: 0.7799 - 51ms/epoch - 2ms/step\n",
            "Epoch 990/1000\n",
            "27/27 - 0s - loss: 0.4296 - binary_accuracy: 0.8138 - val_loss: 0.4787 - val_binary_accuracy: 0.7895 - 61ms/epoch - 2ms/step\n",
            "Epoch 991/1000\n",
            "27/27 - 0s - loss: 0.4201 - binary_accuracy: 0.8239 - val_loss: 0.4787 - val_binary_accuracy: 0.7895 - 52ms/epoch - 2ms/step\n",
            "Epoch 992/1000\n",
            "27/27 - 0s - loss: 0.4303 - binary_accuracy: 0.8194 - val_loss: 0.4786 - val_binary_accuracy: 0.7895 - 60ms/epoch - 2ms/step\n",
            "Epoch 993/1000\n",
            "27/27 - 0s - loss: 0.4476 - binary_accuracy: 0.8126 - val_loss: 0.4787 - val_binary_accuracy: 0.7919 - 62ms/epoch - 2ms/step\n",
            "Epoch 994/1000\n",
            "27/27 - 0s - loss: 0.4295 - binary_accuracy: 0.8217 - val_loss: 0.4776 - val_binary_accuracy: 0.7799 - 47ms/epoch - 2ms/step\n",
            "Epoch 995/1000\n",
            "27/27 - 0s - loss: 0.4239 - binary_accuracy: 0.8217 - val_loss: 0.4778 - val_binary_accuracy: 0.7799 - 63ms/epoch - 2ms/step\n",
            "Epoch 996/1000\n",
            "27/27 - 0s - loss: 0.4262 - binary_accuracy: 0.8126 - val_loss: 0.4781 - val_binary_accuracy: 0.7799 - 65ms/epoch - 2ms/step\n",
            "Epoch 997/1000\n",
            "27/27 - 0s - loss: 0.4346 - binary_accuracy: 0.8047 - val_loss: 0.4782 - val_binary_accuracy: 0.7847 - 61ms/epoch - 2ms/step\n",
            "Epoch 998/1000\n",
            "27/27 - 0s - loss: 0.4400 - binary_accuracy: 0.8149 - val_loss: 0.4781 - val_binary_accuracy: 0.7799 - 62ms/epoch - 2ms/step\n",
            "Epoch 999/1000\n",
            "27/27 - 0s - loss: 0.4225 - binary_accuracy: 0.8273 - val_loss: 0.4787 - val_binary_accuracy: 0.7895 - 58ms/epoch - 2ms/step\n",
            "Epoch 1000/1000\n",
            "27/27 - 0s - loss: 0.4371 - binary_accuracy: 0.8239 - val_loss: 0.4786 - val_binary_accuracy: 0.7895 - 54ms/epoch - 2ms/step\n",
            "Modelo entrenado\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Graficar la pérdida\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.plot(historia.history[\"loss\"], label='Train Loss')\n",
        "plt.plot(historia.history[\"val_loss\"], label='Validation Loss')\n",
        "plt.legend()\n",
        "\n",
        "# Graficar el accuracy\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.plot(historia.history[\"binary_accuracy\"], color = \"green\", label='Train Accuracy')\n",
        "plt.plot(historia.history[\"val_binary_accuracy\"], label='Validation Accuracy')\n",
        "\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "frcmOUI2_0Of",
        "outputId": "c04040ea-3ea5-49da-ae1b-549da14cf795"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate = model_tf.evaluate(test_X_scaled, test_Y)\n",
        "print(evaluate)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tzo8TNN5AJli",
        "outputId": "b3e2b590-7e41-4640-fbbe-15d8a5c222e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14/14 [==============================] - 0s 2ms/step - loss: 0.4786 - binary_accuracy: 0.7895\n",
            "[0.47858184576034546, 0.7894737124443054]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Resultados\n",
        "\n",
        "Para este primer modelo usando redes neuronales, se usa regresión para encontrar si un determinado pasajero sobrevivió o no sobrevivió; por esto tenemos 1 sola capa de salida. \n",
        "\n",
        "El modelo cuenta con una sola capa escondida con 5 neuronas; y para la función de activación de la capa de salida se usa Sigmoid para obtener un valor entre 0 y 1. Si el valor es más cercano a 0, significa que el pasajero no sobrevivió, si es más cercano a 1 significa que no sobrevivió.\n",
        "\n",
        "Con este modelo se obtuvo un mejor Accuracy que con Logistic Regression, sin embargo, la mejora fue muy poca. Algo importante a destacar es que este fue de los mejores resultados obtenidos en las pruebas con diferentes configuraciones, usando una red neuronal no tan compleja con solo 1 capa escondida. \n",
        "\n",
        "Cómo métrica se uso una métrica de accuracy binaría y entropía binaria para la función de pérdida. Se utilizó la técnica de regularización de dropout para evitar que la red genere overfitting. \n",
        "\n",
        "Se utilizaron los datos de test como validación para mostrar cómo se comportan la función de pérdida y de accuracy. Como se puede observar el acurracy tanto como para train como para test es bueno, sin embargo, por lo que tenemos varianza y sesgo bajo. Y de igual forma el modelo esta ajustado correctamente.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "fIGE6m3T8_w2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tensorflow Model RMSprop"
      ],
      "metadata": {
        "id": "fcE3xzPGlZz9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_X_scaled = pd.DataFrame(scaler_train.transform(train_X)).to_numpy()\n",
        "test_X_scaled = pd.DataFrame(scaler_train.transform(test_X)).to_numpy()"
      ],
      "metadata": {
        "id": "bEA__XbOk13J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_Y = tf.one_hot(train_Y, tf.constant(2))\n",
        "test_Y = tf.one_hot(test_Y, tf.constant(2))\n",
        "train_Y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lr2cHtHbk6mU",
        "outputId": "a75d1569-741e-45e8-dc36-d91cecb63cac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(886, 2), dtype=float32, numpy=\n",
              "array([[1., 0.],\n",
              "       [0., 1.],\n",
              "       [0., 1.],\n",
              "       ...,\n",
              "       [1., 0.],\n",
              "       [0., 1.],\n",
              "       [1., 0.]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_dim = train_X_scaled.shape[1]\n",
        "input_dim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OJpbWrpglCU2",
        "outputId": "462e71ea-5bec-4ab5-b61b-74bf8d31520b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential()\n",
        "model.add(tf.keras.Input(shape=(input_dim,)))\n",
        "model.add(tf.keras.layers.Dense(units=8, activation=\"relu\", kernel_initializer=tf.keras.initializers.HeUniform()))\n",
        "model.add(tf.keras.layers.Dropout(0.1))\n",
        "model.add(tf.keras.layers.Dense(units=2, activation=\"softmax\", kernel_initializer=tf.keras.initializers.GlorotNormal()))"
      ],
      "metadata": {
        "id": "66MIQpCUlElS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer=tf.keras.optimizers.RMSprop(\n",
        "        learning_rate=0.001,\n",
        "        rho=0.9,\n",
        "        momentum=0.999,\n",
        "        epsilon=1e-07,\n",
        "        centered=False,\n",
        "        name='RMSprop'\n",
        "    ),\n",
        "    loss=\"categorical_crossentropy\",\n",
        "    metrics=[tf.keras.metrics.CategoricalAccuracy()]\n",
        ")"
      ],
      "metadata": {
        "id": "dNvbmCyplISg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BMZ7oTnYlLHN",
        "outputId": "014f1c5b-1c24-4b93-8e5b-acc17d677a48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_8 (Dense)             (None, 8)                 104       \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 8)                 0         \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 2)                 18        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 122\n",
            "Trainable params: 122\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 32\n",
        "history = model.fit(train_X_scaled, train_Y, \n",
        "    epochs=120, \n",
        "    steps_per_epoch=len(train_X_scaled)/batch_size, \n",
        "    verbose=2,\n",
        "    validation_split=0.1\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UEiLBUwSlNEO",
        "outputId": "00d94f1b-829b-4695-971a-9b2baac02ab4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/120\n",
            "27/27 - 0s - loss: 0.5489 - categorical_accuracy: 0.7629 - val_loss: 0.5409 - val_categorical_accuracy: 0.7640 - 394ms/epoch - 14ms/step\n",
            "Epoch 2/120\n",
            "27/27 - 0s - loss: 0.6496 - categorical_accuracy: 0.7591 - val_loss: 0.4638 - val_categorical_accuracy: 0.8315 - 41ms/epoch - 1ms/step\n",
            "Epoch 3/120\n",
            "27/27 - 0s - loss: 0.5477 - categorical_accuracy: 0.7340 - val_loss: 0.4776 - val_categorical_accuracy: 0.8652 - 35ms/epoch - 1ms/step\n",
            "Epoch 4/120\n",
            "27/27 - 0s - loss: 0.5586 - categorical_accuracy: 0.7240 - val_loss: 0.4729 - val_categorical_accuracy: 0.8764 - 35ms/epoch - 1ms/step\n",
            "Epoch 5/120\n",
            "27/27 - 0s - loss: 0.5018 - categorical_accuracy: 0.7654 - val_loss: 0.3782 - val_categorical_accuracy: 0.8764 - 56ms/epoch - 2ms/step\n",
            "Epoch 6/120\n",
            "27/27 - 0s - loss: 0.4822 - categorical_accuracy: 0.8018 - val_loss: 0.3748 - val_categorical_accuracy: 0.8427 - 45ms/epoch - 2ms/step\n",
            "Epoch 7/120\n",
            "27/27 - 0s - loss: 0.4691 - categorical_accuracy: 0.8043 - val_loss: 0.4165 - val_categorical_accuracy: 0.8202 - 45ms/epoch - 2ms/step\n",
            "Epoch 8/120\n",
            "27/27 - 0s - loss: 0.4816 - categorical_accuracy: 0.7892 - val_loss: 0.4491 - val_categorical_accuracy: 0.7978 - 51ms/epoch - 2ms/step\n",
            "Epoch 9/120\n",
            "27/27 - 0s - loss: 0.4800 - categorical_accuracy: 0.8005 - val_loss: 0.4648 - val_categorical_accuracy: 0.8090 - 45ms/epoch - 2ms/step\n",
            "Epoch 10/120\n",
            "27/27 - 0s - loss: 0.4624 - categorical_accuracy: 0.8231 - val_loss: 0.4230 - val_categorical_accuracy: 0.7978 - 45ms/epoch - 2ms/step\n",
            "Epoch 11/120\n",
            "27/27 - 0s - loss: 0.4631 - categorical_accuracy: 0.7905 - val_loss: 0.3856 - val_categorical_accuracy: 0.8202 - 58ms/epoch - 2ms/step\n",
            "Epoch 12/120\n",
            "27/27 - 0s - loss: 0.4548 - categorical_accuracy: 0.8093 - val_loss: 0.3939 - val_categorical_accuracy: 0.8427 - 45ms/epoch - 2ms/step\n",
            "Epoch 13/120\n",
            "27/27 - 0s - loss: 0.4648 - categorical_accuracy: 0.7980 - val_loss: 0.3631 - val_categorical_accuracy: 0.8876 - 43ms/epoch - 2ms/step\n",
            "Epoch 14/120\n",
            "27/27 - 0s - loss: 0.4790 - categorical_accuracy: 0.7980 - val_loss: 0.3576 - val_categorical_accuracy: 0.8989 - 62ms/epoch - 2ms/step\n",
            "Epoch 15/120\n",
            "27/27 - 0s - loss: 0.4597 - categorical_accuracy: 0.7854 - val_loss: 0.3737 - val_categorical_accuracy: 0.8876 - 42ms/epoch - 2ms/step\n",
            "Epoch 16/120\n",
            "27/27 - 0s - loss: 0.4501 - categorical_accuracy: 0.7892 - val_loss: 0.3749 - val_categorical_accuracy: 0.8876 - 42ms/epoch - 2ms/step\n",
            "Epoch 17/120\n",
            "27/27 - 0s - loss: 0.4764 - categorical_accuracy: 0.8068 - val_loss: 0.3900 - val_categorical_accuracy: 0.8652 - 45ms/epoch - 2ms/step\n",
            "Epoch 18/120\n",
            "27/27 - 0s - loss: 0.4454 - categorical_accuracy: 0.8168 - val_loss: 0.3902 - val_categorical_accuracy: 0.8876 - 42ms/epoch - 2ms/step\n",
            "Epoch 19/120\n",
            "27/27 - 0s - loss: 0.4523 - categorical_accuracy: 0.7980 - val_loss: 0.3596 - val_categorical_accuracy: 0.8427 - 59ms/epoch - 2ms/step\n",
            "Epoch 20/120\n",
            "27/27 - 0s - loss: 0.4658 - categorical_accuracy: 0.7980 - val_loss: 0.3679 - val_categorical_accuracy: 0.8427 - 43ms/epoch - 2ms/step\n",
            "Epoch 21/120\n",
            "27/27 - 0s - loss: 0.4594 - categorical_accuracy: 0.7967 - val_loss: 0.3756 - val_categorical_accuracy: 0.8315 - 43ms/epoch - 2ms/step\n",
            "Epoch 22/120\n",
            "27/27 - 0s - loss: 0.4477 - categorical_accuracy: 0.7942 - val_loss: 0.4093 - val_categorical_accuracy: 0.8202 - 45ms/epoch - 2ms/step\n",
            "Epoch 23/120\n",
            "27/27 - 0s - loss: 0.4791 - categorical_accuracy: 0.8055 - val_loss: 0.3919 - val_categorical_accuracy: 0.8315 - 42ms/epoch - 2ms/step\n",
            "Epoch 24/120\n",
            "27/27 - 0s - loss: 0.4690 - categorical_accuracy: 0.8080 - val_loss: 0.3738 - val_categorical_accuracy: 0.8427 - 44ms/epoch - 2ms/step\n",
            "Epoch 25/120\n",
            "27/27 - 0s - loss: 0.4881 - categorical_accuracy: 0.7867 - val_loss: 0.3765 - val_categorical_accuracy: 0.8539 - 42ms/epoch - 2ms/step\n",
            "Epoch 26/120\n",
            "27/27 - 0s - loss: 0.4573 - categorical_accuracy: 0.8018 - val_loss: 0.3686 - val_categorical_accuracy: 0.8539 - 39ms/epoch - 1ms/step\n",
            "Epoch 27/120\n",
            "27/27 - 0s - loss: 0.4661 - categorical_accuracy: 0.7967 - val_loss: 0.3465 - val_categorical_accuracy: 0.8652 - 41ms/epoch - 1ms/step\n",
            "Epoch 28/120\n",
            "27/27 - 0s - loss: 0.4802 - categorical_accuracy: 0.7980 - val_loss: 0.3830 - val_categorical_accuracy: 0.8539 - 63ms/epoch - 2ms/step\n",
            "Epoch 29/120\n",
            "27/27 - 0s - loss: 0.4829 - categorical_accuracy: 0.7465 - val_loss: 0.4118 - val_categorical_accuracy: 0.7978 - 41ms/epoch - 1ms/step\n",
            "Epoch 30/120\n",
            "27/27 - 0s - loss: 0.4517 - categorical_accuracy: 0.7829 - val_loss: 0.3972 - val_categorical_accuracy: 0.8652 - 39ms/epoch - 1ms/step\n",
            "Epoch 31/120\n",
            "27/27 - 0s - loss: 0.4950 - categorical_accuracy: 0.8005 - val_loss: 0.3831 - val_categorical_accuracy: 0.8652 - 49ms/epoch - 2ms/step\n",
            "Epoch 32/120\n",
            "27/27 - 0s - loss: 0.4920 - categorical_accuracy: 0.8018 - val_loss: 0.3588 - val_categorical_accuracy: 0.8764 - 44ms/epoch - 2ms/step\n",
            "Epoch 33/120\n",
            "27/27 - 0s - loss: 0.5072 - categorical_accuracy: 0.7716 - val_loss: 0.3728 - val_categorical_accuracy: 0.8652 - 42ms/epoch - 2ms/step\n",
            "Epoch 34/120\n",
            "27/27 - 0s - loss: 0.5558 - categorical_accuracy: 0.7992 - val_loss: 0.4804 - val_categorical_accuracy: 0.8652 - 44ms/epoch - 2ms/step\n",
            "Epoch 35/120\n",
            "27/27 - 0s - loss: 0.5248 - categorical_accuracy: 0.8018 - val_loss: 0.5269 - val_categorical_accuracy: 0.7303 - 48ms/epoch - 2ms/step\n",
            "Epoch 36/120\n",
            "27/27 - 0s - loss: 0.6120 - categorical_accuracy: 0.7014 - val_loss: 0.3922 - val_categorical_accuracy: 0.8764 - 68ms/epoch - 2ms/step\n",
            "Epoch 37/120\n",
            "27/27 - 0s - loss: 0.5246 - categorical_accuracy: 0.7992 - val_loss: 0.4194 - val_categorical_accuracy: 0.8539 - 39ms/epoch - 1ms/step\n",
            "Epoch 38/120\n",
            "27/27 - 0s - loss: 0.4654 - categorical_accuracy: 0.7942 - val_loss: 0.4255 - val_categorical_accuracy: 0.8315 - 37ms/epoch - 1ms/step\n",
            "Epoch 39/120\n",
            "27/27 - 0s - loss: 0.4991 - categorical_accuracy: 0.7716 - val_loss: 0.3599 - val_categorical_accuracy: 0.8764 - 36ms/epoch - 1ms/step\n",
            "Epoch 40/120\n",
            "27/27 - 0s - loss: 0.5048 - categorical_accuracy: 0.7829 - val_loss: 0.3945 - val_categorical_accuracy: 0.8764 - 54ms/epoch - 2ms/step\n",
            "Epoch 41/120\n",
            "27/27 - 0s - loss: 0.4950 - categorical_accuracy: 0.7616 - val_loss: 0.3906 - val_categorical_accuracy: 0.7865 - 45ms/epoch - 2ms/step\n",
            "Epoch 42/120\n",
            "27/27 - 0s - loss: 0.5157 - categorical_accuracy: 0.7039 - val_loss: 0.4480 - val_categorical_accuracy: 0.7753 - 45ms/epoch - 2ms/step\n",
            "Epoch 43/120\n",
            "27/27 - 0s - loss: 0.4874 - categorical_accuracy: 0.7428 - val_loss: 0.3986 - val_categorical_accuracy: 0.7865 - 41ms/epoch - 1ms/step\n",
            "Epoch 44/120\n",
            "27/27 - 0s - loss: 0.5300 - categorical_accuracy: 0.7528 - val_loss: 0.3904 - val_categorical_accuracy: 0.7753 - 38ms/epoch - 1ms/step\n",
            "Epoch 45/120\n",
            "27/27 - 0s - loss: 0.4929 - categorical_accuracy: 0.7917 - val_loss: 0.3941 - val_categorical_accuracy: 0.8427 - 44ms/epoch - 2ms/step\n",
            "Epoch 46/120\n",
            "27/27 - 0s - loss: 0.5358 - categorical_accuracy: 0.7315 - val_loss: 0.4224 - val_categorical_accuracy: 0.8427 - 42ms/epoch - 2ms/step\n",
            "Epoch 47/120\n",
            "27/27 - 0s - loss: 0.5068 - categorical_accuracy: 0.7829 - val_loss: 0.3808 - val_categorical_accuracy: 0.8652 - 40ms/epoch - 1ms/step\n",
            "Epoch 48/120\n",
            "27/27 - 0s - loss: 0.5542 - categorical_accuracy: 0.7842 - val_loss: 0.3877 - val_categorical_accuracy: 0.8652 - 38ms/epoch - 1ms/step\n",
            "Epoch 49/120\n",
            "27/27 - 0s - loss: 0.5418 - categorical_accuracy: 0.7177 - val_loss: 0.4859 - val_categorical_accuracy: 0.7416 - 56ms/epoch - 2ms/step\n",
            "Epoch 50/120\n",
            "27/27 - 0s - loss: 0.5199 - categorical_accuracy: 0.7691 - val_loss: 0.4133 - val_categorical_accuracy: 0.8539 - 39ms/epoch - 1ms/step\n",
            "Epoch 51/120\n",
            "27/27 - 0s - loss: 0.5390 - categorical_accuracy: 0.7905 - val_loss: 0.3833 - val_categorical_accuracy: 0.8652 - 41ms/epoch - 1ms/step\n",
            "Epoch 52/120\n",
            "27/27 - 0s - loss: 0.4937 - categorical_accuracy: 0.7980 - val_loss: 0.4073 - val_categorical_accuracy: 0.8539 - 41ms/epoch - 1ms/step\n",
            "Epoch 53/120\n",
            "27/27 - 0s - loss: 0.4759 - categorical_accuracy: 0.7867 - val_loss: 0.3781 - val_categorical_accuracy: 0.8652 - 38ms/epoch - 1ms/step\n",
            "Epoch 54/120\n",
            "27/27 - 0s - loss: 0.4910 - categorical_accuracy: 0.8043 - val_loss: 0.4492 - val_categorical_accuracy: 0.8202 - 41ms/epoch - 1ms/step\n",
            "Epoch 55/120\n",
            "27/27 - 0s - loss: 0.4843 - categorical_accuracy: 0.7992 - val_loss: 0.4152 - val_categorical_accuracy: 0.8315 - 42ms/epoch - 2ms/step\n",
            "Epoch 56/120\n",
            "27/27 - 0s - loss: 0.4895 - categorical_accuracy: 0.7967 - val_loss: 0.3732 - val_categorical_accuracy: 0.8539 - 74ms/epoch - 3ms/step\n",
            "Epoch 57/120\n",
            "27/27 - 0s - loss: 0.4494 - categorical_accuracy: 0.8080 - val_loss: 0.3331 - val_categorical_accuracy: 0.8764 - 38ms/epoch - 1ms/step\n",
            "Epoch 58/120\n",
            "27/27 - 0s - loss: 0.4850 - categorical_accuracy: 0.7955 - val_loss: 0.3461 - val_categorical_accuracy: 0.8427 - 56ms/epoch - 2ms/step\n",
            "Epoch 59/120\n",
            "27/27 - 0s - loss: 0.4657 - categorical_accuracy: 0.7955 - val_loss: 0.3384 - val_categorical_accuracy: 0.8652 - 39ms/epoch - 1ms/step\n",
            "Epoch 60/120\n",
            "27/27 - 0s - loss: 0.4710 - categorical_accuracy: 0.7942 - val_loss: 0.3649 - val_categorical_accuracy: 0.8652 - 38ms/epoch - 1ms/step\n",
            "Epoch 61/120\n",
            "27/27 - 0s - loss: 0.4820 - categorical_accuracy: 0.7930 - val_loss: 0.3902 - val_categorical_accuracy: 0.8652 - 40ms/epoch - 1ms/step\n",
            "Epoch 62/120\n",
            "27/27 - 0s - loss: 0.4587 - categorical_accuracy: 0.8193 - val_loss: 0.3614 - val_categorical_accuracy: 0.8539 - 37ms/epoch - 1ms/step\n",
            "Epoch 63/120\n",
            "27/27 - 0s - loss: 0.4649 - categorical_accuracy: 0.8181 - val_loss: 0.3551 - val_categorical_accuracy: 0.8539 - 35ms/epoch - 1ms/step\n",
            "Epoch 64/120\n",
            "27/27 - 0s - loss: 0.4683 - categorical_accuracy: 0.7892 - val_loss: 0.3580 - val_categorical_accuracy: 0.8539 - 39ms/epoch - 1ms/step\n",
            "Epoch 65/120\n",
            "27/27 - 0s - loss: 0.4923 - categorical_accuracy: 0.7905 - val_loss: 0.3975 - val_categorical_accuracy: 0.8315 - 36ms/epoch - 1ms/step\n",
            "Epoch 66/120\n",
            "27/27 - 0s - loss: 0.4636 - categorical_accuracy: 0.7854 - val_loss: 0.4226 - val_categorical_accuracy: 0.8315 - 38ms/epoch - 1ms/step\n",
            "Epoch 67/120\n",
            "27/27 - 0s - loss: 0.5059 - categorical_accuracy: 0.7704 - val_loss: 0.4365 - val_categorical_accuracy: 0.8315 - 40ms/epoch - 1ms/step\n",
            "Epoch 68/120\n",
            "27/27 - 0s - loss: 0.5046 - categorical_accuracy: 0.7892 - val_loss: 0.4007 - val_categorical_accuracy: 0.8315 - 42ms/epoch - 2ms/step\n",
            "Epoch 69/120\n",
            "27/27 - 0s - loss: 0.4384 - categorical_accuracy: 0.8143 - val_loss: 0.3805 - val_categorical_accuracy: 0.8539 - 43ms/epoch - 2ms/step\n",
            "Epoch 70/120\n",
            "27/27 - 0s - loss: 0.5104 - categorical_accuracy: 0.8130 - val_loss: 0.3815 - val_categorical_accuracy: 0.8539 - 38ms/epoch - 1ms/step\n",
            "Epoch 71/120\n",
            "27/27 - 0s - loss: 0.5245 - categorical_accuracy: 0.7503 - val_loss: 0.3870 - val_categorical_accuracy: 0.8539 - 39ms/epoch - 1ms/step\n",
            "Epoch 72/120\n",
            "27/27 - 0s - loss: 0.5269 - categorical_accuracy: 0.8080 - val_loss: 0.4516 - val_categorical_accuracy: 0.7865 - 56ms/epoch - 2ms/step\n",
            "Epoch 73/120\n",
            "27/27 - 0s - loss: 0.5461 - categorical_accuracy: 0.7679 - val_loss: 0.3689 - val_categorical_accuracy: 0.8764 - 38ms/epoch - 1ms/step\n",
            "Epoch 74/120\n",
            "27/27 - 0s - loss: 0.5131 - categorical_accuracy: 0.7704 - val_loss: 0.3579 - val_categorical_accuracy: 0.8764 - 40ms/epoch - 1ms/step\n",
            "Epoch 75/120\n",
            "27/27 - 0s - loss: 0.5041 - categorical_accuracy: 0.7942 - val_loss: 0.3549 - val_categorical_accuracy: 0.8764 - 46ms/epoch - 2ms/step\n",
            "Epoch 76/120\n",
            "27/27 - 0s - loss: 0.5099 - categorical_accuracy: 0.7829 - val_loss: 0.3520 - val_categorical_accuracy: 0.8764 - 37ms/epoch - 1ms/step\n",
            "Epoch 77/120\n",
            "27/27 - 0s - loss: 0.5052 - categorical_accuracy: 0.7767 - val_loss: 0.3773 - val_categorical_accuracy: 0.8652 - 37ms/epoch - 1ms/step\n",
            "Epoch 78/120\n",
            "27/27 - 0s - loss: 0.5520 - categorical_accuracy: 0.7064 - val_loss: 0.4290 - val_categorical_accuracy: 0.8652 - 47ms/epoch - 2ms/step\n",
            "Epoch 79/120\n",
            "27/27 - 0s - loss: 0.5053 - categorical_accuracy: 0.7390 - val_loss: 0.4242 - val_categorical_accuracy: 0.7978 - 44ms/epoch - 2ms/step\n",
            "Epoch 80/120\n",
            "27/27 - 0s - loss: 0.5638 - categorical_accuracy: 0.7227 - val_loss: 0.4051 - val_categorical_accuracy: 0.7978 - 45ms/epoch - 2ms/step\n",
            "Epoch 81/120\n",
            "27/27 - 0s - loss: 0.4917 - categorical_accuracy: 0.7742 - val_loss: 0.3625 - val_categorical_accuracy: 0.8764 - 40ms/epoch - 1ms/step\n",
            "Epoch 82/120\n",
            "27/27 - 0s - loss: 0.4877 - categorical_accuracy: 0.7804 - val_loss: 0.3436 - val_categorical_accuracy: 0.8764 - 41ms/epoch - 1ms/step\n",
            "Epoch 83/120\n",
            "27/27 - 0s - loss: 0.5132 - categorical_accuracy: 0.7729 - val_loss: 0.3748 - val_categorical_accuracy: 0.8652 - 41ms/epoch - 1ms/step\n",
            "Epoch 84/120\n",
            "27/27 - 0s - loss: 0.4872 - categorical_accuracy: 0.7955 - val_loss: 0.4166 - val_categorical_accuracy: 0.8652 - 39ms/epoch - 1ms/step\n",
            "Epoch 85/120\n",
            "27/27 - 0s - loss: 0.4964 - categorical_accuracy: 0.7880 - val_loss: 0.4669 - val_categorical_accuracy: 0.7753 - 38ms/epoch - 1ms/step\n",
            "Epoch 86/120\n",
            "27/27 - 0s - loss: 0.4873 - categorical_accuracy: 0.7528 - val_loss: 0.4255 - val_categorical_accuracy: 0.8090 - 38ms/epoch - 1ms/step\n",
            "Epoch 87/120\n",
            "27/27 - 0s - loss: 0.4614 - categorical_accuracy: 0.8030 - val_loss: 0.4091 - val_categorical_accuracy: 0.8315 - 46ms/epoch - 2ms/step\n",
            "Epoch 88/120\n",
            "27/27 - 0s - loss: 0.4966 - categorical_accuracy: 0.8055 - val_loss: 0.3704 - val_categorical_accuracy: 0.8652 - 39ms/epoch - 1ms/step\n",
            "Epoch 89/120\n",
            "27/27 - 0s - loss: 0.4783 - categorical_accuracy: 0.8068 - val_loss: 0.4076 - val_categorical_accuracy: 0.8652 - 41ms/epoch - 1ms/step\n",
            "Epoch 90/120\n",
            "27/27 - 0s - loss: 0.4994 - categorical_accuracy: 0.8005 - val_loss: 0.4283 - val_categorical_accuracy: 0.8202 - 38ms/epoch - 1ms/step\n",
            "Epoch 91/120\n",
            "27/27 - 0s - loss: 0.4615 - categorical_accuracy: 0.7880 - val_loss: 0.4944 - val_categorical_accuracy: 0.7978 - 38ms/epoch - 1ms/step\n",
            "Epoch 92/120\n",
            "27/27 - 0s - loss: 0.4917 - categorical_accuracy: 0.8030 - val_loss: 0.5688 - val_categorical_accuracy: 0.7865 - 41ms/epoch - 1ms/step\n",
            "Epoch 93/120\n",
            "27/27 - 0s - loss: 0.4725 - categorical_accuracy: 0.8118 - val_loss: 0.5069 - val_categorical_accuracy: 0.8202 - 42ms/epoch - 2ms/step\n",
            "Epoch 94/120\n",
            "27/27 - 0s - loss: 0.4735 - categorical_accuracy: 0.8005 - val_loss: 0.4150 - val_categorical_accuracy: 0.8202 - 58ms/epoch - 2ms/step\n",
            "Epoch 95/120\n",
            "27/27 - 0s - loss: 0.4760 - categorical_accuracy: 0.8080 - val_loss: 0.3758 - val_categorical_accuracy: 0.8652 - 56ms/epoch - 2ms/step\n",
            "Epoch 96/120\n",
            "27/27 - 0s - loss: 0.5024 - categorical_accuracy: 0.8093 - val_loss: 0.3935 - val_categorical_accuracy: 0.8652 - 39ms/epoch - 1ms/step\n",
            "Epoch 97/120\n",
            "27/27 - 0s - loss: 0.4801 - categorical_accuracy: 0.8156 - val_loss: 0.4151 - val_categorical_accuracy: 0.8202 - 63ms/epoch - 2ms/step\n",
            "Epoch 98/120\n",
            "27/27 - 0s - loss: 0.5013 - categorical_accuracy: 0.8130 - val_loss: 0.4302 - val_categorical_accuracy: 0.8202 - 59ms/epoch - 2ms/step\n",
            "Epoch 99/120\n",
            "27/27 - 0s - loss: 0.5080 - categorical_accuracy: 0.8030 - val_loss: 0.3789 - val_categorical_accuracy: 0.8764 - 38ms/epoch - 1ms/step\n",
            "Epoch 100/120\n",
            "27/27 - 0s - loss: 0.4732 - categorical_accuracy: 0.8043 - val_loss: 0.3816 - val_categorical_accuracy: 0.8539 - 39ms/epoch - 1ms/step\n",
            "Epoch 101/120\n",
            "27/27 - 0s - loss: 0.5096 - categorical_accuracy: 0.7967 - val_loss: 0.3593 - val_categorical_accuracy: 0.8539 - 39ms/epoch - 1ms/step\n",
            "Epoch 102/120\n",
            "27/27 - 0s - loss: 0.4851 - categorical_accuracy: 0.7942 - val_loss: 0.3667 - val_categorical_accuracy: 0.8427 - 41ms/epoch - 1ms/step\n",
            "Epoch 103/120\n",
            "27/27 - 0s - loss: 0.4842 - categorical_accuracy: 0.7930 - val_loss: 0.4198 - val_categorical_accuracy: 0.8652 - 58ms/epoch - 2ms/step\n",
            "Epoch 104/120\n",
            "27/27 - 0s - loss: 0.5031 - categorical_accuracy: 0.7905 - val_loss: 0.3704 - val_categorical_accuracy: 0.8539 - 45ms/epoch - 2ms/step\n",
            "Epoch 105/120\n",
            "27/27 - 0s - loss: 0.4920 - categorical_accuracy: 0.8005 - val_loss: 0.3586 - val_categorical_accuracy: 0.8539 - 39ms/epoch - 1ms/step\n",
            "Epoch 106/120\n",
            "27/27 - 0s - loss: 0.5057 - categorical_accuracy: 0.7854 - val_loss: 0.3475 - val_categorical_accuracy: 0.8652 - 39ms/epoch - 1ms/step\n",
            "Epoch 107/120\n",
            "27/27 - 0s - loss: 0.5144 - categorical_accuracy: 0.7992 - val_loss: 0.4137 - val_categorical_accuracy: 0.8652 - 45ms/epoch - 2ms/step\n",
            "Epoch 108/120\n",
            "27/27 - 0s - loss: 0.4987 - categorical_accuracy: 0.7829 - val_loss: 0.3328 - val_categorical_accuracy: 0.8764 - 42ms/epoch - 2ms/step\n",
            "Epoch 109/120\n",
            "27/27 - 0s - loss: 0.5511 - categorical_accuracy: 0.7930 - val_loss: 0.3483 - val_categorical_accuracy: 0.8764 - 41ms/epoch - 1ms/step\n",
            "Epoch 110/120\n",
            "27/27 - 0s - loss: 0.5064 - categorical_accuracy: 0.7553 - val_loss: 0.4038 - val_categorical_accuracy: 0.8427 - 42ms/epoch - 2ms/step\n",
            "Epoch 111/120\n",
            "27/27 - 0s - loss: 0.5556 - categorical_accuracy: 0.8043 - val_loss: 0.4223 - val_categorical_accuracy: 0.8202 - 39ms/epoch - 1ms/step\n",
            "Epoch 112/120\n",
            "27/27 - 0s - loss: 0.5138 - categorical_accuracy: 0.7817 - val_loss: 0.4088 - val_categorical_accuracy: 0.8539 - 46ms/epoch - 2ms/step\n",
            "Epoch 113/120\n",
            "27/27 - 0s - loss: 0.5078 - categorical_accuracy: 0.8005 - val_loss: 0.3506 - val_categorical_accuracy: 0.8764 - 57ms/epoch - 2ms/step\n",
            "Epoch 114/120\n",
            "27/27 - 0s - loss: 0.5112 - categorical_accuracy: 0.7829 - val_loss: 0.3517 - val_categorical_accuracy: 0.8652 - 40ms/epoch - 1ms/step\n",
            "Epoch 115/120\n",
            "27/27 - 0s - loss: 0.4774 - categorical_accuracy: 0.7992 - val_loss: 0.3602 - val_categorical_accuracy: 0.8764 - 63ms/epoch - 2ms/step\n",
            "Epoch 116/120\n",
            "27/27 - 0s - loss: 0.4578 - categorical_accuracy: 0.8043 - val_loss: 0.3775 - val_categorical_accuracy: 0.8652 - 46ms/epoch - 2ms/step\n",
            "Epoch 117/120\n",
            "27/27 - 0s - loss: 0.4805 - categorical_accuracy: 0.7905 - val_loss: 0.4289 - val_categorical_accuracy: 0.8764 - 47ms/epoch - 2ms/step\n",
            "Epoch 118/120\n",
            "27/27 - 0s - loss: 0.4732 - categorical_accuracy: 0.8005 - val_loss: 0.4058 - val_categorical_accuracy: 0.8427 - 46ms/epoch - 2ms/step\n",
            "Epoch 119/120\n",
            "27/27 - 0s - loss: 0.4856 - categorical_accuracy: 0.7917 - val_loss: 0.4366 - val_categorical_accuracy: 0.8427 - 64ms/epoch - 2ms/step\n",
            "Epoch 120/120\n",
            "27/27 - 0s - loss: 0.5048 - categorical_accuracy: 0.8068 - val_loss: 0.4083 - val_categorical_accuracy: 0.8427 - 42ms/epoch - 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Loss\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.plot(history.history[\"loss\"], label=\"Train\")\n",
        "plt.plot(history.history[\"val_loss\"], label=\"Validation\")\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Accuracy\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Categorical Accuracy\")\n",
        "plt.plot(history.history[\"categorical_accuracy\"], label=\"Train\")\n",
        "plt.plot(history.history[\"val_categorical_accuracy\"], label=\"Validation\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "cbGIRrFhlQx5",
        "outputId": "d521cc47-3fc5-42f6-dc28-933d4ee4a75f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f4ffe1bb4d0>"
            ]
          },
          "metadata": {},
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate = model.evaluate(test_X_scaled, test_Y)\n",
        "print(\"Model Categorical Test Accuracy:\", evaluate[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ju84O5PrlS3u",
        "outputId": "82187414-2061-465b-c463-e36320925f8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14/14 [==============================] - 0s 1ms/step - loss: 0.5897 - categorical_accuracy: 0.7919\n",
            "Model Categorical Test Accuracy: 0.7918660044670105\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_nn = model.predict(test_X_scaled)\n",
        "# From one_hot encoding to 0 or 1\n",
        "y_pred_nn = tf.argmax(y_pred_nn, axis=1).numpy()"
      ],
      "metadata": {
        "id": "dS-ZPulLlUs_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results_nn = [pd.read_csv(\"test.csv\")['PassengerId'], y_pred_nn]\n",
        "submission_nn = pd.DataFrame(results_nn)\n",
        "submission_nn = pd.DataFrame(results_nn).transpose()\n",
        "submission_nn.columns = ['PassengerId', 'Survived']\n",
        "# submission_nn.to_csv(\"Results/results_nn.csv\",index = False)"
      ],
      "metadata": {
        "id": "5JyftpN6lWbe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Resultados"
      ],
      "metadata": {
        "id": "VrzHJGvV_Pb1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Este modelo implementado con Tensorflow utiliza la supervivencia como clasificación dado que la red cuenta con 2 neuronas de salida, las cuales a través de la función softmax muestran la probabilidad de que un pasajero haya sobrevivido y de que no haya sobrevivido. El resultado final de la predicción será aquella neurona con mayor probabilidad.\n",
        "A través de este modelo se obtuvo mejor accuracy en las predicciones por lo que se puede concluir que es el modelo con mayor efectividad al momento de predecir la supervivencia a bordo del titanic con los datos procesados anteriormente.\n",
        "\n",
        "Al tratarse de una clasificación se utilizó una métrica de accuracy específica de clasificaciones. Se utilizó entropía de clasificación para la función de pérdida de la red con la finalidad de que se minimice los errores tanto de clasificación de supervivencia incorrectas como las clasificaciones de no supervivencia incorrectas y premiar aquellas que si se estén clasificando correctamente. \n",
        "Se utilizó la técnica de regularización de dropout para evitar que la red genere overfitting y el optimizador utilizado utilizó momento para aprovechar os gradientes encontrados y converger más rápido. Al utilizar momento fue necesario disminuir el valor del learning rate para compensar la magnitud del movimiento de gradiente descendiente y converger ccon movimientos más tranquilos pero asegurados.\n",
        "\n",
        "Se utilizó el 10% de los datos de entrenamiento como validación para mostrar cómo se comportan la función de pérdida y de accuracy para datos sobre los cuales no se ajusta el modelo y asegurarse de que no se genere overfitting.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "vzAtvsC6_dRL"
      }
    }
  ]
}