ElderlyMortalityPrediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "All_Methods.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sJ6L0DXPUOma",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47a364b7-bb80-4fd6-9dfe-a1f3aa95a7a3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting ann_visualizer\n",
            "  Downloading ann_visualizer-2.5.tar.gz (4.7 kB)\n",
            "Building wheels for collected packages: ann-visualizer\n",
            "  Building wheel for ann-visualizer (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ann-visualizer: filename=ann_visualizer-2.5-py3-none-any.whl size=4168 sha256=382f7a67aeed78a68242645a2e7100d5586ffd6d5591e3784e4fd6ec7c1adff5\n",
            "  Stored in directory: /root/.cache/pip/wheels/1b/fc/58/2ab1c3b30350105929308becddda4fb59b1358e54f985e1f4a\n",
            "Successfully built ann-visualizer\n",
            "Installing collected packages: ann-visualizer\n",
            "Successfully installed ann-visualizer-2.5\n"
          ]
        }
      ],
      "source": [
        "# !pip3 install keras-visualizer\n",
        "!pip3 install ann_visualizer\n",
        "# %tensorflow_version 1.x\n",
        "# !pip3 install keras==2.3.1\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.pyplot import figure\n",
        "\n",
        "# example of training a final classification model\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "# import keras\n",
        "# print(keras.__version__)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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        "outputId": "ed1086c9-b033-4320-a529-3ed9c2e59853"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving FinalDataset.csv to FinalDataset.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing the datasets\n",
        "\n",
        "datasets = pd.read_csv('FinalDataset.csv', sep=',')\n",
        "# numpyData = datasets.to_numpy()\n",
        "\n",
        "X = datasets.iloc[:, [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]].values\n",
        "Y = datasets.iloc[:, 18].values\n",
        "# X\n",
        "# Y\n",
        "# X\n",
        "# datasets\n",
        "# numpyData\n",
        "datasets.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "4L-COnqcUaIh",
        "outputId": "3e7ffd38-a0db-494e-9203-1d1b7b3e69e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\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>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Marital State</th>\n",
              "      <th>Job</th>\n",
              "      <th>Location</th>\n",
              "      <th>Hour of the Incident</th>\n",
              "      <th>Day of the Week</th>\n",
              "      <th>Season</th>\n",
              "      <th>Month</th>\n",
              "      <th>TBSA</th>\n",
              "      <th>Inhalation Injury</th>\n",
              "      <th>Burn Degree</th>\n",
              "      <th>Burn Cause</th>\n",
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              "      <th>Anatomical Site</th>\n",
              "      <th>Past Medical History</th>\n",
              "      <th>AgeGroup</th>\n",
              "      <th>TBSA Groups</th>\n",
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              "      <td>6</td>\n",
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              "      <td>11</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <th>2</th>\n",
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              "      <td>0.08</td>\n",
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              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>92</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>92</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>0.10</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d9fd15c1-3ad4-4021-9271-9631c830c17a');\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Age  Sex  Marital State  ...  AgeGroup  TBSA Groups  Final\n",
              "0   94    1              3  ...         4            2      1\n",
              "1   93    1              1  ...         4            1      1\n",
              "2   93    1              1  ...         4            1      2\n",
              "3   92    1              1  ...         4            3      1\n",
              "4   92    1              1  ...         4            1      2\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting the dataset into the Training set and Test set\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size = 0.25, random_state = 0)\n"
      ],
      "metadata": {
        "id": "PvJ7bB6GUjPo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "\n",
        "# Fit only to the training data\n",
        "scaler.fit(X_Train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "exulHw2mbHez",
        "outputId": "43c74340-c89a-4af8-f24e-636fc1c792ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StandardScaler()"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Now apply the transformations to the data:\n",
        "\n",
        "X_train1 = scaler.transform(X_Train)\n",
        "X_train1 = X_train1.astype(np.float32)\n",
        "\n",
        "# X_test\n",
        "Y_train1 = Y_Train.copy()\n",
        "i=0\n",
        "for z in Y_train1:\n",
        "  if z == 1:\n",
        "    Y_train1[i] = 0\n",
        "  else:\n",
        "    Y_train1[i] = 1\n",
        "  i = i+1\n",
        "\n",
        "Y_test1 = Y_Test.copy()\n",
        "i=0\n",
        "for z in Y_test1:\n",
        "  if z == 1:\n",
        "    Y_test1[i] = 0\n",
        "  else:\n",
        "    Y_test1[i] = 1\n",
        "  i = i+1\n",
        "\n",
        "X_test1 = scaler.transform(X_Test)\n",
        "X_test1 = X_test1.astype(np.float32)"
      ],
      "metadata": {
        "id": "oY92fluCbmbX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting the classifier into the Training set\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import HistGradientBoostingClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from xgboost import plot_importance\n",
        "\n",
        "# instantiate the model (using the default parameters)\n",
        "model_LogisticRegression = LogisticRegression()\n",
        "model_LogisticRegression.fit(X_Train, Y_Train)\n",
        "\n",
        "model_RandomForestClassifier = RandomForestClassifier(n_estimators = 200)\n",
        "model_RandomForestClassifier.fit(X_Train,Y_Train)\n",
        "\n",
        "model_HistGradientBoostingClassifier = HistGradientBoostingClassifier(max_bins=10, learning_rate=0.6)\n",
        "model_HistGradientBoostingClassifier.fit(X_Train, Y_Train)\n",
        "\n",
        "model_GaussianNB = GaussianNB()\n",
        "model_GaussianNB.fit(X_Train, Y_Train)\n",
        "\n",
        "model_XGBClassifier = XGBClassifier(learning_rate=0.2, max_depth=1)\n",
        "model_XGBClassifier.fit(X_Train, Y_Train)\n",
        "\n",
        "# from sklearn.model_selection import cross_validate\n",
        "# import matplotlib.pyplot as plt\n",
        "\n",
        "# cv_dict = cross_validate(model_HistGradientBoostingClassifier, X_Train, Y_Train, return_train_score=True)\n",
        "# print(cv_dict)\n",
        "# model_svm = svm.SVC(kernel='linear') # Linear Kernel\n",
        "# model_svm.fit(X_Train, Y_Train)\n",
        "\n",
        "# plot of train and test scores vs tree depth\n",
        "# plt.plot([0,1,2,3,4], cv_dict['train_score'], '-o', label='Train')\n",
        "# plt.plot([0,1,2,3,4], cv_dict['test_score'], '-o', label='Test')\n",
        "# plt.legend()\n",
        "# plt.show()\n",
        "\n",
        "\n",
        "age = 63\n",
        "sex = 2\n",
        "marital_state = 1\n",
        "job = 2\n",
        "location = 2\n",
        "hour_of_the_incident = 2\n",
        "day_of_the_week = 1\n",
        "season = 4\n",
        "month = 11\n",
        "tbsa = 0.6\n",
        "inhalation_injury = 0\n",
        "burn_degree = 4\n",
        "burn_cause = 1\n",
        "place_of_injury = 1\n",
        "anatomical_site = 2\n",
        "past_medical_history = 6\n",
        "age_group = 1\n",
        "tbsa_groups = 3\n",
        "# 2\n",
        "\n",
        "# Y_Pred = model.predict([[age, sex, marital_state, job, location, hour_group, day_of_the_week, season, month, tbsa, \n",
        "#                          inhalation_injury, degree, cause, place_of_injury, anatomical_site, pmh, age_group, tbsa_groups]])\n",
        "Y_Pred_LogisticRegression = model_LogisticRegression.predict(X_Test)\n",
        "Y_Pred_LogisticRegression1 = model_LogisticRegression.predict(X_Train)\n",
        "# print(\"predict LogisticRegression: \", Y_Pred_LogisticRegression)\n",
        "# print(\"accuracy LogisticRegression11: \", accuracy_score(Y_Train, Y_Pred_LogisticRegression1))\n",
        "\n",
        "Y_Pred_RandomForestClassifier = model_RandomForestClassifier.predict(X_Test)\n",
        "# print(\"predict RandomForestClassifier: \", Y_Pred_RandomForestClassifier)\n",
        "\n",
        "Y_Pred_HistGradientBoostingClassifier = model_HistGradientBoostingClassifier.predict(X_Test)\n",
        "# print(\"predict HistGradientBoostingClassifier: \", Y_Pred_HistGradientBoostingClassifier)\n",
        "\n",
        "Y_Pred_GaussianNB = model_GaussianNB.predict(X_Test)\n",
        "# print(\"predict GaussianNB: \", Y_Pred_GaussianNB)\n",
        "\n",
        "Y_Pred_XGBClassifier = model_XGBClassifier.predict(X_Test)\n",
        "print(\"predict XGBClassifier: \", Y_Pred_XGBClassifier)\n",
        "\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "\n",
        "print(\"accuracy LogisticRegression: \", accuracy_score(Y_Test, Y_Pred_LogisticRegression))\n",
        "print(\"accurac RandomForestClassifiery: \", accuracy_score(Y_Test, Y_Pred_RandomForestClassifier))\n",
        "print(\"accuracy HistGradientBoostingClassifier: \", accuracy_score(Y_Test, Y_Pred_HistGradientBoostingClassifier))\n",
        "print(\"accuracy GaussianNB: \", accuracy_score(Y_Test, Y_Pred_GaussianNB))\n",
        "print(\"accuracy XGBClassifier: \", accuracy_score(Y_Test, Y_Pred_XGBClassifier))\n",
        "\n",
        "# from sklearn.metrics import mean_absolute_error\n",
        "# mae_train = []\n",
        "# mae_test = []\n",
        "# i=0\n",
        "# for train in Y_Train:\n",
        "#   predict = model_LogisticRegression.predict(X_Train[i])\n",
        "#   mae_train.append(mean_absolute_error(train, predict))\n",
        "#   i +=1\n",
        "\n",
        "# mae_test.append(mean_absolute_error(Y_Test, Y_Pred_LogisticRegression))\n",
        "\n",
        "# print(X_Train)\n",
        "# folds = range(1, 10)\n",
        "# plt.plot(folds, mae_train, 'o-', color='green', label='train')\n",
        "# plt.plot(folds, mae_test, 'o-', color='red', label='test')\n",
        "# plt.legend()\n",
        "# plt.grid()\n",
        "# plt.xlabel('Number of fold')\n",
        "# plt.ylabel('Mean Absolute Error')\n",
        "# plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bKDsi5AmUn-E",
        "outputId": "d257a210-1477-41e5-910c-e839bec21532"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "predict XGBClassifier:  [2 2 2 2 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 1 2 2 2\n",
            " 2 2 1 2 2 2 1 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 2 2 2\n",
            " 1 2 2 1 2 2 2 2 2 1 2 2 2 1 2 2 2 2 2 2 2 2 1 1 2 1 2 2 2 2 2 2 2 2 2 2 2\n",
            " 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 1 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 1 2 2 2]\n",
            "accuracy LogisticRegression:  0.8888888888888888\n",
            "accurac RandomForestClassifiery:  0.8627450980392157\n",
            "accuracy HistGradientBoostingClassifier:  0.9019607843137255\n",
            "accuracy GaussianNB:  0.8562091503267973\n",
            "accuracy XGBClassifier:  0.8823529411764706\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# xg_reg = model_XGBClassifier.train( num_boost_round=10)\n",
        "\n",
        "from sklearn.inspection import permutation_importance\n",
        "\n",
        "a = []\n",
        "i = 0\n",
        "for col in datasets.columns:\n",
        "    a.append(col)\n",
        "    i += 1\n",
        "a.pop()\n",
        "\n",
        "figure(figsize=(15, 10), dpi=80)\n",
        "fig1 = plt.gcf()\n",
        "\n",
        "importances = model_RandomForestClassifier.feature_importances_\n",
        "indices = np.argsort(importances)\n",
        "# print(indices)\n",
        "indices = np.delete(indices, 16)\n",
        "indices = np.delete(indices, 8)\n",
        "\n",
        "plt.title('Feature Importance (Random Forest Classifier)')\n",
        "# plt.barh(range(len(indices)), importances[indices], color='b', align='center')\n",
        "plt.barh(range(len(indices)), importances[indices],  color=['#FFF59D', '#FFF59D', '#FFF176', '#FFEB3B', '#FFEE58','#FDD835', \n",
        "                                                             '#FFC107','#FFB300', '#FFA000', '#FF8F00', '#FF6F00', '#F44336', '#E53935','#D32F2F', '#C62828', '#B71C1C'])\n",
        "plt.yticks(range(len(indices)), [a[i] for i in indices])\n",
        "plt.xlabel('Importance')\n",
        "plt.ylabel('Features')\n",
        "plt.show()\n",
        "\n",
        "from google.colab import files\n",
        "fig1.savefig(\"Feature_Importance(Random Forest Classifier).png\", dpi=200)\n",
        "files.download(\"Feature_Importance(Random Forest Classifier).png\") \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 685
        },
        "id": "ApQI9fv9LwQk",
        "outputId": "b7b8cc39-2034-471f-9a1d-12d8c56173c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_7ca0db62-0461-485d-a98b-24aa78b48b76\", \"Feature_Importance(Random Forest Classifier).png\", 112129)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "importances = model_XGBClassifier.feature_importances_\n",
        "indices = importances.argsort()\n",
        "indices = np.delete(indices, 0)\n",
        "indices = np.delete(indices, 0)\n",
        "indices = np.delete(indices, 0)\n",
        "indices = np.delete(indices, 0)\n",
        "indices = np.delete(indices, 0)\n",
        "indices = np.delete(indices, 0)\n",
        "indices = np.delete(indices, 0)\n",
        "\n",
        "# fig1 = plt.gcf()\n",
        "figure(figsize=(15, 10), dpi=80)\n",
        "fig1 = plt.gcf()\n",
        "\n",
        "plt.title('Feature Importance (XGBoost Classifier)')\n",
        "plt.barh(range(len(indices)), importances[indices],  color=[ '#FDD835', '#FFC107','#FFB300', '#FFA000', '#FF8F00', '#FF6F00', '#F44336', '#E53935','#D32F2F', '#C62828', '#B71C1C'])\n",
        "plt.yticks(range(len(indices)), [a[i] for i in indices])\n",
        "plt.xlabel('Importance')\n",
        "plt.ylabel('Features')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "from google.colab import files\n",
        "fig1.savefig(\"Feature_Importance(XGBoost Classifier).png\", dpi=200)\n",
        "files.download(\"Feature_Importance(XGBoost Classifier).png\") "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 685
        },
        "id": "OVnmKvkViVRS",
        "outputId": "1875d10c-dbb5-4216-d5c9-38214786d5a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_9a0d2d96-55ae-4e65-be38-c06cc4b37639\", \"Feature_Importance(XGBoost Classifier).png\", 97251)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from keras.regularizers import l2\n",
        "# from keras_visualizer import visualizer \n",
        "from ann_visualizer.visualize import ann_viz\n",
        "\n",
        "model = Sequential()\n",
        "# model.add(Dense(18, input_dim=18, activation='relu'))\n",
        "model.add(Dense(18, input_dim=18, activation='relu', kernel_regularizer=l2(0.2)))\n",
        "# model.add(Dense(18, activation='relu'))\n",
        "# model.add(Dense(18, activation='relu'))\n",
        "# model.add(Dense(20, activation='relu'))\n",
        "# model.add(Dense(10, activation='relu'))\n",
        "# model.add(Dense(5, activation='relu'))\n",
        "# model.add(Dense(5, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "# model.add(Dense(1, activation='sigmoid'))\n",
        "# model.compile(loss='binary_crossentropy', optimizer='adam')\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "model.summary() \n",
        "\n",
        "# visual = visualizer(model, format='png', view=True)\n",
        "\n",
        "# from google.colab import files\n",
        "# visual.savefig(\"nn_resolve_overfitting.png\", dpi=200)\n",
        "# files.download(\"nn_resolve_overfitting.png\") \n",
        "ann_viz(model)\n",
        "\n",
        "history = model.fit(X_train1, Y_train1, validation_data = (X_test1, Y_test1), epochs=300, verbose=2)\n",
        "\n",
        "model.save(\"model.h5\")\n",
        "print(\"Saved model to disk\")\n",
        "\n",
        "# model.fit(x, y, epochs=1000, verbose=2)\n",
        "\n",
        "# serialize model to JSON\n",
        "# model_json = model.to_json()\n",
        "# with open(\"model.json\", \"w\") as json_file:\n",
        "#     json_file.write(model_json)\n",
        "# # serialize weights to HDF5\n",
        "# model.save_weights(\"model.h5\")\n",
        "# print(\"Saved model to disk\")"
      ],
      "metadata": {
        "id": "_ldyEHpicsUP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2b1044c-d3a8-4fa3-e3cd-3f76fce1ce5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 18)                342       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 19        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 361\n",
            "Trainable params: 361\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/300\n",
            "15/15 - 1s - loss: 4.0525 - accuracy: 0.5120 - val_loss: 3.8502 - val_accuracy: 0.5098 - 1s/epoch - 70ms/step\n",
            "Epoch 2/300\n",
            "15/15 - 0s - loss: 3.6590 - accuracy: 0.6231 - val_loss: 3.4865 - val_accuracy: 0.5817 - 66ms/epoch - 4ms/step\n",
            "Epoch 3/300\n",
            "15/15 - 0s - loss: 3.3041 - accuracy: 0.6972 - val_loss: 3.1597 - val_accuracy: 0.6471 - 69ms/epoch - 5ms/step\n",
            "Epoch 4/300\n",
            "15/15 - 0s - loss: 2.9865 - accuracy: 0.7625 - val_loss: 2.8664 - val_accuracy: 0.7059 - 64ms/epoch - 4ms/step\n",
            "Epoch 5/300\n",
            "15/15 - 0s - loss: 2.7026 - accuracy: 0.8083 - val_loss: 2.6031 - val_accuracy: 0.7255 - 63ms/epoch - 4ms/step\n",
            "Epoch 6/300\n",
            "15/15 - 0s - loss: 2.4477 - accuracy: 0.8475 - val_loss: 2.3673 - val_accuracy: 0.7516 - 72ms/epoch - 5ms/step\n",
            "Epoch 7/300\n",
            "15/15 - 0s - loss: 2.2191 - accuracy: 0.8562 - val_loss: 2.1553 - val_accuracy: 0.7582 - 70ms/epoch - 5ms/step\n",
            "Epoch 8/300\n",
            "15/15 - 0s - loss: 2.0136 - accuracy: 0.8584 - val_loss: 1.9637 - val_accuracy: 0.7843 - 64ms/epoch - 4ms/step\n",
            "Epoch 9/300\n",
            "15/15 - 0s - loss: 1.8286 - accuracy: 0.8649 - val_loss: 1.7916 - val_accuracy: 0.7974 - 75ms/epoch - 5ms/step\n",
            "Epoch 10/300\n",
            "15/15 - 0s - loss: 1.6624 - accuracy: 0.8715 - val_loss: 1.6361 - val_accuracy: 0.7974 - 62ms/epoch - 4ms/step\n",
            "Epoch 11/300\n",
            "15/15 - 0s - loss: 1.5121 - accuracy: 0.8715 - val_loss: 1.4965 - val_accuracy: 0.7974 - 66ms/epoch - 4ms/step\n",
            "Epoch 12/300\n",
            "15/15 - 0s - loss: 1.3768 - accuracy: 0.8715 - val_loss: 1.3703 - val_accuracy: 0.7974 - 64ms/epoch - 4ms/step\n",
            "Epoch 13/300\n",
            "15/15 - 0s - loss: 1.2550 - accuracy: 0.8715 - val_loss: 1.2563 - val_accuracy: 0.7974 - 62ms/epoch - 4ms/step\n",
            "Epoch 14/300\n",
            "15/15 - 0s - loss: 1.1438 - accuracy: 0.8715 - val_loss: 1.1549 - val_accuracy: 0.7974 - 70ms/epoch - 5ms/step\n",
            "Epoch 15/300\n",
            "15/15 - 0s - loss: 1.0445 - accuracy: 0.8715 - val_loss: 1.0619 - val_accuracy: 0.7974 - 64ms/epoch - 4ms/step\n",
            "Epoch 16/300\n",
            "15/15 - 0s - loss: 0.9541 - accuracy: 0.8715 - val_loss: 0.9796 - val_accuracy: 0.7974 - 73ms/epoch - 5ms/step\n",
            "Epoch 17/300\n",
            "15/15 - 0s - loss: 0.8727 - accuracy: 0.8715 - val_loss: 0.9054 - val_accuracy: 0.7974 - 69ms/epoch - 5ms/step\n",
            "Epoch 18/300\n",
            "15/15 - 0s - loss: 0.7990 - accuracy: 0.8736 - val_loss: 0.8396 - val_accuracy: 0.8039 - 65ms/epoch - 4ms/step\n",
            "Epoch 19/300\n",
            "15/15 - 0s - loss: 0.7327 - accuracy: 0.8758 - val_loss: 0.7796 - val_accuracy: 0.8170 - 63ms/epoch - 4ms/step\n",
            "Epoch 20/300\n",
            "15/15 - 0s - loss: 0.6733 - accuracy: 0.8780 - val_loss: 0.7257 - val_accuracy: 0.8105 - 72ms/epoch - 5ms/step\n",
            "Epoch 21/300\n",
            "15/15 - 0s - loss: 0.6192 - accuracy: 0.8780 - val_loss: 0.6784 - val_accuracy: 0.8170 - 76ms/epoch - 5ms/step\n",
            "Epoch 22/300\n",
            "15/15 - 0s - loss: 0.5711 - accuracy: 0.8824 - val_loss: 0.6376 - val_accuracy: 0.8235 - 79ms/epoch - 5ms/step\n",
            "Epoch 23/300\n",
            "15/15 - 0s - loss: 0.5278 - accuracy: 0.8845 - val_loss: 0.6008 - val_accuracy: 0.8366 - 70ms/epoch - 5ms/step\n",
            "Epoch 24/300\n",
            "15/15 - 0s - loss: 0.4894 - accuracy: 0.8889 - val_loss: 0.5696 - val_accuracy: 0.8366 - 65ms/epoch - 4ms/step\n",
            "Epoch 25/300\n",
            "15/15 - 0s - loss: 0.4553 - accuracy: 0.8976 - val_loss: 0.5419 - val_accuracy: 0.8366 - 58ms/epoch - 4ms/step\n",
            "Epoch 26/300\n",
            "15/15 - 0s - loss: 0.4258 - accuracy: 0.9063 - val_loss: 0.5187 - val_accuracy: 0.8366 - 72ms/epoch - 5ms/step\n",
            "Epoch 27/300\n",
            "15/15 - 0s - loss: 0.4006 - accuracy: 0.9085 - val_loss: 0.4994 - val_accuracy: 0.8301 - 77ms/epoch - 5ms/step\n",
            "Epoch 28/300\n",
            "15/15 - 0s - loss: 0.3780 - accuracy: 0.9063 - val_loss: 0.4809 - val_accuracy: 0.8431 - 70ms/epoch - 5ms/step\n",
            "Epoch 29/300\n",
            "15/15 - 0s - loss: 0.3587 - accuracy: 0.9085 - val_loss: 0.4657 - val_accuracy: 0.8431 - 76ms/epoch - 5ms/step\n",
            "Epoch 30/300\n",
            "15/15 - 0s - loss: 0.3427 - accuracy: 0.9107 - val_loss: 0.4533 - val_accuracy: 0.8431 - 63ms/epoch - 4ms/step\n",
            "Epoch 31/300\n",
            "15/15 - 0s - loss: 0.3283 - accuracy: 0.9150 - val_loss: 0.4423 - val_accuracy: 0.8431 - 60ms/epoch - 4ms/step\n",
            "Epoch 32/300\n",
            "15/15 - 0s - loss: 0.3164 - accuracy: 0.9172 - val_loss: 0.4333 - val_accuracy: 0.8562 - 76ms/epoch - 5ms/step\n",
            "Epoch 33/300\n",
            "15/15 - 0s - loss: 0.3054 - accuracy: 0.9194 - val_loss: 0.4256 - val_accuracy: 0.8562 - 63ms/epoch - 4ms/step\n",
            "Epoch 34/300\n",
            "15/15 - 0s - loss: 0.2963 - accuracy: 0.9194 - val_loss: 0.4182 - val_accuracy: 0.8627 - 73ms/epoch - 5ms/step\n",
            "Epoch 35/300\n",
            "15/15 - 0s - loss: 0.2882 - accuracy: 0.9194 - val_loss: 0.4124 - val_accuracy: 0.8627 - 61ms/epoch - 4ms/step\n",
            "Epoch 36/300\n",
            "15/15 - 0s - loss: 0.2812 - accuracy: 0.9194 - val_loss: 0.4073 - val_accuracy: 0.8627 - 74ms/epoch - 5ms/step\n",
            "Epoch 37/300\n",
            "15/15 - 0s - loss: 0.2754 - accuracy: 0.9194 - val_loss: 0.4020 - val_accuracy: 0.8627 - 79ms/epoch - 5ms/step\n",
            "Epoch 38/300\n",
            "15/15 - 0s - loss: 0.2699 - accuracy: 0.9216 - val_loss: 0.3988 - val_accuracy: 0.8627 - 63ms/epoch - 4ms/step\n",
            "Epoch 39/300\n",
            "15/15 - 0s - loss: 0.2647 - accuracy: 0.9216 - val_loss: 0.3963 - val_accuracy: 0.8627 - 75ms/epoch - 5ms/step\n",
            "Epoch 40/300\n",
            "15/15 - 0s - loss: 0.2603 - accuracy: 0.9216 - val_loss: 0.3930 - val_accuracy: 0.8627 - 65ms/epoch - 4ms/step\n",
            "Epoch 41/300\n",
            "15/15 - 0s - loss: 0.2565 - accuracy: 0.9216 - val_loss: 0.3909 - val_accuracy: 0.8627 - 80ms/epoch - 5ms/step\n",
            "Epoch 42/300\n",
            "15/15 - 0s - loss: 0.2536 - accuracy: 0.9237 - val_loss: 0.3876 - val_accuracy: 0.8627 - 74ms/epoch - 5ms/step\n",
            "Epoch 43/300\n",
            "15/15 - 0s - loss: 0.2508 - accuracy: 0.9237 - val_loss: 0.3870 - val_accuracy: 0.8627 - 69ms/epoch - 5ms/step\n",
            "Epoch 44/300\n",
            "15/15 - 0s - loss: 0.2481 - accuracy: 0.9237 - val_loss: 0.3840 - val_accuracy: 0.8627 - 72ms/epoch - 5ms/step\n",
            "Epoch 45/300\n",
            "15/15 - 0s - loss: 0.2456 - accuracy: 0.9237 - val_loss: 0.3821 - val_accuracy: 0.8627 - 70ms/epoch - 5ms/step\n",
            "Epoch 46/300\n",
            "15/15 - 0s - loss: 0.2438 - accuracy: 0.9259 - val_loss: 0.3812 - val_accuracy: 0.8627 - 77ms/epoch - 5ms/step\n",
            "Epoch 47/300\n",
            "15/15 - 0s - loss: 0.2417 - accuracy: 0.9237 - val_loss: 0.3805 - val_accuracy: 0.8627 - 74ms/epoch - 5ms/step\n",
            "Epoch 48/300\n",
            "15/15 - 0s - loss: 0.2398 - accuracy: 0.9216 - val_loss: 0.3783 - val_accuracy: 0.8627 - 70ms/epoch - 5ms/step\n",
            "Epoch 49/300\n",
            "15/15 - 0s - loss: 0.2383 - accuracy: 0.9216 - val_loss: 0.3783 - val_accuracy: 0.8627 - 62ms/epoch - 4ms/step\n",
            "Epoch 50/300\n",
            "15/15 - 0s - loss: 0.2367 - accuracy: 0.9216 - val_loss: 0.3763 - val_accuracy: 0.8562 - 70ms/epoch - 5ms/step\n",
            "Epoch 51/300\n",
            "15/15 - 0s - loss: 0.2355 - accuracy: 0.9194 - val_loss: 0.3751 - val_accuracy: 0.8627 - 77ms/epoch - 5ms/step\n",
            "Epoch 52/300\n",
            "15/15 - 0s - loss: 0.2345 - accuracy: 0.9216 - val_loss: 0.3749 - val_accuracy: 0.8627 - 70ms/epoch - 5ms/step\n",
            "Epoch 53/300\n",
            "15/15 - 0s - loss: 0.2329 - accuracy: 0.9216 - val_loss: 0.3740 - val_accuracy: 0.8562 - 61ms/epoch - 4ms/step\n",
            "Epoch 54/300\n",
            "15/15 - 0s - loss: 0.2323 - accuracy: 0.9216 - val_loss: 0.3741 - val_accuracy: 0.8562 - 65ms/epoch - 4ms/step\n",
            "Epoch 55/300\n",
            "15/15 - 0s - loss: 0.2307 - accuracy: 0.9237 - val_loss: 0.3734 - val_accuracy: 0.8562 - 68ms/epoch - 5ms/step\n",
            "Epoch 56/300\n",
            "15/15 - 0s - loss: 0.2300 - accuracy: 0.9237 - val_loss: 0.3723 - val_accuracy: 0.8562 - 78ms/epoch - 5ms/step\n",
            "Epoch 57/300\n",
            "15/15 - 0s - loss: 0.2290 - accuracy: 0.9237 - val_loss: 0.3716 - val_accuracy: 0.8562 - 72ms/epoch - 5ms/step\n",
            "Epoch 58/300\n",
            "15/15 - 0s - loss: 0.2284 - accuracy: 0.9237 - val_loss: 0.3717 - val_accuracy: 0.8562 - 66ms/epoch - 4ms/step\n",
            "Epoch 59/300\n",
            "15/15 - 0s - loss: 0.2276 - accuracy: 0.9281 - val_loss: 0.3711 - val_accuracy: 0.8562 - 70ms/epoch - 5ms/step\n",
            "Epoch 60/300\n",
            "15/15 - 0s - loss: 0.2267 - accuracy: 0.9281 - val_loss: 0.3704 - val_accuracy: 0.8562 - 66ms/epoch - 4ms/step\n",
            "Epoch 61/300\n",
            "15/15 - 0s - loss: 0.2263 - accuracy: 0.9216 - val_loss: 0.3714 - val_accuracy: 0.8562 - 70ms/epoch - 5ms/step\n",
            "Epoch 62/300\n",
            "15/15 - 0s - loss: 0.2253 - accuracy: 0.9216 - val_loss: 0.3708 - val_accuracy: 0.8562 - 76ms/epoch - 5ms/step\n",
            "Epoch 63/300\n",
            "15/15 - 0s - loss: 0.2242 - accuracy: 0.9259 - val_loss: 0.3708 - val_accuracy: 0.8562 - 73ms/epoch - 5ms/step\n",
            "Epoch 64/300\n",
            "15/15 - 0s - loss: 0.2236 - accuracy: 0.9281 - val_loss: 0.3684 - val_accuracy: 0.8562 - 69ms/epoch - 5ms/step\n",
            "Epoch 65/300\n",
            "15/15 - 0s - loss: 0.2235 - accuracy: 0.9281 - val_loss: 0.3699 - val_accuracy: 0.8562 - 67ms/epoch - 4ms/step\n",
            "Epoch 66/300\n",
            "15/15 - 0s - loss: 0.2225 - accuracy: 0.9281 - val_loss: 0.3677 - val_accuracy: 0.8562 - 63ms/epoch - 4ms/step\n",
            "Epoch 67/300\n",
            "15/15 - 0s - loss: 0.2216 - accuracy: 0.9303 - val_loss: 0.3676 - val_accuracy: 0.8562 - 67ms/epoch - 4ms/step\n",
            "Epoch 68/300\n",
            "15/15 - 0s - loss: 0.2215 - accuracy: 0.9281 - val_loss: 0.3689 - val_accuracy: 0.8562 - 71ms/epoch - 5ms/step\n",
            "Epoch 69/300\n",
            "15/15 - 0s - loss: 0.2205 - accuracy: 0.9303 - val_loss: 0.3709 - val_accuracy: 0.8627 - 70ms/epoch - 5ms/step\n",
            "Epoch 70/300\n",
            "15/15 - 0s - loss: 0.2204 - accuracy: 0.9303 - val_loss: 0.3703 - val_accuracy: 0.8627 - 59ms/epoch - 4ms/step\n",
            "Epoch 71/300\n",
            "15/15 - 0s - loss: 0.2199 - accuracy: 0.9281 - val_loss: 0.3664 - val_accuracy: 0.8627 - 71ms/epoch - 5ms/step\n",
            "Epoch 72/300\n",
            "15/15 - 0s - loss: 0.2197 - accuracy: 0.9303 - val_loss: 0.3659 - val_accuracy: 0.8627 - 63ms/epoch - 4ms/step\n",
            "Epoch 73/300\n",
            "15/15 - 0s - loss: 0.2188 - accuracy: 0.9303 - val_loss: 0.3656 - val_accuracy: 0.8627 - 60ms/epoch - 4ms/step\n",
            "Epoch 74/300\n",
            "15/15 - 0s - loss: 0.2180 - accuracy: 0.9303 - val_loss: 0.3656 - val_accuracy: 0.8627 - 68ms/epoch - 5ms/step\n",
            "Epoch 75/300\n",
            "15/15 - 0s - loss: 0.2177 - accuracy: 0.9259 - val_loss: 0.3672 - val_accuracy: 0.8627 - 73ms/epoch - 5ms/step\n",
            "Epoch 76/300\n",
            "15/15 - 0s - loss: 0.2173 - accuracy: 0.9325 - val_loss: 0.3656 - val_accuracy: 0.8627 - 82ms/epoch - 5ms/step\n",
            "Epoch 77/300\n",
            "15/15 - 0s - loss: 0.2167 - accuracy: 0.9303 - val_loss: 0.3655 - val_accuracy: 0.8627 - 82ms/epoch - 5ms/step\n",
            "Epoch 78/300\n",
            "15/15 - 0s - loss: 0.2164 - accuracy: 0.9303 - val_loss: 0.3663 - val_accuracy: 0.8627 - 66ms/epoch - 4ms/step\n",
            "Epoch 79/300\n",
            "15/15 - 0s - loss: 0.2155 - accuracy: 0.9303 - val_loss: 0.3675 - val_accuracy: 0.8627 - 73ms/epoch - 5ms/step\n",
            "Epoch 80/300\n",
            "15/15 - 0s - loss: 0.2155 - accuracy: 0.9303 - val_loss: 0.3662 - val_accuracy: 0.8627 - 64ms/epoch - 4ms/step\n",
            "Epoch 81/300\n",
            "15/15 - 0s - loss: 0.2149 - accuracy: 0.9325 - val_loss: 0.3676 - val_accuracy: 0.8627 - 78ms/epoch - 5ms/step\n",
            "Epoch 82/300\n",
            "15/15 - 0s - loss: 0.2148 - accuracy: 0.9346 - val_loss: 0.3632 - val_accuracy: 0.8693 - 75ms/epoch - 5ms/step\n",
            "Epoch 83/300\n",
            "15/15 - 0s - loss: 0.2142 - accuracy: 0.9325 - val_loss: 0.3642 - val_accuracy: 0.8693 - 61ms/epoch - 4ms/step\n",
            "Epoch 84/300\n",
            "15/15 - 0s - loss: 0.2135 - accuracy: 0.9303 - val_loss: 0.3651 - val_accuracy: 0.8627 - 70ms/epoch - 5ms/step\n",
            "Epoch 85/300\n",
            "15/15 - 0s - loss: 0.2134 - accuracy: 0.9325 - val_loss: 0.3650 - val_accuracy: 0.8627 - 87ms/epoch - 6ms/step\n",
            "Epoch 86/300\n",
            "15/15 - 0s - loss: 0.2134 - accuracy: 0.9303 - val_loss: 0.3652 - val_accuracy: 0.8627 - 58ms/epoch - 4ms/step\n",
            "Epoch 87/300\n",
            "15/15 - 0s - loss: 0.2127 - accuracy: 0.9303 - val_loss: 0.3671 - val_accuracy: 0.8627 - 62ms/epoch - 4ms/step\n",
            "Epoch 88/300\n",
            "15/15 - 0s - loss: 0.2121 - accuracy: 0.9325 - val_loss: 0.3666 - val_accuracy: 0.8627 - 80ms/epoch - 5ms/step\n",
            "Epoch 89/300\n",
            "15/15 - 0s - loss: 0.2119 - accuracy: 0.9325 - val_loss: 0.3663 - val_accuracy: 0.8627 - 60ms/epoch - 4ms/step\n",
            "Epoch 90/300\n",
            "15/15 - 0s - loss: 0.2119 - accuracy: 0.9325 - val_loss: 0.3634 - val_accuracy: 0.8627 - 63ms/epoch - 4ms/step\n",
            "Epoch 91/300\n",
            "15/15 - 0s - loss: 0.2109 - accuracy: 0.9325 - val_loss: 0.3622 - val_accuracy: 0.8693 - 77ms/epoch - 5ms/step\n",
            "Epoch 92/300\n",
            "15/15 - 0s - loss: 0.2125 - accuracy: 0.9325 - val_loss: 0.3632 - val_accuracy: 0.8758 - 68ms/epoch - 5ms/step\n",
            "Epoch 93/300\n",
            "15/15 - 0s - loss: 0.2108 - accuracy: 0.9325 - val_loss: 0.3648 - val_accuracy: 0.8693 - 81ms/epoch - 5ms/step\n",
            "Epoch 94/300\n",
            "15/15 - 0s - loss: 0.2104 - accuracy: 0.9325 - val_loss: 0.3645 - val_accuracy: 0.8693 - 72ms/epoch - 5ms/step\n",
            "Epoch 95/300\n",
            "15/15 - 0s - loss: 0.2099 - accuracy: 0.9325 - val_loss: 0.3638 - val_accuracy: 0.8693 - 60ms/epoch - 4ms/step\n",
            "Epoch 96/300\n",
            "15/15 - 0s - loss: 0.2098 - accuracy: 0.9346 - val_loss: 0.3646 - val_accuracy: 0.8693 - 58ms/epoch - 4ms/step\n",
            "Epoch 97/300\n",
            "15/15 - 0s - loss: 0.2091 - accuracy: 0.9346 - val_loss: 0.3651 - val_accuracy: 0.8693 - 59ms/epoch - 4ms/step\n",
            "Epoch 98/300\n",
            "15/15 - 0s - loss: 0.2076 - accuracy: 0.9346 - val_loss: 0.3616 - val_accuracy: 0.8693 - 67ms/epoch - 4ms/step\n",
            "Epoch 99/300\n",
            "15/15 - 0s - loss: 0.2084 - accuracy: 0.9346 - val_loss: 0.3615 - val_accuracy: 0.8693 - 63ms/epoch - 4ms/step\n",
            "Epoch 100/300\n",
            "15/15 - 0s - loss: 0.2079 - accuracy: 0.9325 - val_loss: 0.3627 - val_accuracy: 0.8693 - 66ms/epoch - 4ms/step\n",
            "Epoch 101/300\n",
            "15/15 - 0s - loss: 0.2077 - accuracy: 0.9346 - val_loss: 0.3649 - val_accuracy: 0.8693 - 74ms/epoch - 5ms/step\n",
            "Epoch 102/300\n",
            "15/15 - 0s - loss: 0.2082 - accuracy: 0.9346 - val_loss: 0.3638 - val_accuracy: 0.8693 - 70ms/epoch - 5ms/step\n",
            "Epoch 103/300\n",
            "15/15 - 0s - loss: 0.2083 - accuracy: 0.9325 - val_loss: 0.3607 - val_accuracy: 0.8824 - 77ms/epoch - 5ms/step\n",
            "Epoch 104/300\n",
            "15/15 - 0s - loss: 0.2069 - accuracy: 0.9303 - val_loss: 0.3626 - val_accuracy: 0.8693 - 61ms/epoch - 4ms/step\n",
            "Epoch 105/300\n",
            "15/15 - 0s - loss: 0.2068 - accuracy: 0.9346 - val_loss: 0.3619 - val_accuracy: 0.8693 - 66ms/epoch - 4ms/step\n",
            "Epoch 106/300\n",
            "15/15 - 0s - loss: 0.2058 - accuracy: 0.9325 - val_loss: 0.3625 - val_accuracy: 0.8758 - 63ms/epoch - 4ms/step\n",
            "Epoch 107/300\n",
            "15/15 - 0s - loss: 0.2059 - accuracy: 0.9325 - val_loss: 0.3621 - val_accuracy: 0.8758 - 75ms/epoch - 5ms/step\n",
            "Epoch 108/300\n",
            "15/15 - 0s - loss: 0.2057 - accuracy: 0.9325 - val_loss: 0.3633 - val_accuracy: 0.8693 - 64ms/epoch - 4ms/step\n",
            "Epoch 109/300\n",
            "15/15 - 0s - loss: 0.2053 - accuracy: 0.9325 - val_loss: 0.3632 - val_accuracy: 0.8693 - 63ms/epoch - 4ms/step\n",
            "Epoch 110/300\n",
            "15/15 - 0s - loss: 0.2049 - accuracy: 0.9325 - val_loss: 0.3654 - val_accuracy: 0.8693 - 75ms/epoch - 5ms/step\n",
            "Epoch 111/300\n",
            "15/15 - 0s - loss: 0.2055 - accuracy: 0.9325 - val_loss: 0.3649 - val_accuracy: 0.8824 - 66ms/epoch - 4ms/step\n",
            "Epoch 112/300\n",
            "15/15 - 0s - loss: 0.2040 - accuracy: 0.9346 - val_loss: 0.3656 - val_accuracy: 0.8758 - 77ms/epoch - 5ms/step\n",
            "Epoch 113/300\n",
            "15/15 - 0s - loss: 0.2051 - accuracy: 0.9325 - val_loss: 0.3629 - val_accuracy: 0.8824 - 59ms/epoch - 4ms/step\n",
            "Epoch 114/300\n",
            "15/15 - 0s - loss: 0.2039 - accuracy: 0.9325 - val_loss: 0.3605 - val_accuracy: 0.8758 - 59ms/epoch - 4ms/step\n",
            "Epoch 115/300\n",
            "15/15 - 0s - loss: 0.2041 - accuracy: 0.9325 - val_loss: 0.3605 - val_accuracy: 0.8758 - 63ms/epoch - 4ms/step\n",
            "Epoch 116/300\n",
            "15/15 - 0s - loss: 0.2037 - accuracy: 0.9281 - val_loss: 0.3606 - val_accuracy: 0.8824 - 68ms/epoch - 5ms/step\n",
            "Epoch 117/300\n",
            "15/15 - 0s - loss: 0.2029 - accuracy: 0.9325 - val_loss: 0.3637 - val_accuracy: 0.8758 - 65ms/epoch - 4ms/step\n",
            "Epoch 118/300\n",
            "15/15 - 0s - loss: 0.2027 - accuracy: 0.9325 - val_loss: 0.3607 - val_accuracy: 0.8758 - 67ms/epoch - 4ms/step\n",
            "Epoch 119/300\n",
            "15/15 - 0s - loss: 0.2027 - accuracy: 0.9346 - val_loss: 0.3619 - val_accuracy: 0.8824 - 65ms/epoch - 4ms/step\n",
            "Epoch 120/300\n",
            "15/15 - 0s - loss: 0.2019 - accuracy: 0.9325 - val_loss: 0.3655 - val_accuracy: 0.8824 - 81ms/epoch - 5ms/step\n",
            "Epoch 121/300\n",
            "15/15 - 0s - loss: 0.2023 - accuracy: 0.9325 - val_loss: 0.3643 - val_accuracy: 0.8824 - 79ms/epoch - 5ms/step\n",
            "Epoch 122/300\n",
            "15/15 - 0s - loss: 0.2027 - accuracy: 0.9325 - val_loss: 0.3649 - val_accuracy: 0.8824 - 89ms/epoch - 6ms/step\n",
            "Epoch 123/300\n",
            "15/15 - 0s - loss: 0.2017 - accuracy: 0.9346 - val_loss: 0.3601 - val_accuracy: 0.8693 - 72ms/epoch - 5ms/step\n",
            "Epoch 124/300\n",
            "15/15 - 0s - loss: 0.2011 - accuracy: 0.9346 - val_loss: 0.3615 - val_accuracy: 0.8693 - 62ms/epoch - 4ms/step\n",
            "Epoch 125/300\n",
            "15/15 - 0s - loss: 0.2015 - accuracy: 0.9325 - val_loss: 0.3616 - val_accuracy: 0.8824 - 70ms/epoch - 5ms/step\n",
            "Epoch 126/300\n",
            "15/15 - 0s - loss: 0.2013 - accuracy: 0.9368 - val_loss: 0.3613 - val_accuracy: 0.8758 - 63ms/epoch - 4ms/step\n",
            "Epoch 127/300\n",
            "15/15 - 0s - loss: 0.2015 - accuracy: 0.9325 - val_loss: 0.3598 - val_accuracy: 0.8758 - 62ms/epoch - 4ms/step\n",
            "Epoch 128/300\n",
            "15/15 - 0s - loss: 0.2012 - accuracy: 0.9325 - val_loss: 0.3608 - val_accuracy: 0.8758 - 86ms/epoch - 6ms/step\n",
            "Epoch 129/300\n",
            "15/15 - 0s - loss: 0.1998 - accuracy: 0.9346 - val_loss: 0.3603 - val_accuracy: 0.8758 - 88ms/epoch - 6ms/step\n",
            "Epoch 130/300\n",
            "15/15 - 0s - loss: 0.2002 - accuracy: 0.9346 - val_loss: 0.3610 - val_accuracy: 0.8824 - 70ms/epoch - 5ms/step\n",
            "Epoch 131/300\n",
            "15/15 - 0s - loss: 0.2018 - accuracy: 0.9281 - val_loss: 0.3607 - val_accuracy: 0.8824 - 60ms/epoch - 4ms/step\n",
            "Epoch 132/300\n",
            "15/15 - 0s - loss: 0.1999 - accuracy: 0.9303 - val_loss: 0.3617 - val_accuracy: 0.8758 - 65ms/epoch - 4ms/step\n",
            "Epoch 133/300\n",
            "15/15 - 0s - loss: 0.1991 - accuracy: 0.9325 - val_loss: 0.3627 - val_accuracy: 0.8824 - 76ms/epoch - 5ms/step\n",
            "Epoch 134/300\n",
            "15/15 - 0s - loss: 0.1997 - accuracy: 0.9346 - val_loss: 0.3603 - val_accuracy: 0.8758 - 57ms/epoch - 4ms/step\n",
            "Epoch 135/300\n",
            "15/15 - 0s - loss: 0.1998 - accuracy: 0.9346 - val_loss: 0.3647 - val_accuracy: 0.8824 - 70ms/epoch - 5ms/step\n",
            "Epoch 136/300\n",
            "15/15 - 0s - loss: 0.1993 - accuracy: 0.9303 - val_loss: 0.3623 - val_accuracy: 0.8758 - 75ms/epoch - 5ms/step\n",
            "Epoch 137/300\n",
            "15/15 - 0s - loss: 0.1985 - accuracy: 0.9346 - val_loss: 0.3615 - val_accuracy: 0.8758 - 62ms/epoch - 4ms/step\n",
            "Epoch 138/300\n",
            "15/15 - 0s - loss: 0.1981 - accuracy: 0.9346 - val_loss: 0.3612 - val_accuracy: 0.8758 - 78ms/epoch - 5ms/step\n",
            "Epoch 139/300\n",
            "15/15 - 0s - loss: 0.1981 - accuracy: 0.9325 - val_loss: 0.3591 - val_accuracy: 0.8824 - 59ms/epoch - 4ms/step\n",
            "Epoch 140/300\n",
            "15/15 - 0s - loss: 0.1978 - accuracy: 0.9346 - val_loss: 0.3609 - val_accuracy: 0.8824 - 80ms/epoch - 5ms/step\n",
            "Epoch 141/300\n",
            "15/15 - 0s - loss: 0.1976 - accuracy: 0.9346 - val_loss: 0.3617 - val_accuracy: 0.8758 - 58ms/epoch - 4ms/step\n",
            "Epoch 142/300\n",
            "15/15 - 0s - loss: 0.1980 - accuracy: 0.9325 - val_loss: 0.3596 - val_accuracy: 0.8824 - 68ms/epoch - 5ms/step\n",
            "Epoch 143/300\n",
            "15/15 - 0s - loss: 0.1978 - accuracy: 0.9325 - val_loss: 0.3618 - val_accuracy: 0.8824 - 66ms/epoch - 4ms/step\n",
            "Epoch 144/300\n",
            "15/15 - 0s - loss: 0.1974 - accuracy: 0.9303 - val_loss: 0.3603 - val_accuracy: 0.8824 - 64ms/epoch - 4ms/step\n",
            "Epoch 145/300\n",
            "15/15 - 0s - loss: 0.1974 - accuracy: 0.9303 - val_loss: 0.3588 - val_accuracy: 0.8824 - 62ms/epoch - 4ms/step\n",
            "Epoch 146/300\n",
            "15/15 - 0s - loss: 0.1968 - accuracy: 0.9325 - val_loss: 0.3629 - val_accuracy: 0.8824 - 76ms/epoch - 5ms/step\n",
            "Epoch 147/300\n",
            "15/15 - 0s - loss: 0.1968 - accuracy: 0.9346 - val_loss: 0.3651 - val_accuracy: 0.8824 - 72ms/epoch - 5ms/step\n",
            "Epoch 148/300\n",
            "15/15 - 0s - loss: 0.1969 - accuracy: 0.9325 - val_loss: 0.3639 - val_accuracy: 0.8824 - 59ms/epoch - 4ms/step\n",
            "Epoch 149/300\n",
            "15/15 - 0s - loss: 0.1965 - accuracy: 0.9346 - val_loss: 0.3610 - val_accuracy: 0.8824 - 75ms/epoch - 5ms/step\n",
            "Epoch 150/300\n",
            "15/15 - 0s - loss: 0.1955 - accuracy: 0.9303 - val_loss: 0.3612 - val_accuracy: 0.8758 - 72ms/epoch - 5ms/step\n",
            "Epoch 151/300\n",
            "15/15 - 0s - loss: 0.1960 - accuracy: 0.9346 - val_loss: 0.3628 - val_accuracy: 0.8824 - 58ms/epoch - 4ms/step\n",
            "Epoch 152/300\n",
            "15/15 - 0s - loss: 0.1961 - accuracy: 0.9346 - val_loss: 0.3644 - val_accuracy: 0.8824 - 73ms/epoch - 5ms/step\n",
            "Epoch 153/300\n",
            "15/15 - 0s - loss: 0.1955 - accuracy: 0.9346 - val_loss: 0.3605 - val_accuracy: 0.8824 - 60ms/epoch - 4ms/step\n",
            "Epoch 154/300\n",
            "15/15 - 0s - loss: 0.1960 - accuracy: 0.9325 - val_loss: 0.3585 - val_accuracy: 0.8824 - 68ms/epoch - 5ms/step\n",
            "Epoch 155/300\n",
            "15/15 - 0s - loss: 0.1958 - accuracy: 0.9281 - val_loss: 0.3601 - val_accuracy: 0.8824 - 59ms/epoch - 4ms/step\n",
            "Epoch 156/300\n",
            "15/15 - 0s - loss: 0.1955 - accuracy: 0.9303 - val_loss: 0.3603 - val_accuracy: 0.8824 - 74ms/epoch - 5ms/step\n",
            "Epoch 157/300\n",
            "15/15 - 0s - loss: 0.1950 - accuracy: 0.9325 - val_loss: 0.3638 - val_accuracy: 0.8824 - 71ms/epoch - 5ms/step\n",
            "Epoch 158/300\n",
            "15/15 - 0s - loss: 0.1958 - accuracy: 0.9303 - val_loss: 0.3620 - val_accuracy: 0.8824 - 59ms/epoch - 4ms/step\n",
            "Epoch 159/300\n",
            "15/15 - 0s - loss: 0.1945 - accuracy: 0.9303 - val_loss: 0.3607 - val_accuracy: 0.8824 - 63ms/epoch - 4ms/step\n",
            "Epoch 160/300\n",
            "15/15 - 0s - loss: 0.1956 - accuracy: 0.9346 - val_loss: 0.3622 - val_accuracy: 0.8693 - 65ms/epoch - 4ms/step\n",
            "Epoch 161/300\n",
            "15/15 - 0s - loss: 0.1951 - accuracy: 0.9346 - val_loss: 0.3642 - val_accuracy: 0.8758 - 71ms/epoch - 5ms/step\n",
            "Epoch 162/300\n",
            "15/15 - 0s - loss: 0.1945 - accuracy: 0.9346 - val_loss: 0.3619 - val_accuracy: 0.8824 - 67ms/epoch - 4ms/step\n",
            "Epoch 163/300\n",
            "15/15 - 0s - loss: 0.1944 - accuracy: 0.9281 - val_loss: 0.3621 - val_accuracy: 0.8824 - 59ms/epoch - 4ms/step\n",
            "Epoch 164/300\n",
            "15/15 - 0s - loss: 0.1941 - accuracy: 0.9325 - val_loss: 0.3611 - val_accuracy: 0.8824 - 67ms/epoch - 4ms/step\n",
            "Epoch 165/300\n",
            "15/15 - 0s - loss: 0.1938 - accuracy: 0.9303 - val_loss: 0.3598 - val_accuracy: 0.8824 - 64ms/epoch - 4ms/step\n",
            "Epoch 166/300\n",
            "15/15 - 0s - loss: 0.1934 - accuracy: 0.9325 - val_loss: 0.3614 - val_accuracy: 0.8824 - 55ms/epoch - 4ms/step\n",
            "Epoch 167/300\n",
            "15/15 - 0s - loss: 0.1930 - accuracy: 0.9325 - val_loss: 0.3612 - val_accuracy: 0.8824 - 59ms/epoch - 4ms/step\n",
            "Epoch 168/300\n",
            "15/15 - 0s - loss: 0.1942 - accuracy: 0.9346 - val_loss: 0.3594 - val_accuracy: 0.8758 - 82ms/epoch - 5ms/step\n",
            "Epoch 169/300\n",
            "15/15 - 0s - loss: 0.1937 - accuracy: 0.9303 - val_loss: 0.3613 - val_accuracy: 0.8758 - 80ms/epoch - 5ms/step\n",
            "Epoch 170/300\n",
            "15/15 - 0s - loss: 0.1935 - accuracy: 0.9325 - val_loss: 0.3640 - val_accuracy: 0.8824 - 63ms/epoch - 4ms/step\n",
            "Epoch 171/300\n",
            "15/15 - 0s - loss: 0.1927 - accuracy: 0.9325 - val_loss: 0.3604 - val_accuracy: 0.8824 - 79ms/epoch - 5ms/step\n",
            "Epoch 172/300\n",
            "15/15 - 0s - loss: 0.1940 - accuracy: 0.9303 - val_loss: 0.3624 - val_accuracy: 0.8824 - 84ms/epoch - 6ms/step\n",
            "Epoch 173/300\n",
            "15/15 - 0s - loss: 0.1922 - accuracy: 0.9346 - val_loss: 0.3624 - val_accuracy: 0.8824 - 71ms/epoch - 5ms/step\n",
            "Epoch 174/300\n",
            "15/15 - 0s - loss: 0.1924 - accuracy: 0.9325 - val_loss: 0.3618 - val_accuracy: 0.8824 - 55ms/epoch - 4ms/step\n",
            "Epoch 175/300\n",
            "15/15 - 0s - loss: 0.1929 - accuracy: 0.9325 - val_loss: 0.3651 - val_accuracy: 0.8824 - 78ms/epoch - 5ms/step\n",
            "Epoch 176/300\n",
            "15/15 - 0s - loss: 0.1916 - accuracy: 0.9325 - val_loss: 0.3635 - val_accuracy: 0.8758 - 75ms/epoch - 5ms/step\n",
            "Epoch 177/300\n",
            "15/15 - 0s - loss: 0.1932 - accuracy: 0.9303 - val_loss: 0.3600 - val_accuracy: 0.8824 - 67ms/epoch - 4ms/step\n",
            "Epoch 178/300\n",
            "15/15 - 0s - loss: 0.1919 - accuracy: 0.9303 - val_loss: 0.3590 - val_accuracy: 0.8758 - 61ms/epoch - 4ms/step\n",
            "Epoch 179/300\n",
            "15/15 - 0s - loss: 0.1912 - accuracy: 0.9346 - val_loss: 0.3638 - val_accuracy: 0.8758 - 63ms/epoch - 4ms/step\n",
            "Epoch 180/300\n",
            "15/15 - 0s - loss: 0.1917 - accuracy: 0.9346 - val_loss: 0.3603 - val_accuracy: 0.8824 - 59ms/epoch - 4ms/step\n",
            "Epoch 181/300\n",
            "15/15 - 0s - loss: 0.1906 - accuracy: 0.9346 - val_loss: 0.3594 - val_accuracy: 0.8824 - 67ms/epoch - 4ms/step\n",
            "Epoch 182/300\n",
            "15/15 - 0s - loss: 0.1910 - accuracy: 0.9281 - val_loss: 0.3592 - val_accuracy: 0.8824 - 75ms/epoch - 5ms/step\n",
            "Epoch 183/300\n",
            "15/15 - 0s - loss: 0.1921 - accuracy: 0.9346 - val_loss: 0.3616 - val_accuracy: 0.8824 - 83ms/epoch - 6ms/step\n",
            "Epoch 184/300\n",
            "15/15 - 0s - loss: 0.1915 - accuracy: 0.9303 - val_loss: 0.3633 - val_accuracy: 0.8758 - 69ms/epoch - 5ms/step\n",
            "Epoch 185/300\n",
            "15/15 - 0s - loss: 0.1920 - accuracy: 0.9346 - val_loss: 0.3621 - val_accuracy: 0.8824 - 64ms/epoch - 4ms/step\n",
            "Epoch 186/300\n",
            "15/15 - 0s - loss: 0.1921 - accuracy: 0.9325 - val_loss: 0.3596 - val_accuracy: 0.8824 - 72ms/epoch - 5ms/step\n",
            "Epoch 187/300\n",
            "15/15 - 0s - loss: 0.1903 - accuracy: 0.9368 - val_loss: 0.3620 - val_accuracy: 0.8758 - 71ms/epoch - 5ms/step\n",
            "Epoch 188/300\n",
            "15/15 - 0s - loss: 0.1904 - accuracy: 0.9368 - val_loss: 0.3622 - val_accuracy: 0.8758 - 88ms/epoch - 6ms/step\n",
            "Epoch 189/300\n",
            "15/15 - 0s - loss: 0.1902 - accuracy: 0.9346 - val_loss: 0.3626 - val_accuracy: 0.8824 - 80ms/epoch - 5ms/step\n",
            "Epoch 190/300\n",
            "15/15 - 0s - loss: 0.1907 - accuracy: 0.9325 - val_loss: 0.3596 - val_accuracy: 0.8758 - 67ms/epoch - 4ms/step\n",
            "Epoch 191/300\n",
            "15/15 - 0s - loss: 0.1906 - accuracy: 0.9303 - val_loss: 0.3614 - val_accuracy: 0.8824 - 66ms/epoch - 4ms/step\n",
            "Epoch 192/300\n",
            "15/15 - 0s - loss: 0.1898 - accuracy: 0.9281 - val_loss: 0.3598 - val_accuracy: 0.8824 - 69ms/epoch - 5ms/step\n",
            "Epoch 193/300\n",
            "15/15 - 0s - loss: 0.1906 - accuracy: 0.9303 - val_loss: 0.3579 - val_accuracy: 0.8824 - 88ms/epoch - 6ms/step\n",
            "Epoch 194/300\n",
            "15/15 - 0s - loss: 0.1909 - accuracy: 0.9325 - val_loss: 0.3602 - val_accuracy: 0.8824 - 66ms/epoch - 4ms/step\n",
            "Epoch 195/300\n",
            "15/15 - 0s - loss: 0.1892 - accuracy: 0.9346 - val_loss: 0.3604 - val_accuracy: 0.8758 - 72ms/epoch - 5ms/step\n",
            "Epoch 196/300\n",
            "15/15 - 0s - loss: 0.1898 - accuracy: 0.9303 - val_loss: 0.3595 - val_accuracy: 0.8758 - 58ms/epoch - 4ms/step\n",
            "Epoch 197/300\n",
            "15/15 - 0s - loss: 0.1887 - accuracy: 0.9325 - val_loss: 0.3606 - val_accuracy: 0.8758 - 66ms/epoch - 4ms/step\n",
            "Epoch 198/300\n",
            "15/15 - 0s - loss: 0.1892 - accuracy: 0.9346 - val_loss: 0.3621 - val_accuracy: 0.8824 - 76ms/epoch - 5ms/step\n",
            "Epoch 199/300\n",
            "15/15 - 0s - loss: 0.1902 - accuracy: 0.9325 - val_loss: 0.3617 - val_accuracy: 0.8758 - 66ms/epoch - 4ms/step\n",
            "Epoch 200/300\n",
            "15/15 - 0s - loss: 0.1890 - accuracy: 0.9325 - val_loss: 0.3595 - val_accuracy: 0.8758 - 65ms/epoch - 4ms/step\n",
            "Epoch 201/300\n",
            "15/15 - 0s - loss: 0.1888 - accuracy: 0.9303 - val_loss: 0.3580 - val_accuracy: 0.8824 - 68ms/epoch - 5ms/step\n",
            "Epoch 202/300\n",
            "15/15 - 0s - loss: 0.1914 - accuracy: 0.9346 - val_loss: 0.3635 - val_accuracy: 0.8824 - 75ms/epoch - 5ms/step\n",
            "Epoch 203/300\n",
            "15/15 - 0s - loss: 0.1900 - accuracy: 0.9346 - val_loss: 0.3593 - val_accuracy: 0.8758 - 66ms/epoch - 4ms/step\n",
            "Epoch 204/300\n",
            "15/15 - 0s - loss: 0.1882 - accuracy: 0.9346 - val_loss: 0.3577 - val_accuracy: 0.8824 - 79ms/epoch - 5ms/step\n",
            "Epoch 205/300\n",
            "15/15 - 0s - loss: 0.1884 - accuracy: 0.9346 - val_loss: 0.3602 - val_accuracy: 0.8758 - 78ms/epoch - 5ms/step\n",
            "Epoch 206/300\n",
            "15/15 - 0s - loss: 0.1882 - accuracy: 0.9325 - val_loss: 0.3606 - val_accuracy: 0.8824 - 65ms/epoch - 4ms/step\n",
            "Epoch 207/300\n",
            "15/15 - 0s - loss: 0.1888 - accuracy: 0.9303 - val_loss: 0.3634 - val_accuracy: 0.8824 - 60ms/epoch - 4ms/step\n",
            "Epoch 208/300\n",
            "15/15 - 0s - loss: 0.1882 - accuracy: 0.9346 - val_loss: 0.3624 - val_accuracy: 0.8758 - 73ms/epoch - 5ms/step\n",
            "Epoch 209/300\n",
            "15/15 - 0s - loss: 0.1884 - accuracy: 0.9346 - val_loss: 0.3639 - val_accuracy: 0.8824 - 63ms/epoch - 4ms/step\n",
            "Epoch 210/300\n",
            "15/15 - 0s - loss: 0.1880 - accuracy: 0.9325 - val_loss: 0.3646 - val_accuracy: 0.8758 - 68ms/epoch - 5ms/step\n",
            "Epoch 211/300\n",
            "15/15 - 0s - loss: 0.1871 - accuracy: 0.9325 - val_loss: 0.3635 - val_accuracy: 0.8824 - 76ms/epoch - 5ms/step\n",
            "Epoch 212/300\n",
            "15/15 - 0s - loss: 0.1878 - accuracy: 0.9346 - val_loss: 0.3609 - val_accuracy: 0.8758 - 80ms/epoch - 5ms/step\n",
            "Epoch 213/300\n",
            "15/15 - 0s - loss: 0.1888 - accuracy: 0.9346 - val_loss: 0.3613 - val_accuracy: 0.8758 - 61ms/epoch - 4ms/step\n",
            "Epoch 214/300\n",
            "15/15 - 0s - loss: 0.1871 - accuracy: 0.9346 - val_loss: 0.3594 - val_accuracy: 0.8824 - 79ms/epoch - 5ms/step\n",
            "Epoch 215/300\n",
            "15/15 - 0s - loss: 0.1878 - accuracy: 0.9303 - val_loss: 0.3601 - val_accuracy: 0.8758 - 58ms/epoch - 4ms/step\n",
            "Epoch 216/300\n",
            "15/15 - 0s - loss: 0.1873 - accuracy: 0.9303 - val_loss: 0.3602 - val_accuracy: 0.8824 - 65ms/epoch - 4ms/step\n",
            "Epoch 217/300\n",
            "15/15 - 0s - loss: 0.1867 - accuracy: 0.9325 - val_loss: 0.3616 - val_accuracy: 0.8824 - 62ms/epoch - 4ms/step\n",
            "Epoch 218/300\n",
            "15/15 - 0s - loss: 0.1872 - accuracy: 0.9346 - val_loss: 0.3630 - val_accuracy: 0.8758 - 73ms/epoch - 5ms/step\n",
            "Epoch 219/300\n",
            "15/15 - 0s - loss: 0.1865 - accuracy: 0.9325 - val_loss: 0.3621 - val_accuracy: 0.8824 - 71ms/epoch - 5ms/step\n",
            "Epoch 220/300\n",
            "15/15 - 0s - loss: 0.1867 - accuracy: 0.9281 - val_loss: 0.3615 - val_accuracy: 0.8758 - 75ms/epoch - 5ms/step\n",
            "Epoch 221/300\n",
            "15/15 - 0s - loss: 0.1872 - accuracy: 0.9346 - val_loss: 0.3583 - val_accuracy: 0.8824 - 65ms/epoch - 4ms/step\n",
            "Epoch 222/300\n",
            "15/15 - 0s - loss: 0.1873 - accuracy: 0.9346 - val_loss: 0.3583 - val_accuracy: 0.8758 - 59ms/epoch - 4ms/step\n",
            "Epoch 223/300\n",
            "15/15 - 0s - loss: 0.1859 - accuracy: 0.9325 - val_loss: 0.3610 - val_accuracy: 0.8758 - 80ms/epoch - 5ms/step\n",
            "Epoch 224/300\n",
            "15/15 - 0s - loss: 0.1862 - accuracy: 0.9325 - val_loss: 0.3624 - val_accuracy: 0.8824 - 77ms/epoch - 5ms/step\n",
            "Epoch 225/300\n",
            "15/15 - 0s - loss: 0.1866 - accuracy: 0.9325 - val_loss: 0.3598 - val_accuracy: 0.8758 - 79ms/epoch - 5ms/step\n",
            "Epoch 226/300\n",
            "15/15 - 0s - loss: 0.1866 - accuracy: 0.9303 - val_loss: 0.3639 - val_accuracy: 0.8693 - 84ms/epoch - 6ms/step\n",
            "Epoch 227/300\n",
            "15/15 - 0s - loss: 0.1853 - accuracy: 0.9325 - val_loss: 0.3606 - val_accuracy: 0.8758 - 78ms/epoch - 5ms/step\n",
            "Epoch 228/300\n",
            "15/15 - 0s - loss: 0.1869 - accuracy: 0.9346 - val_loss: 0.3610 - val_accuracy: 0.8824 - 76ms/epoch - 5ms/step\n",
            "Epoch 229/300\n",
            "15/15 - 0s - loss: 0.1875 - accuracy: 0.9368 - val_loss: 0.3560 - val_accuracy: 0.8758 - 63ms/epoch - 4ms/step\n",
            "Epoch 230/300\n",
            "15/15 - 0s - loss: 0.1858 - accuracy: 0.9281 - val_loss: 0.3619 - val_accuracy: 0.8758 - 61ms/epoch - 4ms/step\n",
            "Epoch 231/300\n",
            "15/15 - 0s - loss: 0.1854 - accuracy: 0.9325 - val_loss: 0.3595 - val_accuracy: 0.8824 - 60ms/epoch - 4ms/step\n",
            "Epoch 232/300\n",
            "15/15 - 0s - loss: 0.1854 - accuracy: 0.9325 - val_loss: 0.3583 - val_accuracy: 0.8824 - 60ms/epoch - 4ms/step\n",
            "Epoch 233/300\n",
            "15/15 - 0s - loss: 0.1862 - accuracy: 0.9303 - val_loss: 0.3580 - val_accuracy: 0.8758 - 77ms/epoch - 5ms/step\n",
            "Epoch 234/300\n",
            "15/15 - 0s - loss: 0.1850 - accuracy: 0.9346 - val_loss: 0.3604 - val_accuracy: 0.8824 - 75ms/epoch - 5ms/step\n",
            "Epoch 235/300\n",
            "15/15 - 0s - loss: 0.1855 - accuracy: 0.9346 - val_loss: 0.3624 - val_accuracy: 0.8758 - 69ms/epoch - 5ms/step\n",
            "Epoch 236/300\n",
            "15/15 - 0s - loss: 0.1848 - accuracy: 0.9325 - val_loss: 0.3598 - val_accuracy: 0.8758 - 63ms/epoch - 4ms/step\n",
            "Epoch 237/300\n",
            "15/15 - 0s - loss: 0.1853 - accuracy: 0.9281 - val_loss: 0.3603 - val_accuracy: 0.8758 - 71ms/epoch - 5ms/step\n",
            "Epoch 238/300\n",
            "15/15 - 0s - loss: 0.1862 - accuracy: 0.9259 - val_loss: 0.3606 - val_accuracy: 0.8693 - 74ms/epoch - 5ms/step\n",
            "Epoch 239/300\n",
            "15/15 - 0s - loss: 0.1855 - accuracy: 0.9303 - val_loss: 0.3643 - val_accuracy: 0.8758 - 64ms/epoch - 4ms/step\n",
            "Epoch 240/300\n",
            "15/15 - 0s - loss: 0.1838 - accuracy: 0.9325 - val_loss: 0.3625 - val_accuracy: 0.8758 - 76ms/epoch - 5ms/step\n",
            "Epoch 241/300\n",
            "15/15 - 0s - loss: 0.1856 - accuracy: 0.9325 - val_loss: 0.3604 - val_accuracy: 0.8693 - 76ms/epoch - 5ms/step\n",
            "Epoch 242/300\n",
            "15/15 - 0s - loss: 0.1844 - accuracy: 0.9303 - val_loss: 0.3588 - val_accuracy: 0.8758 - 78ms/epoch - 5ms/step\n",
            "Epoch 243/300\n",
            "15/15 - 0s - loss: 0.1849 - accuracy: 0.9325 - val_loss: 0.3586 - val_accuracy: 0.8693 - 58ms/epoch - 4ms/step\n",
            "Epoch 244/300\n",
            "15/15 - 0s - loss: 0.1847 - accuracy: 0.9346 - val_loss: 0.3616 - val_accuracy: 0.8758 - 60ms/epoch - 4ms/step\n",
            "Epoch 245/300\n",
            "15/15 - 0s - loss: 0.1838 - accuracy: 0.9325 - val_loss: 0.3624 - val_accuracy: 0.8758 - 60ms/epoch - 4ms/step\n",
            "Epoch 246/300\n",
            "15/15 - 0s - loss: 0.1851 - accuracy: 0.9303 - val_loss: 0.3613 - val_accuracy: 0.8758 - 57ms/epoch - 4ms/step\n",
            "Epoch 247/300\n",
            "15/15 - 0s - loss: 0.1838 - accuracy: 0.9325 - val_loss: 0.3601 - val_accuracy: 0.8758 - 60ms/epoch - 4ms/step\n",
            "Epoch 248/300\n",
            "15/15 - 0s - loss: 0.1846 - accuracy: 0.9325 - val_loss: 0.3613 - val_accuracy: 0.8758 - 74ms/epoch - 5ms/step\n",
            "Epoch 249/300\n",
            "15/15 - 0s - loss: 0.1838 - accuracy: 0.9346 - val_loss: 0.3622 - val_accuracy: 0.8758 - 72ms/epoch - 5ms/step\n",
            "Epoch 250/300\n",
            "15/15 - 0s - loss: 0.1836 - accuracy: 0.9346 - val_loss: 0.3600 - val_accuracy: 0.8758 - 58ms/epoch - 4ms/step\n",
            "Epoch 251/300\n",
            "15/15 - 0s - loss: 0.1833 - accuracy: 0.9281 - val_loss: 0.3600 - val_accuracy: 0.8758 - 62ms/epoch - 4ms/step\n",
            "Epoch 252/300\n",
            "15/15 - 0s - loss: 0.1844 - accuracy: 0.9325 - val_loss: 0.3638 - val_accuracy: 0.8758 - 67ms/epoch - 4ms/step\n",
            "Epoch 253/300\n",
            "15/15 - 0s - loss: 0.1843 - accuracy: 0.9303 - val_loss: 0.3630 - val_accuracy: 0.8758 - 64ms/epoch - 4ms/step\n",
            "Epoch 254/300\n",
            "15/15 - 0s - loss: 0.1841 - accuracy: 0.9303 - val_loss: 0.3615 - val_accuracy: 0.8693 - 62ms/epoch - 4ms/step\n",
            "Epoch 255/300\n",
            "15/15 - 0s - loss: 0.1841 - accuracy: 0.9281 - val_loss: 0.3643 - val_accuracy: 0.8693 - 78ms/epoch - 5ms/step\n",
            "Epoch 256/300\n",
            "15/15 - 0s - loss: 0.1848 - accuracy: 0.9303 - val_loss: 0.3595 - val_accuracy: 0.8758 - 71ms/epoch - 5ms/step\n",
            "Epoch 257/300\n",
            "15/15 - 0s - loss: 0.1834 - accuracy: 0.9325 - val_loss: 0.3619 - val_accuracy: 0.8758 - 70ms/epoch - 5ms/step\n",
            "Epoch 258/300\n",
            "15/15 - 0s - loss: 0.1837 - accuracy: 0.9325 - val_loss: 0.3631 - val_accuracy: 0.8758 - 74ms/epoch - 5ms/step\n",
            "Epoch 259/300\n",
            "15/15 - 0s - loss: 0.1843 - accuracy: 0.9346 - val_loss: 0.3594 - val_accuracy: 0.8758 - 61ms/epoch - 4ms/step\n",
            "Epoch 260/300\n",
            "15/15 - 0s - loss: 0.1829 - accuracy: 0.9325 - val_loss: 0.3622 - val_accuracy: 0.8824 - 66ms/epoch - 4ms/step\n",
            "Epoch 261/300\n",
            "15/15 - 0s - loss: 0.1834 - accuracy: 0.9303 - val_loss: 0.3606 - val_accuracy: 0.8758 - 68ms/epoch - 5ms/step\n",
            "Epoch 262/300\n",
            "15/15 - 0s - loss: 0.1824 - accuracy: 0.9303 - val_loss: 0.3603 - val_accuracy: 0.8758 - 78ms/epoch - 5ms/step\n",
            "Epoch 263/300\n",
            "15/15 - 0s - loss: 0.1822 - accuracy: 0.9325 - val_loss: 0.3629 - val_accuracy: 0.8758 - 77ms/epoch - 5ms/step\n",
            "Epoch 264/300\n",
            "15/15 - 0s - loss: 0.1826 - accuracy: 0.9325 - val_loss: 0.3616 - val_accuracy: 0.8758 - 83ms/epoch - 6ms/step\n",
            "Epoch 265/300\n",
            "15/15 - 0s - loss: 0.1830 - accuracy: 0.9281 - val_loss: 0.3569 - val_accuracy: 0.8758 - 82ms/epoch - 5ms/step\n",
            "Epoch 266/300\n",
            "15/15 - 0s - loss: 0.1829 - accuracy: 0.9281 - val_loss: 0.3558 - val_accuracy: 0.8758 - 70ms/epoch - 5ms/step\n",
            "Epoch 267/300\n",
            "15/15 - 0s - loss: 0.1828 - accuracy: 0.9281 - val_loss: 0.3620 - val_accuracy: 0.8693 - 61ms/epoch - 4ms/step\n",
            "Epoch 268/300\n",
            "15/15 - 0s - loss: 0.1822 - accuracy: 0.9325 - val_loss: 0.3621 - val_accuracy: 0.8758 - 76ms/epoch - 5ms/step\n",
            "Epoch 269/300\n",
            "15/15 - 0s - loss: 0.1822 - accuracy: 0.9303 - val_loss: 0.3591 - val_accuracy: 0.8758 - 76ms/epoch - 5ms/step\n",
            "Epoch 270/300\n",
            "15/15 - 0s - loss: 0.1828 - accuracy: 0.9325 - val_loss: 0.3587 - val_accuracy: 0.8758 - 78ms/epoch - 5ms/step\n",
            "Epoch 271/300\n",
            "15/15 - 0s - loss: 0.1832 - accuracy: 0.9325 - val_loss: 0.3604 - val_accuracy: 0.8758 - 82ms/epoch - 5ms/step\n",
            "Epoch 272/300\n",
            "15/15 - 0s - loss: 0.1826 - accuracy: 0.9303 - val_loss: 0.3570 - val_accuracy: 0.8758 - 60ms/epoch - 4ms/step\n",
            "Epoch 273/300\n",
            "15/15 - 0s - loss: 0.1817 - accuracy: 0.9325 - val_loss: 0.3609 - val_accuracy: 0.8693 - 61ms/epoch - 4ms/step\n",
            "Epoch 274/300\n",
            "15/15 - 0s - loss: 0.1818 - accuracy: 0.9325 - val_loss: 0.3593 - val_accuracy: 0.8758 - 72ms/epoch - 5ms/step\n",
            "Epoch 275/300\n",
            "15/15 - 0s - loss: 0.1817 - accuracy: 0.9303 - val_loss: 0.3576 - val_accuracy: 0.8758 - 62ms/epoch - 4ms/step\n",
            "Epoch 276/300\n",
            "15/15 - 0s - loss: 0.1814 - accuracy: 0.9325 - val_loss: 0.3552 - val_accuracy: 0.8693 - 64ms/epoch - 4ms/step\n",
            "Epoch 277/300\n",
            "15/15 - 0s - loss: 0.1820 - accuracy: 0.9303 - val_loss: 0.3568 - val_accuracy: 0.8693 - 78ms/epoch - 5ms/step\n",
            "Epoch 278/300\n",
            "15/15 - 0s - loss: 0.1824 - accuracy: 0.9281 - val_loss: 0.3567 - val_accuracy: 0.8693 - 82ms/epoch - 5ms/step\n",
            "Epoch 279/300\n",
            "15/15 - 0s - loss: 0.1821 - accuracy: 0.9259 - val_loss: 0.3582 - val_accuracy: 0.8693 - 84ms/epoch - 6ms/step\n",
            "Epoch 280/300\n",
            "15/15 - 0s - loss: 0.1816 - accuracy: 0.9303 - val_loss: 0.3598 - val_accuracy: 0.8758 - 69ms/epoch - 5ms/step\n",
            "Epoch 281/300\n",
            "15/15 - 0s - loss: 0.1808 - accuracy: 0.9303 - val_loss: 0.3595 - val_accuracy: 0.8758 - 86ms/epoch - 6ms/step\n",
            "Epoch 282/300\n",
            "15/15 - 0s - loss: 0.1809 - accuracy: 0.9303 - val_loss: 0.3583 - val_accuracy: 0.8824 - 67ms/epoch - 4ms/step\n",
            "Epoch 283/300\n",
            "15/15 - 0s - loss: 0.1838 - accuracy: 0.9325 - val_loss: 0.3595 - val_accuracy: 0.8693 - 61ms/epoch - 4ms/step\n",
            "Epoch 284/300\n",
            "15/15 - 0s - loss: 0.1809 - accuracy: 0.9346 - val_loss: 0.3622 - val_accuracy: 0.8758 - 80ms/epoch - 5ms/step\n",
            "Epoch 285/300\n",
            "15/15 - 0s - loss: 0.1819 - accuracy: 0.9325 - val_loss: 0.3572 - val_accuracy: 0.8824 - 81ms/epoch - 5ms/step\n",
            "Epoch 286/300\n",
            "15/15 - 0s - loss: 0.1817 - accuracy: 0.9303 - val_loss: 0.3593 - val_accuracy: 0.8758 - 69ms/epoch - 5ms/step\n",
            "Epoch 287/300\n",
            "15/15 - 0s - loss: 0.1809 - accuracy: 0.9281 - val_loss: 0.3577 - val_accuracy: 0.8693 - 65ms/epoch - 4ms/step\n",
            "Epoch 288/300\n",
            "15/15 - 0s - loss: 0.1805 - accuracy: 0.9303 - val_loss: 0.3604 - val_accuracy: 0.8693 - 64ms/epoch - 4ms/step\n",
            "Epoch 289/300\n",
            "15/15 - 0s - loss: 0.1809 - accuracy: 0.9303 - val_loss: 0.3582 - val_accuracy: 0.8758 - 78ms/epoch - 5ms/step\n",
            "Epoch 290/300\n",
            "15/15 - 0s - loss: 0.1815 - accuracy: 0.9325 - val_loss: 0.3623 - val_accuracy: 0.8758 - 70ms/epoch - 5ms/step\n",
            "Epoch 291/300\n",
            "15/15 - 0s - loss: 0.1806 - accuracy: 0.9325 - val_loss: 0.3621 - val_accuracy: 0.8824 - 79ms/epoch - 5ms/step\n",
            "Epoch 292/300\n",
            "15/15 - 0s - loss: 0.1800 - accuracy: 0.9303 - val_loss: 0.3584 - val_accuracy: 0.8824 - 85ms/epoch - 6ms/step\n",
            "Epoch 293/300\n",
            "15/15 - 0s - loss: 0.1808 - accuracy: 0.9303 - val_loss: 0.3583 - val_accuracy: 0.8693 - 78ms/epoch - 5ms/step\n",
            "Epoch 294/300\n",
            "15/15 - 0s - loss: 0.1802 - accuracy: 0.9325 - val_loss: 0.3552 - val_accuracy: 0.8758 - 71ms/epoch - 5ms/step\n",
            "Epoch 295/300\n",
            "15/15 - 0s - loss: 0.1813 - accuracy: 0.9281 - val_loss: 0.3578 - val_accuracy: 0.8758 - 79ms/epoch - 5ms/step\n",
            "Epoch 296/300\n",
            "15/15 - 0s - loss: 0.1804 - accuracy: 0.9281 - val_loss: 0.3573 - val_accuracy: 0.8824 - 61ms/epoch - 4ms/step\n",
            "Epoch 297/300\n",
            "15/15 - 0s - loss: 0.1809 - accuracy: 0.9346 - val_loss: 0.3613 - val_accuracy: 0.8693 - 67ms/epoch - 4ms/step\n",
            "Epoch 298/300\n",
            "15/15 - 0s - loss: 0.1804 - accuracy: 0.9346 - val_loss: 0.3566 - val_accuracy: 0.8758 - 67ms/epoch - 4ms/step\n",
            "Epoch 299/300\n",
            "15/15 - 0s - loss: 0.1805 - accuracy: 0.9325 - val_loss: 0.3602 - val_accuracy: 0.8824 - 65ms/epoch - 4ms/step\n",
            "Epoch 300/300\n",
            "15/15 - 0s - loss: 0.1811 - accuracy: 0.9281 - val_loss: 0.3609 - val_accuracy: 0.8824 - 73ms/epoch - 5ms/step\n",
            "Saved model to disk\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import tensorflow.python.keras\n",
        "# print(keras.__version__)\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oE-QzC9SDeOo",
        "outputId": "b7443aa0-9c66-4eaf-be69-19bd9b7f9f48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.15.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# X_test1 = scaler.transform([[60,2,1,4,2,2,6,2,6,0.15,0,4,2,1,1,6,1,1]])\n",
        "# X_test1 = scaler.transform([[61,2,1,4,1,3,0,3,7,0.8,0,4,4,1,5,1,1,4]])\n",
        "# X_test1 = scaler.transform([[73,1,1,1,2,2,3,1,1,0.3,0,3,3,2,3,3,2,2]])\n",
        "# X_test3 = scaler.transform([[74,2,1,3,1,2,2,4,11,0.18,1,4,3,1,2,2,2,1]])\n",
        "# X_test3 = X_test3.astype(np.float32)\n",
        "\n",
        "# X_test4 = scaler.transform(X_test)\n",
        "# X_test4 = X_test4.astype(np.float32)\n",
        "# pred1 = model.predict(X_test4, verbose=1)\n",
        "# pred2 = model.predict(X_test4, batch_size=64, verbose=1)\n",
        "# print(pred1)\n",
        "# classes_x=np.argmax(pred2,axis=1)\n",
        "\n",
        "# print(classes_x)\n",
        "\n",
        "# X_test4 = scaler.transform(X_test)\n",
        "# X_test4 = X_test4.astype(np.float32)\n",
        "\n",
        "# print(X_test1)\n",
        "# print(y_test2)\n",
        "pred1 = model.predict(X_test1)\n",
        "# print(pred1)\n",
        "\n",
        "# Y_test2 = Y_test1.copy()\n",
        "\n",
        "n = 0\n",
        "for r in pred1:\n",
        "  if pred1[n][0] < 0.5:\n",
        "    pred1[n][0] = 0\n",
        "  else:\n",
        "    pred1[n][0] = 1\n",
        "  n = n+1\n",
        "print(\"accuracy neural network: \", accuracy_score(Y_test1, pred1))\n"
      ],
      "metadata": {
        "id": "XA2G5rVGdL_4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f412abe-c9a4-4d3b-c294-c49ab77c0d41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy neural network:  0.8823529411764706\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "figure(figsize=(15, 10), dpi=80)\n",
        "\n",
        "plt.plot(history.history['acc'])\n",
        "plt.plot(history.history['val_acc'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Test'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 685
        },
        "id": "gVFt2KWJdcyj",
        "outputId": "b4081b45-525f-4ece-810a-0e54f5cd0b7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "figure(figsize=(15, 10), dpi=80)\n",
        "fig1 = plt.gcf()\n",
        "\n",
        "plt.plot(history.history['loss']) \n",
        "plt.plot(history.history['val_loss']) \n",
        "plt.title('Model loss') \n",
        "plt.ylabel('Loss') \n",
        "plt.xlabel('Epoch') \n",
        "plt.legend(['Train', 'Test'], loc='upper left') \n",
        "plt.show()\n",
        "plt.draw()\n",
        "\n",
        "from google.colab import files\n",
        "fig1.savefig(\"nn_resolve_overfitting.png\", dpi=200)\n",
        "files.download(\"nn_resolve_overfitting.png\") "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 702
        },
        "id": "dofoOcUTdgMi",
        "outputId": "f9198496-3a10-41d7-d93d-3449cf2bc8ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UzpR7sgEAAFpOyR6Q2pn2nGwAAICWU7IHpHR6G5+5JxsAAKC9lOxB6Uy7JxsAAKDlOjv9BaWUP0xybZJukrNJ/kWt9S8vuOYbk/xBko9vevvraq1LO51vUMYmpt2TDQAA0HI7XrKTfHet9VSSlFK+M8mvJ3nmRa77eK31lgHkaUSZcE82AABA2+34cvHzBbtvX5K60985jMY6npMNAADQdoOYZKeU8h+S3No//LZLXHZTKeXDSTaS/Fqt9Zcu8Vm3Jbnt/PG+ffu2M+qOKRPTKaVmfW216SgAAADskIGU7FrrS5KklPI9SX42X1i0P5zkaK31dCnlaJJ3lVIeqrX+9kU+6/Ykt58/Pnr06GhMxjtTSZK6vtxwEAAAAHbKQHcXr7X+RpJbSymHLnj/TK31dP/18ST/KcmzBpltx3WmkyR1VckGAABoqx0t2aWU/aWU6zcdf0eSk0kevuC660opY/3Xe5I8N8nf24F85J0v2SbZAAAArbXTy8X3JXlbKWUmvUd4PZjkubXWWkp5c5J31lrfmeT5Sf5ZKWW9n+ltSX5th7MNVn+5eNZXms0BAADAjtnRkl1rvTfJP7jEuZdtev26JK/bySyN60+yY5INAADQWgO9J3tXe3SSrWQDAAC0lZI9KP1JdtmwXBwAAKCtlOxB6U+yi3uyAQAAWkvJHpT+JHvMJBsAAKC1lOxB6U+yO92VbHRrw2EAAADYCUr2oPQn2VNZy+p6t+EwAAAA7AQle1D6k+ypspaV9Y2GwwAAALATlOxBeXSSvZoVk2wAAIBWUrIH5fwkO2tZXjPJBgAAaCMle1A23ZNtkg0AANBOSvagnC/ZZS0ra0o2AABAGynZg7J5ubiNzwAAAFpJyR6UzRufmWQDAAC0kpI9KJsm2R7hBQAA0E5K9qBsuid72SQbAACglZTsQRnrpGbMJBsAAKDFlOxBKSXd8SmP8AIAAGgxJXuAzpfs5TWTbAAAgDZSsgeodqYyXVZNsgEAAFpKyR6kznRvubiNzwAAAFpJyR6kzkzvOdk2PgMAAGglJXuQJnrLxT3CCwAAoJ2U7AEqEzOZNskGAABoLSV7gD5fsk2yAQAA2kjJHqCxiRmP8AIAAGixTtMBdpOxyZmMlW7W11abjgIAAMAOMMkepM5MkqS7ttRwEAAAAHaCkj1IE9NJkrqqZAMAALSRkj1InV7JzvpyszkAAADYEUr2IE30lovHcnEAAIBWUrIHqT/JLibZAAAAraRkD9KjJdskGwAAoI2U7EHqb3xWNlYaDgIAAMBOULIHqf8Ir3ElGwAAoJWU7EHqT7LH3JMNAADQSkr2IPUn2RN1Jesb3YbDAAAAsN2U7EHqT7Kny1pWlWwAAIDWUbIHqT/Jns5qlteUbAAAgLZRsgfp/CQ7q1lZ32g4DAAAANtNyR6k/iR7qqxmxSQbAACgdZTsQdo0yV42yQYAAGgdJXuQzk+ys2aSDQAA0EJK9iB1ppKcvydbyQYAAGgbJXuQJvq7ixcbnwEAALSRkj1I45OpKZnOmkd4AQAAtJCSPUilZGN82iO8AAAAWkrJHrDu+FRvubhJNgAAQOso2QNWO9Me4QUAANBSSvaA1c50pmKSDQAA0EZK9oDVzoxJNgAAQEsp2YPWmc5Usbs4AABAGynZA1YmepPslTWTbAAAgLZRsgesTEz3n5OtZAMAALRNp+kAu83Y5Ewmspql1fWmowAAALDNTLIHbHxyJmOlZn11pekoAAAAbDMle8DGJmaSJBurSw0nAQAAYLsp2YN2vmSvLTYcBAAAgO2mZA9aZ7r339XlZnMAAACw7ZTsQetPsuu6kg0AANA2Svag9SfZdc092QAAAG2jZA9af5KddSUbAACgbZTsQetPssu6R3gBAAC0jZI9aP2SPbbhnmwAAIC2UbIHbaJfsm18BgAA0DpK9qB1evdkT9SVbHRrw2EAAADYTjteskspf1hK+Wgp5c5Syv9XSvmKS1z3vaWUT5RSPllKeVMpZWKnszWiP8mezmqW1zYaDgMAAMB2GsQk+7trrV9ea70lye1Jfv3CC0opX5Tkp5M8K8nNSY4k+b4BZBu8/iR7KqtZUrIBAABaZcdLdq311KbDfUkutkb6BUneWWt9oNZak7w+yYt3Olsj+o/wms6aSTYAAEDLdAbxJaWU/5Dk1v7ht13kkhuS3Lvp+J7+e+0zMZskmS6rWV7rNhwGAACA7TSQjc9qrS+ptT4pyb9K8rNX81mllNtKKcfP/ywsLGxPyEHpT7JnsmKSDQAA0DID3V281vobSW4tpRy64NR9SW7cdHys/97FPuP2WuvR8z/z8/M7E3anPFqybXwGAADQNjtasksp+0sp1286/o4kJ5M8fMGl/znJ80op15ZSSpJXJHnrTmZrzKPLxVcsFwcAAGiZnb4ne1+St5VSZpJ0kzyY5Lm11lpKeXN6m529s9b6qVLKv07y/v7v3ZHkDTucrRmdqdQUk2wAAIAW2tGSXWu9N8k/uMS5l11w/KYkb9rJPEOhlGyMz2RmYyULSjYAAECrDPSebHq6nenMFJNsAACAtlGyG9DtzGQ6K1led082AABAmyjZDaidmcxkNSsm2QAAAK2iZDdhYiYzWcnSqpINAADQJkp2EyZme/dkryvZAAAAbaJkN6BMzmY6q56TDQAA0DJKdgPGJmf7y8XXm44CAADANlKyGzA2OZtO6WZtbaXpKAAAAGwjJbsB41OzSZK6stRwEgAAALaTkt2AMtEr2d3Vcw0nAQAAYDsp2U2YmEmS1LXFhoMAAACwnZTsJvQn2VmzXBwAAKBNlOwm9CfZSjYAAEC7KNlN6JfsMSUbAACgVZTsJvRLdllXsgEAANpEyW5Cv2SPbyw3HAQAAIDtpGQ3ob/x2diGSTYAAECbKNlN6E+yOxvLqbU2HAYAAIDtomQ3oT/Jns5KVje6DYcBAABguyjZTehPsmeymuU1JRsAAKAtlOwmnC/ZZSXLaxsNhwEAAGC7KNlNeHS5+KqSDQAA0CJKdhMeXS6+kiUlGwAAoDWU7Cb0J9kzZTVLq0o2AABAWyjZTRifSLd0epNsJRsAAKA1lOyGbIxPZzqrlosDAAC0iJLdkI3xmcwU92QDAAC0iZLdkNqZzkxWs2i5OAAAQGso2Q2pEzOZiedkAwAAtImS3ZA6MZvpYpINAADQJkp2Q0p/km13cQAAgPZQshtSJmczY3dxAACAVuk0HWC3GpuczVRZzfLKWtNRAAAA2CYm2Q0Zm5xNkqyuLDWcBAAAgO2iZDekMzWXJOmuLjacBAAAgO2iZDek9CfZGyvnGk4CAADAdlGymzIxkySpq0o2AABAWyjZTZnoTbKz5p5sAACAtlCymzLZuye7rJlkAwAAtIWS3ZT+JHts3cZnAAAAbaFkN6W/8dnYmpINAADQFkp2UybnkySddfdkAwAAtIWS3ZT+cvHxjaXUWhsOAwAAwHZQspvSXy4+m+WsrHcbDgMAAMB2ULKbMtHbXXymrGRpdaPhMAAAAGwHJbsp/Ud4zWYlS2tKNgAAQBso2U3ZVLIXTbIBAABaQcluSn/js9mynGWTbAAAgFZQspvSmUo3Y5aLAwAAtIiS3ZRSstGZyYzl4gAAAK2hZDdoozObWbuLAwAAtIaS3aCNzmx/ufh601EAAADYBkp2g+rEbGaznKXVbtNRAAAA2AZKdpMmesvFF1dNsgEAANpAyW7SxGxmsuIRXgAAAC3RaTrAblam5jOT5SyumGQDAAC0gUl2g8Ym5zJeatZWl5qOAgAAwDZQshs0NjWXJNlYXmg4CQAAANtByW5QZ3o+SdJdPddwEgAAALaDkt2g85PsuqJkAwAAtIGS3aAy2S/Zq4sNJwEAAGA7KNlN6pfssmaSDQAA0AZKdpMmZpMkxSQbAACgFZTsJk32SvbYupINAADQBkp2kyZ7u4sr2QAAAO2gZDepv1x8fGOp4SAAAABshx0t2aWU6VLKO0opd5VSPlJKeW8p5eaLXHeslLJRSrlz089NO5ltKPSXi3fWF1NrbTgMAAAAV6szgO94Y5I/qLXWUsoPJHlzkm+8yHVna623DCDP8Jjo7S4+nZWsrHczPTHecCAAAACuxo5Osmuty7XWd9XPj2k/kOTYTn7nSOlPsmezkqXVjYbDAAAAcLUGfU/2DyX53Uucmyul/Hkp5cOllFeVUi461i2l3FZKOX7+Z2FhYefS7rT+c7Jnspxzq+sNhwEAAOBqDaxkl1J+PMnNSX7sIqc/m+SJtdavSfKcJM9K8n9d7HNqrbfXWo+e/5mfn9+xzDuuv1x8rqxk0SQbAABg5A2kZJdSXpnku5J8a631C55XVWtdqbWe6L9+OMmvple0260zmY3SyWxWcm7FJBsAAGDU7XjJLqXcluTFSb6p1nrqEtc8oZQy0X89lV4h/8udzjYMNsZnMpNl92QDAAC0wE4/wutoktck2Z/kff1Hc32wf+7VpZRX9C/9R0n+spTykSQfTvJAkp/ZyWzDYqMzm9myknNKNgAAwMjb0Ud41VqPJymXOPeqTa9/J8nv7GSWYdXtzGQmK1m08RkAAMDIG/Tu4lygOzGXuSzb+AwAAKAFlOymTc71lovb+AwAAGDkKdlNm5zPfJZMsgEAAFpAyW7Y2PR8pstallZWmo4CAADAVVKyGzY2tSdJsrF0tuEkAAAAXC0lu2GdmV7JXl9eaDgJAAAAV0vJblhner73YtUkGwAAYNQp2Q0r/eXi1SQbAABg5CnZTZs6P8lWsgEAAEadkt20yV7JLqvnGg4CAADA1VKym9Yv2ePrSjYAAMCoU7KbNjmXJBlfU7IBAABGnZLdtP7GZ52NxYaDAAAAcLWU7Kb1J9mTG4uptTYcBgAAgKuhZDetf0/2TJazst5tOAwAAABXQ8luWn+SPZelnFtZbzgMAAAAV0PJblp/kj1XVrK4utFwGAAAAK6Gkt20zmTWy0TmsqRkAwAAjDglewisd+Yyl5WcW7VcHAAAYJQp2UNgfXw2c2UpSybZAAAAI03JHgIbE3OZy7KNzwAAAEackj0E6sRc5sqye7IBAABGnJI9BOrkXGajZAMAAIw6JXsYTM5nLstZXFlrOgkAAABXQckeAmPTezJealaWFpuOAgAAwFVQsofA2NR8kmR9+XTDSQAAALgaSvYQ6MzsSZKsL51tOAkAAABXQ8keAudLdndZyQYAABhlSvYQ6Ez3S/bKQsNJAAAAuBpK9hAoU72SXVfONZwEAACAq6FkD4PJuSRJWTPJBgAAGGVK9jDoT7LLmkk2AADAKFOyh0F/kj2+apINAAAwypTsYTDZe052Z32x4SAAAABcDSV7GPQn2ZPdxXS7teEwAAAAXCklexhM7U2SzGU5i2sbDYcBAADgSinZw6C/8dl8Wcq5lfWGwwAAAHCllOxh0JnKRulkT5ayoGQDAACMLCV7GJSS1fG5zMckGwAAYJQp2UNifWI+82XRJBsAAGCEKdlDYmNiPvNZysKykg0AADCqlOwh0Z3c09v4bFXJBgAAGFVK9rCY2tPf+MwjvAAAAEaVkj0kytSeTJe1LC0tNR0FAACAK6RkD4mx6b1JkrXF0w0nAQAA4Eop2UNifKZXsteXzjScBAAAgCulZA+Jzuy+JElXyQYAABhZSvaQmDxfsleUbAAAgFGlZA+J8/dkZ2Wh2SAAAABcMSV7WEztSZKMrZxtOAgAAABXSskeFv2SPb6mZAMAAIwqJXtYTPWWi4+vWS4OAAAwqpTsYdGfZE+un2s4CAAAAFdKyR4W50v2hpINAAAwqpTsYdEv2bN1MSvrGw2HAQAA4Eoo2cNici7dlMyXpZxbUbIBAABGkZI9LErJ6vhc5rOUcyvrTacBAADgClx2yS6l/JNSyt7+61eWUt5eSvmynYu2+6x15rKnLGVByQYAABhJW5lk/0yt9Uwp5ZlJ/vck703yyzsTa3da68ybZAMAAIywrZTs883vf03yxlrrG5LMbX+k3as7MZ/5spSzSjYAAMBI2krJHi+lfG2S5yd5X/+9ie2PtHvVqT2Zz1LOLivZAAAAo2grJftfJXlDkj+ptf7PUspTk9y1M7F2qak9mS/LWVhabjoJAAAAV6BzuRfWWn8vye9tOv54emlWQHQAACAASURBVFNttkmZ3pskWTl3puEkAAAAXImt7C7+6lLK/tLz+6WUh0opSvY26sz0Svaqkg0AADCStrJc/NtrraeSPCe9TdD+l/SWkLNNJmb2JUnWFk81nAQAAIArsZWS3e3/9xuSvK2/XLw+1i+UUqZLKe8opdxVSvlIKeW9pZSbL3Htc0spHyulfKKU8jvnn8m9m0zM9Up2d9kkGwAAYBRtpWSfK6X8aJIXJXlvKaUkmbyM33tjkqfWWp+Z5HeTvPnCC0op80l+Jcl31FqfnOQzSX5yC9laYWK2V7Lr8umGkwAAAHAltlKyX5rkuiT/d631c0luSvIfH+sXaq3LtdZ31VrPT7w/kOTYRS791iR/WWv9WP/4l5K8eAvZWqHM7O/9V8kGAAAYSZddsmutd9da/2WSD5RSru8f/z9b/L4fSm+afaEbkty76fieJNeVUr5g9/NSym2llOPnfxYWFrYYYYhN9VbIj6+dbTgIAAAAV2Iru4s/rZTyN0n+OsnflFL+qv+s7Mv9/R9PcnOSH9t6zM+rtd5eaz16/md+fv5qPm64TPeWi3dWlWwAAIBRtJXl4r+U5GdqrQdrrQeS/EyS11/OL5ZSXpnku5J8a6118SKX3Jfkxk3Hx5J8tta6voV8o69fsifXlWwAAIBRtJWSfaDW+pbzB7XWtyY58Hi/VEq5Lb37q7+p/wiwi3l3kq8spXxJ//j7k7x1C9naYbp3T/bUeouWwAMAAOwiWynZG6WULz1/0H+98Vi/UEo5muQ1SfYneV8p5c5Sygf7515dSnlFktRazyZ5WZJ3lFLuTnI0yU9v6V/SBtO9e7Lncy7La4/5pwUAAGAIfcHGYo/hx5P8cSnlo/3jZ6S3kdkl1VqPJymXOPeqC47fmeSdW8jTPuMTWR2bzt6NxZxdXs/0xHjTiQAAANiCyy7Ztdb3lFKeluRr+299MMmHkrzl0r/FVq2M78ne9XNZWFnP4T1TTccBAABgC7YyyU6t9cEk/+X8cSnlolNqrtzaxHz2rCzl7PJa01EAAADYoq3ck30xdVtS8Kj1yb3ZW87l7PLu2lgdAACgDR53kl1K+fLHOD2xjVlI0p3cm31ZVLIBAABG0OUsF//dxzi3tF1B6Jvel5mymoXFiz1OHAAAgGH2uCW71vpFgwhCT5nelyRZXXik4SQAAABs1dXek802G5/dnyRZWzzVcBIAAAC2SskeMp1+yd44Z5INAAAwapTsITM11yvZ3eUzDScBAABgq5TsITM5f6D3Yul0s0EAAADYMiV7yIzP9kp2WVWyAQAARo2SPWz6u4uPr1ouDgAAMGqU7GEzvTdJ0lk923AQAAAAtkrJHjb9SfbUupINAAAwapTsYXO+ZG8sNBwEAACArVKyh01nOuvpZKZ7Lusb3abTAAAAsAVK9rApJcud+ewtizmzvN50GgAAALZAyR5Cq5092ZtzObO01nQUAAAAtkDJHkJrE3uztyzmtJINAAAwUpTsIdSd3JO9WcyZZSUbAABglCjZQ6hO78+espSz55aajgIAAMAWKNnDaOZAkmTpzMMNBwEAAGArlOwhND53MEmyek7JBgAAGCVK9hDq9Ev2+sLJhpMAAACwFUr2EJra0yvZ3cVHGk4CAADAVijZQ2h67zW9F8unmg0CAADAlijZQ6gz29v4rCjZAAAAI0XJHkb93cU7K6cbDgIAAMBWKNnDaGZ/kmRqTckGAAAYJUr2MOpPsqfWzzQcBAAAgK1QsofRxGzWMpHZjbOptTadBgAAgMukZA+jUrI0vid7spDltW7TaQAAALhMSvaQWpnYm/05lzPLa01HAQAA4DIp2UNqdWJv9pVzObOkZAMAAIwKJXtIbUztz74s5PTiatNRAAAAuExK9pDqTu/PVFnPwrmzTUcBAADgMinZQ6r0n5W9fOZkw0kAAAC4XEr2kBqbPZgkWTn7cMNJAAAAuFxK9pDqzPVK9vqCSTYAAMCoULKH1OSeXsnuLppkAwAAjAole0jN7L0mSVKXTjWcBAAAgMulZA+pmb2HkiRlWckGAAAYFUr2kCozveXi4yunG04CAADA5VKyh1X/EV4TaybZAAAAo0LJHlbTvZI9s2aSDQAAMCqU7GE13sm5sfnMbZxJrbXpNAAAAFwGJXuILXb2ZX/OZmlto+koAAAAXAYle4itTBzI/rKQU4trTUcBAADgMijZQ2xtan8O5mxOnVttOgoAAACXQckeYt3pg5kqazlzxg7jAAAAo0DJHmazh5IkS6dPNBwEAACAy6FkD7Gx+V7JXj7zUMNJAAAAuBxK9hCbmL8mSbJ29sGGkwAAAHA5lOwhNr3vcJKknjvZcBIAAAAuh5I9xGb3PyFJUhcfbjgJAAAAl0PJHmIz/Un2+LKSDQAAMAqU7CFWZnv3ZE+ueIQXAADAKFCyh9nMgSTJ9NojDQcBAADgcijZw2y8k7NlPrMbZ5pOAgAAwGVQsofc4vi+7OmeTq216SgAAAA8DiV7yC1N7M/+nM3i6kbTUQAAAHgcSvaQW53cnwM5m1OLq01HAQAA4HEo2UNuY/pApsp6zpyxwzgAAMCwU7KHXHfmYJLk3CMnGk4CAADA41Gyh9zYXO9Z2UunlWwAAIBht+Mlu5Tyi6WUe0optZRyyyWu+cZSylIp5c5NPzM7nW0UTMwfSpKsnHmw4SQAAAA8ns4AvuPtSX4uyZ88znUfr7VetITvZlP7npAkWT+rZAMAAAy7HS/ZtdY/TpJSyk5/VSvNHbyu9+LcQ80GAQAA4HEN0z3ZN5VSPlxK+fNSyvdf6qJSym2llOPnfxYWFgaZceD2HOqV7PFFk2wAAIBhN4jl4pfjw0mO1lpPl1KOJnlXKeWhWutvX3hhrfX2JLefPz569GgdYM6Bm9jTWy4+uXKy4SQAAAA8nqGYZNdaz9RaT/dfH0/yn5I8q9lUQ2JqT1YymZnVR5pOAgAAwOMYipJdSrmulDLWf70nyXOT/GWzqYZEKTk9ti/zG0o2AADAsBvEI7zeUEo5nuRokveUUu7uv//mUsrz+pc9P8lflVI+kuQDSd6b5Nd2OtuoONc5kH3d06m11SvjAQAARt4gdhd/+SXef9mm169L8rqdzjKqlicP5rqVT2dheS17ZiabjgMAAMAlDMVycR7b2vShTJe1PHLKknEAAIBhpmSPgO7sNUmSsyc/03ASAAAAHouSPQLK3OEkydIjDzScBAAAgMeiZI+Azt4jSZLl059rOAkAAACPRckeAVP7eyW7e/ZEw0kAAAB4LEr2CJg9cG2SpC482HASAAAAHouSPQL2HrouSTK+dLLhJAAAADwWJXsEzB3oLRefXH6o4SQAAAA8FiV7BJTOVE5nPjNrnpMNAAAwzJTsEXFmbF/m15VsAACAYaZkj4iFzsHsq6eajgEAAMBjULJHxPLkweyvC1ldXW06CgAAAJegZI+ItZlrMlZqHnnw/qajAAAAcAlK9ojozvV2GD+jZAMAAAwtJXtEjO29Nkmy9LCSDQAAMKyU7BExua9XsldOfabhJAAAAFyKkj0iZg8+MUnSPfO5hpMAAABwKUr2iJg/fDRJMnZOyQYAABhWSvaIOHj4+mzUkomlh5qOAgAAwCUo2SNiemoyD2dfZlcfbDoKAAAAl6Bkj5BHxg9m79rJpmMAAABwCUr2CFnoHMz+eiqptekoAAAAXISSPUKWpg5nOqtZXzzVdBQAAAAuQskeIWuzT0iSnH7weMNJAAAAuBgle4TUuV7JPvvQ/Q0nAQAA4GKU7BEyvu/aJMnSw0o2AADAMFKyR8jU/uuSJGunP9twEgAAAC5GyR4hs4eOJknq2QcaTgIAAMDFKNkjZP81T0ySjJ870XASAAAALkbJHiEHD+zPmTqbyaUHm44CAADARSjZI2RuqpMTOZj5VZNsAACAYaRkj5iHO9fkwPpDSa1NRwEAAOACSvaIOTf5hMxkOVk+3XQUAAAALqBkj5jlmSNJktVTxxtOAgAAwIWU7BHT3dN7Vvbpz93bcBIAAAAupGSPmLF9vcd4nXvQJBsAAGDYKNkjZvrg0STJ2iN/13ASAAAALqRkj5i5wzckSbpnPtNwEgAAAC6kZI+Yg9dcl5U6kc7CZ5uOAgAAwAWU7BFzZN90HqgHMrP0uaajAAAAcAEle8TMT3VyohzKnrUHm44CAADABZTsEVNKyenONdnTPZOsLTUdBwAAgE2U7BF0bvpI78VZ92UDAAAMEyV7BK3N9kr26iOelQ0AADBMlOwRVPc+MUly9nP3NpwEAACAzZTsEdTZ3yvZSyfvazgJAAAAmynZI2j6mmNJku7DSjYAAMAwUbJH0L7D12elTmTszN81HQUAAIBNlOwRdN3+udxfD2Vq8TNNRwEAAGATJXsEXbdvOvfXa7J3+bNJrU3HAQAAoE/JHkHTE+N5qHMkU3U5WXqk6TgAAAD0KdkjamHq2t6L0+7LBgAAGBZK9ohane89xqv7iB3GAQAAhoWSPaLq/iclSc6d+HTDSQAAADhPyR5Rk4eOJUmWH7yn0RwAAAB8npI9ovYcflI2asnGKcvFAQAAhoWSPaKuPbA3n8uBdM7c33QUAAAA+pTsEXX9/t6zsmeXPtN0FAAAAPqU7BF1ZG+/ZK+fSlbPNR0HAACAKNkja3piPA91rusduC8bAABgKCjZI2xhtves7DxyT6M5AAAA6FGyR9jqnmNJku7JTzUbBAAAgCRK9kgrB78oSbJ84u6GkwAAAJAo2SNt7xOelJU6kbUHTbIBAACGgZI9wo4enM/f1cMZO31v01EAAADIAEp2KeUXSyn3lFJqKeWWx7jue0spnyilfLKU8qZSysROZxt1Rw/M5N56JLPnjifdjabjAAAA7HqDmGS/Pck/SnLJcWsp5YuS/HSSZyW5OcmRJN83gGwj7eiBmdxXn5Dxupac+UzTcQAAAHa9HS/ZtdY/rrUef5zLXpDknbXWB2qtNcnrk7x4p7ONuoNzk/ns2LW9A4/xAgAAaNyw3JN9Q/7+pPue/ntfoJRyWynl+PmfhYWFQeQbSqWULM71/0yPfLrZMAAAAAxNyb5stdbba61Hz//Mz883HalR3QM3JknqSSUbAACgacNSsu9LcuOm42P993gcU9d8cZJk5UHPygYAAGjasJTs/5zkeaWUa0spJckrkry14Uwj4dpD+/PZejAbJz0rGwAAoGmDeITXG0opx5McTfKeUsrd/fffXEp5XpLUWj+V5F8neX+Su5M8mOQNO52tDY4emM2nu9dm8vSnk1qbjgMAALCrdXb6C2qtL7/E+y+74PhNSd6003na5uiBmfxVvS5fv/63ycLnkj3XNh0JAABg1xqW5eJcoaMHZvKpen3v4KFPNBsGAABgl1OyR9zBuckcH3ti7+Ckkg0AANAkJXvElVKyvL+3w3gessM4AABAk5TsFpg6dCyrtZPuQ3c1HQUAAGBXU7Jb4MZr9uSeeiQbnpUNAADQKCW7BW48NJtP1evTOXNfsr7adBwAAIBdS8lugRsPzeVT9bqUupE88umm4wAAAOxaSnYL9CbZ1/UOPMYLAACgMUp2Czxx/0zuTf9Z2R7jBQAA0BgluwU642NZ3HdT7+DBjzcbBgAAYBdTslvi0DVH8kA9mHrib5uOAgAAsGsp2S1x48HZ3NV9YvLgXUl3o+k4AAAAu5KS3RI3HprNXfVoyvpS8sg9TccBAADYlZTslrjx0Fw+Xp/UO3jwY82GAQAA2KWU7JY4dmg2n+ge7R24LxsAAKARSnZL3HBoNp/ME3sHJ0yyAQAAmqBkt8RUZzyHDh7KA+UJlosDAAA0RMlukS8+PJ+PbTwx9aG7ko31puMAAADsOkp2i9x0eC4f6x5N2VhNHv5k03EAAAB2HSW7Rb748Hz+Z7e/w/gDf9VsGAAAgF1IyW6Rmw7P52/qsd7BAx9tNAsAAMBupGS3yE2H5/Kpen1Wy1TyWSUbAABg0JTsFjk4N5k9M1O5t3Ost1y81qYjAQAA7CpKdouUUnLT4bl8ZP2GZPGh5Oxnm44EAACwqyjZLfPFh+fz4dUbegeWjAMAAAyUkt0yNx2ez992b+wd2GEcAABgoJTslnnqtfP5WH1SuhlLHvhI03EAAAB2FSW7ZZ5yZE+WM5WHpm9MPqtkAwAADJKS3TJP3D+T+alO/me5OTl1X3LuoaYjAQAA7BpKdsuUUvKUI/N5//Kx3hvH/6LRPAAAALuJkt1CT712z+dL9v1KNgAAwKAo2S30lCN78vH6pGyMT5lkAwAADJCS3UJPPbIn6+nkofmnJfd/OOl2m44EAACwKyjZLfSUa/ckSe6aeGqycjo5eXfDiQAAAHYHJbuFrpmfyqG5yXxg9Yt6b7gvGwAAYCCU7JZ66rV78u5TT+od/N2fNRsGAABgl1CyW+rp1+/NJ1f3Z23++uTe/9F0HAAAgF1ByW6pp1+/L0nJ5w58VfLQx5OFB5uOBAAA0HpKdks9/fq9SZKPdp7ee+O+P20wDQAAwO6gZLfUFx+ez/TEWN63+OTeG5aMAwAA7LhO0wHYGeNjJU+7bm/+6MRY6twTUu79/9u78zhJzvrO858nIvKozKy7u7qq75Za3ZJaNxJCIMAcNr4wWICBNfaYsT2w+/LYHox3PB6vZ732zA7rHV/jsQ2GATyMwTYYW16DuYy4hJCEbqSW1Or7qO6uuyrvjHj2j19kVXWrutWSqruu7/v1yldWRkZlPhHxxBPP73meePKbS50kERERERGRVU892avYno1djFdb1DbeCsOPQXViqZMkIiIiIiKyqinIXsVs8jM4VLoB8BoyLiIiIiIicpEpyF7FrkmD7Hu4zhY885UlTI2IiIiIiMjqpyB7Fds1WCITOr4+1gPdW2GfgmwREREREZGLSUH2KpaLQq4e6uKho5P4y18L4wdgbP9SJ0tERERERGTVUpC9yt2wpYexcoPRwdttgXqzRURERERELhoF2avc9Vt6ALg3uA5cCM/88xKnSEREREREZPVSkL3K3ZAG2d8djmHLS+HA16FVX+JUiYiIiIiIrE4Ksle5HeuKdOUjHjoyAbveAI0ZOPCNpU6WiIiIiIjIqqQge5VzznH9lh4eOzZJ84ofsoVP/uPSJkpERERERGSVUpC9Bty4pYd6K2Fvcwj6d8Lez0GSLHWyREREREREVh0F2WvAjVt7Abj/0Bhc+SMwMwzHH1ziVImIiIiIiKw+CrLXgJu29eIc3HdwDHb/iC3c+w9LmygREREREZFVSEH2GtDdkeHKwS7uPTCO3/QSKA3CY38L3i910kRERERERFYVBdlrxEu39zIyU+fgeB2ufStMHIIj9y51skRERERERFYVBdlrxC07+gC478AYXPd2W/jIp5YwRSIiIiIiIquPguw14qXbLci+9+AYDF4LA1fbkPFWY4lTJiIiIiIisnooyF4jBrrybOsv2ORnzllvdm0CnvqnpU6aiIiIiIjIqqEgew152Y5+Do1WODZRhevfAUEE9//3pU6WiIiIiIjIqqEgew25/Yp1AHzz6dPQOQhXvRH2fxVG9i1xykRERERERFYHBdlryCt2rsM5+MbTI7bglp+z5/s/snSJEhERERERWUUUZK8hfcUsezZ2cfczoySJh22vgPVXwoP/E+ozS508ERERERGRFU9B9hpz+871jJUbPH5iyiZAu/W9UJ+EBz6+1EkTERERERFZ8RRkrzGvTO/Lnh0yfv07obQB7v5j/ZyXiIiIiIjIi3TRg2zn3BXOubudc0855+5zzu1ZYJ3vc85VnXMPzXt0XOy0rUUv2dZLPhNw15OnbEEmDy/7X2H6ODz610ubOBERERERkRXuUvRkfxD4kPd+F/AB4GPnWO9J7/0N8x7VS5C2NSefCbl95zruPzTOZKVpC2/+l5Drhq99AJq1pU2giIiIiIjICnZRg2zn3ABwM/CJdNFngC3OuZ0X83vl/F5/1QbixHPXU2lvdr4bXvV+mDgM3/7jpU2ciIiIiIjICnaxe7K3ACe89y0A770HDgNbF1j3cufcA+mQ8v/tXB/onHufc+5o+zEzo1mxn6/XXjkAwJefODW38Nb3Qt/l8I3fg6njS5QyERERERGRlW25THz2ALDZe38T8OPAe51zP7HQit773/Peb24/SqXSJU3oajDQlef6zd3c9eQpmnFiC6Ms/OB/hmYZ7vxF8H5pEykiIiIiIrICXewg+wgw5JyLAJxzDuvFPjx/Je/9lPd+Mv37KPBJ4JUXOW1r2uuu2sB0rcW9B8bmFu76AbjhJ2Hfl+A7H1y6xImIiIiIiKxQFzXI9t6fwnqp35Uuegtw1Hu/b/56zrkh51yQ/t0J/Cjw4MVM21r3hj2DAHzu0RNnvvFDH4DeHfCl34Rj312ClImIiIiIiKxcl2K4+HuA9zjnngJ+DXg3gHPuw865H0vXeQvwqHPuYeAe4EvARy9B2tas3YOd7NpQ4vOPDc8NGQfIdcLbPgrOwafeBdMnly6RIiIiIiIiK8xFD7K9909672/z3u/y3t/svX80Xf5z3vs707//2Hu/x3t/ffr8f6aTpMlF9MbrNjJWbnD3M6NnvrHxRnjjH9lvZ3/qnVCbWpoEioiIiIiIrDDLZeIzWQI/ev1GAP7h4QVmE7/+7fDK99uQ8U/coUBbRERERETkAijIXsN2rCty7aZuvvDYMLVm/OwVXvsb8IpfgqP3wUd/GCaPXvpEioiIiIiIrCAKste4N9+4iel6i88/duLZbzoHr/8tC7ZPPgp//jo4rvnoREREREREzkVB9hp3x42byEYBn7z3yMIrOAev+lV460ehOm492t/77KVNpIiIiIiIyAqhIHuN6y1m+eFrBrn3wBj7Ts2ce8Vr7oCf+UfIFuFvfgY+8RY4/eQlS6eIiIiIiMhKoCBbeMdLtwLwyXsPn3/FLbfAe74B170d9n0Z/uQ2+Py/hcrYJUiliIiIiIjI8qcgW7h1Rx87B0r89X1HmK41z79y1xDc8SH42S/bT31958/gj26Ar/8uTJxjyLmIiIiIiMgaoSBbcM7xc7fvYLre4q/uu8BAecst8LNfgrd8BHLd8M+/A39wDXzkB+A7H4SpBSZSExERERERWeWc936p0/CibN682R89qp+WerFqzZjbP/BVsqHja//7a8iEz6P9pVWHfV+Bxz4DT34OmhXAwaaXwNaXweabYdPN0L3ZJlITERERERFZwZxzx7z3mxd6L7rUiZHlKZ8J+ZmXb+P//eJT/MPDx7njpgXzy8KiHFz5w/ZolOGpL8Djfw/7vwrH7p9brzRoPeBbb4NtL4cN10KoLCgiIiIiIquHerJl1mSlye3/zz/TW8jy5fe9mmz0Iu8mSBIYewaO3jf3OPk98Im9ny1BvgcyeQu8t7wUiuuhsA46eiBuWgDfuwMC3dkgIiIiIiLLw/l6shVkyxn+21f38btfeJLfftMefuq27Yv/BbUpOHovHPo2HPmODS2vjMH4gXP/T64Lhq6Hweugc4MF5vluCEIbqp7vseWlDVDot+UiIiIiIiIXiYJsuWCVRotX/+5deA9f+9Xvo5i7RMO5xw/B6b1QHoHKCFQnIMxCfQpOPGyPxnl+x3u+qMN+zztXgmznvL+LkClAs2rD2htlyBagcwi6NlmAnjSth71niwXv7TR4D4U+ex1mLKCPctbbXpuytOW77aH7zkVEROTFajVs9F8mv9QpWXxJolGKl0plzOq0z2d/T5+EqWOw6aaLl65VQPdkywUrZCP+zet38euffZTf/9JT/MaPXn1pvrh3mz3OJUlg4iBUxqE2bkG4TyzorU1YYTAzbMsbM1CfsefGDJRP2etmee7zMgV7NMrQqr6ABDvIdKSTvM0TZm3Ie3G9BeWNin1+adBeRzn7u3sTuMB+9uzId6BVg1ynTRC3fhdUx6EyatsDFvgPXGXpHX7ECssoB4futjQMXA0broa+yy3wb297uzHBOfs+F9pzmIG+y2wfPPMV+66OXvvcjh77e+YkHLnPGg427IEN11jjg09g4rA1jPRut+PWKNv25bth6jicetz2Qe8OyHfN7R/vYfqE3TZQ2mCf2S7069M2K31lJB2dMGjpSGJLX3G9rZvEtn+aFduWVt32SRLD5a+1dCyYfw7ZvmlWYPKI/W/f5bYN7QqM9xA37Dgu1FiSJHDiIctzO179/EZNeG/7qTZp+z/Xdf6Kk/dw/EEYfhT6dkDnRoiydjtFtnBmels1aNbsuVW3PNeqz73G2bHr2Wr59vlqNex7ciV7XZ2w49iqQf9Oy7sX0/GHbD/sefOFf5f3lieSljWeJS0IMvO2YRwyRdunZ/9fY+a5v8d7GNtvDYNBaBM9XqwGNu9t+zsHoTRgDXvTJyz/HvwG7P1HuPrHYMernt/nJgkc+qaVSet3Xdj/TA/D6DN2vg5dbz/r2BY37VyNG3a+niuvVcct/7fPH++tXGm/btXtXGmPWJqvNmnHNdcJ4wftWA1evziV9eqE3da06SULlyOtBjzzz1aG7Hz9mWXb2Rpl2x8dPS8+XW1xEw58zY7Bnh+3hmOw/VefPn96zpbEVsbne+bKk3OZf3zi1twxznUuvH1JAo1pa9AuDjz72CSJnZMLlbNnfFcTxg5AXLfG895tVnbOX3fmlJXlpfV2fKZP2LVlobK1PgP3ftD+vvZtVh6CfQ/MfXajYscvCO0a5Jydcwe+BiNPAx4G9tj1ZmbY6h/dm6w8HHkaDt9jwcmGPdaAHzfsOj9+EK79Cfvus8udhfb5o38Dn3u//X3TT8NLf96uV/PXATtfjn0XqmPQs83qCmfvJ1i4fGrW7DgkLTj5mJ2X63Y+e73apJWXFzKPTty0tOc6YevLodg/910+sXLzH3/FrsN3fAi2327vV8bgiTth/11Wd3jV++fyONjxHX7U0pfvnvvMp79gZdH8fTM9bMewa9OZeWH6JOz9/2xeKmXBnQAAIABJREFUoIGr5pafftKOcb7bPqd9fFp1K+9GnrLju+0Vtl0zJy2NUdb279RxOP0EnHrCPufmd0PXRnuvWZ2rr4w+A3v/wa5F/Tth8qjtk1t+ForrztyPYwcsXc0KXP4ay4ttJx6B0aetzMoULE25Thi67sz1WnX46n+Eb/2RvXf7++yczXfD+qvOXQd56gvw2fdYWf2Wj8C1b7Xl3lv9p9WwEagjT8Hlr7P839as2v6Pm7DuijPzXWXM5muaOGL11p6tlpbOQXt/+BHbjr7LFk7XCqOebHmWOPHc8ad389ixSe78hVewZ2P3UidpcSSJBR9R/szKXXXcLsyVMbsw1aYsIKtPWSGR67JCojJmFYNWA6aPp5WatPc6U7CCpzxiF/3yiFVEskX7vplhu9AuJOqwQqU2aZWJ5yPIWEW2PvXi9s1i6dpklYtZDtbvtuC+Omb7cP42ZoqW/lZt4ZEKYc4u/j5Og/I9cPS7UJ9c+PtdYBfI0gYon7Zj1NFrF8Bz7iNnF7d2xd7H9r2lDXOVz3ajTly34wR2gRy63j67HTCP7LPKZTa94EVZy1/l0bRietbx7dpsFWOfPPvRKNs2LCTbafulVQOeZxleHLALW6HP0tQopw0waeNLz1Y7LuMHLe31acvv3sPgtbafRp486zPThqXBa63hpD5llZDyiO3fXKftr3yXVTLKp2yf1iYgiKyyOnSDnUutKsyctorY2H5rsDn1uH1P73Z47f9haT34DTj4LVvuE9uWTMGC0OkT9ljoWG+51f7/8Ldte9ftmvuMKGcNSJVRu8hvudXO4Q3XwFVvtMrfvq9YHh9+1PJ028YbLcg9cp+luzpu21zsh8ljNlJm4w2Wvs4h2HyLVUaOP2D5zcfWaDZz0l53Dtp6jRkL7E4+ZpXhHa+aa1wLs2eWK3vusO8ES9vI07YtQzdA33YLrDqHrPI3/Ag89EmrqIF97sYb7btH99n/jh+w453psApRdfzZxz7fY/m/MXPm/gBrHCwN2PcPXW95bu/n4KnPWx7uv9wqmZVRwNvn5Ltt/87O3ZEGcvke2+axZ559WEuD1ohUGbUK+5aXwcOftLRf/w5L/8Rh29/VMWsgLPRB9xYLiMYPWOPV2DO2P7Mla9AJIjunTzxkeSVJ5sqeMGfB0it+0V6XT1sQt+9L1ugxfWIuv22+2c6B0X2W76/7Cavgn3jERmlNHLL9VB2HA1+3fNyzzcoW5+Cat1i58/jfpfsqPed2vcHOy/13pUHdtfYrHhOH7Lxt1a3MaTeSFdfZ++XT9tlJy65PO14Fu37Q9v29H7J8dfsv23nyzFfhsb+1z8932+f6eO582nRTGpTkLW/Vp2z9yoitkuu2oKhRtu2rTablFrZ/syXbN9mSBXDjhywvlQafXWYGkX1HR6/lhemTVt6CHb/ahG0TWABx40/C5pfa9f7o/fDt/waTh+c+r3e7/d/wo3Zt79xo6Z9/rQizdv1aqKwNornvuxDt9Qvr4Jo7bC6aIIT7/7ttyw3vtHPgyHfm8mVxwPLq6b12PLbeZvtyetjK0fZxmD0mzH1+9xYrx7/3Wfve/p2w45W2fO8/WhlQGbX/DyLbB2D5rWebff/YAXuuTdo5uPlmC6zKI1Yuhlk71lf8gAWWOLjzX1v53Lb+Sssfw4/OpdMFdg7Fdbj6zVY+PvypueMJls6Bq+y8bdXg1F57P98DN77LzqWnPm/bkO+BO/7ctvPhv7Tt84mlZ+ONVv5URuHpL9pnudAaLi5/jZVJj3zqzONU2mD/P3Nyriw6W7ZkQeTo/mfXSTJF21fHHjhzm84l2wmXvdq+OwgtCD1679z7fZfBT37aypC7/+uZ+/fsNN3wv9hxP/Gw1WdbVei/Iu1omHe9cKGVIVf9mO3HkX2W18YP2XmS77HjVhmFV/2qlUWP/a01JsyX74E3/CcrI5/+Ijx+59x5O3QDXP0mK3cP32ONQQvtz+J6K19G99k+eM2/h1f88ooY6aDh4vK8PXZskh/7429yzaZuPv3el7/4SdDWuiRJexVrVlmZOgF4u7AMXW+FU6thFe6JI1bQFdelLZLOKicnv2dBwMYbrRJSm7JKe6Zgn3nye1awRrl5FZd0iDykPQSxFXDtFvfqOFz2fdYiW5uwi1Z13P7OFq2y2izbZw8/Zo0FLrCKTu82CyamTti6U8esFXfDNTaJXXXCAoPjD6Y9An22XV2bYPAaq1wff9AaMsKMfWbnkG13dWKuhyDM2H46fI8VwJtusu+Y38K9YY8FEPu+ZNs1c9IqJ7lO257iettX2ZJVCro3274Yfca2YXrYguRsySrktUn7jGY6yiHfM9cws+WldhG498N2YS30W8UzbliA2tE7N5KiVbPXhXW2XqHfLiRJyyomY/vtO9pBLu0RB2mlpz0T/8RhqxjHjTR4PW3bEeWskSbK2cUwk7fns5cnLes1GD9kF9qJI3bRK6yzfdQO7Fs1q3zj7XgV16UV/i2WtsPftorRttusghpkrMI1edSCiolDZ+b7IJrLd2drB0/1qbmGi4V0DsEV32/79mu/e2aluzvtmXfO9m2jbPunc9DyWZSzNLYrLpVRCxp8Ale83oKQ00/a+85Zr0jnoH3X0ftsny0k12UV1q0vs7w0fhC++7G0d6/LAvd8V3qOTVhQO3PSzqvnki1ZutoV3va+uu5tdg4evRc23mT54tTj1pu958fhrv/72RWvTDFtZDrHNTLXBS/5GdvOx+888zh1b5kb7VKftnM5U7BK6YY9tt+PfRfGDqYNSyXLL8WBuYp3+bQ1MIw8xVyA4ux41mes8t6z1c7PMDM3eqd3u6W7Npk+JuzZhVZeZgvpetvsfNn7OXs/W5yb3yPM2rGfP4Jp/n5pVixN2U4YuNK+u2ebBSGP/M1cRTJTtDIXb2XV7h+yc/iRv4Yj9yy8X9ftsnS2f95yfhoWCsxcmO77NGhNYjsmXRtt9Fb7+K3bbT1K+W745u/PNST17rAGroPftGPVs8XWCXPW0BfmbH/MDNvxKK2349u10Y7Nobvn0hR1zI3+mJ8Xhq63fZ7vttELUd7KpQNfe/b5238FbL3VvvfU4xao5bvm5lPJFm0/NCuW3vbos7hux6Dd0NLRa9uVLdk6I/tseXVsLr/1XW5pPbU3HT213fbLga9Z2T9ftgSv+037ju991q4/5dNz19HJI2mv2pCVi3HDrkF4+99tL0/zgoODX7cGkb7L0sbl41be9F1mwVXPNjtn2iNdBq+17bn/o/DgJ+Yat9p5Itd1ZiNV3+XW+PiaX7f/2/9V+PafwKFvWVnXOWiBYNumm6yRYHSfNcaM7pt7b/2Vtm9OPT7XSBNmLQDq2WL5Om5aT+exB+w62ta50UZS9Wy1QP/4Q1b2dQ7a9vnE9t3ZjZq3/Jxdpw/dbY9WzRqccp1W1t76Xvu/z77H6j3t8+YVv2wNPo992sq0Zs32a6bD9unWW+3YTRye209Xvwnu+/CZjSNXvMEa+Mf2W7nYzqNDN1hjwIOfsDK+bef3W5BbGbW8VE5HSHQOWrrW7Z5r3E1adgyO3m/7ed0V1hgwcJU17tQm4Iu/YdfFTS+xdGcLdl4X+qzBtt2Y2bPFrstf/U9n5omoA676UaufTR2399tlqAut8XDn6+0a2kw7Kcqn4bsft4bQILJzNt9jZdptv2DH6OkvWv4tn4Yj91oDbvtze7baOd45ZNe2V/6KXVM/+oNz+y/fY8cn12n7ptAPX/mtM8+1rS+3c6A+BQ//1dxo0VyXbc/O11kZcfqJuU6po/fZNfKqN1o5dnovvOTd8MY/YLlTkC0vyAf+aS9/etczvOfVl/Hvfuiq5/4HkYstbi2fn31r1e2imS3MDX+MckudqhevWbPKbns43vNRHrHAstBnlbr2UMv6tFXm69NWKeocmjuOcct+6m/sgFVOMh124e7dYZW7+Y0po89YL0+YtQrc+t3Pf4j2hd7j2B6C26xYxeTpL1qv2PXvsF7Hs02njR8DVy18G4H3c722E4esghNmreJJOux/y61WcfE+HRUzbJX7zsG5odfViYWH6HpvlZTqOOBsnfZQ3elhewSRVU7bQ1mHbpgbrdGsWmWuWbVA4bmGED8f1QlrzKiOWeVqoeGoi+XYA9ZDt/tH7Bg/+U9zwU+2kN4S02uVx8kjFjDMH1oLdj7PnLTj09Fz7uN54OtW4S/02b7OdFjANnjdXL5sD2GuTVhl2yfWy14ZtUrw0PW2vDKWNkb2PDstR75jQerA1XOfm8SWP9sVd+dsXfzzn/yzOmGV7elh62VvlOGBj1sj3JZbLbA8V49S+7aMZiUdgeDtHFjquUmaNTtnJw5bgDd4nQVoL6RcW2zepyN0nrDzdfcPW4PBk5+3Y7flZdYQ8mI+f3Rf2vBUmisn27c7TR61gPJc++LUExZQLnR7URI/O38liQ3Zfuaf7bu33Wa94ReqNmVpWr/7wvJuq2EBfs/WuSHWp/baKIx1u6x3tm/H3Ppx2qjd0Tc3DDxJ7DNOPGzXo52vW9w8670F42eXLRf0f3Ha0D5vX3zvs9aDfcUbbMRD+1aHsyWxbVP/5ReW10f22S1Dl73m3LdsVsbs2utju2acfe2cPGYjB0rrrTybP9S7PGp5sbjO0nwh+6NRgS//BxtpMHjtc6+/xBRkywvSjBPe9mff5qEjE3z03bfwmt0LVCxFRERERETWmPMF2RoDLOeUCQP+6ztvpCsf8Yt/+SBPn7yA+0pERERERETWMAXZcl5b+gr82bteQrUZ8y8/fh+npmpLnSQREREREZFlS0G2PKeX71zHf/zxazgyVuUdH7pHgbaIiIiIiMg5KMiWC/L2W7byf71pD/tHyrztg9/mwMgCM7aKiIiIiIiscQqy5YL99G3b0x7tCj/+J9/i7mdGljpJIiIiIiIiy4qCbHlefvLWbXzkX9xCs5Xwrg9/hz/88tO04gV+WF5ERERERGQNUpAtz9trrhzgzn99O7s2dPL7X36KN//Jt3j4yMRSJ0tERERERGTJ6Xey5QWrNWP+8CtP8+df30/sPT/1sm38yvfvprtwAT82LyIiIiIiskKd73eyFWTLi/bUyWn+/Wcf5b6D4xSyIXfctImfvm07uzZ0LnXSREREREREFp2CbLnoksRz58PH+fA39/PYsSkAbrusn3/x8m28/qoNRKHuTBARERERkdVBQbZcMt57Hjg8wcfvPsjnHj1BK/EMdOZ40w0bec3uAW7c2ktHNlzqZIqIiIiIiLxgCrJlSZyaqvHJe4/wmQeOcnisAkAUOK7d3M1Ld/Rx644+XrKtj+4O3cMtIiIiIiIrh4JsWVLeex45Osk9+0e598AY9x0cY6rWAsA52L2hkz0bu7lqqJMrB7u4cqiTdaXcEqdaRERERERkYQqyZVlJEs+TJ6e598AY9x4Y47uHxhmeqp2xzrpSLg26LfDe1l9gQ1ee9Z058hkNNxcRERERkaWjIFuWvfFyg73D0zw5PMXe4WmeGJ7mqeFpqs34Wev2FjJctr7Elt4O1nfmGOjMp8+52dddHRHOuSXYEhERERERWe0UZMuKFCeew2MV9p6Y4thElZNTNU5N1zk6XmX/6RnGK81z/m82DFifBt3tALy/mKWnkKW3mKGnI0tXR4bujoiujgylXERHJlRgLiIiIiIiz+l8QXZ0qRMjcqHCwLFjXZEd64oLvl9txJyernN6psapqTqnZ+qcnq6f8feJySqPHpskTp67MSkTOga78xQyEY04oZAN6e7I0FPI0N2Robsje8brQtaC8p6ODBu68ng83kMpH1HMRoSBAnYRERERkbVGQbasWB3ZkK39Bbb2F867XpJ4xioNxssNxitNxisNJioNpqotpmpNJqtNyvWYyWqDYxM16s2YXBQwXWtxZKzCdL3FCxnwUcyGdOYzlPIRpVxEZ/rckQnJZQJykT3no5B8JqSQDenIhHSkz4Vs+nc2JBeFtEN256CvmKUzn6E9EkU98CIiIiIiy4OCbFn1gsCxrpR7wTOWx4lnOg3GJyrpc7VJtdEi8TBWbnBqqkaQ9lyX6y1m6i2ma3PPw5M19p2y14slGwY0k4TQOXoKWfqKGbryGTyQeE/iLdDvK2bpK2Yp5SJi74ljey+fCchGAc04oSMTsqErTyHtgY8CRximz4EjCgLCwJGLgtke/Djx9BezlPIRlXrMTKNFtdGir5ijt5BR4C8iIiIia5KCbJHnEAYWxPYUsmzrf3GflSSeRpxQa8bUWwn1ZkKtFVNtxFSb9lyZ/btFtWmv661k7jO8Z3TGeuYzYUArSayHvtzg4GgF5yB0DueYDfIvtXwmYGN3B8VcRKOVEAaObBSQiwJymdCeI+vNz0YB3nvGKw3ymZDB7jy5KJwN8MPAETpHEDhCB2EYEDpHGEDgHJkwmB0JUMiGtBJPvWUNB1HoqDVjOnMZhnry5KIA5xyBA4fto8DNe8ZGCqiBQEREREReKAXZIpdQEDjyQXhJf4asGSeMlxvM1FvWIx1akFlrJtRbMZkwoFKPOTlVo95KaCUJceJpJZ5W7ImThFbiidPgtVxvzQalIzN1pmstSrmIYjoUfrRc59hEjeMTVcbKDXJRQK3lGSsnNOKEetrA0DrrPvnAwQXcOn9J5KKAUi6ikAvx6WgF75kdwp+LAsBuWejKZ2i0EhsRkA0p12NOTFbpymfY3FdgS28HXR0Zqo2YWjOm0UrIRtYw0L41IBcFeGzURJx4HJCJAqK0cSIK7O8gsMaFTBgw2JWnM59hpt7kiRPT7Ds1w9a+ApetL842XuQiG60QBjbyoBV7Eu9nGz2yYUAmDKxhAWtcaI9iyKSjFzKhW7DRod6Kmaw26S/mzjv/gPeeZuzJpvtMREREZLXT7OIisiTixNNoWaDvcHTmI2qtmFNTdZpxMhfkews8k/ZzcuayZuyppT3+lUY8G5jWmjHNOCGfCZmqNjkxWZsNMhNvwV/iPR7wnnl/e2rNhJl6i0rDRgH0FXM4bLK9SrNFvWkjC6rNmKlqk2wUkg0dlWZMPgrZ2JNnqtbi6HiFWjM55z5YKQIHUTg/6HeMpg0PgYOBzjwDXTnK6ciJ7o4MYeAYr9g8CI1WQj4T0F/M0VfMArbvOvMRvYUsvYUstWbM8FSNYi6it5AheB6jCfKZkM29HeQzIbVmPPuoNmM6MiEDnXkqjZiZepOufIYwdJTrls7B7g46MiFx4pmqNQmcm21MKWRCCtmIiWqDY+NVwsBRyEazoyYK2YhWklBtxtSblmejsxoo2reR5KOQYi5kZKZBrRmzpbdANgqYrjUppnM2tPNyo5XMztdQbcYk3lPMRrQSe3/2lo70Vo4odGcs00gMERGRi08/4SUisgS895yeqVOux7OT2mXDgEbLArN2IFhrxgRubni899BKrHe8GfvZRodk3oiCk1M1pmstirmQHetK7N7QyaGxMkfHqzRaiT3ihHorIU6SM3rDW7GnEcdpD7yfTWviSUctJLTidDRD+neceJqJp5n22m/oytNbzHB6us7wpP28XjtYnKo2STz0FDL0FbIUcxGT1SZj5QZj5QZgowCma03Gy00asTVErCtlmam3VkXDxFJqDyxw826ByEX2awmtJKHWTOjuyBCFjolKk1wUzL522EgX5t8+wdxtFQ5b0H4vTjzVZkwuCunpyNBdyJCLwtkGtGacnNEYkAkCggBOTdUZrzQoZCOKuZBSLkMpF1LIRbO3hwSO2ZEe07UWrcSzoStPFLjZ22wa6a0082/zcC5tOEssT2ciR/6siSbDwNFKEk5N1am1Yjb1dAA2aiVIbykpN6yBIwock9UmlUbMrg2dbOktEKVzVgCMzNSptxJ6C1kCZ7eo1Frx7Gih/mKOq4e6qDZjRst1mrFPG39yzNRbTFSaZ4wuyUYBmdBex4lnZKZBZz5ia19htrGxmLVz6uBomWwU0Fe0xqrEe05P18mljTrt8ykKHH1FOxfbpmpNWrEnn+6XYN6IlEYrOWMUS5x4Ko0W1UZMK/EMdOaIwoVHp7TS83n++804wZ21TERkpdNPeImILAHnHAOdeeg8c3lHNqSbzKJ/33PNtL8cee8pN2IyoSMXhXhv8xY8n/bfcr3FkfEqrXTkQj4TpM8hlXrMqWnrIS+lwX6c+DTwbzA8WafeskaOro4I70lHRbRmR0d05SM291qAc/Z7mdDRkZkXuM1rnGjG1jDi8VQbCeVGi/5illwUcGS8Spx4uvIR0/UWM7VWOsTfhvq3G2I6MiHOQaVht3vkMwGJJ72lI5m9DaDdCNKKExuV4ZkdpQFQa8ZMVBuzQdxktUUzTti5vkQ9TpioNIjrfnb9uc+Y/1ln/Q1EgUtHENR5tDrXYNLWDnjPlgltrotawyZNXOHt/StGd0fGRvo0YqbPmogzGwXko4BmbA0nUeAo5qLZOUTmiwJHfylLFAQ04oRqei54YKLSBOz2mlIuInCOU9M1wsCxrb9IMRfZ/BrpqIswbWBsN6y0R8wANFr+jAbHOPGzDYLtfJgNA7o67Kc1u/IR3R0Zqs2Yfadm0jwasO/UNCMzDbo7MvQWM7MjaDrz0WwDEthopUZsjVAdmTBt1GhPJjo3gmqq2mKy2mSouz2KJwZsVE3HvDJopt7i6Hh1dn/M/wWRajOenTMlSG/BsvRlOTlVo1xvsXOgRBg4jk/UKOasjNl7Yhrn4PrNPQB2207JypXxShOfTmyaz4TWWFS3xqJMGKSNQ9aIY6NtrPE1TjxHJ6pUGzGlXEQpH5ENA05P15mpt8hlAjb3FtjeX2D/6TKnp+us78ox2JWnr5jl4EiZ0zN1tvcXOT1T5zv7x2zi1VKWY+NVMmHAbZf3M9CZo9qMefz4FLVmzM3b++jIhIzMWMNQZ96+uzMfEQUBTw5Pc2KyyrWbu+kv5pisNsmEjmLWwpfpeovT0zVKuQwDnTmCwJEknul6iyTxaYNVQL01lx+29hUopY1NY+UGceLpLtj1uJ42jHkPxZw1AOaiC7+9L0k8IzN1jk1UKeUidg6UcM4xVm7w5cdPUm60eNMNm2ZHdDXjhNPTdaLQ0dORfVG3VSXp/XbzG8tkaaknW0RERF407/3s0PlcxnplozCYvS9/fsNDV3pLwfz/m6m3qNRjYm+BVCvx6VwGCV0d7WCtTpL42TkNMmH7M5h99vi0N9qCp2ZsvfftYLGWDsG3RrAcuSjg2ESV0Flvr8cqrO1e9Uac0FPIkA0DnjgxxcnpOnE8N6+EBTgh4xUbpZGLwtne4Vwm4MREjb3D05RyIes6c2TCgHK9xenpOqVcRG8xa40k6ciTZpzMjkYJ0x7oqVqTI2PV2R70mXqLYjZix/oirdinP03ZxAHrOnM0WnbLSykX0ZG12yFOT9c5PmGNO9koYGNPB7komD1m7ZE1UWgjG6qNmHK9RT4TUMhF6e0T9usSR8erjMzUiRNPLgroyIY008ax/lIW5xwztRbTtSatxDPUnacVew6MlKk249lgtd1glLRvCfJ+tqEKrDGmHQhmwiANzOdPVOmotxKmFmjgyYY2aqLeStjRX2SoJ89k1UbPjFcaVBrxC87rzkEhE1J+EZ8h53f2PC3PNW9L+3aZZpws6vwumdBuE8pFAUlaLsVpHm03dmajgM58holKY3Z0GMC2/gIOODRWmS2jclHAlUNdOGDv8NTsyK0wcGzutduX5k/IGqSjdNrP2TCgmIuot+x2tTBwVBox+0fKdqtRFLChK89gV55Czhqah6dqJN7P3lrV05GhObsd6a1OgaOUizg+UWP/yAzrSjlymZCDI2U6MiG7BjsJnDVCt29b6u6wcnF2ktr0UcxaQwnYBLwnJmtkw4D+UpZMGMxuy4GRMt87NklfKcu6Uo4TEzWcg+39RX7kuiHeeP3GxTuQF4mGi4uIiIjIipAk/nn90oP3dhvNZNV+ZjMKHFv7CkRhQJL4BXv36q2YmVpr9mcvAQrZiExotwfUm8ls77r9Ykfa4+6glIuIwoDxcoPRcoNSLsI56wlv3ypQbcQUsiFb+izQqqS/INL+NZGOjPXctm+7SLxnstpkdKbBQFeeQja03lcPQz352Uai3Rs6iRPPw0cnZnvxx8uNtDEoi4P0loUEn87nEARYQ9e8xq5WnMyOfgmcY2NPB8WcTd45U7e5NNaVcnTmM9SaMQdHyxwcqbBjXYGh7g5Oz9itQqPlOlt6C2zoynNwtEwxG/GKneuIE89ouc6mng6m6y2+s3+MmXqTKAjYPdhJNgy479AYeFjfmaOeNgxN15rM1Gy00OUDJTb1dPDQkQmma036ijlasY0Kcs6lPz+aY6ra4sRk1W4PSXuFw9DNNlZFoePy9SUC5zg8VqHWjPHe01fMEQYwXmkSOBuJ0J7YtNyIqdRbzNRt9FK9lZwx90X77yBtiJuqNuktZNnY08FQT57hyRpfeeIUHdmQ3Rs6ee2VA0Sh43/ec5ij4xUasWf3YIkrBjpJvOfkVI2DIxUa6WikxHuStN1o9rW3Wzms8SukKx+ReBuFsmNdkVIuotxozd7CVWnYekPdHUSBzUVydKI6e5sN2P6KgmA2X3TmIy5fX2Ks3KDajNnRX2Sm3mLf6RlC52ZHYrQSO9/aI0zOpz356kLLrxgozeb7jT15Yu85Nl7lF193Bb/8+l0XdP4vJQXZIiIiIiIia1iSeJpJks6PMdf41J70NZ8JXtDkmUky17vfbixpN4Ss78yRpD/V2v4VlSSBdZ1ZCtln37ls83n42WH9y5nuyRYREREREVnDgsCRC559n7lLe6lfzOdm06C9I2uB9Xwh6Rw1FyAXhayA+Po5aZpHERERERERkUWiIFtERERERERkkSjIFhEREREREVkkCrJFREREREREFomCbBEREREREZFFoiBbREREREREZJEoyBYRERERERFZJAqyRURERERERBaJgmw3mY+4AAAIQUlEQVQRERERERGRRaIgW0RERERERGSRKMgWERERERERWSQKskVEREREREQWiYJsERERERERkUWiIFtERERERERkkSjIFhEREREREVkkCrJFREREREREFomCbBEREREREZFFoiBbREREREREZJEoyBYRERERERFZJBc9yHbOXeGcu9s595Rz7j7n3J5zrPezzrmnnXPPOOf+3DmXudhpExEREREREVlMl6In+4PAh7z3u4APAB87ewXn3A7gt4FXAjuBDcC/ugRpExEREREREVk0FzXIds4NADcDn0gXfQbY4pzbedaqbwXu9N4Pe+898GfAOy9m2kREREREREQW28Xuyd4CnPDetwDSAPowsPWs9bYCh+a9PrjAOgA4597nnDvafszMzCx+qkVERERERERegBU38Zn3/ve895vbj1KptNRJEhEREREREQEufpB9BBhyzkUAzjmH9VAfPmu9w8C2ea+3L7COiIiIiIiIyLLmbAT3RfwC5+4CPua9/5hz7q3Ar3nvbz5rncuAbwI3ASeBvwe+6L3/4wv4/DpwetETvvhKgMa2y3KnfCrLnfKorATKp7LcKY/KcrcS8uh6731uoTcuRZC9G5tRvB+YAt7tvX/UOfdhbLKzO9P1fh74tfTf7gLe671vXtTEXULOuaPe+81LnQ6R81E+leVOeVRWAuVTWe6UR2W5W+l59KIH2WJWekaRtUH5VJY75VFZCZRPZblTHpXlbqXn0RU38ZmIiIiIiIjIcqUg+9L5vaVOgMgFUD6V5U55VFYC5VNZ7pRHZblb0XlUw8VFREREREREFol6skVEREREREQWiYJsERERERERkUWiIPsic85d4Zy72zn3lHPuPufcnqVOk6w9zrk/cs4ddM5559wN85afM38q78ql5JzLO+f+Ls1vDzvnvuSc25m+N+Cc+yfn3NPOucecc6+a93/nfE/kYnDOfdE594hz7iHn3Decczemy1WeyrLinHt3et1/c/paZaksG2m99Mm0LH3IOff2dPmqKEsVZF98HwQ+5L3fBXwA+81wkUvt08DtwKGzlp8vfyrvyqX2IWC39/564O+BD6fL/zNwj/f+CuDdwF865zIX8J7IxfAT3vvrvPc3YBPzfCxdrvJUlg3n3Hbg54F75i1WWSrLzdu99zekj79Kl62KslQTn11EzrkBYB/Q571vOecccAK43Xu/b2lTJ2uRc+4g8Gbv/UPny5/A1LneU96VS8E5dzPwae/9dufcDLDTez+cvncv8Ove+y+f770lS7ysGc65nwF+GfgBVJ7KMuGcC4AvAv8W+C/AH3jv/05lqSwn8+uk85atmrqperIvri3ACe99C8Bbi8ZhYOuSpkrEnC9/Ku/KUvsl4O+dc/1Apl3xSx0Etp7vvUuWSlmTnHN/4Zw7Avw28FOoPJXl5X3At7z3320vUFkqy9RfOOcedc59xDm3nlVUlirIFhGRZcU59+vATuDfLXVaRBbivf9p7/0W4DewIYsiy4Jz7hrgLcDvLHVaRJ7Dq7z31wE3ASPAx5c4PYtKQfbFdQQYcs5FAOmwhq1Yq4vIUjtf/lTelSXhnHs/cAfwQ977ivd+FGg55wbnrbYdOHy+9y5VemVt895/HHgNcBSVp7I8vBIrB59Oh+O+DJvv4idQWSrLiPf+cPrcBP4Ay7urpm6qIPsi8t6fAh4A3pUuegtwdDneNyBrz/nyp/KuLAXn3PuAdwLf772fmPfW3wDvTde5BdgEfO0C3hNZVM65Hufcxnmv3wyMAipPZVnw3v+p937Ie7/de78dm/jsX3nv/xSVpbJMOOeKzrmeeYveCTy4muqmmvjsInPO7cZmvuvHbth/t/f+0SVNlKw5zrkPAj8CDGIVwmnv/c7z5U/lXbmUnHObsVbq/cB0urjuvb/VObcB+B/ADqAB/IL3/qvp/53zPZHF5pzbhgUjHUACnAben04mqfJUlh3n3F3MTXymslSWBefcZcBngBBw2LX/l7z3B1dLWaogW0RERERERGSRaLi4iIiIiIiIyCJRkC0iIiIiIiKySBRki4iIiIiIiCwSBdkiIiIiIiIii0RBtoiIiIiIiMgiUZAtIiIiIiIiskiipU6AiIiIvDDOuYNAHajOW/xTi/m7oc657cBD3vuexfpMERGR1UxBtoiIyMr2du/9Q0udCBERETEaLi4iIrLKOOe8c+53nHMPOueecs795Lz33uCce8A594hz7mvOuavnvfdu59xDzrmHnXP3p73Y7fd+yzn3XefcPufcD1/aLRIREVk51JMtIiKysv2Vc27+cPHb0mfvvb/ROXcZcL9z7ltABfhL4Pu894+mwfennXN7gFcDvwm83Ht/wjlXSD9nAOgGHvHe/wfn3A8Cfwh87hJsm4iIyIrjvPdLnQYRERF5AdJ7st989nBx55wHtnvvD6Wv/w74W2Ac+BXv/ffNW3cCuAb4JaDqvf/Nsz5rO/AEUPDee+dcNzDqvVdDvYiIyAI0XFxERGRteDGt6nU/1yofA+EipEdERGRVUpAtIiKyOr0bZnuiXwl8A7gHuNY5d0363juAY+njH4B3OeeG0vcK84aMi4iIyAXSUC8REZGV7ex7sv9N+hw65x4EisAveu8PAqT3Yf+Fcy7Cho+/Le2l/rpz7reAL6TDzRvAWy/VRoiIiKwWuidbRERklUmD5F7v/cRSp0VERGSt0XBxERERERERkUWinmwRERERERGRRaKebBEREREREZFFoiBbREREREREZJEoyBYRERERERFZJAqyRURERERERBaJgmwRERERERGRRaIgW0RERERERGSR/P/pxf6o26bi/gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_83a82c3b-4534-4cdd-ae1c-d870e4d1d4ed\", \"nn_resolve_overfitting.png\", 101385)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve, auc\n",
        "from matplotlib.pyplot import figure\n",
        "\n",
        "# fpr_keras, tpr_keras, thresholds_keras = roc_curve(y1, pred1)\n",
        "figure(figsize=(15, 10), dpi=80)\n",
        "\n",
        "Y_Pred_rh_LogisticRegression = model_LogisticRegression.predict_proba(X_Test)\n",
        "Y_rh_LogisticRegression = model_LogisticRegression.predict(X_Test)\n",
        "\n",
        "Y_Pred_rh_RandomForestClassifier = model_RandomForestClassifier.predict_proba(X_Test)\n",
        "Y_rh_RandomForestClassifier = model_RandomForestClassifier.predict(X_Test)\n",
        "\n",
        "Y_Pred_rh_HistGradientBoostingClassifier = model_HistGradientBoostingClassifier.predict_proba(X_Test)\n",
        "Y_rh_HistGradientBoostingClassifier = model_HistGradientBoostingClassifier.predict(X_Test)\n",
        "\n",
        "Y_Pred_rh_GaussianNB = model_GaussianNB.predict_proba(X_Test)\n",
        "Y_rh_GaussianNB = model_GaussianNB.predict(X_Test)\n",
        "\n",
        "Y_Pred_rh_XGBClassifier = model_XGBClassifier.predict_proba(X_Test)\n",
        "Y_rh_XGBClassifier = model_XGBClassifier.predict(X_Test)\n",
        "\n",
        "\n",
        "pred1 = model.predict(X_test1)\n",
        "\n",
        "\n",
        "Y_Pred_rh_LogisticRegression = Y_Pred_rh_LogisticRegression[:, 1]\n",
        "Y_Pred_rh_RandomForestClassifier = Y_Pred_rh_RandomForestClassifier[:, 1]\n",
        "Y_Pred_rh_HistGradientBoostingClassifier = Y_Pred_rh_HistGradientBoostingClassifier[:, 1]\n",
        "Y_Pred_rh_GaussianNB = Y_Pred_rh_GaussianNB[:, 1]\n",
        "Y_Pred_rh_XGBClassifier = Y_Pred_rh_XGBClassifier[:, 1]\n",
        "\n",
        "# n=0\n",
        "# for r in Y_Test:\n",
        "#   if r == 1:\n",
        "#     Y_Test[n] = 0\n",
        "#   else:\n",
        "#     Y_Test[n] = 1\n",
        "#   n = n+1\n",
        "\n",
        "fpr_rf_LogisticRegression, tpr_rf_LogisticRegression, thresholds_rf_LogisticRegression = roc_curve(Y_test1, Y_Pred_rh_LogisticRegression)\n",
        "fpr_rf_RandomForestClassifier, tpr_rf_RandomForestClassifier, thresholds_rf__RandomForestClassifier = roc_curve(Y_test1, Y_Pred_rh_RandomForestClassifier)\n",
        "fpr_rf_HistGradientBoostingClassifier, tpr_rf_HistGradientBoostingClassifier, thresholds_rf_HistGradientBoostingClassifier = roc_curve(Y_test1, Y_Pred_rh_HistGradientBoostingClassifier)\n",
        "fpr_rf_GaussianNB, tpr_rf_GaussianNB, thresholds_rf_GaussianNB = roc_curve(Y_test1, Y_Pred_rh_GaussianNB)\n",
        "fpr_rf_XGBClassifier, tpr_rf_XGBClassifier, thresholds_rf_XGBClassifier = roc_curve(Y_test1, Y_Pred_rh_XGBClassifier)\n",
        "fpr_rf_neural_network, tpr_rf_neural_network, thresholds_rf_neural_network = roc_curve(Y_test1, pred1)\n",
        "\n",
        "\n",
        "auc_rf_LogisticRegression = auc(fpr_rf_LogisticRegression, tpr_rf_LogisticRegression)\n",
        "auc_rf_RandomForestClassifier = auc(fpr_rf_RandomForestClassifier, tpr_rf_RandomForestClassifier)\n",
        "auc_rf_HistGradientBoostingClassifier = auc(fpr_rf_HistGradientBoostingClassifier, tpr_rf_HistGradientBoostingClassifier)\n",
        "auc_rf_GaussianNB = auc(fpr_rf_GaussianNB, tpr_rf_GaussianNB)\n",
        "auc_rf_XGBClassifier = auc(fpr_rf_XGBClassifier, tpr_rf_XGBClassifier)\n",
        "auc_rf_neural_network = auc(fpr_rf_neural_network, tpr_rf_neural_network)\n",
        "\n",
        "print(\"auc LogisticRegression: \", auc_rf_LogisticRegression)\n",
        "print(\"auc RandomForestClassifier: \", auc_rf_RandomForestClassifier)\n",
        "print(\"auc HistGradientBoostingClassifier: \", auc_rf_HistGradientBoostingClassifier)\n",
        "print(\"auc GaussianNB: \", auc_rf_GaussianNB)\n",
        "print(\"auc XGBoost Classifier: \", auc_rf_XGBClassifier)\n",
        "print(\"auc Neural Network: \", auc_rf_neural_network)\n",
        "\n",
        "fig1 = plt.gcf()\n",
        "\n",
        "plt.figure(1)\n",
        "# plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.plot(fpr_rf_neural_network, tpr_rf_neural_network, label='Neural Network (AUROC = {:.3f})'.format(auc_rf_neural_network))\n",
        "plt.plot(fpr_rf_HistGradientBoostingClassifier, tpr_rf_HistGradientBoostingClassifier, label='Histogram-based Gradient Boosting (AUROC = {:.3f})'.format(auc_rf_HistGradientBoostingClassifier))\n",
        "plt.plot(fpr_rf_LogisticRegression, tpr_rf_LogisticRegression, label='Logistic Regression (AUROC = {:.3f})'.format(auc_rf_LogisticRegression))\n",
        "plt.plot(fpr_rf_GaussianNB, tpr_rf_GaussianNB, label='Gaussian Naive Bayes (AUROC = {:.3f})'.format(auc_rf_GaussianNB))\n",
        "plt.plot(fpr_rf_XGBClassifier, tpr_rf_XGBClassifier, label='XGBoost Classifier (AUROC = {:.3f})'.format(auc_rf_XGBClassifier))\n",
        "plt.plot(fpr_rf_RandomForestClassifier, tpr_rf_RandomForestClassifier, label='Random Forest Classifier (AUROC = {:.3f})'.format(auc_rf_RandomForestClassifier))\n",
        "\n",
        "plt.xlabel('False positive rate')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.title('ROC curve')\n",
        "plt.legend(loc='best')\n",
        "plt.show()\n",
        "\n",
        "from google.colab import files\n",
        "fig1.savefig(\"ROC_Curve.png\", dpi=200)\n",
        "files.download(\"ROC_Curve.png\") "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 789
        },
        "id": "PpAYn7u3WeKE",
        "outputId": "967a1ed2-d503-43aa-89b9-9deb3f3bfbbb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "auc LogisticRegression:  0.8751983077736647\n",
            "auc RandomForestClassifier:  0.8796932839767319\n",
            "auc HistGradientBoostingClassifier:  0.8878900052882073\n",
            "auc GaussianNB:  0.8709677419354839\n",
            "auc XGBoost Classifier:  0.8648863035430989\n",
            "auc Neural Network:  0.9016393442622951\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_d0957c45-3b31-45f9-9ab6-b1ce602e1acf\", \"ROC_Curve.png\", 154975)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_recall_curve\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.metrics import classification_report, precision_score, recall_score, f1_score, precision_recall_fscore_support\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "\n",
        "pred1 = model.predict(X_test1)\n",
        "\n",
        "Y_Pred_rh_LogisticRegression = model_LogisticRegression.predict_proba(X_Test)\n",
        "Y_rh_LogisticRegression = model_LogisticRegression.predict(X_Test)\n",
        "Y_Pred_rh_RandomForestClassifier = model_RandomForestClassifier.predict_proba(X_Test)\n",
        "Y_rh_RandomForestClassifier = model_RandomForestClassifier.predict(X_Test)\n",
        "Y_Pred_rh_HistGradientBoostingClassifier = model_HistGradientBoostingClassifier.predict_proba(X_Test)\n",
        "Y_rh_HistGradientBoostingClassifier = model_HistGradientBoostingClassifier.predict(X_Test)\n",
        "Y_Pred_rh_GaussianNB = model_GaussianNB.predict_proba(X_Test)\n",
        "Y_rh_GaussianNB = model_GaussianNB.predict(X_Test)\n",
        "Y_Pred_rh_XGBClassifier = model_XGBClassifier.predict_proba(X_Test)\n",
        "Y_rh_XGBClassifier = model_XGBClassifier.predict(X_Test)\n",
        "\n",
        "Y_Pred_rh_LogisticRegression = Y_Pred_rh_LogisticRegression[:, 1]\n",
        "Y_Pred_rh_RandomForestClassifier = Y_Pred_rh_RandomForestClassifier[:, 1]\n",
        "Y_Pred_rh_HistGradientBoostingClassifier = Y_Pred_rh_HistGradientBoostingClassifier[:, 1]\n",
        "Y_Pred_rh_GaussianNB = Y_Pred_rh_GaussianNB[:, 1]\n",
        "Y_Pred_rh_XGBClassifier = Y_Pred_rh_XGBClassifier[:, 1]\n",
        "\n",
        "lr_precision_LogisticRegression, lr_recall_LogisticRegression, _ = precision_recall_curve(Y_test1, Y_Pred_rh_LogisticRegression)\n",
        "lr_precision_RandomForestClassifier, lr_recall_RandomForestClassifier, _ = precision_recall_curve(Y_test1, Y_Pred_rh_RandomForestClassifier)\n",
        "lr_precision_HistGradientBoostingClassifier, lr_recall_HistGradientBoostingClassifier, _ = precision_recall_curve(Y_test1, Y_Pred_rh_HistGradientBoostingClassifier)\n",
        "lr_precision_GaussianNB, lr_recall_GaussianNB, _ = precision_recall_curve(Y_test1, Y_Pred_rh_GaussianNB)\n",
        "lr_precision_XGBClassifier, lr_recall_XGBClassifier, _ = precision_recall_curve(Y_test1, Y_Pred_rh_XGBClassifier)\n",
        "lr_precision_Deep, lr_recall_Deep, _ = precision_recall_curve(Y_test1, pred1)\n",
        "\n",
        "\n",
        "tn_rh_LogisticRegression, fp_rh_LogisticRegression, fn_rh_LogisticRegression, tp_rh_LogisticRegression = confusion_matrix(Y_Test, Y_rh_LogisticRegression).ravel()\n",
        "tn_rh_RandomForestClassifie, fp_rh_RandomForestClassifie, fn_rh_RandomForestClassifie, tp_rh_RandomForestClassifie = confusion_matrix(Y_Test, Y_rh_RandomForestClassifier).ravel()\n",
        "tn_rh_HistGradientBoostingClassifier, fp_rh_HistGradientBoostingClassifier, fn_rh_HistGradientBoostingClassifier, tp_rh_HistGradientBoostingClassifier = confusion_matrix(Y_Test, Y_rh_HistGradientBoostingClassifier).ravel()\n",
        "tn_rh_GaussianNB, fp_rh_GaussianNB, fn_rh_GaussianNB, tp_rh_GaussianNB = confusion_matrix(Y_Test, Y_rh_GaussianNB).ravel()\n",
        "tn_rh_XGBClassifier, fp_rh_XGBClassifier, fn_rh_XGBClassifier, tp_rh_XGBClassifier = confusion_matrix(Y_Test, Y_rh_XGBClassifier).ravel()\n",
        "\n",
        "\n",
        "precision_score_rh_LogisticRegression = tp_rh_LogisticRegression / (tp_rh_LogisticRegression + fp_rh_LogisticRegression)\n",
        "recall_score_rh_LogisticRegression = tp_rh_LogisticRegression / (tp_rh_LogisticRegression + fn_rh_LogisticRegression)\n",
        "specificity_rh_LogisticRegression = tn_rh_LogisticRegression / (tn_rh_LogisticRegression + fp_rh_LogisticRegression)\n",
        "f1_rh_LogisticRegression = 2*(recall_score_rh_LogisticRegression * precision_score_rh_LogisticRegression) / (recall_score_rh_LogisticRegression + precision_score_rh_LogisticRegression)\n",
        "\n",
        "precision_score_rh_RandomForestClassifier = tp_rh_RandomForestClassifie / (tp_rh_RandomForestClassifie + fp_rh_RandomForestClassifie)\n",
        "recall_score_rh_RandomForestClassifier = tp_rh_RandomForestClassifie / (tp_rh_RandomForestClassifie + fn_rh_RandomForestClassifie)\n",
        "specificity_rh_RandomForestClassifier = tn_rh_RandomForestClassifie / (tn_rh_RandomForestClassifie + fp_rh_RandomForestClassifie)\n",
        "f1_rh_RandomForestClassifier = 2 * (recall_score_rh_RandomForestClassifier * precision_score_rh_RandomForestClassifier) / (recall_score_rh_RandomForestClassifier + precision_score_rh_RandomForestClassifier)\n",
        "\n",
        "precision_score_rh_HistGradientBoostingClassifier = tp_rh_HistGradientBoostingClassifier / (tp_rh_HistGradientBoostingClassifier + fp_rh_HistGradientBoostingClassifier)\n",
        "recall_score_rh_HistGradientBoostingClassifier = tp_rh_HistGradientBoostingClassifier / (tp_rh_HistGradientBoostingClassifier + fn_rh_HistGradientBoostingClassifier)\n",
        "specificity_rh_HistGradientBoostingClassifier = tn_rh_HistGradientBoostingClassifier / (tn_rh_HistGradientBoostingClassifier + fp_rh_HistGradientBoostingClassifier)\n",
        "f1_rh_HistGradientBoostingClassifier = 2 * (recall_score_rh_HistGradientBoostingClassifier * precision_score_rh_HistGradientBoostingClassifier) / (recall_score_rh_HistGradientBoostingClassifier + precision_score_rh_HistGradientBoostingClassifier)\n",
        "\n",
        "precision_score_rh_GaussianNB = tp_rh_GaussianNB / (tp_rh_GaussianNB + fp_rh_GaussianNB)\n",
        "recall_score_rh_GaussianNB = tp_rh_GaussianNB / (tp_rh_GaussianNB + fn_rh_GaussianNB)\n",
        "specificity_rh_GaussianNB = tn_rh_GaussianNB / (tn_rh_GaussianNB + fp_rh_GaussianNB)\n",
        "f1_rh_GaussianNB = 2 * (recall_score_rh_GaussianNB * precision_score_rh_GaussianNB) / (recall_score_rh_GaussianNB + precision_score_rh_GaussianNB)\n",
        "\n",
        "precision_score_rh_XGBClassifier = tp_rh_XGBClassifier / (tp_rh_XGBClassifier + fp_rh_XGBClassifier)\n",
        "recall_score_rh_XGBClassifier = tp_rh_XGBClassifier / (tp_rh_XGBClassifier + fn_rh_XGBClassifier)\n",
        "specificity_rh_XGBClassifier = tn_rh_XGBClassifier / (tn_rh_XGBClassifier + fp_rh_XGBClassifier)\n",
        "f1_rh_XGBClassifier = 2 * (recall_score_rh_XGBClassifier * precision_score_rh_XGBClassifier) / (recall_score_rh_XGBClassifier + precision_score_rh_XGBClassifier)\n",
        "\n",
        "\n",
        "# f1_rh_LogisticRegression = f1_score(Y_Test, Y_rh_LogisticRegression)\n",
        "# f1_rh_RandomForestClassifier = f1_score(Y_Test, Y_rh_RandomForestClassifier)\n",
        "# f1_rh_HistGradientBoostingClassifier = f1_score(Y_Test, Y_rh_HistGradientBoostingClassifier)\n",
        "# f1_rh_GaussianNB = f1_score(Y_Test, Y_rh_GaussianNB)\n",
        "# f1_rh_XGBClassifier = f1_score(Y_Test, Y_rh_XGBClassifier)\n",
        "\n",
        "\n",
        "# precision_score_rh_LogisticRegression = precision_score(Y_Test, Y_rh_LogisticRegression)\n",
        "# precision_score_rh_RandomForestClassifier = precision_score(Y_Test, Y_rh_RandomForestClassifier)\n",
        "# precision_score_rh_HistGradientBoostingClassifier = precision_score(Y_Test, Y_rh_HistGradientBoostingClassifier)\n",
        "# precision_score_rh_GaussianNB = precision_score(Y_Test, Y_rh_GaussianNB)\n",
        "# precision_score_rh_XGBClassifier = precision_score(Y_Test, Y_rh_XGBClassifier)\n",
        "\n",
        "# recall_score_rh_LogisticRegression = recall_score(Y_Test, Y_rh_LogisticRegression)\n",
        "# recall_score_rh_RandomForestClassifier = recall_score(Y_Test, Y_rh_RandomForestClassifier)\n",
        "# recall_score_rh_HistGradientBoostingClassifier = recall_score(Y_Test, Y_rh_HistGradientBoostingClassifier)\n",
        "# recall_score_rh_GaussianNB = recall_score(Y_Test, Y_rh_GaussianNB)\n",
        "# recall_score_rh_XGBClassifier = recall_score(Y_Test, Y_rh_XGBClassifier)\n",
        "\n",
        "n=0\n",
        "for r in pred1:\n",
        "  if pred1[n][0] < 0.5:\n",
        "    pred1[n][0] = 0\n",
        "  else:\n",
        "    pred1[n][0] = 1\n",
        "  n = n+1\n",
        "\n",
        "tn_rh_Deep, fp_rh_Deep, fn_rh_Deep, tp_rh_Deep = confusion_matrix(Y_test1, pred1).ravel()\n",
        "\n",
        "# f1_rh_Deep = f1_score(Y_test1, pred1)\n",
        "# precision_score_rh_Deep = precision_score(Y_test1, pred1)\n",
        "# recall_score_rh_Deep = recall_score(Y_test1, pred1)\n",
        "\n",
        "precision_score_rh_Deep = tp_rh_Deep / (tp_rh_Deep + fp_rh_Deep)\n",
        "recall_score_rh_Deep = tp_rh_Deep / (tp_rh_Deep + fn_rh_Deep)\n",
        "specificity_rh_Deep = tn_rh_Deep / (tn_rh_Deep + fp_rh_Deep)\n",
        "f1_rh_Deep = 2 * (recall_score_rh_Deep * precision_score_rh_Deep) / (recall_score_rh_Deep + precision_score_rh_Deep)\n",
        "\n",
        "\n",
        "\n",
        "# print(precision_score_rh_LogisticRegression)\n",
        "# print(recall_score_rh_LogisticRegression)\n",
        "# print(specificity_rh_LogisticRegression)\n",
        "# print(f1_rh_LogisticRegression)\n",
        "\n",
        "print(\"F1-score (aka F-Score / F-Measure)\")\n",
        "\n",
        "print(\"Logistic Regression\", f1_rh_LogisticRegression)\n",
        "print(\"Random Forest Classifier\", f1_rh_RandomForestClassifier)\n",
        "print(\"Histogram-based Gradient Boosting\", f1_rh_HistGradientBoostingClassifier)\n",
        "print(\"Gaussian Naive Bayes\", f1_rh_GaussianNB)\n",
        "print(\"XGBoost Classifier\", f1_rh_XGBClassifier)\n",
        "print(\"Neural Network: \", f1_rh_Deep)\n",
        "\n",
        "print(\"Precision\")\n",
        "\n",
        "print(\"Logistic Regression\", precision_score_rh_LogisticRegression)\n",
        "print(\"Random Forest Classifier\", precision_score_rh_RandomForestClassifier)\n",
        "print(\"Histogram-based Gradient Boosting\", precision_score_rh_HistGradientBoostingClassifier)\n",
        "print(\"Gaussian Naive Bayes\", precision_score_rh_GaussianNB)\n",
        "print(\"XGBoost Classifier\", precision_score_rh_XGBClassifier)\n",
        "print(\"Neural Network: \", precision_score_rh_Deep)\n",
        "\n",
        "print(\"Recall (aka Sensitivity)\")\n",
        "\n",
        "print(\"Logistic Regression\", recall_score_rh_LogisticRegression)\n",
        "print(\"Random Forest Classifier\", recall_score_rh_RandomForestClassifier)\n",
        "print(\"Histogram-based Gradient Boosting\", recall_score_rh_HistGradientBoostingClassifier)\n",
        "print(\"Gaussian Naive Bayes\", recall_score_rh_GaussianNB)\n",
        "print(\"XGBoost Classifier\", recall_score_rh_XGBClassifier)\n",
        "print(\"Neural Network: \", recall_score_rh_Deep)\n",
        "\n",
        "print(\"Specificity\")\n",
        "\n",
        "print(\"Logistic Regression\", specificity_rh_LogisticRegression)\n",
        "print(\"Random Forest Classifier\",  specificity_rh_RandomForestClassifier)\n",
        "print(\"Histogram-based Gradient Boosting\",  specificity_rh_HistGradientBoostingClassifier)\n",
        "print(\"Gaussian Naive Bayes\",  specificity_rh_GaussianNB)\n",
        "print(\"XGBoost Classifier\", specificity_rh_XGBClassifier)\n",
        "print(\"Neural Network: \", specificity_rh_Deep)\n",
        "\n",
        "\n",
        "# print(classification_report(Y_Test, Y_rh_LogisticRegression))\n",
        "\n",
        "# print(Y_Pred_rh_LogisticRegression)\n",
        "# print(Y_rh_LogisticRegression)\n",
        "\n",
        "fig1 = plt.gcf()\n",
        "\n",
        "# plt.rcParams['figure.figsize'] = [20, 15]\n",
        "plt.figure(1)\n",
        "\n",
        "plt.plot(lr_recall_Deep, lr_precision_Deep, label='Neural Network')\n",
        "plt.plot(lr_recall_HistGradientBoostingClassifier, lr_precision_HistGradientBoostingClassifier, label='Histogram-based Gradient Boosting')\n",
        "plt.plot(lr_recall_LogisticRegression, lr_precision_LogisticRegression, label='Logistic Regression')\n",
        "plt.plot(lr_recall_GaussianNB, lr_precision_GaussianNB, label='Gaussian Naive Bayes')\n",
        "plt.plot(lr_recall_XGBClassifier, lr_precision_XGBClassifier, label='XGBoost Classifier')\n",
        "plt.plot(lr_recall_RandomForestClassifier, lr_precision_RandomForestClassifier, label='Random Forest Classifier')\n",
        "# plt.plot(lr_recall_Deep, lr_precision_Deep, marker='.', label='Neural Network')\n",
        "\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.legend(loc='best')\n",
        "plt.title('Precision-Recall Curve')\n",
        "plt.show()\n",
        "\n",
        "from google.colab import files\n",
        "fig1.savefig(\"Precision-Recall.png\", dpi=200)\n",
        "files.download(\"Precision-Recall.png\") "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 781
        },
        "id": "c_TkwByQsGCP",
        "outputId": "90281649-d536-4fec-8de6-b4b038c555e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F1-score (aka F-Score / F-Measure)\n",
            "Logistic Regression 0.9328063241106719\n",
            "Random Forest Classifier 0.921259842519685\n",
            "Histogram-based Gradient Boosting 0.9397590361445783\n",
            "Gaussian Naive Bayes 0.9098360655737705\n",
            "XGBoost Classifier 0.9279999999999999\n",
            "Neural Network:  0.9279999999999999\n",
            "Precision\n",
            "Logistic Regression 0.9007633587786259\n",
            "Random Forest Classifier 0.8863636363636364\n",
            "Histogram-based Gradient Boosting 0.9212598425196851\n",
            "Gaussian Naive Bayes 0.9098360655737705\n",
            "XGBoost Classifier 0.90625\n",
            "Neural Network:  0.90625\n",
            "Recall (aka Sensitivity)\n",
            "Logistic Regression 0.9672131147540983\n",
            "Random Forest Classifier 0.9590163934426229\n",
            "Histogram-based Gradient Boosting 0.9590163934426229\n",
            "Gaussian Naive Bayes 0.9098360655737705\n",
            "XGBoost Classifier 0.9508196721311475\n",
            "Neural Network:  0.9508196721311475\n",
            "Specificity\n",
            "Logistic Regression 0.5806451612903226\n",
            "Random Forest Classifier 0.5161290322580645\n",
            "Histogram-based Gradient Boosting 0.6774193548387096\n",
            "Gaussian Naive Bayes 0.6451612903225806\n",
            "XGBoost Classifier 0.6129032258064516\n",
            "Neural Network:  0.6129032258064516\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_37e6faa1-2095-4902-a69b-ce10b7114b79\", \"Precision-Recall.png\", 120076)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "y_true = [0, 0, 0, 1, 1, 1, 1, 1]\n",
        "y_pred = [0, 1, 0, 1, 0, 1, 0, 1]\n",
        "\n",
        "Y_rh_LogisticRegression = model_LogisticRegression.predict(X_Test)\n",
        "tn, fp, fn, tp = confusion_matrix(Y_Test, Y_rh_LogisticRegression).ravel()\n",
        "specificity = tn / (tn+fp)\n",
        "specificityw = tp / (tp+fn)\n",
        "precission = tp / (tp+fp)\n",
        "\n",
        "print(recall_score(Y_Test, Y_rh_LogisticRegression, average=None))\n",
        "specificity"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y3qBR4k1A8Yp",
        "outputId": "f10dcf5f-0502-423d-db21-51dcf86ad3d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.58064516 0.96721311]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5806451612903226"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    }
  ]
}