imbalanced_data.ipynb

imbalanced_data.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/gist/kn1kn1/faa3e8b78afcfb26d2925642f3f7a922/imbalanced_data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dUeKVCYTbcyT"
      },
      "source": [
        "#### Copyright 2019 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "4ellrPx7tdxq"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7JfLUlawto_D"
      },
      "source": [
        "# Classification on imbalanced data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DwdpaTKJOoPu"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/structured_data/imbalanced_data\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/structured_data/imbalanced_data.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/imbalanced_data.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/structured_data/imbalanced_data.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mthoSGBAOoX-"
      },
      "source": [
        "This tutorial demonstrates how to classify a highly imbalanced dataset in which the number of examples in one class greatly outnumbers the examples in another. You will work with the [Credit Card Fraud Detection](https://www.kaggle.com/mlg-ulb/creditcardfraud) dataset hosted on Kaggle. The aim is to detect a mere 492 fraudulent transactions from 284,807 transactions in total. You will use [Keras](https://www.tensorflow.org/guide/keras/overview) to define the model and [class weights](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model) to help the model learn from the imbalanced data. .\n",
        "\n",
        "This tutorial contains complete code to:\n",
        "\n",
        "* Load a CSV file using Pandas.\n",
        "* Create train, validation, and test sets.\n",
        "* Define and train a model using Keras (including setting class weights).\n",
        "* Evaluate the model using various metrics (including precision and recall).\n",
        "* Try common techniques for dealing with imbalanced data like:\n",
        "    * Class weighting \n",
        "    * Oversampling\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kRHmSyHxEIhN"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JM7hDSNClfoK"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "import os\n",
        "import tempfile\n",
        "\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "import sklearn\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c8o1FHzD-_y_"
      },
      "outputs": [],
      "source": [
        "mpl.rcParams['figure.figsize'] = (12, 10)\n",
        "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make Keras program deterministic with rand seed:\n",
        "cf. https://www.tensorflow.org/api_docs/python/tf/keras/utils/set_random_seed"
      ],
      "metadata": {
        "id": "7MTt6J-NJ7qb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "\n",
        "seed = 42\n",
        "random.seed(seed)\n",
        "np.random.seed(seed)\n",
        "tf.random.set_seed(seed)"
      ],
      "metadata": {
        "id": "SZRVGoVIJsy8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z3iZVjziKHmX"
      },
      "source": [
        "## Data processing and exploration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4sA9WOcmzH2D"
      },
      "source": [
        "### Download the Kaggle Credit Card Fraud data set\n",
        "\n",
        "Pandas is a Python library with many helpful utilities for loading and working with structured data. It can be used to download CSVs into a Pandas [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame).\n",
        "\n",
        "Note: This dataset has been collected and analysed during a research collaboration of Worldline and the [Machine Learning Group](http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available [here](https://www.researchgate.net/project/Fraud-detection-5) and the page of the [DefeatFraud](https://mlg.ulb.ac.be/wordpress/portfolio_page/defeatfraud-assessment-and-validation-of-deep-feature-engineering-and-learning-solutions-for-fraud-detection/) project"
      ]
    },
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              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
              "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
              "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
              "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
              "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
              "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
              "\n",
              "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
              "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
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              "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
              "\n",
              "        V26       V27       V28  Amount  Class  \n",
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              "3 -0.221929  0.062723  0.061458  123.50      0  \n",
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              "      <td>-1.514654</td>\n",
              "      <td>...</td>\n",
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              "      <td>0.771679</td>\n",
              "      <td>0.909412</td>\n",
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              "      <td>-0.139097</td>\n",
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          "metadata": {},
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        }
      ],
      "source": [
        "file = tf.keras.utils\n",
        "raw_df = pd.read_csv('https://storage.googleapis.com/download.tensorflow.org/data/creditcard.csv')\n",
        "raw_df.head()"
      ]
    },
    {
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              "                Time            V1            V2            V3            V4  \\\n",
              "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
              "mean    94813.859575  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-15   \n",
              "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
              "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
              "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
              "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
              "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
              "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
              "\n",
              "                 V5           V26           V27           V28         Amount  \\\n",
              "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
              "mean   9.604066e-16  1.683437e-15 -3.660091e-16 -1.227390e-16      88.349619   \n",
              "std    1.380247e+00  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
              "min   -1.137433e+02 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
              "25%   -6.915971e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
              "50%   -5.433583e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
              "75%    6.119264e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
              "max    3.480167e+01  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
              "\n",
              "               Class  \n",
              "count  284807.000000  \n",
              "mean        0.001727  \n",
              "std         0.041527  \n",
              "min         0.000000  \n",
              "25%         0.000000  \n",
              "50%         0.000000  \n",
              "75%         0.000000  \n",
              "max         1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b588e8a1-ed69-44be-996d-d735e2fbe240\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
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              "  <tbody>\n",
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              "      <th>count</th>\n",
              "      <td>284807.000000</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>284807.000000</td>\n",
              "      <td>284807.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>94813.859575</td>\n",
              "      <td>1.168375e-15</td>\n",
              "      <td>3.416908e-16</td>\n",
              "      <td>-1.379537e-15</td>\n",
              "      <td>2.074095e-15</td>\n",
              "      <td>9.604066e-16</td>\n",
              "      <td>1.683437e-15</td>\n",
              "      <td>-3.660091e-16</td>\n",
              "      <td>-1.227390e-16</td>\n",
              "      <td>88.349619</td>\n",
              "      <td>0.001727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>47488.145955</td>\n",
              "      <td>1.958696e+00</td>\n",
              "      <td>1.651309e+00</td>\n",
              "      <td>1.516255e+00</td>\n",
              "      <td>1.415869e+00</td>\n",
              "      <td>1.380247e+00</td>\n",
              "      <td>4.822270e-01</td>\n",
              "      <td>4.036325e-01</td>\n",
              "      <td>3.300833e-01</td>\n",
              "      <td>250.120109</td>\n",
              "      <td>0.041527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-5.640751e+01</td>\n",
              "      <td>-7.271573e+01</td>\n",
              "      <td>-4.832559e+01</td>\n",
              "      <td>-5.683171e+00</td>\n",
              "      <td>-1.137433e+02</td>\n",
              "      <td>-2.604551e+00</td>\n",
              "      <td>-2.256568e+01</td>\n",
              "      <td>-1.543008e+01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>54201.500000</td>\n",
              "      <td>-9.203734e-01</td>\n",
              "      <td>-5.985499e-01</td>\n",
              "      <td>-8.903648e-01</td>\n",
              "      <td>-8.486401e-01</td>\n",
              "      <td>-6.915971e-01</td>\n",
              "      <td>-3.269839e-01</td>\n",
              "      <td>-7.083953e-02</td>\n",
              "      <td>-5.295979e-02</td>\n",
              "      <td>5.600000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>84692.000000</td>\n",
              "      <td>1.810880e-02</td>\n",
              "      <td>6.548556e-02</td>\n",
              "      <td>1.798463e-01</td>\n",
              "      <td>-1.984653e-02</td>\n",
              "      <td>-5.433583e-02</td>\n",
              "      <td>-5.213911e-02</td>\n",
              "      <td>1.342146e-03</td>\n",
              "      <td>1.124383e-02</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>139320.500000</td>\n",
              "      <td>1.315642e+00</td>\n",
              "      <td>8.037239e-01</td>\n",
              "      <td>1.027196e+00</td>\n",
              "      <td>7.433413e-01</td>\n",
              "      <td>6.119264e-01</td>\n",
              "      <td>2.409522e-01</td>\n",
              "      <td>9.104512e-02</td>\n",
              "      <td>7.827995e-02</td>\n",
              "      <td>77.165000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>172792.000000</td>\n",
              "      <td>2.454930e+00</td>\n",
              "      <td>2.205773e+01</td>\n",
              "      <td>9.382558e+00</td>\n",
              "      <td>1.687534e+01</td>\n",
              "      <td>3.480167e+01</td>\n",
              "      <td>3.517346e+00</td>\n",
              "      <td>3.161220e+01</td>\n",
              "      <td>3.384781e+01</td>\n",
              "      <td>25691.160000</td>\n",
              "      <td>1.000000</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "raw_df[['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V26', 'V27', 'V28', 'Amount', 'Class']].describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xWKB_CVZFLpB"
      },
      "source": [
        "### Examine the class label imbalance\n",
        "\n",
        "Let's look at the dataset imbalance:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HCJFrtuY2iLF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0216b95e-abce-45b2-de2d-f97cbf451761"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Examples:\n",
            "    Total: 284807\n",
            "    Positive: 492 (0.17% of total)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "neg, pos = np.bincount(raw_df['Class'])\n",
        "total = neg + pos\n",
        "print('Examples:\\n    Total: {}\\n    Positive: {} ({:.2f}% of total)\\n'.format(\n",
        "    total, pos, 100 * pos / total))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KnLKFQDsCBUg"
      },
      "source": [
        "This shows the small fraction of positive samples."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6qox6ryyzwdr"
      },
      "source": [
        "### Clean, split and normalize the data\n",
        "\n",
        "The raw data has a few issues. First the `Time` and `Amount` columns are too variable to use directly. Drop the `Time` column (since it's not clear what it means) and take the log of the `Amount` column to reduce its range."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ef42jTuxEjnj"
      },
      "outputs": [],
      "source": [
        "cleaned_df = raw_df.copy()\n",
        "\n",
        "# You don't want the `Time` column.\n",
        "cleaned_df.pop('Time')\n",
        "\n",
        "# The `Amount` column covers a huge range. Convert to log-space.\n",
        "eps = 0.001 # 0 => 0.1¢\n",
        "cleaned_df['Log Amount'] = np.log(cleaned_df.pop('Amount')+eps)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uSNgdQFFFQ6u"
      },
      "source": [
        "Split the dataset into train, validation, and test sets. The validation set is used during the model fitting to evaluate the loss and any metrics, however the model is not fit with this data. The test set is completely unused during the training phase and is only used at the end to evaluate how well the model generalizes to new data. This is especially important with imbalanced datasets where [overfitting](https://developers.google.com/machine-learning/crash-course/generalization/peril-of-overfitting) is a significant concern from the lack of training data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xfxhKg7Yr1-b"
      },
      "outputs": [],
      "source": [
        "# Use a utility from sklearn to split and shuffle your dataset.\n",
        "train_df, test_df = train_test_split(cleaned_df, test_size=0.2, random_state=42)\n",
        "train_df, val_df = train_test_split(train_df, test_size=0.2, random_state=42)\n",
        "\n",
        "# Form np arrays of labels and features.\n",
        "train_labels = np.array(train_df.pop('Class'))\n",
        "bool_train_labels = train_labels != 0\n",
        "val_labels = np.array(val_df.pop('Class'))\n",
        "test_labels = np.array(test_df.pop('Class'))\n",
        "\n",
        "train_features = np.array(train_df)\n",
        "val_features = np.array(val_df)\n",
        "test_features = np.array(test_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8a_Z_kBmr7Oh"
      },
      "source": [
        "Normalize the input features using the sklearn StandardScaler.\n",
        "This will set the mean to 0 and standard deviation to 1.\n",
        "\n",
        "Note: The `StandardScaler` is only fit using the `train_features` to be sure the model is not peeking at the validation or test sets. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IO-qEUmJ5JQg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "363e5b2c-d1d3-4ac8-9652-699ab61099eb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training labels shape: (182276,)\n",
            "Validation labels shape: (45569,)\n",
            "Test labels shape: (56962,)\n",
            "Training features shape: (182276, 29)\n",
            "Validation features shape: (45569, 29)\n",
            "Test features shape: (56962, 29)\n"
          ]
        }
      ],
      "source": [
        "scaler = StandardScaler()\n",
        "train_features = scaler.fit_transform(train_features)\n",
        "\n",
        "val_features = scaler.transform(val_features)\n",
        "test_features = scaler.transform(test_features)\n",
        "\n",
        "train_features = np.clip(train_features, -5, 5)\n",
        "val_features = np.clip(val_features, -5, 5)\n",
        "test_features = np.clip(test_features, -5, 5)\n",
        "\n",
        "\n",
        "print('Training labels shape:', train_labels.shape)\n",
        "print('Validation labels shape:', val_labels.shape)\n",
        "print('Test labels shape:', test_labels.shape)\n",
        "\n",
        "print('Training features shape:', train_features.shape)\n",
        "print('Validation features shape:', val_features.shape)\n",
        "print('Test features shape:', test_features.shape)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XF2nNfWKJ33w"
      },
      "source": [
        "Caution: If you want to deploy a model, it's critical that you preserve the preprocessing calculations. The easiest way to implement them as layers, and attach them to your model before export.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uQ7m9nqDC3W6"
      },
      "source": [
        "### Look at the data distribution\n",
        "\n",
        "Next compare the distributions of the positive and negative examples over a few features. Good questions to ask yourself at this point are:\n",
        "\n",
        "* Do these distributions make sense? \n",
        "    * Yes. You've normalized the input and these are mostly concentrated in the `+/- 2` range.\n",
        "* Can you see the difference between the distributions?\n",
        "    * Yes the positive examples contain a much higher rate of extreme values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "raK7hyjd_vf6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 869
        },
        "outputId": "dcd3f87c-0e01-401b-b8e3-a34823118444"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "pos_df = pd.DataFrame(train_features[ bool_train_labels], columns=train_df.columns)\n",
        "neg_df = pd.DataFrame(train_features[~bool_train_labels], columns=train_df.columns)\n",
        "\n",
        "sns.jointplot(x=pos_df['V5'], y=pos_df['V6'],\n",
        "              kind='hex', xlim=(-5,5), ylim=(-5,5))\n",
        "plt.suptitle(\"Positive distribution\")\n",
        "\n",
        "sns.jointplot(x=neg_df['V5'], y=neg_df['V6'],\n",
        "              kind='hex', xlim=(-5,5), ylim=(-5,5))\n",
        "_ = plt.suptitle(\"Negative distribution\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qFK1u4JX16D8"
      },
      "source": [
        "## Define the model and metrics\n",
        "\n",
        "Define a function that creates a simple neural network with a densly connected hidden layer, a [dropout](https://developers.google.com/machine-learning/glossary/#dropout_regularization) layer to reduce overfitting, and an output sigmoid layer that returns the probability of a transaction being fraudulent: "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3JQDzUqT3UYG"
      },
      "outputs": [],
      "source": [
        "METRICS = [\n",
        "      keras.metrics.TruePositives(name='tp'),\n",
        "      keras.metrics.FalsePositives(name='fp'),\n",
        "      keras.metrics.TrueNegatives(name='tn'),\n",
        "      keras.metrics.FalseNegatives(name='fn'), \n",
        "      keras.metrics.BinaryAccuracy(name='accuracy'),\n",
        "      keras.metrics.Precision(name='precision'),\n",
        "      keras.metrics.Recall(name='recall'),\n",
        "      keras.metrics.AUC(name='auc'),\n",
        "      keras.metrics.AUC(name='prc', curve='PR'), # precision-recall curve\n",
        "]\n",
        "\n",
        "def make_model(metrics=METRICS, output_bias=None):\n",
        "  if output_bias is not None:\n",
        "    output_bias = tf.keras.initializers.Constant(output_bias)\n",
        "  model = keras.Sequential([\n",
        "      keras.layers.Dense(\n",
        "          16, activation='relu',\n",
        "          input_shape=(train_features.shape[-1],)),\n",
        "      keras.layers.Dropout(0.5),\n",
        "      keras.layers.Dense(1, activation='sigmoid',\n",
        "                         bias_initializer=output_bias),\n",
        "  ])\n",
        "\n",
        "  model.compile(\n",
        "      optimizer=keras.optimizers.Adam(learning_rate=1e-3),\n",
        "      loss=keras.losses.BinaryCrossentropy(),\n",
        "      metrics=metrics)\n",
        "\n",
        "  return model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SU0GX6E6mieP"
      },
      "source": [
        "### Understanding useful metrics\n",
        "\n",
        "Notice that there are a few metrics defined above that can be computed by the model that will be helpful when evaluating the performance.\n",
        "\n",
        "\n",
        "\n",
        "*   **False** negatives and **false** positives are samples that were **incorrectly** classified\n",
        "*   **True** negatives and **true** positives are samples that were **correctly** classified\n",
        "*   **Accuracy** is the percentage of examples correctly classified\n",
        ">   $\\frac{\\text{true samples}}{\\text{total samples}}$\n",
        "*   **Precision** is the percentage of **predicted** positives that were correctly classified\n",
        ">   $\\frac{\\text{true positives}}{\\text{true positives + false positives}}$\n",
        "*   **Recall** is the percentage of **actual** positives that were correctly classified\n",
        ">   $\\frac{\\text{true positives}}{\\text{true positives + false negatives}}$\n",
        "*   **AUC** refers to the Area Under the Curve of a Receiver Operating Characteristic curve (ROC-AUC). This metric is equal to the probability that a classifier will rank a random positive sample higher than a random negative sample.\n",
        "*   **AUPRC** refers to Area Under the Curve of the Precision-Recall Curve. This metric computes precision-recall pairs for different probability thresholds. \n",
        "\n",
        "Note: Accuracy is not a helpful metric for this task. You can have 99.8%+ accuracy on this task by predicting False all the time.  \n",
        "\n",
        "Read more:\n",
        "*  [True vs. False and Positive vs. Negative](https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative)\n",
        "*  [Accuracy](https://developers.google.com/machine-learning/crash-course/classification/accuracy)\n",
        "*   [Precision and Recall](https://developers.google.com/machine-learning/crash-course/classification/precision-and-recall)\n",
        "*   [ROC-AUC](https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc)\n",
        "*   [Relationship between Precision-Recall and ROC Curves](https://www.biostat.wisc.edu/~page/rocpr.pdf)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FYdhSAoaF_TK"
      },
      "source": [
        "## Baseline model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IDbltVPg2m2q"
      },
      "source": [
        "### Build the model\n",
        "\n",
        "Now create and train your model using the function that was defined earlier. Notice that the model is fit using a larger than default batch size of 2048, this is important to ensure that each batch has a decent chance of containing a few positive samples. If the batch size was too small, they would likely have no fraudulent transactions to learn from.\n",
        "\n",
        "\n",
        "Note: this model will not handle the class imbalance well. You will improve it later in this tutorial."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ouUkwPcGQsy3"
      },
      "outputs": [],
      "source": [
        "EPOCHS = 100\n",
        "BATCH_SIZE = 2048\n",
        "\n",
        "early_stopping = tf.keras.callbacks.EarlyStopping(\n",
        "    monitor='val_prc', \n",
        "    verbose=1,\n",
        "    patience=10,\n",
        "    mode='max',\n",
        "    restore_best_weights=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1xlR_dekzw7C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ef982f0-66b5-4c64-d4fb-48bb3b845a93"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 16)                480       \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 16)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 17        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 497\n",
            "Trainable params: 497\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model = make_model()\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wx7ND3_SqckO"
      },
      "source": [
        "Test run the model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LopSd-yQqO3a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b6e739f-2210-4e91-b7f2-802758015a22"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 1s 540ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.66212314],\n",
              "       [0.9795485 ],\n",
              "       [0.9611054 ],\n",
              "       [0.5962904 ],\n",
              "       [0.8575357 ],\n",
              "       [0.82157207],\n",
              "       [0.85708076],\n",
              "       [0.85908675],\n",
              "       [0.7135863 ],\n",
              "       [0.92740554]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "model.predict(train_features[:10])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YKIgWqHms_03"
      },
      "source": [
        "### Optional: Set the correct initial bias."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qk_3Ry6EoYDq"
      },
      "source": [
        "These initial guesses are not great. You know the dataset is imbalanced. Set the output layer's bias to reflect that (See: [A Recipe for Training Neural Networks: \"init well\"](http://karpathy.github.io/2019/04/25/recipe/#2-set-up-the-end-to-end-trainingevaluation-skeleton--get-dumb-baselines)). This can help with initial convergence."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PdbfWDuVpo6k"
      },
      "source": [
        "With the default bias initialization the loss should be about `math.log(2) = 0.69314` "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H-oPqh3SoGXk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5096542-355a-4392-e646-82f53fe9e8dd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss: 1.8883\n"
          ]
        }
      ],
      "source": [
        "results = model.evaluate(train_features, train_labels, batch_size=BATCH_SIZE, verbose=0)\n",
        "print(\"Loss: {:0.4f}\".format(results[0]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hE-JRzfKqfhB"
      },
      "source": [
        "The correct bias to set can be derived from:\n",
        "\n",
        "$$ p_0 = pos/(pos + neg) = 1/(1+e^{-b_0}) $$\n",
        "$$ b_0 = -log_e(1/p_0 - 1) $$\n",
        "$$ b_0 = log_e(pos/neg)$$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F5KWPSjjstUS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b15a204a-c3b3-496f-bc95-f35bbf6c356a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-6.35935934])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "initial_bias = np.log([pos/neg])\n",
        "initial_bias"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d1juXI9yY1KD"
      },
      "source": [
        "Set that as the initial bias, and the model will give much more reasonable initial guesses. \n",
        "\n",
        "It should be near: `pos/total = 0.0018`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "50oyu1uss0i-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c29ba2a0-363a-4c41-d60b-e5844f3998d3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 70ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.00171803],\n",
              "       [0.00110678],\n",
              "       [0.00230583],\n",
              "       [0.00033414],\n",
              "       [0.00359259],\n",
              "       [0.00960666],\n",
              "       [0.00155863],\n",
              "       [0.00273352],\n",
              "       [0.00070974],\n",
              "       [0.00354959]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "model = make_model(output_bias=initial_bias)\n",
        "model.predict(train_features[:10])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4xqFYb2KqRHQ"
      },
      "source": [
        "With this initialization the initial loss should be approximately:\n",
        "\n",
        "$$-p_0log(p_0)-(1-p_0)log(1-p_0) = 0.01317$$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xVDqCWXDqHSc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9aa009a-9bc5-4e34-bd4d-17c8a17a46c8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss: 0.0175\n"
          ]
        }
      ],
      "source": [
        "results = model.evaluate(train_features, train_labels, batch_size=BATCH_SIZE, verbose=0)\n",
        "print(\"Loss: {:0.4f}\".format(results[0]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FrDC8hvNr9yw"
      },
      "source": [
        "This initial loss is about 50 times less than if would have been with naive initialization.\n",
        "\n",
        "This way the model doesn't need to spend the first few epochs just learning that positive examples are unlikely. This also makes it easier to read plots of the loss during training."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0EJj9ixKVBMT"
      },
      "source": [
        "### Checkpoint the initial weights\n",
        "\n",
        "To make the various training runs more comparable, keep this initial model's weights in a checkpoint file, and load them into each model before training:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_tSUm4yAVIif"
      },
      "outputs": [],
      "source": [
        "initial_weights = os.path.join(tempfile.mkdtemp(), 'initial_weights')\n",
        "model.save_weights(initial_weights)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EVXiLyqyZ8AX"
      },
      "source": [
        "### Confirm that the bias fix helps\n",
        "\n",
        "Before moving on, confirm quick that the careful bias initialization actually helped.\n",
        "\n",
        "Train the model for 20 epochs, with and without this careful initialization, and compare the losses: "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dm4-4K5RZ63Q"
      },
      "outputs": [],
      "source": [
        "model = make_model()\n",
        "model.load_weights(initial_weights)\n",
        "model.layers[-1].bias.assign([0.0])\n",
        "zero_bias_history = model.fit(\n",
        "    train_features,\n",
        "    train_labels,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    epochs=20,\n",
        "    validation_data=(val_features, val_labels), \n",
        "    verbose=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j8DsLXHQaSql"
      },
      "outputs": [],
      "source": [
        "model = make_model()\n",
        "model.load_weights(initial_weights)\n",
        "careful_bias_history = model.fit(\n",
        "    train_features,\n",
        "    train_labels,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    epochs=20,\n",
        "    validation_data=(val_features, val_labels), \n",
        "    verbose=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E3XsMBjhauFV"
      },
      "outputs": [],
      "source": [
        "def plot_loss(history, label, n):\n",
        "  # Use a log scale on y-axis to show the wide range of values.\n",
        "  plt.semilogy(history.epoch, history.history['loss'],\n",
        "               color=colors[n], label='Train ' + label)\n",
        "  plt.semilogy(history.epoch, history.history['val_loss'],\n",
        "               color=colors[n], label='Val ' + label,\n",
        "               linestyle=\"--\")\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('Loss')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dxFaskm7beC7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "outputId": "23f0e39e-3634-438f-f5d3-1e63c5156add"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_loss(zero_bias_history, \"Zero Bias\", 0)\n",
        "plot_loss(careful_bias_history, \"Careful Bias\", 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fKMioV0ddG3R"
      },
      "source": [
        "The above figure makes it clear: In terms of validation loss, on this problem, this careful initialization gives a clear advantage. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RsA_7SEntRaV"
      },
      "source": [
        "### Train the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yZKAc8NCDnoR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8b9bb6f-eed2-46a4-cc84-52ddb82bf917"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "90/90 [==============================] - 3s 12ms/step - loss: 0.0153 - tp: 90.0000 - fp: 149.0000 - tn: 227302.0000 - fn: 304.0000 - accuracy: 0.9980 - precision: 0.3766 - recall: 0.2284 - auc: 0.7630 - prc: 0.1514 - val_loss: 0.0059 - val_tp: 13.0000 - val_fp: 5.0000 - val_tn: 45500.0000 - val_fn: 51.0000 - val_accuracy: 0.9988 - val_precision: 0.7222 - val_recall: 0.2031 - val_auc: 0.9345 - val_prc: 0.6460\n",
            "Epoch 2/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0081 - tp: 125.0000 - fp: 42.0000 - tn: 181904.0000 - fn: 205.0000 - accuracy: 0.9986 - precision: 0.7485 - recall: 0.3788 - auc: 0.8666 - prc: 0.4829 - val_loss: 0.0043 - val_tp: 30.0000 - val_fp: 5.0000 - val_tn: 45500.0000 - val_fn: 34.0000 - val_accuracy: 0.9991 - val_precision: 0.8571 - val_recall: 0.4688 - val_auc: 0.9365 - val_prc: 0.7418\n",
            "Epoch 3/100\n",
            "90/90 [==============================] - 0s 3ms/step - loss: 0.0067 - tp: 160.0000 - fp: 36.0000 - tn: 181910.0000 - fn: 170.0000 - accuracy: 0.9989 - precision: 0.8163 - recall: 0.4848 - auc: 0.9196 - prc: 0.5668 - val_loss: 0.0038 - val_tp: 35.0000 - val_fp: 5.0000 - val_tn: 45500.0000 - val_fn: 29.0000 - val_accuracy: 0.9993 - val_precision: 0.8750 - val_recall: 0.5469 - val_auc: 0.9369 - val_prc: 0.7499\n",
            "Epoch 4/100\n",
            "90/90 [==============================] - 0s 3ms/step - loss: 0.0061 - tp: 176.0000 - fp: 38.0000 - tn: 181908.0000 - fn: 154.0000 - accuracy: 0.9989 - precision: 0.8224 - recall: 0.5333 - auc: 0.9100 - prc: 0.6103 - val_loss: 0.0035 - val_tp: 42.0000 - val_fp: 5.0000 - val_tn: 45500.0000 - val_fn: 22.0000 - val_accuracy: 0.9994 - val_precision: 0.8936 - val_recall: 0.6562 - val_auc: 0.9292 - val_prc: 0.7644\n",
            "Epoch 5/100\n",
            "90/90 [==============================] - 0s 3ms/step - loss: 0.0057 - tp: 181.0000 - fp: 29.0000 - tn: 181917.0000 - fn: 149.0000 - accuracy: 0.9990 - precision: 0.8619 - recall: 0.5485 - auc: 0.9186 - prc: 0.6417 - val_loss: 0.0032 - val_tp: 47.0000 - val_fp: 5.0000 - val_tn: 45500.0000 - val_fn: 17.0000 - val_accuracy: 0.9995 - val_precision: 0.9038 - val_recall: 0.7344 - val_auc: 0.9293 - val_prc: 0.7797\n",
            "Epoch 6/100\n",
            "90/90 [==============================] - 0s 3ms/step - loss: 0.0054 - tp: 185.0000 - fp: 29.0000 - tn: 181917.0000 - fn: 145.0000 - accuracy: 0.9990 - precision: 0.8645 - recall: 0.5606 - auc: 0.9162 - prc: 0.6716 - val_loss: 0.0030 - val_tp: 48.0000 - val_fp: 5.0000 - val_tn: 45500.0000 - val_fn: 16.0000 - val_accuracy: 0.9995 - val_precision: 0.9057 - val_recall: 0.7500 - val_auc: 0.9293 - val_prc: 0.7863\n",
            "Epoch 7/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0049 - tp: 193.0000 - fp: 29.0000 - tn: 181917.0000 - fn: 137.0000 - accuracy: 0.9991 - precision: 0.8694 - recall: 0.5848 - auc: 0.9227 - prc: 0.6994 - val_loss: 0.0029 - val_tp: 48.0000 - val_fp: 5.0000 - val_tn: 45500.0000 - val_fn: 16.0000 - val_accuracy: 0.9995 - val_precision: 0.9057 - val_recall: 0.7500 - val_auc: 0.9293 - val_prc: 0.7921\n",
            "Epoch 8/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0049 - tp: 195.0000 - fp: 30.0000 - tn: 181916.0000 - fn: 135.0000 - accuracy: 0.9991 - precision: 0.8667 - recall: 0.5909 - auc: 0.9227 - prc: 0.6841 - val_loss: 0.0027 - val_tp: 49.0000 - val_fp: 5.0000 - val_tn: 45500.0000 - val_fn: 15.0000 - val_accuracy: 0.9996 - val_precision: 0.9074 - val_recall: 0.7656 - val_auc: 0.9293 - val_prc: 0.8046\n",
            "Epoch 9/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0047 - tp: 195.0000 - fp: 27.0000 - tn: 181919.0000 - fn: 135.0000 - accuracy: 0.9991 - precision: 0.8784 - recall: 0.5909 - auc: 0.9274 - prc: 0.7149 - val_loss: 0.0027 - val_tp: 49.0000 - val_fp: 5.0000 - val_tn: 45500.0000 - val_fn: 15.0000 - val_accuracy: 0.9996 - val_precision: 0.9074 - val_recall: 0.7656 - val_auc: 0.9293 - val_prc: 0.8070\n",
            "Epoch 10/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0049 - tp: 190.0000 - fp: 33.0000 - tn: 181913.0000 - fn: 140.0000 - accuracy: 0.9991 - precision: 0.8520 - recall: 0.5758 - auc: 0.9154 - prc: 0.6790 - val_loss: 0.0026 - val_tp: 49.0000 - val_fp: 5.0000 - val_tn: 45500.0000 - val_fn: 15.0000 - val_accuracy: 0.9996 - val_precision: 0.9074 - val_recall: 0.7656 - val_auc: 0.9293 - val_prc: 0.8052\n",
            "Epoch 11/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0046 - tp: 199.0000 - fp: 26.0000 - tn: 181920.0000 - fn: 131.0000 - accuracy: 0.9991 - precision: 0.8844 - recall: 0.6030 - auc: 0.9215 - prc: 0.7030 - val_loss: 0.0025 - val_tp: 50.0000 - val_fp: 5.0000 - val_tn: 45500.0000 - val_fn: 14.0000 - val_accuracy: 0.9996 - val_precision: 0.9091 - val_recall: 0.7812 - val_auc: 0.9293 - val_prc: 0.8134\n",
            "Epoch 12/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0042 - tp: 200.0000 - fp: 39.0000 - tn: 181907.0000 - fn: 130.0000 - accuracy: 0.9991 - precision: 0.8368 - recall: 0.6061 - auc: 0.9277 - prc: 0.7415 - val_loss: 0.0025 - val_tp: 51.0000 - val_fp: 5.0000 - val_tn: 45500.0000 - val_fn: 13.0000 - val_accuracy: 0.9996 - val_precision: 0.9107 - val_recall: 0.7969 - val_auc: 0.9294 - val_prc: 0.8209\n",
            "Epoch 13/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0045 - tp: 193.0000 - fp: 33.0000 - tn: 181913.0000 - fn: 137.0000 - accuracy: 0.9991 - precision: 0.8540 - recall: 0.5848 - auc: 0.9278 - prc: 0.7027 - val_loss: 0.0025 - val_tp: 51.0000 - val_fp: 5.0000 - val_tn: 45500.0000 - val_fn: 13.0000 - val_accuracy: 0.9996 - val_precision: 0.9107 - val_recall: 0.7969 - val_auc: 0.9294 - val_prc: 0.8114\n",
            "Epoch 14/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0043 - tp: 202.0000 - fp: 28.0000 - tn: 181918.0000 - fn: 128.0000 - accuracy: 0.9991 - precision: 0.8783 - recall: 0.6121 - auc: 0.9308 - prc: 0.7246 - val_loss: 0.0024 - val_tp: 51.0000 - val_fp: 5.0000 - val_tn: 45500.0000 - val_fn: 13.0000 - val_accuracy: 0.9996 - val_precision: 0.9107 - val_recall: 0.7969 - val_auc: 0.9294 - val_prc: 0.8245\n",
            "Epoch 15/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0042 - tp: 199.0000 - fp: 34.0000 - tn: 181912.0000 - fn: 131.0000 - accuracy: 0.9991 - precision: 0.8541 - recall: 0.6030 - auc: 0.9309 - prc: 0.7312 - val_loss: 0.0023 - val_tp: 51.0000 - val_fp: 4.0000 - val_tn: 45501.0000 - val_fn: 13.0000 - val_accuracy: 0.9996 - val_precision: 0.9273 - val_recall: 0.7969 - val_auc: 0.9294 - val_prc: 0.8310\n",
            "Epoch 16/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0043 - tp: 199.0000 - fp: 27.0000 - tn: 181919.0000 - fn: 131.0000 - accuracy: 0.9991 - precision: 0.8805 - recall: 0.6030 - auc: 0.9247 - prc: 0.7244 - val_loss: 0.0023 - val_tp: 52.0000 - val_fp: 6.0000 - val_tn: 45499.0000 - val_fn: 12.0000 - val_accuracy: 0.9996 - val_precision: 0.8966 - val_recall: 0.8125 - val_auc: 0.9294 - val_prc: 0.8290\n",
            "Epoch 17/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0040 - tp: 217.0000 - fp: 34.0000 - tn: 181912.0000 - fn: 113.0000 - accuracy: 0.9992 - precision: 0.8645 - recall: 0.6576 - auc: 0.9295 - prc: 0.7406 - val_loss: 0.0023 - val_tp: 51.0000 - val_fp: 4.0000 - val_tn: 45501.0000 - val_fn: 13.0000 - val_accuracy: 0.9996 - val_precision: 0.9273 - val_recall: 0.7969 - val_auc: 0.9295 - val_prc: 0.8306\n",
            "Epoch 18/100\n",
            "90/90 [==============================] - 0s 3ms/step - loss: 0.0043 - tp: 207.0000 - fp: 34.0000 - tn: 181912.0000 - fn: 123.0000 - accuracy: 0.9991 - precision: 0.8589 - recall: 0.6273 - auc: 0.9310 - prc: 0.7250 - val_loss: 0.0023 - val_tp: 51.0000 - val_fp: 4.0000 - val_tn: 45501.0000 - val_fn: 13.0000 - val_accuracy: 0.9996 - val_precision: 0.9273 - val_recall: 0.7969 - val_auc: 0.9295 - val_prc: 0.8343\n",
            "Epoch 19/100\n",
            "90/90 [==============================] - 0s 3ms/step - loss: 0.0041 - tp: 220.0000 - fp: 37.0000 - tn: 181909.0000 - fn: 110.0000 - accuracy: 0.9992 - precision: 0.8560 - recall: 0.6667 - auc: 0.9309 - prc: 0.7281 - val_loss: 0.0023 - val_tp: 50.0000 - val_fp: 2.0000 - val_tn: 45503.0000 - val_fn: 14.0000 - val_accuracy: 0.9996 - val_precision: 0.9615 - val_recall: 0.7812 - val_auc: 0.9295 - val_prc: 0.8387\n",
            "Epoch 20/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0042 - tp: 205.0000 - fp: 35.0000 - tn: 181911.0000 - fn: 125.0000 - accuracy: 0.9991 - precision: 0.8542 - recall: 0.6212 - auc: 0.9294 - prc: 0.7311 - val_loss: 0.0022 - val_tp: 51.0000 - val_fp: 2.0000 - val_tn: 45503.0000 - val_fn: 13.0000 - val_accuracy: 0.9997 - val_precision: 0.9623 - val_recall: 0.7969 - val_auc: 0.9294 - val_prc: 0.8390\n",
            "Epoch 21/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0041 - tp: 201.0000 - fp: 30.0000 - tn: 181916.0000 - fn: 129.0000 - accuracy: 0.9991 - precision: 0.8701 - recall: 0.6091 - auc: 0.9339 - prc: 0.7383 - val_loss: 0.0022 - val_tp: 51.0000 - val_fp: 4.0000 - val_tn: 45501.0000 - val_fn: 13.0000 - val_accuracy: 0.9996 - val_precision: 0.9273 - val_recall: 0.7969 - val_auc: 0.9294 - val_prc: 0.8379\n",
            "Epoch 22/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0039 - tp: 214.0000 - fp: 33.0000 - tn: 181913.0000 - fn: 116.0000 - accuracy: 0.9992 - precision: 0.8664 - recall: 0.6485 - auc: 0.9311 - prc: 0.7588 - val_loss: 0.0022 - val_tp: 51.0000 - val_fp: 2.0000 - val_tn: 45503.0000 - val_fn: 13.0000 - val_accuracy: 0.9997 - val_precision: 0.9623 - val_recall: 0.7969 - val_auc: 0.9294 - val_prc: 0.8386\n",
            "Epoch 23/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0042 - tp: 193.0000 - fp: 36.0000 - tn: 181910.0000 - fn: 137.0000 - accuracy: 0.9991 - precision: 0.8428 - recall: 0.5848 - auc: 0.9310 - prc: 0.7141 - val_loss: 0.0022 - val_tp: 52.0000 - val_fp: 4.0000 - val_tn: 45501.0000 - val_fn: 12.0000 - val_accuracy: 0.9996 - val_precision: 0.9286 - val_recall: 0.8125 - val_auc: 0.9294 - val_prc: 0.8380\n",
            "Epoch 24/100\n",
            "90/90 [==============================] - 0s 3ms/step - loss: 0.0040 - tp: 208.0000 - fp: 36.0000 - tn: 181910.0000 - fn: 122.0000 - accuracy: 0.9991 - precision: 0.8525 - recall: 0.6303 - auc: 0.9310 - prc: 0.7407 - val_loss: 0.0022 - val_tp: 52.0000 - val_fp: 2.0000 - val_tn: 45503.0000 - val_fn: 12.0000 - val_accuracy: 0.9997 - val_precision: 0.9630 - val_recall: 0.8125 - val_auc: 0.9295 - val_prc: 0.8387\n",
            "Epoch 25/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0040 - tp: 201.0000 - fp: 34.0000 - tn: 181912.0000 - fn: 129.0000 - accuracy: 0.9991 - precision: 0.8553 - recall: 0.6091 - auc: 0.9294 - prc: 0.7305 - val_loss: 0.0022 - val_tp: 52.0000 - val_fp: 2.0000 - val_tn: 45503.0000 - val_fn: 12.0000 - val_accuracy: 0.9997 - val_precision: 0.9630 - val_recall: 0.8125 - val_auc: 0.9294 - val_prc: 0.8387\n",
            "Epoch 26/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0042 - tp: 200.0000 - fp: 34.0000 - tn: 181912.0000 - fn: 130.0000 - accuracy: 0.9991 - precision: 0.8547 - recall: 0.6061 - auc: 0.9234 - prc: 0.7162 - val_loss: 0.0022 - val_tp: 52.0000 - val_fp: 1.0000 - val_tn: 45504.0000 - val_fn: 12.0000 - val_accuracy: 0.9997 - val_precision: 0.9811 - val_recall: 0.8125 - val_auc: 0.9294 - val_prc: 0.8390\n",
            "Epoch 27/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0041 - tp: 195.0000 - fp: 39.0000 - tn: 181907.0000 - fn: 135.0000 - accuracy: 0.9990 - precision: 0.8333 - recall: 0.5909 - auc: 0.9340 - prc: 0.7351 - val_loss: 0.0022 - val_tp: 52.0000 - val_fp: 3.0000 - val_tn: 45502.0000 - val_fn: 12.0000 - val_accuracy: 0.9997 - val_precision: 0.9455 - val_recall: 0.8125 - val_auc: 0.9295 - val_prc: 0.8376\n",
            "Epoch 28/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0039 - tp: 216.0000 - fp: 34.0000 - tn: 181912.0000 - fn: 114.0000 - accuracy: 0.9992 - precision: 0.8640 - recall: 0.6545 - auc: 0.9326 - prc: 0.7486 - val_loss: 0.0022 - val_tp: 52.0000 - val_fp: 4.0000 - val_tn: 45501.0000 - val_fn: 12.0000 - val_accuracy: 0.9996 - val_precision: 0.9286 - val_recall: 0.8125 - val_auc: 0.9295 - val_prc: 0.8399\n",
            "Epoch 29/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0040 - tp: 204.0000 - fp: 33.0000 - tn: 181913.0000 - fn: 126.0000 - accuracy: 0.9991 - precision: 0.8608 - recall: 0.6182 - auc: 0.9326 - prc: 0.7350 - val_loss: 0.0021 - val_tp: 52.0000 - val_fp: 1.0000 - val_tn: 45504.0000 - val_fn: 12.0000 - val_accuracy: 0.9997 - val_precision: 0.9811 - val_recall: 0.8125 - val_auc: 0.9372 - val_prc: 0.8449\n",
            "Epoch 30/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0039 - tp: 205.0000 - fp: 41.0000 - tn: 181905.0000 - fn: 125.0000 - accuracy: 0.9991 - precision: 0.8333 - recall: 0.6212 - auc: 0.9402 - prc: 0.7496 - val_loss: 0.0021 - val_tp: 52.0000 - val_fp: 2.0000 - val_tn: 45503.0000 - val_fn: 12.0000 - val_accuracy: 0.9997 - val_precision: 0.9630 - val_recall: 0.8125 - val_auc: 0.9373 - val_prc: 0.8455\n",
            "Epoch 31/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0038 - tp: 205.0000 - fp: 33.0000 - tn: 181913.0000 - fn: 125.0000 - accuracy: 0.9991 - precision: 0.8613 - recall: 0.6212 - auc: 0.9387 - prc: 0.7518 - val_loss: 0.0021 - val_tp: 52.0000 - val_fp: 2.0000 - val_tn: 45503.0000 - val_fn: 12.0000 - val_accuracy: 0.9997 - val_precision: 0.9630 - val_recall: 0.8125 - val_auc: 0.9372 - val_prc: 0.8451\n",
            "Epoch 32/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0038 - tp: 213.0000 - fp: 35.0000 - tn: 181911.0000 - fn: 117.0000 - accuracy: 0.9992 - precision: 0.8589 - recall: 0.6455 - auc: 0.9372 - prc: 0.7557 - val_loss: 0.0021 - val_tp: 51.0000 - val_fp: 1.0000 - val_tn: 45504.0000 - val_fn: 13.0000 - val_accuracy: 0.9997 - val_precision: 0.9808 - val_recall: 0.7969 - val_auc: 0.9372 - val_prc: 0.8455\n",
            "Epoch 33/100\n",
            "90/90 [==============================] - 0s 3ms/step - loss: 0.0039 - tp: 198.0000 - fp: 29.0000 - tn: 181917.0000 - fn: 132.0000 - accuracy: 0.9991 - precision: 0.8722 - recall: 0.6000 - auc: 0.9356 - prc: 0.7521 - val_loss: 0.0021 - val_tp: 52.0000 - val_fp: 5.0000 - val_tn: 45500.0000 - val_fn: 12.0000 - val_accuracy: 0.9996 - val_precision: 0.9123 - val_recall: 0.8125 - val_auc: 0.9372 - val_prc: 0.8435\n",
            "Epoch 34/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0039 - tp: 212.0000 - fp: 34.0000 - tn: 181912.0000 - fn: 118.0000 - accuracy: 0.9992 - precision: 0.8618 - recall: 0.6424 - auc: 0.9310 - prc: 0.7465 - val_loss: 0.0021 - val_tp: 52.0000 - val_fp: 3.0000 - val_tn: 45502.0000 - val_fn: 12.0000 - val_accuracy: 0.9997 - val_precision: 0.9455 - val_recall: 0.8125 - val_auc: 0.9372 - val_prc: 0.8450\n",
            "Epoch 35/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0039 - tp: 210.0000 - fp: 35.0000 - tn: 181911.0000 - fn: 120.0000 - accuracy: 0.9991 - precision: 0.8571 - recall: 0.6364 - auc: 0.9310 - prc: 0.7367 - val_loss: 0.0021 - val_tp: 51.0000 - val_fp: 1.0000 - val_tn: 45504.0000 - val_fn: 13.0000 - val_accuracy: 0.9997 - val_precision: 0.9808 - val_recall: 0.7969 - val_auc: 0.9372 - val_prc: 0.8457\n",
            "Epoch 36/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0038 - tp: 215.0000 - fp: 32.0000 - tn: 181914.0000 - fn: 115.0000 - accuracy: 0.9992 - precision: 0.8704 - recall: 0.6515 - auc: 0.9342 - prc: 0.7546 - val_loss: 0.0021 - val_tp: 50.0000 - val_fp: 1.0000 - val_tn: 45504.0000 - val_fn: 14.0000 - val_accuracy: 0.9997 - val_precision: 0.9804 - val_recall: 0.7812 - val_auc: 0.9372 - val_prc: 0.8474\n",
            "Epoch 37/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0038 - tp: 208.0000 - fp: 31.0000 - tn: 181915.0000 - fn: 122.0000 - accuracy: 0.9992 - precision: 0.8703 - recall: 0.6303 - auc: 0.9371 - prc: 0.7612 - val_loss: 0.0021 - val_tp: 52.0000 - val_fp: 3.0000 - val_tn: 45502.0000 - val_fn: 12.0000 - val_accuracy: 0.9997 - val_precision: 0.9455 - val_recall: 0.8125 - val_auc: 0.9373 - val_prc: 0.8455\n",
            "Epoch 38/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0038 - tp: 210.0000 - fp: 32.0000 - tn: 181914.0000 - fn: 120.0000 - accuracy: 0.9992 - precision: 0.8678 - recall: 0.6364 - auc: 0.9326 - prc: 0.7474 - val_loss: 0.0021 - val_tp: 52.0000 - val_fp: 3.0000 - val_tn: 45502.0000 - val_fn: 12.0000 - val_accuracy: 0.9997 - val_precision: 0.9455 - val_recall: 0.8125 - val_auc: 0.9373 - val_prc: 0.8476\n",
            "Epoch 39/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0037 - tp: 217.0000 - fp: 29.0000 - tn: 181917.0000 - fn: 113.0000 - accuracy: 0.9992 - precision: 0.8821 - recall: 0.6576 - auc: 0.9357 - prc: 0.7725 - val_loss: 0.0021 - val_tp: 52.0000 - val_fp: 4.0000 - val_tn: 45501.0000 - val_fn: 12.0000 - val_accuracy: 0.9996 - val_precision: 0.9286 - val_recall: 0.8125 - val_auc: 0.9372 - val_prc: 0.8464\n",
            "Epoch 40/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0039 - tp: 215.0000 - fp: 34.0000 - tn: 181912.0000 - fn: 115.0000 - accuracy: 0.9992 - precision: 0.8635 - recall: 0.6515 - auc: 0.9295 - prc: 0.7487 - val_loss: 0.0021 - val_tp: 51.0000 - val_fp: 1.0000 - val_tn: 45504.0000 - val_fn: 13.0000 - val_accuracy: 0.9997 - val_precision: 0.9808 - val_recall: 0.7969 - val_auc: 0.9372 - val_prc: 0.8451\n",
            "Epoch 41/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0038 - tp: 211.0000 - fp: 31.0000 - tn: 181915.0000 - fn: 119.0000 - accuracy: 0.9992 - precision: 0.8719 - recall: 0.6394 - auc: 0.9387 - prc: 0.7512 - val_loss: 0.0021 - val_tp: 51.0000 - val_fp: 1.0000 - val_tn: 45504.0000 - val_fn: 13.0000 - val_accuracy: 0.9997 - val_precision: 0.9808 - val_recall: 0.7969 - val_auc: 0.9372 - val_prc: 0.8462\n",
            "Epoch 42/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0038 - tp: 211.0000 - fp: 32.0000 - tn: 181914.0000 - fn: 119.0000 - accuracy: 0.9992 - precision: 0.8683 - recall: 0.6394 - auc: 0.9372 - prc: 0.7551 - val_loss: 0.0021 - val_tp: 52.0000 - val_fp: 2.0000 - val_tn: 45503.0000 - val_fn: 12.0000 - val_accuracy: 0.9997 - val_precision: 0.9630 - val_recall: 0.8125 - val_auc: 0.9372 - val_prc: 0.8439\n",
            "Epoch 43/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0040 - tp: 200.0000 - fp: 37.0000 - tn: 181909.0000 - fn: 130.0000 - accuracy: 0.9991 - precision: 0.8439 - recall: 0.6061 - auc: 0.9326 - prc: 0.7454 - val_loss: 0.0021 - val_tp: 52.0000 - val_fp: 2.0000 - val_tn: 45503.0000 - val_fn: 12.0000 - val_accuracy: 0.9997 - val_precision: 0.9630 - val_recall: 0.8125 - val_auc: 0.9372 - val_prc: 0.8451\n",
            "Epoch 44/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0038 - tp: 223.0000 - fp: 30.0000 - tn: 181916.0000 - fn: 107.0000 - accuracy: 0.9992 - precision: 0.8814 - recall: 0.6758 - auc: 0.9447 - prc: 0.7534 - val_loss: 0.0021 - val_tp: 52.0000 - val_fp: 2.0000 - val_tn: 45503.0000 - val_fn: 12.0000 - val_accuracy: 0.9997 - val_precision: 0.9630 - val_recall: 0.8125 - val_auc: 0.9294 - val_prc: 0.8402\n",
            "Epoch 45/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0039 - tp: 206.0000 - fp: 29.0000 - tn: 181917.0000 - fn: 124.0000 - accuracy: 0.9992 - precision: 0.8766 - recall: 0.6242 - auc: 0.9342 - prc: 0.7476 - val_loss: 0.0021 - val_tp: 52.0000 - val_fp: 4.0000 - val_tn: 45501.0000 - val_fn: 12.0000 - val_accuracy: 0.9996 - val_precision: 0.9286 - val_recall: 0.8125 - val_auc: 0.9372 - val_prc: 0.8422\n",
            "Epoch 46/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0036 - tp: 227.0000 - fp: 31.0000 - tn: 181915.0000 - fn: 103.0000 - accuracy: 0.9993 - precision: 0.8798 - recall: 0.6879 - auc: 0.9357 - prc: 0.7731 - val_loss: 0.0021 - val_tp: 52.0000 - val_fp: 1.0000 - val_tn: 45504.0000 - val_fn: 12.0000 - val_accuracy: 0.9997 - val_precision: 0.9811 - val_recall: 0.8125 - val_auc: 0.9373 - val_prc: 0.8472\n",
            "Epoch 47/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0036 - tp: 219.0000 - fp: 30.0000 - tn: 181916.0000 - fn: 111.0000 - accuracy: 0.9992 - precision: 0.8795 - recall: 0.6636 - auc: 0.9403 - prc: 0.7680 - val_loss: 0.0021 - val_tp: 52.0000 - val_fp: 2.0000 - val_tn: 45503.0000 - val_fn: 12.0000 - val_accuracy: 0.9997 - val_precision: 0.9630 - val_recall: 0.8125 - val_auc: 0.9373 - val_prc: 0.8457\n",
            "Epoch 48/100\n",
            "90/90 [==============================] - ETA: 0s - loss: 0.0038 - tp: 210.0000 - fp: 34.0000 - tn: 181912.0000 - fn: 120.0000 - accuracy: 0.9992 - precision: 0.8607 - recall: 0.6364 - auc: 0.9372 - prc: 0.7622Restoring model weights from the end of the best epoch: 38.\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.0038 - tp: 210.0000 - fp: 34.0000 - tn: 181912.0000 - fn: 120.0000 - accuracy: 0.9992 - precision: 0.8607 - recall: 0.6364 - auc: 0.9372 - prc: 0.7622 - val_loss: 0.0021 - val_tp: 52.0000 - val_fp: 2.0000 - val_tn: 45503.0000 - val_fn: 12.0000 - val_accuracy: 0.9997 - val_precision: 0.9630 - val_recall: 0.8125 - val_auc: 0.9373 - val_prc: 0.8449\n",
            "Epoch 48: early stopping\n"
          ]
        }
      ],
      "source": [
        "model = make_model()\n",
        "model.load_weights(initial_weights)\n",
        "baseline_history = model.fit(\n",
        "    train_features,\n",
        "    train_labels,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    epochs=EPOCHS,\n",
        "    callbacks=[early_stopping],\n",
        "    validation_data=(val_features, val_labels))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iSaDBYU9xtP6"
      },
      "source": [
        "### Check training history\n",
        "\n",
        "In this section, you will produce plots of your model's accuracy and loss on the training and validation set. These are useful to check for overfitting, which you can learn more about in the [Overfit and underfit](https://www.tensorflow.org/tutorials/keras/overfit_and_underfit) tutorial.\n",
        "\n",
        "Additionally, you can produce these plots for any of the metrics you created above. False negatives are included as an example."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WTSkhT1jyGu6"
      },
      "outputs": [],
      "source": [
        "def plot_metrics(history):\n",
        "  metrics = ['loss', 'prc', 'precision', 'recall']\n",
        "  for n, metric in enumerate(metrics):\n",
        "    name = metric.replace(\"_\",\" \").capitalize()\n",
        "    plt.subplot(2,2,n+1)\n",
        "    plt.plot(history.epoch, history.history[metric], color=colors[0], label='Train')\n",
        "    plt.plot(history.epoch, history.history['val_'+metric],\n",
        "             color=colors[0], linestyle=\"--\", label='Val')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel(name)\n",
        "    if metric == 'loss':\n",
        "      plt.ylim([0, plt.ylim()[1]])\n",
        "    elif metric == 'auc':\n",
        "      plt.ylim([0.8,1])\n",
        "    else:\n",
        "      plt.ylim([0,1])\n",
        "\n",
        "    plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u6LReDsqlZlk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "outputId": "467f2042-620b-432d-bd00-d0024979ea09"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 4 Axes>"
            ],
            "image/png": 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cDkO0y4Un6j87zFux66CkvVtsFKcN7dn4/IZTBpAaH82YjGSyUuMOSlZdTgcT+qW0+H1zu5yMykhq8fVol4PstDiy0+KafT09OYabTh3Y4vGx0a7GUelgBZOYtyTa5SDa9Z//NxndWi/T6JcaR7/U5u8NYFzfbo2Pzx/T57DXfzZzJFFO01h2kxof3bjOCsAF49Jbvb7TYUiKiYKYqFb3ay8a6W7FA/PW8Lev8ln5wLdCEJWISNs00i0dYV95DZ9sKmRxXiHbi6voFhvNlVP6cfLg7pRWeflowz5cDgdl1XWNXxeNz2BIrwQ+XL+XW175mkpvfeP5YqOd/Pl7kxnftxs7iqvYXVbD0N4JJHo6Jrlpb36/pcLrw+NyEu1SDwppmUa6j1K820VFrQ9r7XE9cUdERLqWuno/xZVeeiZ62FNWwwm/+ACA1LhoBgfKFqoC3bvy9lXww78eXFfsdjmY0K8bQ3ol0C81jtk5mfTvHseA7vH07x5Hr0RP47+bmSmxIZuY1lEcDtNpf2GQyKGkuxXxHhd+CzV1fmKij/7PLSIiIqG2fk85WalxrZYHWGt5b+1eHn5nPcN6J/L4d8fTK8nDzy4Yyfi+yQzrlXhYLe+ojCQ+vOMUfH5LckwUiTFRB11jQPd4Hjh/RMjuS+R4oaS7FXGBuqD9tXVKukVEJKIUVdTy4fp9fGdcOi6ngzdy83ktdwcXjs/g0kmZDO2VeND+X28v4X8XrOOLbSUM7BHPheP/U+96xQn9WryO2+Wkf/f4kN2HSFehpLsVCYGku6LGR4+EMAcjIiJd3u6yauav3M17a/aS+00xfgt9U2KZ3D+Viydmsm9/La8s286LS7Yxrm8yV0/NYubYdP72VT4/em0FafFu/vc7o7g4J6PZbg4iEjpKultxYKS7sra+jT1FRLomY8wM4PeAE3jWWvvLQ17vC/wJSA7sc7e1dkGHB9qJHZhXtHxHKd/546dYC0N7JXDzaYM4a3hPRvRpGNEe3DOBx+aMo7jSy9++yufVz7fz2ZZiZo5N57ShPbjjzMFce2J2479tItKx9JPXivgm5SUiInIwY4wTeBw4E8gHvjDGzLPWrm2y2/8Ar1lrnzDGDAcWAFkdHmyEWLypkM0FFewtr2FPeQ37ymvpneTh17PHAPDrd9dTVl1Ht9iG1mdLNhcxok8id80Yysg+ifz4W0M4e2TvFlvIAaTERfO9k/pz3YnZVNc1DBolx0Zzy+mDOuQeRaR5SrpbEa+RbhGR1kwC8qy1WwCMMXOBmUDTpNsCB4qLk4BdHRphBKis9TWOLj+xKI9P84pwOQw9Etz0TPIcNPK8amc5q3eWUVLlxdqGPswnDUoDGno+/9f0lnsyH8oYc8Q9mkUkdPTT2Ip4T6CmWyPdIiLNSQd2NHmeD0w+ZJ8HgPeMMbcAccAZHRNa+JVWeXly0Rb+8tk3vH3LiWSlxfGrWWNwuxykxEY3u+LfS9dOAgIr5dX4SIxxqWWtyHFCSXcr4ptMpBQRkaMyB3jRWvuoMWYK8LIxZqS11t90J2PM9cD1AH379g1DmO2nrLqOl5du46mPt1BR6+OCsemNC6qkJ8cEdQ6nw5AUq77QIscTJd2taEy6VV4iItKcnUBmk+cZgW1NXQfMALDWLjXGeIA0YF/Tnay1TwNPQ8OKlKEKOBSKKmrZt7+WYb0Tqfdbpv3yQypqfZw5vCd3nDX4sNZ9ItI1KeluhSfKgdNhVF4iItK8L4BBxphsGpLtS4HLDtlnO3A68KIxZhjgAQo6NMp2VlHrY+nmIj7ZVMCSzUXk7atgZHoi/7zlJJwOw0/PH8HgngmMykgKd6giEkGUdLfCGENctFMTKUVEmmGt9RljbgbepaEd4PPW2jXGmAeBXGvtPOAO4BljzA9pmFR5tbW2U41kW2tZv2c/w3o3jFj/+PUVvLN6DzFRTib3T+Gi8RlM7p/SuP9FEzLCFaqIRDAl3W1I8ESxXzXdIiLNCvTcXnDItvuaPF4LTOvouNrLN0WV3PO3VSzZXMSSu0+jT3IM15/cnyunZDG+XzJul1YrFpHgKOluQ7zbpfISEZEuxlfv57nFW/nt+xuJcji4/7zhJMY0TGwc17dbmKMTiRz1fsvyHSWMy+zWbEeeSGCt5Z8rd+NyGMZkJtM7yROWrkBKutsQ51Z5iYhIV+L3Wy55+jO+/KaEM4f35GczR9IryRPusEQijt9vueuNlbz5VT6zJ2Tw8EWjIy7xttby0Px1PLt4a+O2HgluxmYmMyYzmXGZyYztm9whPe1DeoUglgd2Ay8BE4Ai4BJr7TZjTCrwBjCRhlZTNwf2jwVeBwYA9cDb1tq7Q3kP8Z4oyqo10i0icryr9dXjdjlxOAznj+nDtdOyOWdUL/XJFmmGtZb7563hza/ymZSdwutf5mOBhy8ajTOCEu8/fJjHs4u3cvXULC4Yl86KHaUsD3y9t3YvAC6HYWR6EpOzU5iUnUJOv5SQtOwMWdId5PLA1wEl1tqBxphLgYeBS4Aa4F5gZOCrqUestR8ZY6KBD4wxZ1tr3wnVfcS7newqrQ7V6UVEJAJ8tH4f981bzb3nDuesEb24ampWuEMS6XB19X6W7yjly29KmDogldEZyc3uZ63l4X9t4OXPvuEHJ/fn7rOH8rv3N/H7DzZhLfxqVmQk3n9aso1H/72RC8enc9+3h+NwGMZmJnNV4PXSKi/Ld5TyxbZiPt9azAufNvTXNwaG9krk17NGMzK9/boQhXKkO5jlgWfSsFoZNIxs/8EYY6y1lcBiY8xB691aa6uAjwKPvcaYr2joCxsy8W6XFscRETlO7Sqt5qdvr+HdNXsZ0D2ObnHR4Q5J2vDyZ9/wl8++4bmrJwa92FBnVVZVR3GVl/TkmMYFltrbjuIqFm0s4OONBSzdXMT+2v/kPLMnZPDjGUPokXBwedXjH+Xx5KLNXH5CX+4+eyjGGH545mCMgd+9vwmL5dezxhxR4n2gqVF7/WXp71/nc/+8NZw1vCe/aqHsJTk2mulDejB9SA8AaurqWb6jlM+3NiThafHudonlgFAm3cEsD9y4T6D1VBmQChS2dXJjTDJwHg3lKyET746iolZJt4jI8eYvy77hofnr8FvLXTOG8L0T+4cssZFjV++3/GLBf2pzf/vvjTwye0yYozo21loqan0UVnjZU1ZDXkEFeXv3s2lfBZv2VVCwvxZoWKE0s1sM2Wlx9O8eT3ZaHEN6JTAqPQlP1NF10MnbV8Edry1nRX4Z0LBa6rfH9ObkQd0ZnZnMS0u38fzirSxYtZtbTh/ENdOycLucPLd4K4+8t5ELx6Xz4PkjD0qSbz9jMAbDb9/fCBZ+PfvwxNtX72d3WQ15+yrYtG8/m/Y23GvevgqinIZZEzK4bHI/stPiWvyebdi7n3dX78UYmJSdwtjM5IO+D++t2cOdr69k6oBUHpszDpczuJ9rT5STE/qnckL/1CP8bganU06kNMa4gFeBxw6MpDezT7ssKRzvdlLp9eH324ibHCAiIkdmT1kNMVFOkmKj8LicTBuYxn3fHk5mSmy4Q5NWVHl93D53Oe+t3cvVU7NwGMOLS7Zy/cn9GdwzIWxx1dX7WbihgME94+mX2nyS2NTX20t45pMt7CytoXB/LYUVtdT6/AftE+92MbBHPNMHd2dQz3i6xUazvbiKLYWVbCmoZOmWImrqGo6JdjoYnZFETlYKE7O6MaFfN5Jj2/5rzd++yud//rEaT5STe789nFMGd2dA97iDEuh7zh7GpRP78tD8dfzynfW8+vl2zhrek2c+2cqMEb341azmR49vO2MQxsBv/r2RkiovfZJj2Ftey97yGvaW11BYUYu/Saf+tHg3g3rEc+H4dAr21/LCp9t45pOtnDgwje9O7ssZw3vichg27N3PgpW7+eeq3WwpqMRhGhr/W9vwfRibmcyk7BR6JXl48J9rGZmexNNX5hz1LyWhEMqkO5jlgQ/skx9IpJNomFDZlqeBTdba37W0Q3stKRzvcWEtVNXVNy4LLyIincfe8hreWbWb+at288W2Ev7n3GF876T+XDg+XQvZdAL79tfwvT/lsnpnGfefN5xrpmVTUunl9dwd/PrdDTxzZU6Hx2St5aMN+3ho/jo2F1QS7XRwzbQsbj5tIAmewyfglVXV8at31/PK59tJjYtmWO9E+qfFkRoXTVqCm7R4Nz0T3QzoHt9mOzu/37KnvIbVO8v48psSvthWzHOLt/DkooZUZ2xmMpdN7st5o/sQE31wwlntref+eat5Lbdh8uNjl45rtTNPdlocz16Vw8cbC3jwn2t55pOtnDK4e5ujx7eePginw/C79zcS73bRM9FDz0QPw3on0CvRQ+/kGAb2iGdg9/jDSrr2ldfw1y928Orn27nxL1/RI8FNgsfF5kCiPTk7lWumZTNjRC+inIbcbSV8vq2YZVuLeWLRZur9lsE94/nTNRMjLm8zoVoYLJBEb6Rh+d+dNCwXfJm1dk2TfW4CRllrbwhMpLzQWntxk9evBnIOdC8JbPs5MAyYba09+NfDFuTk5Njc3Nyjuo+/LPuG//f31Sz7yen0TFTLKBHpWMaYL621HZ9VhNGxfGY35fX5uemVr3h/3V6shaG9Ejh3VG/OH9snqFHJzqSk0suTizZz3UnZh9XfhtKSzYW4XU4m9AtN7/INe/Zz7YtfUFzp5f/mjOOM4T0bX/vDh5t45L2NvHnjFCb0S2nxHJsLKlixo5QZI3u1S1u49XvK+fk/17E4r5D+aXHcdsYgPtlUyBtf5pMWH82dZw1hdk4mTofBWss/lu/kofnrKK70cvXUbH501uB2TwZr6upZEahFfmvFLvL2VZDocTFrQiaXTe7LwB7xbNq7n5te+YpN+yq4+dSB3Hb6oKDLLqBhVP/TvEJO6J8a9OjxsVQJ1PstCzfs49XPt1NT5+dbI3sxY0Qvuie0XGddWetj7e5yhvZKaPaXn47Q2md2yJLuwIXPAX7Hf5YHfqjp8sDGGA/wMjAOKAYubTLxchuQCEQDpcBZQDkNNeDrgdrAZf5grX22tTiO5QP8reU7uW3uct7/0SkM7BF/VOcQETlaSrqPXnlNHc98vIXy6jqumNKPgT3CV4YQane+voI3vszn7JG9eOLyCSG/3v6aOn72z7W8lpsPwHcnN0yoa49Ep8rrY/GmQj5Yt4/5q3YTG+3k+asnHtZFosrr4+RfLaR/Whx//cEJzY4Oby+q4sInllBYUUuix8Wlk/pyxQn9jqqcqLCilkff28hfv9hOgieK204fxOUn9GucB7Ayv5QH315L7jclDO+dyA3TB/Dqsu0s3VLE2Mxkfn7ByHbthNESay3Lthbz58++4d01e6irt0zKSmHVzjJio5389pKxnDy4e8jj6KrClnRHimP5AP9w/V6ufTGXf9w0jbGZzbfOEREJFSXd0pbPthRx6dOfkZ0Wx9bCSl64ZiKnBroxHKlaXz3V3vpW64I/zSvkrjdWsrusmutPHtCweuenW+md6OGhC0cd8bX9fsvO0moWbSzgg3V7+XRzEV6fnwS3i+lDe3DP2UPp00KXkpeXbuPet9bwwtUTOXXowdctqqhl1pNLKa708rMLRvLumj38a/Ue/NZyxrCeXD01i6kDUoPqlrF4UyG3vPoV+2t8XH5CP24/Y1Cz36MDKx/+8p317CytJtHj4r/PHsqciX3DMi+sYH8tr+Xu4I0v88lMieXXs0brr/YhpqT7GD7Al20p4pKnP+Mv35vMtIFp7RyZiEjrlHQfvZX5pQzsEd8hK82Fi9fn55zHPqGmrp75t57EhX/8lLp6y3s/PDmoEgBrLVsKK/lkYwEfbypk6eYiquvqGZWexPQh3Zk+pAdjM5NxOgxVXh8Pv7OePy39hv5pcTxy8RjG920oK/lqewl3vbGSvH0VXDgunfvOG96YlFprKar0sqO4iu3FVeSXVAe+qthZUk1+aTXewGTCvimxnD6sB2cM68nErJQ2u8l4fX7O+M0iYqOdLLj1pMbEtrLWx2XPfMb6Pft55fuTG8tPdpdV85fPtvPK59sprvQyMj2Rn54/osXyFGstT328hV/9az0De8Tzx++OD+ovJjV19SzcUMCEft1aLYeQ420NxVkAACAASURBVE9rn9nH7ydRO4n3NHyL9qtXt4hIp1FTV8/sJ5dy+Qn9uPfbw8MdTsg888kW8vZV8PzVOSTFRPGzC0Zy2TPLePyjPO44a0iLx20vquLJjzezaEMBOwMLwGWnxTE7J4O0eDefbCrg8Y/y+L8P80iOjeLkQd1ZmV/KtqIqrpmWxV3fGnrQJL3xfbsx/9YT+cOHefxx4WY+3lTA2MxkdhRXs6Okiipv/UHXT4uPJr1bLMP6JHLmiJ5kdIvlhOwUBvaIP6I+zdEuB3ecNZjb5i5n3opdXDAunbr6hlr+VTvLeOqKnIMS6t5JMdz5rSHcfNpA5q3YxW/e28hFTyxl1oQM7j576EF9mStrfdz15krmr9zNuaN686tZo4kLshbbE+VkxsheQd+HdA1KuttwYLJDpXp1i4h0Git2lFLr8zM5u+UJdp3d9qIqHvtgE2eP7MVpQxsmGE4dkMZ3xqXz5KLNXDAunQHdD5+LtGZXGVc9/wVVXh8nDkzjxukDOGVw94PqnG89fRClVV4+2VTIRxv28fHGAuLcLl75/mSmDmj+r75ul5M7zhrCjJG9+Onba8kvqSYzJZZpA9PomxJDZkosmSmxZHSLade/Ppw3ug9PLdrCo//ewNmjevGTv61m4YYCfnHhKM5sMvGyKU+Uk4tzMjl3VG/+78M8nv1kC++u2cOdZw3hu5P7kl9SzQ9e/pJN+/Zzz9lDuf7k/u22aIt0XUq623Ag6dYCOSIinceyrcWNC2dEOq/PT5TTHFFSZ63l3rdW43IY7jvv4JH8n5wzjA/W7eXef6zmL9+bfNB5l20p4nt/yiXe42LezSe22iAgOTaa88b04bwxfbDWBh3fiD5JvPaDKUHfy7FyOAx3zRjC1S98wewnl7Iyv4wfnjGYOZPaXqMjzu3i7rOHMmtCBg/MW8P989bw6ufb2VVajcNh+NO1kzhpkCYdSvvQ0lttiFPSLSLS6Xy2pYhhvRKDWigknAorajnt0YXcNnc5RzLHasGqPSzaWMAdZw2hd9LBkwy7J7j58YyhLNlcxLwVuxq3v792L1c+/zndE928cePUI+rIFemjvKcM7s4J/VNYmV/GnEl9ufX0gUd0/MAe8bx83SQev2w8ZdV1ZKbE8vbNJyrhlnalke42uF0OopxGSbeISCdR66vny29KuGzy0a9G3BHq/ZZbX/26cWLhWSN68u3Rfdo8bn9NHT99ew0j+iRy5ZR+ze5z2aS+vJG7g5/9cx3Th/Tg/bV7uevNlYzok8gLV08kNf74mtxnjOFXF43hvbV7uHpq1lH9kmCM4dzRvZkxshcGtAq1tDuNdLfBGEO820WFJlKKiHQKUQ4Hb9wwlSunZIU7lFb95t8bWLK5iF9eOIoxGUnc99YaCitq2zzu0fc2UlBRy0PfGdXi4iZOh+Gh74yiuLKW7z77GXe8voLJ2Sm88v0TjruE+4C+qbF876T+R7TgS3OcDqOEW0JCSXcQ4twuTaQUEekkHA7DqIwkstPCs+pkSaWX7UVVre7z/tq9PP7RZi6dmMmlk/ry69ljqKjxcf9ba1o9bt6KXby0dBuXT+7X5toRI9OTuHJKFqt3ljNjRC+evzrylsUW6UqUdAch3u1iv5JuEZFO4cVPt/JpXmFYrl1S6eU7f/yUUx9dyK/fXU9NXf1h+2wvquJHry1nZHoiD5w/AoDBPRO47YxBzF+1mwWrdjd77rdX7OL2uV+Tk5XCPecMDSqeu88eyjNX5vCHy8YFvXS3iISGku4gqLxERKRz8Pr8/PJf6/n32r0dfu1aXz0/ePlLdpXVcOawnjz+0WbOeewTPt9a3LhPTV09N/7lSwCe+O6EgxLhH5zcn1HpSdz7j9UUV3oPOvf8lbu5/a/LyemXwgtXTwy65Z4nysmZw3sec8mFiBw7/RQGId7jotKrpFtEJNKtzC+lps7PCf1TO/S61lrufnMVn28r5pHZY3jyigm8dO0kvD4/Fz+1lP/5xyr219TxwLw1rNlVzm8vGXtQX2wAl9PBr2ePprymjvvn/afMZMGq3dw692vG903mhWsmBr1Ai4hEFv3kBiHe7WqzPk9ERMLvsy1FQMf35/79B5v4+9c7ufOswZw/pqEDycmDu/Pu7Sfz6HsbeWHJVuav3E1JVR03nTqA04c1v2jL0F6J3HLaIH7z742cO6o31lpuefVrxmYm88I1k5Rwi3Ri+ukNQrzbpZaBIiKdwLKtxQztlUBKXMf15/771/n87v1NXDQ+g5tOPbg/dJzbxX3nDefbY3rz//6+mnF9u/GjM1tenh3gxukD+NfqPfz3myuprPUxJiOJF6/RJEiRzk4/wUFQ0i0iEvmsteSXVHPK4I5b0OTzrcX89xurOKF/Cr+4cFSL/aHH9+3GO7edFNTKjlFOB4/MHsPMxxczKiOJP107iQRPVCjCF5EOpKQ7CHFuF1Xeeur9Fqd6d4qIRCRjDB/ecQq1Pn+HXG9rYSXXv5xLRkoMT12eQ7Sr7WlSwS7aMrxPIh/eMZ3uCW51HRE5TmgiZRASPA2/m2gypYhIZDPGdEiSWlHr4/sv5WKAF66eSFJs+49EZ6bEKuEWOY4o6Q7CgTo6tQ0UEYlcP359BY++tyHk1/H7LXe8tpythZU8/t3x9EsNzyI8ItK5KOkOwoHZ4lqVUkQkMtXV+5m/ajdl1XUhv9YfF+bx7pq9/OScYUwdkBby64nI8UFJdxDiA+UlWpVSRCQyrdpZRpW3PuT9uT9av49H/72RC8b24dppWSG9logcX5R0B0HlJSIika0j+nNvK6zk1rlfM6xXIr+4cHTQkyJFREBJd1DiVV4iIhLRlm0pZlCPeNLi3SE5f2Wtj+tfzsXlMDx1xQRiojXBUUSOjFoGBuFA0q3yEhGRyDSkV0LIRrmttfz4jRXk7avg5esmH7Z8u4hIMEI60m2MmWGM2WCMyTPG3N3M625jzF8Dry8zxmQFtqcaYz4yxlQYY/5wyDETjDGrAsc8Zjrg73sa6RYRiWw/OWfYYatBtpenP97CglV7uOfsYUwbqImTInJ0QpZ0G2OcwOPA2cBwYI4xZvghu10HlFhrBwK/BR4ObK8B7gXubObUTwDfBwYFvma0f/QHi1NNt4hIl/TFtmJ+9e4GzhnVi++dlB3ucESkEwvlSPckIM9au8Va6wXmAjMP2Wcm8KfA4zeA040xxlpbaa1dTEPy3cgY0xtItNZ+Zq21wEvABSG8BwCiXQ6iXQ4tBS8i0oUUVdRyyytfk9kthl9epImTInJsQpl0pwM7mjzPD2xrdh9rrQ8oA1rr95QeOE9r5wyJBLdLSbeISBfh91tu/+tyiqu8PP7d8SR62n/FSRHpWo7b7iXGmOuNMbnGmNyCgoJjPl+ckm4RkS7j8Y/y+GRTIT89fwQj+iSFOxwROQ6EMuneCWQ2eZ4R2NbsPsYYF5AEFLVxzow2zgmAtfZpa22OtTane/fuRxj64eLdLk2kFBHpApZsLuS37zcsgHPpxMy2DxARCUIok+4vgEHGmGxjTDRwKTDvkH3mAVcFHs8CPgzUajfLWrsbKDfGnBDoWnIl8Fb7h364eI+L/ZpIKSJyXNu3v4ZbX11OdlocD31nlOq4RaTdhCzpDtRo3wy8C6wDXrPWrjHGPGiMOT+w23NAqjEmD/gR0NhW0BizDfgNcLUxJr9J55P/Ap4F8oDNwDuhuoem4lVeIiJymLZawwb2udgYs9YYs8YY80pHxxiser/ltleXU1FbxxOXT2jsXCUi0h5C+olirV0ALDhk231NHtcAs1s4NquF7bnAyPaLMjjxbhdblHSLiDRq0hr2TBomtn9hjJlnrV3bZJ9BwD3ANGttiTGmR3iibdvLS7exdEsRj8wew+CeCeEOR0SOM8ftRMr2pomUIiKHCaY17PeBx621JQDW2n0dHGNQymvq+P0Hm5g2MJWLxndIUywR6WKUdAcpwaOkW0TkEMG0hh0MDDbGfGqM+cwYE/IFzY7Gkws3U1JVxz1nD1Mdt4iEhArWghTvdlFT56eu3k+UU7+riIgEyUXD6sHTaeg49bExZpS1trTpTsaY64HrAfr27duhAe4uq+a5xVu5YGwfRqarPaCIhIayxyAdmFCjtoEiIo2CaQ2bD8yz1tZZa7cCG2lIwg/S3m1ej8Rv3tuItXDHWUM69Loi0rUo6Q5SQiDpVomJiEijYFrD/oOGUW6MMWk0lJts6cggW7N+TzlvfJXPVVP7kZkSG+5wROQ4pqQ7SHFKukVEDhJka9h3gSJjzFrgI+DH1trWFkHrUL9YsJ5ETxQ3n3rY4LuISLtSTXeQ4j0qLxEROVQQrWEtDesw/KiDQ2vT4k2FLNpYwP87ZxhJsVHhDkdEjnMa6Q5SfGCkW6tSioh0fn6/5RfvrCM9OYYrpvQLdzgi0gUo6Q5SvMpLRESOG/NW7GLNrnJ+/K0heKKc4Q5HRLoAJd1BUnmJiMjxoaaunl+/u4ERfRI5f0yfcIcjIl2Eku4gxUervERE5Hjw8cYCdpZWc8dZg3E4tBCOiHQMJd1BinM3/PmxsrY+zJGIiMixWLixgLhoJycO7Nh+4CLStSnpDpLL6SAmyklFbV24QxERkaNkrWXRhgKmDUwj2qV/AkWk4+gT5wjEuV2aSCki0oltLqhgZ2k104f0CHcoItLFKOk+AgkeFxUqLxER6bQWbigA4JQhKi0RkY6lpPsIxLmdVNSovEREpLNauKGAQT3iSU+OCXcoItLFKOk+AvFulyZSioh0UlVeH59vLWa6RrlFJAyUdB+BeLeL/arpFhHplJZuLsJb7+eUwarnFpGOF1TSbYyJM8Y4Ao8HG2PON8ZEhTa0yBPvdql7iYhIJ7VwQwGx0U4mZncLdygi0gUFO9L9MeAxxqQD7wFXAC+GKqhIFe9ReYmISGdkrWXhxn1MHZCK26Vl30Wk4wWbdBtrbRVwIfBHa+1sYETowopMcW4XFVqRUkSk09laWMmO4mpOUatAEQmToJNuY8wU4LvA/MC2LjdUkOB24a33U+vTaLeIHF+MMTcZY5KbPO9mjPmvcMbUng60Cpw+WJMoRSQ8gk26bwfuAf5urV1jjOkPfNTWQcaYGcaYDcaYPGPM3c287jbG/DXw+jJjTFaT1+4JbN9gjPlWk+0/NMasMcasNsa8aozxBHkPxyzO7QK0FLyIHJe+b60tPfDEWlsCfD+M8bSrhRsL6N89jsyU2HCHIiJdVFBJt7V2kbX2fGvtw4EJlYXW2ltbO8YY4wQeB84GhgNzjDHDD9ntOqDEWjsQ+C3wcODY4cClNJSwzAD+aIxxBmrKbwVyrLUjaRhtvzTIez1m8YGkWyUmInIcchpjzIEngc/w6DDG025q6upZtqWI6epaIiJhFGz3kleMMYnGmDhgNbDWGPPjNg6bBORZa7dYa73AXGDmIfvMBP4UePwGcHrgQ38mMNdaW2ut3QrkBc4H4AJijDEuIBbYFcw9tIe0BDcA+SVVHXVJEZGO8i7wV2PM6caY04FXgX+FOaZ2sXRLEbU+v1ahFJGwCra8ZLi1thy4AHgHyKahg0lr0oEdTZ7nB7Y1u4+11geUAaktHWut3Qk8AmwHdgNl1tr3gryHY5bTrxtOh2HJ5qKOuqSISEf5MfAhcGPg6wPgrrBG1E4WbSjAE+VgcnZKuEMRkS4s2KQ7KtCX+wJgnrW2DrChC6t5xphuNIyCZwN9gDhjzOUt7Hu9MSbXGJNbUFDQLtdP8EQxOiOJxXmF7XI+EZFIECglWWetfdJaOyvw9ZS19riYwLJwwz6m9E/FE9Xl5v+LSAQJNul+CtgGxAEfG2P6AeVtHLMTyGzyPCOwrdl9AuUiSUBRK8eeAWy11hYEEv+/AVObu7i19mlrbY61Nqd79/b7k+KJA9NYmV9KeY0WyRGR40Mgud5gjOkb7lja27bCSrYVVXGKupaISJgFO5HyMWtturX2HNvgG+DUNg77AhhkjMk2xkTTMOFx3iH7zAOuCjyeBXxorbWB7ZcGuptkA4OAz2koKznBGBMbqP0+HVgXzD20l6kD0vBbWLaluCMvKyISat2ANcaYD4wx8w58hTuoY7VoY6BVoPpzi0iYuYLZyRiTBNwPnBzYtAh4kIYa7GZZa33GmJtpmJzjBJ4PtBt8EMi11s4DngNeNsbkAcUEOpEE9nsNWAv4gJsCIzHLjDFvAF8Ftn8NPH2E93xMxvdLxhPl4NO8Qs4c3rMjLy0iEkr3hjuAUFi4YR9ZqbFkpcWFOxQR6eKCSrqB52noWnJx4PkVwAs0rFDZImvtAmDBIdvua/K4BpjdwrEPAQ81s/1+Gn4BCAu3y8nErBQ+VV23iBwHAmsd3AAMBFYBzwUmtnd6tb56lm4p4pKczLZ3FhEJsWBrugdYa+8PtP/bYq39KdA/lIFFsmkD09i0r4J95TXhDkVE5Fj9CcihIeE+G3g0vOG0n71ltdTU+RmZnhTuUEREgk66q40xJx54YoyZBlSHJqTIN21AGoBaB4rI8WC4tfZya+1TNMytOSncAbWXkiovAN1ij4s1fkSkkwu2vOQG4KVAbTdACf+ZANnlDO+TSFJMFIvzCrlg3KGtx0VEOpXGVkyBuTjhjKVdlVY33FpybFSYIxERCTLpttauAMYYYxIDz8uNMbcDK0MZXKRyOgxTB6SyJK8Qay3H0z9SItLljDHGHGgBa2hY8bc88NhaaxPDF9qxKQ2MdCdrpFtEIkCw5SVAQ7IdWJkS4EchiKfTmDowjV1lNWwr0pLwItJ5WWud1trEwFeCtdbV5HGnTbgBSqs00i0ikeOIku5DdOnh3WkDUgG0OqWISIRqTLpjlHSLSPgdS9Ld4cvAR5LstDh6J3lYoqRbRCQilVR5SXC7cDmP5Z86EZH20WpNtzFmP80n1waICUlEnYQxhmkD03h/3V78fovD0aUH/kVEIk5ZdR1JKi0RkQjR6q//B2r6mvlKsNYG2/nkuDVtYCqlVXWs3V3e9s4iItKhSqq8ahcoIhFDf3M7BlMD/bpV1y0iEnlKq+o0iVJEIoaS7mPQM9HDoB7xWhJeRCQClVXXqV2giEQMJd3HaNrANL7YVkytrz7coYiISBMlVV51LhGRiKGk+xhNHZBKTZ2fr74pDXcoIiIS4PfbwEi3km4RiQxKuo/R5P6pOAws2awSExGRSFFeU4e1Wo1SRCKHku5jlBQTxeiMZNV1i4hEEC2MIyKRRkl3OzhxYBor8svIL9GS8CIikaC0uiHp7hanpFtEIoOS7nYwZ3Jfop0OHpi3NtyhiIh0KGPMDGPMBmNMnjHm7lb2u8gYY40xOR0RV0mVF4CkGJWXiEhkUNLdDtKTY/jhmYN4f91e3luzJ9zhiIh0CGOME3gcOBsYDswxxgxvZr8E4DZgWUfFVhYoL+mmiZQiEiGUdLeTa6ZlM7RXAg/MW0NlrS/c4YiIdIRJQJ61dou11gvMBWY2s9/PgIeBmo4K7MBItyZSikikUNLdTqKcDn5+wUh2ldXw+w82hTscEZGOkA7saPI8P7CtkTFmPJBprZ3fkYEdmEiZ6HF15GVFRFqkpLsd5WSlcOnETJ5bvJX1e8rDHY6ISFgZYxzAb4A7gtj3emNMrjEmt6Cg4JivXVZdR6LHhcupf+ZEJDLo06id/feMoSTFRPH//r4av9+GOxwRkVDaCWQ2eZ4R2HZAAjASWGiM2QacAMxrbjKltfZpa22OtTane/fuxxxYSZVXpSUiElFCmnS3NavdGOM2xvw18PoyY0xWk9fuCWzfYIz5VpPtycaYN4wx640x64wxU0J5D0eqW1w0PzlnGF9+U8JruTvaPkBEpPP6AhhkjMk2xkQDlwLzDrxorS2z1qZZa7OstVnAZ8D51trcUAdWWlWnSZQiElFClnQHOav9OqDEWjsQ+C0NE20I7HcpMAKYAfwxcD6A3wP/stYOBcYA60J1D0frovHpTMpO4RfvrKeoojbc4YiIhIS11gfcDLxLw2fxa9baNcaYB40x54czttIqL0ka6RaRCBLKke5gZrXPBP4UePwGcLoxxgS2z7XW1lprtwJ5wCRjTBJwMvAcgLXWa60tDeE9HBVjDA9dMJLKWh8PzY+43wlERNqNtXaBtXawtXaAtfahwLb7rLXzmtl3ekeMckPD4jhajVJEIkkok+42Z7U33ScwYlIGpLZybDZQALxgjPnaGPOsMSYuNOEfm0E9E/iv6QP429c7eXLR5nCHIyLSpai8REQiTWebSOkCxgNPWGvHAZVAsyugtfdM+KNx+xmDOW9MH375znrmfr49LDGIiHQ19X5LeU2dyktEJKKEMulua1b7QfsYY1xAElDUyrH5QL619sCqZm/QkIQfpj1mwlt7bN1HHA7Do7PHcMrg7vzk76t4Z9XuYzqfiIi0rby6Dmu1GqWIRJZQJt2tzmoPmAdcFXg8C/jQNmS684BLA91NsoFBwOfW2j3ADmPMkMAxpwNr2zvwwopaTn1kYbt0H4l2OXji8vGM69uN2+YuZ/GmwnaIUEREWvKf1SiVdItI5AhZ0h3krPbngFRjTB7wIwKlItbaNcBrNCTU/wJustbWB465BfiLMWYlMBb43/aOPTUumvLqOpZtLW6X88VGu3j+qon07x7H9S/nsnxHxM39FBE5bpRWN6xGmRyj8hIRiRwhXR/XWrsAWHDItvuaPK4BZrdw7EPAQ81sXw4ctrBCezLGkJPVjS+2tU/SDZAUG8VL105i1pNLufqFz3n9B1MY1DOh3c4vIiINygJLwGukW0QiSWebSNlhJmalsKO4mj1lNe12zh6JHv583WSinA4ufmopH67f227nFhGRBv8pL9FIt4hEDiXdLZiYlQLQrqPdAH1TY3ntB1PolRTDtS/m8vN/rsXr87frNUREurLSwEi3JlKKSCRR0t2CEX0SmTMpkz7JMe1+7uy0OP7+X1O5cko/nl28lVlPLuGbosp2v46ISFdUWuXFGEjwKOkWkcihpLsFLqeDX1w4mgn9uoXk/J4oJw/OHMmTl09gW2El5z62mLdX7ArJtUREupLS6joSPVE4HSbcoYiINFLS3QprLXn7Kqjy+kJ2jRkje7HgtpMY0iuBW179mof/tT5k1xIR6Qq0GqWIRCIl3a34bEsxZ/xmEcu2tG9d96EyusUy9/oTmDMpkycWbtYiOiIix6CkyqvVKEUk4ijpbsXYzGRcDsPn7TyZsjlRTgc/PX8kYzKSuOvNleworgr5NUVEjkdl1RrpFpHIo6S7FTHRTkamJ/FFOy2S05Zol4P/mzMeLNw692vq6tXVRETkSJVUeUmOUdItIpFFSXcbJmWnsDK/jJq6+rZ3bgd9U2P5xUWj+Hp7KY+8t6FDrikicjwprapTj24RiThKutswMSsFb72flfllHXbNb4/uw5xJfXlq0RYWbtjXYdcVEensfPV+9tf4tBqliEQcJd1tmJSdwlNXTGBY745dsv3+84YzpGcCd7y2gr3l7bcqpojI8aysOrAEvMpLRCTCKOluQ1JMFN8a0avDF1nwRDn5w2XjqPT6uH3ucur9FmhoY1hTV09RRS35JVWN24MRytaHIiKRoDSQdHeLU3mJiEQWV7gD6Ay2FVby3to9XHdi/w5dbGFQzwQePH8kd725khN+8QFen5/KWh++Jon28N6J/HTmiMZl65uzp6yGX76zjrdW7OK6adn899lDiXLq9y0ROf6UVnmBhgETEZFIoqQ7CMt3lPK/C9YzbWAaI/okdei1Z+dkUF5Tx9rd5cS7XcS5XQ3/jXZSb+HZT7Yw+8mlXDC2D/ecM4yeiZ7GY2t99Tz7yVYe/ygPX73lpEHdeXbxVlbml/GHy8bRo8m+IiLHg9KqQHmJJlKKSIRR0h2EnKyGpeC/2Frc4Um3MYbvndS/xdfnTMrkjx9t5umPt/De2r3cctogrj0xi483FvLz+Wv5pqiKs4b35H/OHU7f1FjeWr6Tu99cxTmPLebxy8YxuX9qB96NiEhoHUi61adbRCKNagyCkNEtlj5JHr7YVhLuUA4TG+3izm8N4d8/OpmpA1J5+F/rmfTQB3z/pVyinA5evm4ST1+ZQ9/UWABmjk3nrZunkehxcdmzy3j6481YG3xduIhIJCsJlJckx2ikW0Qii0a6gzQxO4Ulm4uw1mJMx9V1B6tfahzPXjWRjzbs46Ul2zhxUHeunNKv2drtwT0TeOvmafz3myv53wXr+eqbUn5zyRhio/V2EJHOray6DoeBBI8+z0QksmikO0gTs1IorfKyt7w23KG06tQhPXjhmklcd2J2q5MlEzxRPH7ZeP7n3GG8t3YPc55ZRnGltwMjFRFpfyVVXpJionB04KR3EZFgKOkO0oXj01n1wLfolXT8TD48UC/+5OUTWL+7nFlPLGFHcVWL+1tr+dfqPTy5aLMSdBGJSFqNUkQilZLuIMVGu/BEOcMdRkicNaIXf/neZIoqvVz4xBLW7io/bJ9V+WVc8tRn3PDnL/nlO+s58eEP+cU76yiqiOyRfxHpWsqq67QapYhEJCXdR+BvX+Vzy6tfhzuMkMjJSuH1G6bgchgueWopSzYXArC3vIY7X1/B+Y8vZnNBBQ99ZyT/uv0kzhzek2c+3sKJD3/E/y5YR8F+Jd8iEn4lVV6tRikiEUkzTY5Awf5a3l6xizvOHExWWly4w2l3g3sm8OaNU7nq+c+5+vkvmJWTwT++3omv3nL9Sf256bSBJAZW5vz9peO49fRB/OHDPJ79ZAsvLd3GeaP7MDErhfH9kumfFn9YTWVxpZelm4tYsrmQ3G0ljM1M5ifnDmtzEYvKWh/zV+3m/7N33+FRVekDx79nWia9J6QHCL1DpFpgRUUsWEBEV8Wyrm1t67KKu6ur6667+nNta8HeEVEUC1ZQRJFO6CVASCEJ6T2TKef3xwwxgSQEyKTxfp5nnszccu45YTh577mnDOgRxOC4RMG0iwAAIABJREFUoHYbyFprd+JwaQJ85L+JEF1FabWdvlGBHZ0NIYQ4glejCaXUFOApwAi8rLV+9LD9PsCbwCigCJiptc7w7LsPuB5wArdrrb9qcJ4RWAvkaK3P92YZGpo6JIZnl6Vz09vrWHjz+G4ZjMWG+PLBTeO44Y21vLsqkymDenDf1P4khR95k9E7MoD/zhzO7Wf24X/L0vlqax4frMsG3DMHDE8IYURiKNU2Bz/tKWJ7rrvbSoCPiSFxwSxcn83y3QU8eulQzugbeUT6Wmu+2JzHw59tI6+8FoD+PQKZPiqei0fEER7g0+Q5+eU2Cips9IkOOO4uQbllNVz58ipKqur41yVDmDI45rjSAdhTUEmon4UwWZZaCK8rrbYTLN1LhBCdkPLWHM2ewHgXcBaQDawBZmmttzU45hZgqNb6JqXU5cDFWuuZSqmBwHvAaCAW+Bboq7V2es67G0gFgloTdKempuq1a9e2SbmW7yrg2tfXMKlfFPOuGtVtR8jbHE6yimtIiQpo9Tkul2ZvYRUbMktYn1nKhswSduVXYDIaSE0KZXzvcManRDA0LhiT0UBaVil//CCN9IOVzBqdyP3nDai/kdlTUMkDn2xlRXohA2OCmDt1APuKqli4Nou07DJMBsWZA6KYOiSG4qo6duVXsju/gl35FZTXOgCwGA0MSwjmlOQwRvcMY1RSKIHWo/8xziqu5oqXf6Gkyk5imB/bcsuZPiqeBy4Y2Krzwd1K/mnaAd76ZT+bsssA6BXhz8ikUEYlhZKaFErvyCOfBghxOKXUOq11akfnoz0db51td7roc/8S7prclzsm9/FCzoQQomUt1dneDLrHAQ9qrc/xfL4PQGv9rwbHfOU5ZqVSygTkAZHAvQ2PPey4eOAN4BHg7vYOugHe+DmDBz/dyrs3jGVcb1nRsSVVNgdGg2q2xbnW7uSJb3bx0o97iQvx5R8XDWb1vmJe+nEvVrORP53TjyvHJGFsEJzuzKtg4bosFm3IobDSPYtKsK+ZftGB9IkOoF+PQML8LaRllbI6o4QtOWU4XRqDghGJodx9Vl8mpEQ0mZ89BZVc+dIqauxO3rxuNANjg3j6u938b1k6sSG+/HfmcE5JDmu2vFnF1by9aj8L1mRRUm0nJSqAK0YnYnO4WLe/hPWZJfUzvwT7mpk1OpEbT+8lreCiWRJ0t15hpY3Uf3zLQ9MGcfW45LbPmBBCHEVLdbY3+0fEAVkNPmcDY5o7RmvtUEqVAeGe7b8cdm6c5/2TwBygwzrtXT0uidTk0HZfEr4r8j9KFxyr2cjcqQM4e2A093yQxuzX1gBw6ch47j23P5GBR3Yh6dcjkPvPG8icKf3ZdqCcmGArkYE+R/T1Pn9oLADVdQ42ZJayel8xC9dlc+XLqzizfxT3TR3QqCV/e245V72yCoD5N45lQEwQAH88ux8T+0Vy1/tpzHxxJTed0ZvLT0nkQFkN2SU15JTUkFNaTUZRNWsyijEoxVkDorl6XBLjeoc3ypfWmoyiatZmFPP9zgJeXL6Ht1ZmcM34ZH53Wi9Cmwi+y2rsrNxTRGZxFWcN7EHPbjieQIi2UFr96w2tEEJ0Nl2qU7JS6nzgoNZ6nVJq4lGOvRG4ESAxMbGt81EfcK/YXYivxcCopOZbP8XRpSaH8cUdp/Hmyv2MSgptsTX5ELPRwLCEkKMe52cxMSElggkpEdw8sTev/5zB/5amc86Ty7lyTCJ3nNmH7JIarn51Nb5mI+/8bgy9Ixt3qxmV5M7fw59u47nv9/Dc93sa7Y8M9CEuxJdbJ6ZwxZhEYkN8m8yLUoqeEf70jPBnRmoCu/MreOq73Tz/wx7eXLmf2eOTmT0hmYzCKn7cXciPuwtIy3a31AP884sdjOkZxuWjEzh3cIxXprGsc7jIK6slu6Sa7NIajEoR6m8mxM9CqJ+FUD8zQdaut/hIdZ2Dd37J5M1fMugbFcjc8wYc8e8surbSajsAoTJPtxCiE+pS3UuAC4GrAAdgBYKAj7TWv20pL23dveQQh9PFlKd+pKSqjk9um0B8qF+bX0N4R1GljSe/3c27qzPxMxvRQKi/mXdvGEtCWMv/jj+nF5JVUk1ciB9xob7EBFtPOPjdlV/BU9/u5vPNufXbDAqGxIdwep8ITk2JID7Mj0825vD+miz2F1UTaDUxbXgs5w2JJTLQh2BfM0G+JnxMrcuLy6VJL6hk/f4SNmSWsrewkuySGvLLa3EdpVowGhRD44OZ2DeKSf0jGRwb7PUgfEdeOekHKxmVFEpMcNM3NU2prnPw1sr9zFu+l6KqOlKTQtmZV0GN3ck145O5/cw+R7SMaq3ZlF3GO6v2s3THQf54dj9mjW7bm/fWku4lrffNtnx+9+ZaFt82gaHxR78hF0KIttZRfbpNuAdSngnk4B5IeYXWemuDY24FhjQYSHmJ1voypdQg4F1+HUj5HdDn0EBKz7kTgXs6ok93Q+kHK7n4uZ+IC/Fl/o1jZSW0Lib9YAX/+mIHBytszLt61DEFc96wI6+czzflMiAmiPG9w5v8PrlcmlX7ilmwNosvNudic7ga7beaDQT7mgn2NRPqZyE8wFI/e0qon4WS6jo2ZJaSllVKhc096DTEz0zf6EDiQ32JD/Vz/wzxJTbEF437sX1ptZ2S6jpKqu0UVNhYubeITdmlaA0RARbO6BvFGf0i6RnuT2SgDxEBFkzGxksBaK0prqojt6yWnNIabA4Xk/pFtjhAtaDCxv99vZP312ZxqLpKDPNjTM8wxvQKZ0zPMBLC/NBa43BpHE6Nw+Wi1u5i4bpsXvpxL8VVdZzWJ4I7zuxDanIYhZXuNOevySLUz8I9Z/dj5ikJ1NidLN54gHdW7WfrgXJ8zUaSwv3YkVfBPWf35dZJKe02ZeUhEnS33gdrs/jTwk0s/9MkEsOlEUQI0f46JOj2XHgq7j7YRuBVrfUjSqmHgLVa68VKKSvwFjACKAYu11rv9Zx7P3Ad7lbtO7XWSw5LeyKdIOgGdxeT695YQ68If96+YQwRTUxlJ4Q3lFXbWZ9VQnmNnfIaO2U1dsprHZR5AuTSajvF1XWUVNVRUl2HS7tbqfv3CGREYggjEkIZkRhCzwj/4womiyptLN9dwLIdBSzfXVD/eB9AKQjzsxAZ6EOQ1Uxhpa0+0G7I12zkwmGxXDEmkaHxwfX5sDmcvPZTBs8uTafW7mT2+GSmDo1hQ2Ypq/YWsTqjuP56RoOq74JzuNP7RnLHmX0YlRR6xL4tOWU89Nk2Vu8rpleEP/nltVTVOenfI5ArxyQybUQcvmYjcxZuYtGGHGaPT+Zv5w9sdau+1pq07DI+3pDD5pwyFt407ph/zxJ0t97LP+7lH59vZ9ODZ9evKSCEEO2pw4LuzsLbQTe4A+8b3lzD7Wf24ZaJKV69lhDHw+XSlNXY8TEb8LO0/XAOp0uzPbecA6U1FFS650o/WOH+WVZjJzLAh9gQK7EhvsQE+xIX4ovN4eSDtdksTjtAjd3JwJggZo1JJMzPwr+/3EFmcTWTB0Qxd+oAeh3W/9rl0uzMr2DV3iIOVtgwGQ2YDQqT0YDJoDAZFSMSQxl+lH7/h+aDn7d8DylRgVwxJpGRiSGNgmOXS/OPz7fz6k/7uHBYLI/PGIbF1PyCvplF1Xy8MYePN+Swt7AKi8nAWQOiefTSIa2edvIQCbpb77GvdvDCD3tJf+Tcdn8iIYQQIEF3uwTd4J5urpenxVBrLZW+EK1UUWvn440HeHdVZv0iSn2jA/jr+QM5rc+RCyd1BK01z/+wh/98uZPT+0bywm9H4mcx4XC6yCiqYltuBTtyy1m1r5h1+0sAGNsrjItHxDFlcMxxz6ghQXfr3b9oM19uyWPdX8/yQq6EEOLoOmrKwJPOoZkQ9hdVcdf7G3nisuHdcrl4IdpaoNXMVWOT+O2YRNKyy8grq2HygOgj+oR3JKUUt0xMIczPwtxFm7nw2Z+wmg3syq+kztNlxmRQ9OsRyJwp/Zg2PI64ZmaxEd4hq1EKITozCbq9oNLmYF9hFZe9uJJ3fzeGlKgOm1JciC5FKeXuDtKKqSA7yuWjEwn1t/DE17sI9bMwe3wy/XsE0r9HEL2j/Fs9e4xoe6U1dYTIHN1CiE5Kgm4vGBQbzPu/H8cVL63i0udX8sjFg+sXahFCdH3nDOrBOYN6dHQ2xGFKq+30CLJ2dDaEEKJJnefZbTfTNzqQD28eR3KEP7e9u4EFa7KOfpIQQnQxSqkpSqmdSql0pdS9Tey/Wym1TSm1SSn1nVIqyVt5ke4lQojOTIJuL0oK9+fDm8bx1/MHct7QGMC9UIcQQnQHSikj8D/gXGAgMEspNfCwwzYAqVrrocBC4D/eyk9pdZ2sRimE6LQk6PYyk9HA9af2xN/HRK3dyQXPrOBvn2yR4FsI0R2MBtK11nu11nXAfGBawwO01su01tWej78A8d7ISJ3DRVWdU/p0CyE6LQm629nEflG8uXI/Z/7fD7z4wx7Ka+1HP0kIITqnOKBh37lsz7bmXA8saWH/cSutqQMgxF9auoUQnZME3e3Iajby1/MH8v6NY0kO9+dfS3Yw7p/fkX6wsqOzJoQQXqWU+i2QCjzWzP4blVJrlVJrCwoKjjn9Ms/qpNLSLYTorGT2kg4wplc4790YzpacMhanHaCXZy7vxWkHiA22MiopVBbWEUJ0BTlAQoPP8Z5tjSilJgP3A2dorW1NJaS1ngfMA/fiOMeakZJDQbcMpBRCdFISdHegwXHBDI4LBtxLaP/nyx1kl9TQM8KfS0fGcfHIeFlcQwjRma0B+iileuIOti8Hrmh4gFJqBPAiMEVrfdBbGSmtdncvkYGUQojOSrqXdBJGg+KrO0/nselDiQr04fGvd3Hqv5cyb/mejs6aEEI0SWvtAG4DvgK2Awu01luVUg8ppS70HPYYEAB8oJTaqJRa7I28lHpauoOle4kQopOSlu5OxN/HxIzUBGakJpBVXM2H67MZ0zMcgC05ZbyzKpOZpyQwLD5Yup8IIToFrfUXwBeHbftbg/eT2yMfhwZShspASiFEJyVBdyeVEObHnZP71n/ekVfBog3ZvLc6k37RgcxIjeeSkfGEyR8YIYSgtNqOyaDwtxg7OitCCNEk6V7SRUwfFc/q+yfzyMWDsVqM/OPz7VzwzIr6/d/vPMiqvUX1I/iFEOJkUlJtJ8TPLE8BhRCdlrR0dyFBVjNXjkniyjFJ7MyrIC2rtH7fw59tY09BFQAxwVYiA32YkBLBn6f0B+D57/dgNipGJIYwND4Es1Hut4QQ3UdZTR0hMohSCNGJSdDdRfXrEUi/HoH1n9/93Vi255azI6+CXXkVFFXVYWzQ4vPSj3sprnL3efSzGBndM4zLT0lgyuCYds+7EEK0tZIqu8zRLYTo1CTo7iaig6xEB1mZ2C+qyf3r/jKZ4qo6Vu8r5uc9Rfy8p5DMYvfKzAfLa7ntvQ0M6BFI/5gg+nsCej/Lr18Pl0tT63BSU+fEx2wkwEe+OkKIzqO0xi5TrAohOjWJnE4SSinCA3w4d0gM5w5xt267XO71J8pq7DhdmoXrsqmqc3qOh7evH8OElAi+2prH799aV5+WQcHwhBD+eckQ+vcIav/CCCHEYcqq6xgUK/WREKLzkqD7JGYwuLuf9IkO5MObx+NyaXJKa+q7qRxa2S0lKoA7J/fB12zE12KkoMLG8t2FRAT4APD+mky+31nA6J5hpEQF0DsygJhgqwxoEkK0m5Jq6V4ihOjcJOgW9QwGRUKYHwlhfpw9qEf99t6RAY2mLwT449n96t/X1DnZmFXKki159dtC/Mys/8tZGAyK99dksregCqNBYTIaMBsUYQEWrhyTBMBXW/MorLRhMihMBgNBvmZigq31q3UKIURLXC7NeUNjGJEY2tFZEUKIZknQLU7Y7Ak9uWZ8MoWVdewpqGRPQSWl1fb6lvTvdxawdMdBnC6Nw9OlpVekf33Q/cqP+1idUdwozaHxwSy+7VQApj//MzmlNfiajVhMBnxMBkYmhfLABYMA+N+ydAxKERtiJT7Ul9gQX6ICrRg913c4Xbg0uLTGaFAyc4sQ3YzBoHh8xrCOzoYQQrTIq0G3UmoK8BRgBF7WWj962H4f4E1gFFAEzNRaZ3j23QdcDziB27XWXymlEjzHRwMamKe1fsqbZRCto5QiMtCHyEAfxvYKb7Tv+d+Oqn+vtTvwdnqCb4B5V4/C5nDhcGkcThdlNXb0r7uZ1D+KfYVV1Nid1Dlc1DlcWM2/LoDxwdosMoqqG13zkhFxPDFzOAAD//YVdU5X/b5AHxO/HZfEn6f0x+XS3LMwjWBfM34WI34WE34WIyMSQxmeEILD6WJDVimBVhMBPiYCrWYCfEz1Ab0QQgghRGt4LehWShmB/wFnAdnAGqXUYq31tgaHXQ+UaK1TlFKXA/8GZiqlBgKXA4OAWOBbpVRfwAH8UWu9XikVCKxTSn1zWJqiE1NKYTYqGsTMR51b99ZJKS3u//5Pk6i0OcgtrSG7tIYDpTWE+/vU77/zrD71QbzDqSmtqWNgjHvAVbXdyep9xZRV26m2O+tvBm4/sw/DE0Iorqpjxgsrj7jmX84bwA2n9WJPQSVn/3d5o30GBQ9PG8zloxPZklPGzBdXYlAKg0FhNCgMSvGPiwYzZXAP1u0v5tZ3NuDSGpd235Ro4InLhjGxXxQbMkt4Zmk6gVb3zYBSCgX87rReJEf4k5ZVyqINORg9aRsNCqNSXDUuieggK+v2F/PlljzsTk2d04Xd4cLp0sw9bwARAT58sy2fTzbmoDX4mNxde4J9zdxwWk8CrWb2F1Wxr7AKp+dGyenSOLVm6uAYDAbF9zsPsj6zlMpaB1U2B9V2J+H+Fv52/kAMBkV2STVKKaIDfTC18ITB5XLnz2Z34dK6finvjVml5JfXUlHroLzGjt3pIj7Uj/OGugcDr9xThN3pwmRUuH8z7q5NAzz/vltyyjAZFf4W902Tv48Ji8mdD601u/IrKaqyUVJlp6S6DrvTRf8eQYzrHY7TpXl1xT6UAn8fE8G+ZoKsZpIj/IgP9cPp0hRV2rA5XJ6X+6YwKshKXIgvTpemuKoOfx8jvmajjHEQQoiTnDdbukcD6VrrvQBKqfnANKBhgDwNeNDzfiHwrHL/ZZoGzNda24B9Sql0YLTWeiWQC6C1rlBKbQfiDktTnIQCfEz0iQ6kT3TgEftumdh80B7gY2LFn38DuIMwm8NFTZ0Tk9EdIAX5mnnzutFU2hxU1NqpqHVQUetgWEIIAKF+Fm4+o3d9ehqN1tDfE/SF+Vu4fHSiO6j2BKxOl3sBI4BgXwtn9I3EYHDfkBgUaA2xnqnPauxODlbUsqfAQZXN6b6ChktHxZOMP5nF1Xy4Prs+bZcLHC4X5w7pQXSQlV35lbyzKhOz0YDZaMBidAf/tXb3LDWFlTa25ZajgFq7+ylDpc3BdRN6AvDe6ixe+GHPEb+3Xf/ogcWg+H5nAa//nEGAjzuotZoNOLXmwQvdXX/+tWQHn2/KBdw3IwblHjew7J6JANz45lqW7TyI3fnro43+PQL58s7TAXhg8dZGi0ABjO4ZVh903//xZvZ6FoU6ZFK/SF67djQA17+xhvxyW6P9Fw6L5elZIwC44NkV1DlcjfZfNTaJcb3DcWnNI19sP6LsN0/szZ+n9Ke4qo7R//zuiP1zpvTjlokp5JTUcPpjywD3bEALfj+OU5LDjjheCCHEyUHphs/x2zJhpaYDU7TWN3g+XwWM0Vrf1uCYLZ5jsj2f9wBjcAfiv2it3/ZsfwVYorVe2ODcZGA5MFhrXd5SXlJTU/XatWvbrnBCdGMOpwujQaGUIqu4moJKG0alGrWmp0QGYDAo6hwuTAZV33//cOszS9iZV0F+eS1Ol8alNYFWMzd5blTe/mU/OaU1+JgMnv76RiICLEwbHgfAtgPluLQmyGom0GrCbDLgdGmCPbNU7MyroKLW3ihob9jSvWJ3IWU1dqpsDipt7tb43lEBTPVMm/nttnz8fUyE+VsI9TNjNhrwMRvws5jQWlNpc6CByloHZTV2ymrsRAdZ6RnhT63dyYfrs7EYDfiYjZ6fBvpGBxIX4ktZtZ3FaTlU1TmptjmYNSaRmOBjn0daKbVOa516zCd2YVJnCyG6qpbq7C45kFIpFQB8CNzZXMCtlLoRuBEgMTGxHXMnRNfWsBvIodlsmnOoq0ZzRiaGMrKFGSV+OzapxfMHHmXe5Yarsjbl1D4RLe6fPDC62X1KKQKt7uA+yGquf/pwiNVsrB8M3JRgPzNXjUtu8fpCCCFOHt6cxiEHSGjwOd6zrcljlFImIBj3gMpmz1VKmXEH3O9orT9q7uJa63la61StdWpkZOQJFkUIIYQQQojj582gew3QRynVUyllwT0wcvFhxywGrvG8nw4s1e7+LouBy5VSPkqpnkAfYLWnv/crwHat9RNezLsQQgghhBBtxmvdS7TWDqXUbcBXuKcMfFVrvVUp9RCwVmu9GHcA/ZZnoGQx7sAcz3ELcA+QdAC3aq2dSqlTgauAzUqpjZ5LzdVaf+GtcgghhBBCCHGivNqn2xMMf3HYtr81eF8LzGjm3EeARw7btgKQebeEEEIIIUSXIkvzCSGEEEII4WUSdAshhBBCCOFlEnQLIYQQQgjhZRJ0CyGEEEII4WUSdAshhBBCCOFlEnQLIYQQQgjhZRJ0CyGEEEII4WUSdAshhBBCCOFlEnQLIYQQQgjhZRJ0CyGEEEII4WUSdAshhBBCCOFlEnQLIYQQQgjhZRJ0CyGEEEII4WUSdAshhBBCCOFlEnQLIYQQQgjhZRJ0CyGEEEII4WUSdAshhBBCCOFlEnQLIYQQQgjhZRJ0CyGEEEII4WUSdAshhBBCCOFlEnQLIYQQQgjhZRJ0CyGEEEII4WVeDbqVUlOUUjuVUulKqXub2O+jlHrfs3+VUiq5wb77PNt3KqXOaW2aQggh2s+J1PNCCHEy8VrQrZQyAv8DzgUGArOUUgMPO+x6oERrnQL8F/i359yBwOXAIGAK8JxSytjKNIUQQrSDE6nnhRDiZOPNlu7RQLrWeq/Wug6YD0w77JhpwBue9wuBM5VSyrN9vtbaprXeB6R70mtNmkIIIdrHidTzQghxUvFm0B0HZDX4nO3Z1uQxWmsHUAaEt3Bua9IUQgjRPk6knhdCiJOKqaMz4C1KqRuBGz0fK5VSO48jmQigsO1y1WmdLOUEKWt31Z3LmtTRGWgPUmcfMylr93OylBO6d1mbrbO9GXTnAAkNPsd7tjV1TLZSygQEA0VHOfdoaQKgtZ4HzDvezAMopdZqrVNPJI2u4GQpJ0hZu6uTqaydzInU841InX1spKzdz8lSTji5ytqQN7uXrAH6KKV6KqUsuAdGLj7smMXANZ7304GlWmvt2X65Z9R7T6APsLqVaQohhGgfJ1LPCyHEScVrLd1aa4dS6jbgK8AIvKq13qqUeghYq7VeDLwCvKWUSgeKcVfYeI5bAGwDHMCtWmsnQFNpeqsMQgghmnci9bwQQpxslDQ4NE8pdaPnkWe3drKUE6Ss3dXJVFbRvJPpeyBl7X5OlnLCyVXWhiToFkIIIYQQwstkGXghhBBCCCG8TILuJnTnpeaVUq8qpQ4qpbY02BamlPpGKbXb8zO0I/PYVpRSCUqpZUqpbUqprUqpOzzbu115lVJWpdRqpVSap6x/92zv6Vl6O92zFLelo/PaFjwr1G5QSn3m+dwtyylaR+rsrl+HgdTZnu3dsi6TOttNgu7DqO6/1PzrwJTDtt0LfKe17gN85/ncHTiAP2qtBwJjgVs9/5bdsbw24Dda62HAcGCKUmos7iW3/+tZgrsE95Lc3cEdwPYGn7trOcVRSJ3dbeowkDpb6uxuToLuI3Xrpea11stxzyDQUMNlmt8ALmrXTHmJ1jpXa73e874C93/4OLphebVbpeej2fPSwG9wL70N3aSsSql44DzgZc9nRTcsp2g1qbO7yfdd6myps7s7CbqPdDIuNR+ttc71vM8DojsyM96glEoGRgCr6Kbl9Ty+2wgcBL4B9gClnqW3oft8l58E5gAuz+dwumc5RetInd1N6rCGpM4Gus93WepsDwm6RSOeRSu61ZQ2SqkA4EPgTq11ecN93am8Wmun1no47lUBRwP9OzhLbU4pdT5wUGu9rqPzIkRn0J3qsEOkzu4+pM5uzJvLwHdVrVnWuLvJV0rFaK1zlVIxuO+6uwWllBl35f2O1vojz+ZuW14ArXWpUmoZMA4IUUqZPC0K3eG7PAG4UCk1FbACQcBTdL9yitaTOrsb1WFSZ3e7ukzq7AakpftIJ+NS8w2Xab4G+KQD89JmPP3GXgG2a62faLCr25VXKRWplArxvPcFzsLdH3IZ7qW3oRuUVWt9n9Y6XmudjPv/5lKt9ZV0s3KKYyJ1djf5vkudLXV2dyeL4zTBc0f2JL8ua/xIB2epzSil3gMmAhFAPvAA8DGwAEgE9gOXaa0PH7jT5SilTgV+BDbza1+yubj7CHar8iqlhuIejGLEfTO9QGv9kFKqF+6BZWHABuC3Wmtbx+W07SilJgL3aK3P787lFEcndXbXr8NA6myps7tHOVsiQbcQQgghhBBeJt1LhBBCCCGE8DIJuoUQQgghhPAyCbqFEEIIIYTwMgm6hRBCCCGE8DIJuoUQQgghhPAyCbqF8FBKOZVSGxu87m3DtJOVUlvaKj0hhDjZSZ0tuhpZkVKIX9V4luQVQgjR+UmdLboUaekW4iiUUhlKqf8opTYrpVYrpVI825OVUkuVUpuUUt8ppRI926OVUouUUmme13hPUkal1EtKqa1Kqa89q5AJIYRoQ1Jni85Kgm4hfuV72KPKmQ32lWmthwDP4l75DuAZ4A2t9VDgHeBpz/angR+01sOAkcBWz/Y+wP+01oOAUuBSL5dHCCG6M6nRxgVIAAAgAElEQVSzRZciK1IK4aGUqtRaBzSxPQP4jdZ6r1LKDORprcOVUoVAjNba7tmeq7WOUEoVAPENl7RVSiUD32it+3g+/xkwa63/4f2SCSFE9yN1tuhqpKVbiNbRzbw/FrYG753ImAohhPAWqbNFpyNBtxCtM7PBz5We9z8Dl3veXwn86Hn/HXAzgFLKqJQKbq9MCiGEAKTOFp2Q3LUJ8StfpdTGBp+/1FofmoIqVCm1CXfLxyzPtj8Aryml/gQUANd6tt8BzFNKXY+7deRmINfruRdCiJOL1NmiS5E+3UIchad/YKrWurCj8yKEEKJlUmeLzkq6lwghhBBCCOFl0tIthBBCCCGEl0lLtxBCCCGEEF4mQbcQQgghhBBeJkG3EEIIIYQQXiZBtxBCCCGEEF4mQbcQQgghhBBeJkG3EEIIIYQQXiZBtxBCCCGEEF4mQbcQQgghhBBeJkG3EEIIIYQQXiZBtxBCCCGEEF4mQbcQQgghhBBeJkG3EEIIIYQQXiZBtxBCCCGEEF4mQbcQQgghhBBe1qmCbqXUq0qpg0qpLc3sV0qpp5VS6UqpTUqpke2dRyGEEG5SZwshROt1qqAbeB2Y0sL+c4E+nteNwPPtkCchhBBNex2ps4UQolU6VdCttV4OFLdwyDTgTe32CxCilIppn9wJIYRoSOpsIYRovU4VdLdCHJDV4HO2Z5sQQojOR+psIYTwMHV0BrxFKXUj7seZ+Pv7j+rfv38H50gIIY7dunXrCrXWkR2dD2+TOlsI0R20VGd3taA7B0ho8Dnes+0IWut5wDyA1NRUvXbtWu/nTggh2phSan9H5+EESJ0thDiptFRnd7XuJYuBqz0j4scCZVrr3I7OlBBCiCZJnS2EEB6dqqVbKfUeMBGIUEplAw8AZgCt9QvAF8BUIB2oBq7tmJwK0Tpaa1buKWJIfDCBVvNxp1PncLFqXxETekdgMKjjTqegwobWmqgg63GnAbArv4LcslpMBoXR8wr2NdM3OvCE0m1JSVUdG7NKGRQXRFSglcJKG5uzywDo2yOQuBBfr11bNE3qbCGEaL1OFXRrrWcdZb8Gbm2n7AhxQiptDu77aDOfph3gstR4/jN92HGn9czS3TyzNJ2V9/2GmODjCy6/3JLLPR9sIjbEyld3no5Sxxe8V9kcXPf6GrJLahptH9srjPk3jjuuNFvjrgUb+X5nAS9eNYpzBvVgc04Z176+BgBfs5GHLxrM9FHxXrt+Q/nltUQF+qCUosrmwN+nU1Wl7UbqbCGEaL2T8y+FEF62M6+Cm99ZR0ZhFRNSwvnL+QOPO63qOgdv/bKfgTFBxAT7orVmT0EVKVEBx5SOw6XxMRnYlV/J1gPlDI4LPq78+PuY+OTWCezIq8BsNOBwuXC6dH1L/qbsUvwsRlKi2q7Ve0deOd/vLOD6U3sytmc4ACMTQ/n41gnYnS7+7+ud3PNBGr/sLeI/lw49oacBR+Nwurj0+Z8Z3zucyQOimbtoM0/OHMGpfSK8dk0hhOgK7HY72dnZ1NbWdnRWvM5qtRIfH4/Z3Pqn2BJ0C9HGtuSUMf2FnwnwMfP2DWMY39sdjNkcTqptTkL9LceU3sJ12ZRW23n56lQA3lmVyd8/3crcqQOYPT65xRbrfYVVbD1QxvlDYzl/aCzjeoUz5p/f8WnageMKur/emscZ/SIJD/BhQorPEfttDic3vLGWqCAfFt0yAbOxbYaNzFu+Fz+LkT/8JoVgP3cFF+xrZnhCCADv3DCWp77bjc3h9GrADfDl1jyyS2o4c0A0vSL9CfWzcNWrq7hrcl9um5Ti9esLIURnlZ2dTWBgIMnJLf9t6uq01hQVFZGdnU3Pnj1bfV5XG0gpRKc3ICaIa8Yn88Xtp9YH3C6X5rIXVvLHD9JwP3FvHadL8/KP+xiRGMKopFAALhgayxl9I/n7p9u47d0NVNTamzx3cdoBzn/6Rx76dBs1dU4AwgN8OK1PBN9szz+mfACs2F3IjW+t49UVGc0e42My8tC0QWzJKefZpenHlH5zcstqWLzxAJelJhDi1/QNi9GguPusvtw7xT3N3Lr9xSxYk3XMZTwarTUvLd9Lzwh/Jg+IJiUqkE9um8BFw+N44ptdzH59DcVVdW16TSGE6Cpqa2sJDw/v1gE3gFKK8PDwY27Rl5Zu0aUdLK/lue/3YHM4sTlc7pfdxezxya1+3L9sx0E2ZJZw11l9W11RfL4pl4c/29Zom9VsYMFN44gKtHLfuQMa7TMYFNOGx/HQZ9v4cH1Oq/se7y+qorrOyX2n9arPW7CfmXlXpTLvx7089tVOlu8q4C/nD2DmKYnsKajkypdW4dKagxU2RiWF8sysEfhajPVpPjRtMKH+lmOqFMtr7cxZmEbvSH+unZDc4rFTBsdw8Yg4nl2WzuQB0QyJP75uLIdYTUZuOqM3M09JOOqxh8r0zqpMPlqfw2Nf78To2TYyKYTnrhwFwGUvrCSzuLrRuT2Crbxx3WiCfZt/VLh6XzFp2WX846LBGD0t2n4WE09cNoxTksN48NOtLNtxkEtHxTN/dSZPfru7/twXrxrFME/LvBBCdFfdPeA+5HjKKUG36NIqbQ4WbcjBYjLgU/8yUlXnaNX5WcXV/OG9DVTaHEQG+nDVuOQWj6+1O7GajfQI9uGMvo3nvlcKsktqiApsemaQ2eOTWbIll79/upVTUyLoEXz0GUR6RQbw072TMBkaP5QyGBQ3ndGbUUmhfLQ+h7gQPwD8Lab6fPWK9Oe6U3se0cUjIczvqNc93D8+20ZeeS0f3jweq9l41OMfvGAQK/cUcfeCjXz6h1NbdU5zQv0t3HNOv2M657HpwxgSF8yO3Ir6bb0i/evfn9IzlJ4R/o3OmTUmscWAG+D9NVmE+Vu4dGTjmyalFFeMSeS0PhHEh7oHusaF+jb6jhwtbSGEECemqKiIM888E4C8vDyMRiORke56ePXq1VgszXfvXLt2LW+++SZPP/201/Kn2vrxa2ckCy10P+szS+gZ7t9i/+g6h4v9RVX0aWEauxveWMPKPUUMig0mLbuUz28/tdkBgHsKKrl83i/897Lhxz1oLqOwiilPLWd87wheuSa1xTvlokobwb5mTG3UL7qhpTvyeXVFBq9de8pR+10v23GQa19fwy0TezNnSutXCfxhVwFpWaXcPLH3cfft/nxTLhaTgckDotql9cTp0ny26QAXDI1tsm+2zeEk/WAlg2JPrPX+WCil1mmtU9vtgp2A1NlCdE3bt29nwIABRz+wHTz44IMEBARwzz331G9zOByYTG3X3txUeVuqs6VPt+hyCips3PDGWu5esLHF4/784SZmvbSKkhb62D5wwSCeuWIEz145gmnDYwn3P3JwIIDd6eLu9zdid7roG31ss4Y0lBzhz5xz+nOwopbympZb4+cu2sxFz/3U5v2SAexOzYr0Qn5KLzzqsTEhVqYNj+WOyX2O6Rpn9I3k9jP7YDYajqsMdQ4XD3+2jVdX7Gu3x5Vfb83jjvkbeeuXIxcU01rjYzK2a8AthBDixMyePZubbrqJMWPGMGfOHFavXs24ceMYMWIE48ePZ+fOnQB8//33nH/++YA7YL/uuuuYOHEivXr1arPWb+leIroUrTX3friJSpuDuVNbvpv+3Wm9+GzTAf76yRaevWJko33ltXYCfUwkhPnVd7c4NI+21vqIIO/ZpemkZZfx3JUjT3hhmdnjk7lqXFJ9MNpUQLm3oJKvt+Xzh0kpXgk4J/aLJNBqYnHaASb2i2rx2P49gnjq8hHHfa2f0gt5/Oud9TONHHLP2f1anN/6s00HyCuv5V+XDjnuax+rKYN7MLFfJP9asp0JKRH10zIWVNiY+eJKHpo2WKYGFEKIVvj7p1vZdqC8TdMcGBvEAxcMOubzsrOz+fnnnzEajZSXl/Pjjz9iMpn49ttvmTt3Lh9++OER5+zYsYNly5ZRUVFBv379uPnmm49pesCmSEu36FLmr8niux0HuXdK/xa7jYD7P+edk/vy2aZcPk07UL/d4XRxzaur+eOCtCPOyS2r4eLnfmblnqL6bRuzSnl2WToXj4hj6pCYEy6DwaAwGw3YHE4ue3El76/JPKIl+JUV+zAbDUftY368fExGpgzqwddb86m1O5s8ZnHaAe6cv4HqVvaPb47VbOBguY2F67IbvWwOFwBv/bKftRnFjc7RWjNv+V76RQcy8bC+896klOI/lw7FajZy9wL3kw2At1ZmsK+oitiQE7vhEkII0f5mzJiB0egeW1RWVsaMGTMYPHgwd911F1u3bm3ynPPOOw8fHx8iIiKIiooiPz//hPMhLd2iy8gorOLhz7YxISWc2eOTW3XO70/vxdfb8vnrJ1sY0yuMqEAr837cy4bM0ibTCPY1U1Zj5+4FG/nyjtMJ9jOzbMdBogN9ePDCY7+7bkmVzYnJYODPH25m6Y6DPHrJUEL9LRRVugPUS0bEERnYdHeXtnDh8Fg+WJfNsh0HOfewm4ms4mruX7SZPlEBWE6wT/mopDB+uvc3Te6zOZy8tmIfGUVV3DYphT94uqMs313IjrwKHps+tN1HwkcFWfnnxUO45Z31PLs0nZvO6M2bv+znrAHR9Io8/q5FQghxMjmeFmlv8ff/deD8X//6VyZNmsSiRYvIyMhg4sSJTZ7j4/Pr31+j0YjDcWINUCAt3aIL8fcxceaAaB6bPqzVC5CYjAb+b8YwksL9Ka9xsCOvnP9+s4upQ3pw4bDYI473s5h4cuZwCips3P/xZrTW3HVWX76447Q2n30izN/COzeM4b5z+7N0x0GmPLWcFbsL+XjjAWwOFzec1voJ94/HuF7hTB3S44hyOZwu7np/I1rDU5eP8MpAzkN8TEYW/+FULhkZz9NL05nxwkoyCquw2Z2kJoUybXic167dkqlDYrh5Ym9O6xPBwnVZlFbbufH0Xh2SFyGEEG2nrKyMuDj335bXX3+9Xa8tQbfwKpdL43Id2yC6KpuD0uq6Rq+aOieRgT48M2sEsSG+x5ReSlQAH98ynsQwP+56P41gXzMPTxvcbAvqsIQQ7pzch8825fLUd+55lptblOVEGQyK35/Rm0W3TCDQauaRL7ZzzbgkFt0yvk2XUW+KyWjguStHMT6lcR/lZ5els3Z/CY9cPPi4phc8VgE+Jh6fMYz/XTGSvQWVXPjsCsb0CmfhzeOxmDquivrzlP6MSAzl5RWNFycSQgjRdc2ZM4f77ruPESNGtEnr9bGQKQOF16zeV8w9H6SRGObH69ee0uoW07ve38iiDTmNtg2JC+alq1NbNbd1c7JLqrn6ldXce25/zh7Uo8VjnS7N1a+uosrmZNEt49uli0NNnZOiKhvxod4PdBsqqLBRVmMnJSqAilo7Zzz2PRP7RvLEzOHtmg9w96lfva+4w1q4D+dyaX7YVYCvxcjYXuEdkgeZMlAI0VV0pikD28OxThkofbpFm7M5nPz3m928uHwP4f4+/LK3iPWZpYzuGdbsOTV1Toqr64gL8eWiEXEMPWwVQ6vZSJDviX1dA3xM/H3aIE7rc/SBeUaD4q3rxuBsZnYRb/C1GIm3tG/ArbVm+gs/0zPCn9evHU2g1cxnfziVQGvHVA0xwb6dJuAG95OISf1bnt1FCCGEaA0JukWbe/Lb3bzwwx5mjU7gL+cNpLDSRlK4f7PHa62Zu2gzy3cVsPSeiZzRN/KI1R7bQoifpVUB9yEGg8JA917OVinFuYNjeOnHvXyyMYcLhsYec/cdIYQQQhyd9OkWbcLl0hRV2gD3jCGvzT6Ff10yFH8fU33A/c22fPYWVB5x7turMlm0IYdrxifLUtkd4MJhsThdmjvmb2Rxg6kVhRBCCNF2pKW7i6q0OThYXovzsEGKvSIDMBoUBRU2Sqsbr8Sogb6eua1X7iliS04ZpTV1lFbbKa2xg4b/XeleROb29zbwxebcRufHhFj5cY576rfrXl/D8l0F9fscLs2QuGA+umU8IX6WIx7JV9oc3PvhJiICfFh063j8LO6v3obMEh76dCuT+kVy26SUE//FiGM2ICaQwXFBBFnNXNDEjC5CCCGEOHESdHeQjMIqft5TRIlndo7iKjul1XU8eOEgEsL8+HprHm+udC9FrdFU2ZyUVtfxwU3jiQz04aXle+tn1mgo7YGzCfY18/KPe3lx+d4j9u/551SMBsVnmw7wzqpMDMo9N3Won4WIgF/npJzUP5L4UF8admcOtP7aCn3u4B4MiGk8u8aAmCBMzUzlF+Bj4snLh3P1q6uZ+9Fm/jtzOMVVddzyznqig6z8d+bwVk8DKNqWUoqFN43HoBRG+TcQQgghvEKC7naitWb1vmKSI/yJDrKyMauUuYs2A+BrNhLqZybEz4LLM5uMw6WpabBSYKDVvWS5xr3/nEE9SI7ww3zYjCC+ZveKS9OGxzHksMGICuVZ+VAxZ0p//nxufwIspiaD3YtHxLdYnhmpCcf2CwBO6xPJ3ZP78n/f7GJkUiiXjoxnXO9wrh3f02tT8onWsXq+N0IIIYTwDgm6vazW7uSTjTm89lMGO/IquP03Kdx9dj8mD4xm5X2/IdTP0mTAM3VITItLjg+MDWJgbNBx7++ovtO3TkphY1YpD3+2jdP7RPLEZe0/LZ0QQgghup9JkyZx7733cs4559Rve/LJJ9m5cyfPP//8EcdPnDiRxx9/nNTU9pmVVYJuL9Fa8/aqTP7v652UVtvp3yOQRy8ZUj8dWoCPiQCfk+/XbzAonrhsON/tyCcpvH2nxxNCCCFE9zVr1izmz5/fKOieP38+//nPfzowV7+S2Uu8aNXeIgbFBvHe78ay5I7TuHx0Ir4WeYwf7GfmkpHx7Tb/tRBCCCG6v+nTp/P5559TV+eeSCIjI4MDBw7w3nvvkZqayqBBg3jggQc6LH8nX1Orl+0vqgIgKdyfx2cMw2I0dOoBguW1dr7fWcB5Q2JkEJ0QQggh2szMF1cese38oTFcNS6Zmjons19bfcT+6aPimZGaQHFVHTe/va7Rvvd/P67F64WFhTF69GiWLFnCtGnTmD9/Ppdddhlz584lLCwMp9PJmWeeyaZNmxg6dOiJFe44SNDdhr7bns+d729kYEwQ7/9+XKcfnKa15s75G1m64yAOp4tLRrY8eLItfZp2gNgQK6OSml+lUpx8SqvrePzrnWzOLuPRS4cyIKb5cQltqdbuZO5Hm7FajAyNC2ZIfDB9owOPGKistaag0kZmUTW5ZbUAmAwKg0HV/4wM8GFwXHBTlxFCCOFlh7qYHAq6X3nlFRYsWMC8efNwOBzk5uaybds2Cbq7KqdL89S3u3h6aTqD44J4fMawDslHcVUdL/+4lwVrs7j7rH5cMSaxxeNf/SmDpTsOEuBj4pml6Vw4LBaT8dh7HNmdLv70QRoFlTae/+0ogqwtD9J8c2UGf/tkK0rB7PHJzDmnv3S7Ocm5XJoP1mXx6JIdlNc6CLSauPi5n/j3pUPbZVn4p7/bzUcbcgj0MfHuqkwALCYDA2OC6BcdSHF1HZlF1WQWVzeaVag5105IZu7UAUcE7UIIcTJpqWXa12JscX+Yv+WoLdtNmTZtGnfddRfr16+nurqasLAwHn/8cdasWUNoaCizZ8+mtrb2mNNtCxJ0n6DS6jpue3cDK9ILmTEqnocvGtzuLdwFFTZe+nEvb/+ynxq7k4RQP+7/eDNh/mamDG56BpTN2WU8umQ7kwdEM31UPDe9vY7FaQeOubXb4XRxx/wNfLE5D6NBMfvV1bx5/ZhmB4l+sTmXBxZvZfKAKOJCfHntpwyW7TjI4zOGkZosrd4dweXSLN1xELPJwGkpESfUHcrhdOHSoBQYlELhft9S//0tOWX85eMtbMwq5ZTkUB6aNphwfwu3vbuBO+ZvZENmKfef13QA63JpVmcUU1JVR89If5LD/Y/5/9/23HLmLd/LpSPjeWz6UDKLq9mUU8bm7FI2ZZfxzfZ8wv0tJIX7MSElgqRwPxLD/YgL8cWg3NN7Oj0vh0vzadoBXvspg60HyvnfFSOJDPQ5eiaEEEK0iYCAACZNmsR1113HrFmzKC8vx9/fn+DgYPLz81myZAkTJ07skLxJ0H2CrGYj1XUOHr1kCDNPSWizwYEFFTY2ef7ob8kpA9wrQsYE+xLr+Rnsa+aDtdm8u3o/dQ4XFw6L5bbfpBAX4seVL//C7fM38uZ1Fsb2Cm+UdkWtndveW09EgA+PTR9KiJ+ZgTFBx9za7XC6uGtBGl9szuOv5w8kNtjKbe9t4NrXVvP6taPxPyzwXrmniDvnb2RkYijPzBqJr8XIOYN7MGfhJma8uJLrJ/TknnP6eeWmpcrm4NElO/hqax4JYX70jvSnd2SA+xUVQEKo73G18nd1azOKefjz7aRllQLQK8Kfayckc+mo+PpVQ1ujrMbOc9+n8/pPGdgcriP2Gz3dLqKDrfQI8qFHkJXoYCvZJTW8tzqTcH8LT1w2jItHxNX/H3rnd2P41xc7ePWnfWzJKeO5K0cSFWRFa8223HI+2XiAxRsPkFf+a4uFUhAb7EuvSH96Rfhz8ch4hieENJtvp0tz70ebCfI185fzBmAwKJIj/EmO8OfC41ydc2RiKMPiQ7j3o01c8MwKnv/tSEYkhh5XWkIIIY7drFmzuPjii5k/fz79+/dnxIgR9O/fn4SEBCZMmNBh+VJa66Mf1cWlpqbqtWvXtll6TpfmjZ8zmJEaT6DVjMulj7l1UGtNSbWd3LIa8stryS2rJa+slp15FWzOKavvL2pQkBIVgEEpcstqKauxN0rHaFBcNDyOWyf1pldkQP32kqo6Zry4kvyyWhbcNK6+b6zWmjvf38inaQeYf+M4Rvd0ty5/tTWP37+1jicuG9aq1m6nS3PPB2ks2pDDfef25/dn9Abgs00HuGP+RkYlhfL6tafUB27bc8u57IWVRAdbWXjTuEaL4VTZHPxryXbe/iWzPug7pWcYfaMC22QQ6s97CpmzcBM5pTWcPTCa0mo7ewqqKKy01R+TGObH29ePIbGDpzG0OZx8uSUPX7ORpHB/EsP8vNL1Zn9RFf/+cgdfbM4jOsiHe87uh8Vk4NUV+0jLLiPIamLWmESuGZdMbIhvs+nYnS7eXZXJk9/uorTGzoXDYukbHYjWGpcGl9ZoDXVOFwUVNvLL3d/zvLJaKmwODAquHpfMXWf1bXbu+MVpB/jzwk0EWE1clhrPV1vzST9YicmgmNgvkmnD4+gZ4c++wir2FlSxt7CSfYVVpB+sxKU1b1w7mjGH3Xge8vpP+3jw0208OXM4F41o224sWw+UcdPb68gvs/HghYOO2t2rOUqpdVrr9plEtpNo6zpbCNE+tm/fzoABAzo6G+2mqfK2VGdL0H2MCips3Pn+Bn5KL+LhiwZz1dikY07jiW928cIPe6g7rEXQoNyzngyND2ZIXDDDEkIYGBPUqMW4us5BblktuaW1HKyoZVRSKEnh/k1eJ6e0hkuf+xmX1nx483gSwvxYsDaLOQs3cfdZfbn9zD71x2qtOe/pFdTYnXxz1+kttvq6XJo5H25i4bps/nROP26dlNJo/ycbc7jr/Y2M6RnOq7NPobDSxqXP/4xBKT66ZXyzQdxP6YXcv2gzGUXVAARZTaQmh5GaHEpqUhi9I/0J87e0+mlCdZ2Dfy/ZwRsr95Mc7ndEF5ayajt7CivZmVfBv7/cgZ/ZyHs3jm3299ke5ixMY8Ha7EbbIgN9SArzY3BcMHOm9GtVC/TCddl8tD6biAAfegRbiQo89NPKt9vzef2nDIwGxe/P6MWNp/eqT1NrzfrMEl5dkcGSLbkopRieEMLg2CAGxQYzKC6IPlGBmI2Kb7cf5F9LtrO3oIpxvcK5/7wBxzSAsMrmwO50tWo10p15Fdz09jr2FVYxOjmMaSNimTo4hlD/5s8trLRx+bxfOFBaw1vXjz5i0O6B0hrOeuIHRiWH8ca1p3hlCsvS6jpun7+R5bsKuPyUBP4+bRA+pmO7iZKgWwjRVUjQLUF3m1XgWcXVXPL8z5TX2Hl42mBmpB77XNNpWaVc9NxPTOwbyel9I4kJttIj2JceQVYiAixt3sVhV34F05//mYgAHx69dCjXvLqaYQnBvHPD2COmCGxNa7fLpZm7aDPz12Rx1+S+3DG5T5PHLdqQzd0L0hjfO5zcsloKK2wsvHk8faMDW8yv1pqs4hrWZBSzdn8xazJKSD9YWb/fajYQF+JLXKi7T21ciJXwAB9C/SyE+VsI8zcT5u/DzrwK/vzhJrJKqrl2fE/+dE6/FluMtx4o48qXV+FrNvLe78aSHNH6wNvmcJJbWsuB0hrsLo3ZqPAxGbAYjVhMBnzNRhLCfI/6XZn//+zdd3xUVf7/8ddJD2mQkFASegu9BRAQBey9YcFesa6udXV1V9fd/W1R96vuuopiW117xbYoiFjovYTeQ0gghUzaJJPk/P5IISGFBDKZyeT9fDx4LHPvnXs/CdnjJ2c+53OW7uHhT9Zx28l9OGtIZ3ZnFbAnM5/dmQXszipg+a4spibGMfOapAbbO85NTueWt5bTrUP5rH2aw1njFzxjYNqoBB44YwCdIkPqvU9KdgHvLNnD8l3ZbEjNIb+4fAFhkL8fnaKC2ZtVSO/YMH571kBOGRjn9r7rxSVl5DpdxIQ3vkb6gMPJ5S8vJiO3iLduHldVamKt5eY3l7Nweybf3nsS3aLd9wlHaZnl/77bwjfr9/PZnROJOMpC4yMp6RaR1kJJt5LuZhvAX/9lJ3/4IpnZd01kWEL9daL1KS2zXPTvX0jLcTLv/pOb/B/fY7VsVxZXz1pCUUkZ0WFBfHPPpDqTraPNdjucLn77yTq+XLufX03ty/2nD2jwuR+tSOHBj9YQ5O/H2zePY8wxLpTMzCti9d5D7MkqYF92IfsOVfzJLiQzv7je93WPbsdT04bVW1pwpORUB1fNWkxwQAkvJsAAACAASURBVPmMd686Eu+DuUV8vDKFdfty2JddSOqhQg7kFtVxt5rOGNyJ564YWW+9+rqUHC55aSHjekXzxg1j60yq31y4i8dnb+C68T144vzBdSa5G1JzuPSlRfSNC+f9GeMJDfLHWktOoYu0itKOhA6h9I1r+JefI5WVWXZl5rMh1cH61By2pedx8oBYpo/t7vUdOvbnFHL5zMUcKijmnVtOYEh8FF+t3c+d76zk0bMHcstJvVskjsLi0mMqFVLSLSKthZLuhsdsLaRsgp0Z+XRoF8jQY+zB++7SPaxNyeG5K0a0WMINMKZnNP+6chSPfLKOpy4dVu/spjGGe07tx61v1e5ksmJ3Nve8t4r9OU5+c2Yit5189ERl2ugEOoYHER4ccFydSWLCgzllYKc6zzldpWQXFJOVX0x2vovM/CKy84sxxnBpUtMWAw7qGsm7M07gyleWcPnMRbw74wT6xIZjrWXRjkz+u2QP325Iw1Vq6RHTjoQOoUweEEt8+3Z0bR9CfPtQggP9KCopw1VqKS4po7ikjM1pDv45fxvXvLqEWdeOIapdzX/7QwXF3Pb2CjqGBfHcFSPrncW+bkJP9mYVMOvnnXSPCeOmE3vVOJ/ucHLTG8uJCg1k1rVJVQmeMYb27YJo3y6IxM7H1vfaz8/QOzac3rHhnHeMCww9pUtUKO/cMo7LZy7m6leXMPPq0Tw+ewND4iO5YWLPFotDbTFFpC2w1raJHaePZdJaM91NYK3lUIGrwTrS+mTmFTH1mQUM6hLJO7eM88gPZGP+j3DkbLcxhhfmb+O5eVvp2j6E564YySgf78SwOS2XK19ZjJ+f4foJPfl4RQo7MvKJCg1k2ugEpo/tTt+48KPfqJov16Zy3/tr6BHTjjdvHFtV115WZrnxzWUs3JbJB7eNb7DTRuX1d/x3JXOS03jxqtGcOaQzUF6/ftnMRew8mM+Ht01gUNeW2VSmNdmTWcBlMxeR5nDi72f4/M6JrWITG810i0hrsXPnTiIiIoiJifHpxNtaS2ZmJrm5ufTqVXMCTOUlXjCAP/TRGj5ZuY9v7plEv6PUNXtaZW33g2cMYMHmgyzdlcWFI7ryxwuHtOgMvSdtTc9l+itLyMgrYlT39lw1rgfnDOtyXO0MF27P4Nb/rCAsOID/3DSW/p0ieHbuFp6du5U/XTiEqxu5KNfpKmX6K4tJTnXw3owTGJbQntveXsG8jenMui6JqYl1fyog5Z9WXfvaEi4emcC9p/X3dDiNoqRbRFoLl8tFSkqKxzafaUkhISEkJCQQGFgzL1LS3QwD+L5DhTw9ZzMzTurd5K2pV+zO4pIXF3HrSb155Gzvr3WqnO1O3u8gLMifP100hItGttwW8d4iLceJw+k66uLPpkhOdXD960txukqZcVJvnvluCxeNjOeZS4c3aVYgI6+Ii/+9kPyiEqYmxvHhihSeOG8Q10/sdfQ3t3Gt7aNPJd0iIq1HQ2O2d6+A8iLr9+Xw6ap9OBuxBXR1JaVlPPbZBjpHhtRo0efNjDE8ecFgzh3Wha/vmdQmE26AzlEhzZpwQ3nd+Cd3TKBjRDBPf7uFAZ0i+POFQ5ucBHYMD+aNG8ZQai0frkjhuvE9lHA3UmtKuEVExHdoIWUjbU7LBWhyEvb24t1s3F++HfSROzR6s/L+2NqW3R0SOrTj49smMPPHHVw1rvsxL7DrHRvOf24cyw+bD3LH5D7NHKWIiIg0p9aTBXrY5rRcesS0a1LifMDh5JlvtzCpX0fOHtrZjdFJa9MhLIiHz0o87vsMS2h/TO0rRUREpGUp6W6kTWkOBhxllruopJRVew6xcHsmi7dnsmpvNkC9PZVFREREpG1Q0t0IpWWW4AD/etuL7crI53efr2fpziyKSsrwMzAkPoobJ/bi7KFd6BPbtPZyIiIiIuJbvC7pNsacCTwH+AOzrLV/PeJ8d+BNoH3FNQ9ba792Z0z+foav75lU7/nn521lxe5srhrXg/F9YhjbK5qo0LbRWk9E2jZvHLOleblKy1i8I5NJ/WI9HUq9rLV8sXY/n6/aR6m1vHJtEoH+fry1aBfzNh2ocW2An2HWdWMAeOXHHfyyPaPG+fDgAP515SgA/vX9Vpbvzq5xvmN4ME9fOhyAp+ZsYkOqo8b5+Pah/PmioQD88ctkth/Mq3G+b2w4j507CIBHP13HvkOFAESGBHL3KX2PumNwWZnl7SW7+f6Ir+tXU/syukc0JaVlFJeWNWlzuLr8d8luvktOr3HMzxheu778e/fqzzv5aetBAEID/bnt5D4MP8peE0eTV1TCXe+srHX84lEJnD+8Kxl5RTzw4Zpa568c253TB3cmJbuAxz5bX+v8jRN7cVL/WLYdyONPXyXXOn/H5L6M7RXN+n05PP3tZgAuHd2Nc4Z1Oa6v50helXQbY/yBF4DTgBRgmTFmtrW2+nfoMeADa+2LxphBwNdAzxYPtoLTVcqcDWmcP7wrvz9vkKfCEBFpca1xzJamWb8vh4c+WsvGNAdz7zuZPrHh7M7Mp0dMmKdDq5LucPLYZ+v5Ljmd7tHt6FBt19+C4lKy84trXB/gf7hxW15RSa3zJaWHWynn1nE+sPr7nbXPV9/PwlHoqnXeEeGq+ntOtfMrd2fzv/Vp/PHCwVw+pnu9X++mtFwen72BPrHhhFVbiF9cUh73qz/v5O0lu/nbxcOY0LdjvfepT2mZxd/PUFjH9656qWxBte/NuuxC5mxI466p/biviXsgbDuQy4rd2Vw+pjtl1tZ6JoCzuLxzXH3ni0rKys+X0eD5krKyOs8XV5x3lR4+39RudY3hVX26jTHjgSestWdUvH4EwFr7l2rXzAR2WGv/VnH9M9baCQ3d93h7vv5z3lZW7T3Eq9cl1arN/mrtfu58ZyXv3DKOCX2a/sMtItIQb+7T7a1jthy/opJS/jlvGy8u2E6HdkH86cIhnDmkM+v35XDBC79wyah4Hj1nkMc/1d2fU8gZ//cjRSVlPHD6AG48sRf+fq1zDdXB3CKemL2BGyb2rNU9rKS0jEXVPm1Ym3KIofFRda4XW7Ijk998vJZdmQVcOa47j5yV2OiN7T5btY+3Fu9m1rVJTdp92+F08ZevN9IzJoxbT25cN62S0jJm/riD5+ZuJTI0kAUPTm5VXd7q09CY7W1fXTywt9rrFGDcEdc8AXxrjPkVEAac6u6glu7KIruguM4f7s9X7yMuIphxvWLcHYaIiLfxyjFbjk9pmeWSFxeyfp+DS0Yl8LtzB9K+XXkC1jcunNtO7s1LC3bww+aD/PmioZw2qOV3wS0sLiU0yJ8uUaHcMqk35w7vSq+O3jP7fixiI4J54apRVa///FUyoYH+nDaoM49+to61KTn879eTSOwc2WDXqnG9Y/jmnpP4x3ebefXnnczfdIB/XDaC8X0azlO2pufyyCfrGBofRURI09LDyJBA/nLxMConcr9et5+lO7N48IwBdSbSyakOHvp4Dev3OTh7aGf+cP4Qn0i4j6Y1foXTgTestc9UzJq8ZYwZYq0tq36RMWYGMAOge/f6P6ZpjE1puZxURy1bToGLHzYf5JrxPVrtb9YiIm7W4mN2W/TNuv388/tt/PWSoQxLaM+cDWn89ZtNta575drR9I2L4LNV+3hu3tZa59++eRzx7UO5cmwPurQPYcqAuBrnQwL9efCMRM4a0oUHPlzDLf9ZzvnDu/L89JEATH95MWmO8i3ApwyI4/7T+x81mVqz9xC/n70BR6GrxvG/TxvGmJ7RLNhykCdmb6hxLrugmI9um0DfuHB+1Uo2nmuKsjJLZn4xn6zcx/PfbyMmLIgXrhx11C5qlUKD/Hn0nEGcPbQLD3+8jqCAhnOU/KISbv/vSsKC/fnnlSNrlOA0ReXk5Oa0XN5YuIvPVu+jQ8UvbCO7t+cfl42grMxyy3+WU1RSyotXjeKsoc1bN+3NvC3p3gd0q/Y6oeJYdTcBZwJYaxcZY0KAjkCNFQXW2peBl6H8o8pjDSgrv5iDuUUkdq79g/6/DfspLi3jghFdj/X2IiKtmdeN2W3Nwdwifv/5er5Zn0bfuPCqxXMd2gUxtI6OW8EB5TXAMeF1nw+qSLauHNfwLz5D4qOYfdeJvP7LTkrKDv9zJXaJIDYimILiEl5fuJNvk9N49vIRDW621jEimNKyslrxhFck61GhgbXORYQEEBLou5tq+/kZ/nHZCM4d1oXFO7K47eQ+RDeh3KPSyO4d+OaeSfhVTAw+O3cLA7tEcsbgw3uHWGt59NN1bD+Yx9s3jaNTZMhxx3/vaf2Z2Lcj7y3bU1UjX9nJzc/PcM34Hlye1K1JJSy+wNuS7mVAP2NML8oH7iuAK4+4Zg9wCvCGMWYgEAIcdFdAm9LKVyUndqmddH++OpVeHcPqHLhERNoArxuz25LPVu3jiS82UFBUykNnDmDGpN5VM5Rje0Uztlf9ie6kfrHH3Y0kKMCvVv3u4+cNrvr78l1ZPPLJOoICaifH8zam8/W6NJ6+dBjx7UP54q4T693PYkS39lUz6W3N1MROTE08vvKdyoS7qKSUuRvTeXbuVs4d1oU/nD+YmPBgDhW4WL33EPedWp4oN5eGfgZva2Tdt6/xqqTbWltijLkLmEN5a6nXrLUbjDFPAsuttbOB+4FXjDH3Aha43rpxNWigvx+T+nUksXNkjePpDieLdmRy99R+2vhGRNokbxyz25K1KTn07hjG36cNO2qbOU9I6hnNnF+fVJX0PT1nMwM6RzBvYzqfrU5lQKcIsvKLiQkP1n9HW0BwgD+f3jGRmQu28/y8bfyyLYMnzh/M+cO78uXdk2gX6H/0m8hx8aruJe7ijpXws37awZ++2si8+0/W5jci4jbe3L3EXdS9pG7WWt5ftpc+ceGM6RlNUUkpAX5+rWJNUWFxKRf9+xc2peUS4Ge4c0pf7pzSt85ZcHG/rem5PPjRWlylZbw344RGdzeRo2tN3Uu8jqu0rEZPzkqz16QyND5KCbeIiLjd3qwCHv5kLb9sy+SypATG9Iyuqs9uDUKD/Jl914l8uGIvo3t0qPXpsbSsfp0i+Pj2CXy2ah+ZecVKuluIku4GlJVZRj35HTNO6l1jdfSOg3msTcnhsXMGejA6ERHxdWVllrcW7+Zv/9uEnzH8+aIhTG9g4xRvFhTgx1Xjeng6DKng72e4ZHSCp8NoU5R0N2BvdgG5RSXERQbXOD57TSrGwLnD1LVEREQax+kq5WBuEVDeei82ovy/LfsOFVJWVrPUMzTIn47hwXyxNpXHZ2/g5P6x/L+LhxLfPrTF4xaR5qGkuwGb0nIBGFDtYzBrLbNXp3JCrxg6Rx1/Wx0REfF9325I49HP1lcl3acOjGPWdWMAuOBfv5CRV1Tj+gtGdOW5K0Zy7rCuFRukdNJiQ5FWTkl3Azbtz8UY6N/pcN32+n0OdmTkc8tJvT0YmYiItBbPfLuZf36/jYFdInng9P74+/nRpdqkzePnDaKopMZeQXTrUD6j7e9nOL1aT2URab2UdDdgc7qD7tHtqjYbgPJt3wP9DWcN0SAoIiJ1s9ZSUmYJ9Pfj9EGdCfT34/bJfepcmH/ecJUqirQFSrobcEpiJ8ZW20WrtMzyxdpUTu4fR/t2bWsXJRERqclaW1XysXG/o6pEpMzCfxfvJjosiL9eMoyhCVEMTdAmaiJtnZLuBhy5qnfF7mzSHUU8do5mJURE2rK5yenM/HE7b944lnZBAfz7h+18sSa16nxwgB/3n96/RmIuIm2bku565BS6yHW6iG8fWjVg7s7MB2B4QntPhiYiIh6SlV/MH77YwOerU0nsHEFGbjHdYwL49an9uG784XZ43aLb0SlSi+1F5DAl3fX4dkMaD360tsaOkw5nCQBRoWoiLyLSllhr+XpdGr//fD05hS5+fWo/7ph8eEfFPrHhEOvhIEXEqynprsfmtFyCA/zoGRNWdSyn0AVAeIi+bSIibYm18ObCXXRtH8rbN49jYBftqCgiTaPssR6b03Pp3ykCf7/DtXiOQhcRIQE1jomIiO/z8zO8ePUookIDCaijA4mIyNFo5KjHprRcBnSOqHHMUegiMkSlJSIibckTszcwf/MBYsKDlXCLyDHT6FGHzLwiDuYWkXhk0u10Eal6bhGRNmNvVgFvLNzF9gN5ng5FRFo5lZfUISTQn39OH8mQ+Jp9VR2FJUSF6lsmItJWzN2YDsApAzt5OBIRae2UQdYhLDigzh3Ccgpd9Ihp54GIRETEE+ZuTKdvXDi9OoYd/WIRkQaovKQJHE6X2gWKiLQROYUuluzI4lTNcotIM1DS3QQ5harpFhFpK9JynPTrFMFpg+I8HYqI+ACVlzSSq7SMguJSdS8REWkjBnSO4Jt7Jnk6DBHxEZrpbqTcqt0o9XuKiIivKyktw+kq9XQYIuJDlHQ3UuVulCovERHxfUt3ZjHqj9+xak+2p0MRER+hpLuRHBVJtxZSioj4vu82plNaZmttkiYicqyUdDeSZrpFRNoGay1zN6ZzYt+OtAtSSaFIa1dQXMLlMxfx3yW7PRqHRpNGcjgrkm4tpBQR8Wlb0vPYm1XIHZP7ejoUEWkG/1ufxpKdWSzZmYW1cPUJPTwSh5LuRnIUVi6kVNItIuLLqnahTFSrQBFf8NGKFLpHt6NfXDiPfbaeAD/DFWO7t3gcSrob6XB5ib5lIiK+7NSBnYgICSAuMsTToYhIPe7/YA3dokP59an9G7wuJbuARTsy+fUp/bltcm9m/GcFj3y6Dn8/w6VJ3Voo2nKq6W4kh9NFoL8hNNDf06GIiIgbDegcwbXje3o6DBGpx75DhXy8MoV//7Cdg7lFDV776cp9WAsXj4onOMCfmdeM5sS+HXno47V8uiqlhSIup6S7kXIKXUSGBGKM8XQoIiLiJst2ZfHD5gOUlVlPh+Jzysos//fdFpJTHZ4ORVq5r9amAuUbF765cFe911lr+WhlCuN7x9Atuh0AIYH+vHxNEif0iuH+D9Ywe01qS4QMKOluNIe2gBcR8Xkv/bCdxz5bj+ZXmt97y/by3LytPPjRGv1SI8fly7X7GZYQxemDOvGfRbvIKyqp87rlu7PZnVnAtNEJNY6HBvnz6vVJJPWM5t73V/NdcnoLRK2ku9EczhIl3SIiPqyguISft2Vw6sBO+lSzkTalOdh+MO+o12XlF/P3OZvoGB7EhlQH36xPa4HopCUUFpfyxZpU1qXkUNoCv0ztyshnbUoO5w3rym0n98HhLOG9pXvqvPaj5SmEBflz1tDOtc61Cwrg9evHMKRrJL96dyVr9h5yd+hKuhurvLxEiyhFRHzVou2ZFJWUcerATp4OpVUoKinl6llLuOiFX9iTWdDgtX/7ZhN5zhLevnkc/TuF88x3mykpLWuhSN2nrMzyl683tkjC5o2y8ou5ctZifvXuKs7718+MfPJbbn5zOa/+vJON+x1u+UTjq3X7AThnWBdGdu/A2F7RvPrzTlxH/DwVFJfw1br9nD20S7399sOCA5h13RhiI4K56c1l7M1q+Of4eCnpbqTcQpfaBYqI+LAVu7MJ8DMk9ezg6VBahS/W7CcjrxhnSRm3vr2CwuLSOq9bsTub95fv5aYTe5HYOZL7Tx/AjoP5fLJy3zE911rLgVzn8YTebJbtymLmjzv41burKCiuu8TBWy3Zkcm5//yJJ79IPqYZ6r1ZBVzy4kI2pDp45tLhPHfFCM4e2oUt6bn88ctkznruJ8b9ZR6v/Lij3p+NY/HFmlSSenSga/tQAG4/uQ/7c5zMXl2zNnvOhjTyikq45IjSkiPFRgTzxg1jcZVarnt9KYcKipst1iMp6W6kHNV0i4j4tE1puQzqGkmIulQdlbWW137eSf9O4bx09Sg2pTl49NN1WFszeSspLeN3n62nc2QId5/SD4DTB3VieEIUz87dQlFJ05OxNxbu4oT/N4/NabnN8rUcj49WpBAc4MeerAKenrOl2e9/MLeIn7YebNZ75hS6eOSTtVz+8mL2ZRfy2i87uf3tFThdjf+3WL8vh4v+vZCs/GL+e/M4LhmdwAUj4vnrJcP48aEp/PLwVJ6+dDgDOkXw5683Munv83nt551NekZdth3IZVNaLucO61J1bPKAWAZ0imDmj9tr/Px9tCKFbtGhjO0ZfdT79okN55Vrk0jJKuSW/yw/7jjro6S7Eay1OJwu7UYpIuLDZl2bxOvXj/F0GK3Ckp1ZJO93cOPEXkxN7MSvT+nPJ6v28dbimttsv714N8n7Hfzu3EGEBZd/xG+M4cEzEknNcfLOkrprceuT7nDyzLdbKLPw4fK9zfb1HIv8ovLyhQtHxHPNCT14feFOVuzOarb7p+U4mfbSQq55dSkr92Q36j2frdrHo5+uY/aa1FqfBlhr+Xrdfk79xwLeX7aXGSf15peHp/L4eYP4bmM6V81aQnb+0Wd5F2w5yOUzFxEc4MfHt49nTB1JbXz7UKaNTuDtm8fxwa3j6RcXzpNfJnPyU/N5a9GuY/plC8o/XfEzcHa1pNsYw60n92ZLeh7zNx8AylsKLtyeySWjEvDza9z6jLG9onnmsuEs25XNAx+6Z7GvipQbwekqw1VqVV4iIuLD/PwMMeHBng6jVXjt5510aBfIhSPjAfjV1L6sTTnEk18kM7hrJKN7RHMwt4hnvt3CpH4dOfuIhWwT+8YwvncML8zfxmVJ3aoS8qP501cbKS4tY2T39ny2OpXfnJVIoL9n5g//tz6NguJSLhmdwKCukXy/6QAPfbSWr+6edNyflhxwOJn+ymIy84rp0C6Qp+ds5p1bTmjwPWk5Th7+ZC1FJWX8t+KXmb5x4YzvHcOYXtHMXp3K3I3pDO4aWb6AMD4KgBsm9qJTZAi/fn81l7y0kDdvGFvVXq+60jLLxytS+O2n6+jXKYI3bhhDp0ZsIDW2VzTvzjiBhdsz+L/vtvC7zzfw56831vnvNqJbe165NqnO75+1li/WpjKuVwxxETWfe97wrjw9ZzMvLdjB1MROfLoyBWvhklENl5Yc6bzhXUk9VMhfvtlEQod2PHxWYpPefzSa6W4E7UYpIuLb/rd+Pw9/vLbV1eV6wp7MAr7bmM6V47pXJUd+foZ/XD6C+A6h3P72Sg7kOvnL1xspKinjD+cPrtUNxhjDA2cMICOvmDca6LNc3S/bMvhiTSq3ndyHOyf3JSOviB+3NG/pRVNUbi0+pmcHwoMD+MvFQ9l+MJ/n5209rvseyC1PuA84nLx54xjuPqUfC7dn8vPWjAbf99SczZSVwfz7J/P5nRN5+KxE4tuH8vHKFO5+dxU/bzvIo2cP5PM7J1Yl3JXOHtqFt24cS0ZuERe/uJANqTmUlVk2pTl4/ZedzPjPckb98Tse+ngt43pH8/6tJzQq4a5uQp+OfHDreN66aSxXjevBtNEJNf6cM7QLP23N4O//21zn+zfuz2XHwXzOG9611rlAfz9umtSbpTuzWLknm49WpHBC7+g6f3k4mhkn9ebqE7rz0oLtzf7zpSyyERzO8qRbM90iIr7p+00H+DY5nb9cPNTToXi9Nxftwt8YrjmhZ43jUaGBvHT1aC7+90KufGUJ2w7kcdeUvvSODa/zPqN7dODUgXG8tGA7V43rTvt2QfU+s7ikjN9/vp5u0aHcMbkP/n6GmLAgPlqRwilH6TazcHsGv2zL4O5T+hEc0Dz1+pVbi993Wv+qXyhO6h/LpaMTmPnjDs4e2qVWYtsYGXlFXPXKElIPOXnzxrGM7hHNkPgoXvlxB0/N2cTEvhPrbGe5LiWHj1emcOvJvenZMQyA4d3ac9vJfXCVlrF+Xw5dokLpHFV/ojyudwwf3T6B615bymUvLSIk0J/MinKTbtGhnDG4ExP7duSsIV0ICji2OVtjDJP6xTKpX2yd54MD/Hjtl52c1L8jkwfE1Tj3xdpU/P0MZw6p3f4P4Iox3Xh+3lYe+GANuzILuHNK32OO8YnzBjOiWwdO7NvxmO5RH810N0LVTLdqukVEfNKqPYcY2a29+nMfRa7TxfvL9nLOsC51JnADu0Ty10uGsu1AHvHtQ4+a+Nx/+gDyikqY+eOOBq979eedbD+Yzx/OH0xIoD+B/n5cODKeuRvTG6xDLiop5cEP1/LC/O1c/9qyqkm041XZeeXiUfE1jj92ziBiwoJ44MM1FJc0rSViVn4xV89awt7sAl67fgxje5XXSgcH+PPrU/uzJiWHb+vYxMVay5++SiY6LKjO73egvx8ju3doMOGu1L9TBJ/eMZGT+sdy8oBYnpo2jJ9/M4WfHprK36cN54IR8ceccDfGI2cPpH+ncB74cC0ZeYe3d7fW8uXaVE7s25HosLp/OQsLDuDa8T3YkZFPuyB/zh7apc7rGiPA349poxtfD95YSrobwVGomW4REV/lcLrYdjCPkd3VKvBoPlqRQl5RCTdM7FXvNReMiOe5K0bwyrVJhAY1PLM8sEsk5w/vyuu/7GRnRn6d1+w7VMjz87Zy2qBOTE08PKs9bXQCrlLb4Dbe7yzZw75DhVw/oSfLdmVx2UuLSMs5vnaD1lo+WpHChD4xJHSoWb4Q1S6QP180lE1puby0YHvV9dn5xaxNOcTX6/bz2s87mfXTjlp/rpq1hJ0Z+bx63RjG94mpcd+LR8XTu2MYz3y7uVZ7v++S01myM4t7T+vfLJODnaNCePHq0fzjshFcmtSt1tfoTiGB/jx3xUgcThe/+WhtVTeSNSk57M0qrNG1pC7XTehJSKAf5wzt0uh1Ai3J+yLyQpW/GatloIiI71m7NwdrYWT39p4OxauVllneWLiLUd3bM6Jbw9+rC0bEN3i+untP7c93yemc/n8LuHxMN+6c0pcuUaFV5//4RTIWy+/PHVTjfQO7RDK4ayQfrUjhugk9a903v6iEF+ZvY3zvGB4/bxCnDIzjtrdWcPG/f+HNG8fSr1NEo2OsbtmuNLoBawAAIABJREFUbPZkFfDrU/vVef60QZ04f3hX/vn9Vr5et5+U7MJ6tymvLizIn1euTWJiHSUNAf5+3Hd6f+56ZxWz1+zjopHlCwSLS8r4yzeb6BsXzvQx3Y7p6/E2A7tE8vCZiTz5ZTJvLd7NteN78uWaVIL8/Th9cN2lJZU6hgfz1d2TiIvwzgXRSrobIaegsrxE3y4REV+TX1xCr45hDEtoO0l3aZllf05hk2Yxv990gN2ZBTx4xoBmjaVnxzDm3ncyL8zfxvvL9vLB8hSuHNudOyb3IXm/g/9tSOOB0/vXuShu2ugE/vBFMpvTchnQuWYS/cbCXWTkFfPytQOqaonfv3U8N7yxjEteXMis6w6XcDTFRyv2EhbkX29tMcDj5w0iu6CY4AA/TugdQ0KHULpFtyOhQyhdo0IJ8K9dthAU4NdgzfnZQ7owuOt2/u+7rZwztCtBAX68vXg3OzPyef36MQR4qIuLO9wwsScLthzkz19tZGyvaL5cu5+T+sc2quKgTz1rCLyBV/0LGWPONMZsNsZsM8Y8XM81lxljko0xG4wx77REXA5n+W+omukWEanJW8ftpjhjcGfmPzC5zZQQlpVZ7vtgNSf+bT7n/fNn3lq0q2pyqSGv/byTrlEhnHmU2cZj0bV9KH++aCjf3z+Zi0bE89bi3Uz6+3zu/2ANvTuGcctJvet83wUj4gn0N3y8MqXG8ZwCFy8t2M6pAzsxqlrZ0JD4KD65fQIdI4K5+tUlvLRgOwu2HGTHwbxG9Y4uKC7hq7UNby0OEBMezFs3jWPWdWN44vzB3DypN2cM7szgrlF0CAsiIiSw1p+jLfL08yvv+LInq4APlu/lUEExz83byqR+HZk8oO6Fia2VMYanLx1OeHAAV89aSprDyXnDj71G21t4zdStMcYfeAE4DUgBlhljZltrk6td0w94BJhorc02xsTVfbfmlVPool2Qv8d6gYqIeCNvHrcbq7JmtDUtoLTWcqjARUp2IXuzC0jJLiAlu5BcZwl3TO5z1LKJp7/dzOerU7loZDyb0nL53ecb+ONXGzljcGcuS0pgQp+O+B+xgGzjfgeLdmTy8FmJbp1R7Rbdjr9NG8YdU/rw/LxtfL1uP89PH1lvQhodFsTUxDg+WbmPh84YUBXbSz9uJ6+ohPtP71/nMz6+bQIz3lrOX7/ZVONcp8hgunVox5TEOG6e1KvWc+dsSCO/uJRpR9la3F0m949lTM8OPD9vK8n7HTicLn579sBW9fPbWLERwTx16TBufGM5IYF+nHqULjWtgdck3cBYYJu1dgeAMeY94AIgudo1twAvWGuzAay1B1oiMEehq83MgIiINIHXjtuNtTuzgGkvLeLpS4fValHmjbam53Lda0tJPWIxYGRIAGUW5m1M59Xrx9S5SyCULyz89w/buXJcd/584RCMMazfl8OHy/fy2epUvliTSlCAHwFHJN0lpZbQQH+uaKG64R4xYTxz2XCevnTYURPKS0YlMGdDOj9uPcjUxE4cyHXy+i87OX94VwZ2iazzPR3Cgvjg1vGkOZzszSokJbug6n+3H8zjqTmb+WzVPv56yVBG9zj8vTzcm7vpZSnNoXI3z8tmLuKdJXu4Yky3er9GXzA1sVNVOZM3LoxsKm/6CuKB6nu6pgDjjrimP4Ax5hfAH3jCWvs/dwemLeBFROrkteN2Y63ee4iMvKJaO9x5owMOJ9e/vgxXmeWxcwaS0KEd3aJDSejQjqjQQPZmFXDda0u5atYSnr9iBGcOqflx/PxNB/jd5+uZMiCWJ6ttWDMkPooh8VE8cvZA5m5MZ83eQ3U+f3SPDg320naHxszgTkmMq+rZPTWxEy98vw1XqeXeU2vPch957y5RoXSJCq1V2z1/0wEe+2w9015axNXjevDQmQPIKXSxcHsmvz6lf7O3kmuKsb2imTIglqU7s7ivjpl8X3Os/ba9kTcl3Y0RAPQDJgMJwI/GmKHW2lojhDFmBjADoHv37sf10JxCl3ajFBE5No0at5tzzG6KVXuyaRfkT/9O3rv4Cso7cdz45jKyC4p5f8Z4hibU3nilW3Q7Prp9Aje/uYzb/7uSJ84bXNXVY/2+HO58ZyUDu0TwrytH1VkiEhLoz7nDunLusNo7/nmzQH8/LhgRz9uLd7N+Xw7vLN3DZUndqjaJORZTEuP49t6TeObbLbyxcCffJaczLCEKa2v35vaEF68eTVZ+cav4ZVEO86ZMch9Q/XOrhIpj1aUAS6y1LmCnMWYL5YP5siNvZq19GXgZICkpyR55vikchSV0ba8fbBGRIzTbuN2cY3ZTrNp7iGEJUW6rU053OFm0PZNF2zNZvDOTA46iOq+b1K8jT5w/mK7tQ2udKykt41fvriI51cGr142pM+GuFB0WxH9vPoG731vF47M3sD/HyVXjunPDG8vo0C6I164b4xMf0x9p2ugEXvtlJ9e/vhRjDPecUnc7v6YICw7g9+cN4oIRXfnNx2v5Njn9mLcWb24hgf51/qyId/Om/+ctA/oZY3pRPmhfAVx5xDWfAdOB140xHSn/2LLhbayaQU6hi8TOx9bPU0TEh3ntuN0YTlcpyamOejtjHKsNqTm8s2QPi3ZksuNg+YYvkSEBjOsdw+mDOtUqmSgoLuHjFfs47R8LePCMAVwzvmfVQkZrLY/P3sD3mw7w54uGMCXx6HXnoUH+vHT1aB6fvZ6XFmznrUW78PMzvHPzOOIifXMCaVDXSAZ1iSR5v4NbJvVq1O6LjTW8W3u++NWJfLIyRRsoyXHxmqTbWltijLkLmEN53d9r1toNxpgngeXW2tkV5043xiQDpcCD1tpMd8fmcLrULlBE5AjePG43htNVyrXjezKlGRdQrtidzbWvLgHKa2+nj+nO+D4xDOwSWasjSHW3ntSH3366jie+SObzNan85eKhJHaO5KUFO/jvkj3cPrkPV43r0eg4/P0Mf7xgCF2iQnnlpx38+6pRx7wZTGtx/cSePPPtZm6f3Pw1wIH+flw+puXKnsQ3mcp2Sb4sKSnJLl++/JjeW1Zm6fPo1/xqaj/uO833FyyIiHcxxqyw1iZ5Oo6WdDxjtiet3nuIq2ctITYimPdmnECnJs4qW2v5fHUqT36ZjKPQxdlDuzB7TSrnD+/Ks5ePOObFe9Zan2wpJ+KNGhqz3TbTbYyZCDwB9Kh4jgGstbZ5P8dzs1xnCdZqN0oREV+zJ7OAzlEhBAUcfz33upQcrnl1CdFhQbxzy7gmJ9xQ3k3jwpHxnNQ/lj99lcwnK/cxtlc0T1067Li6ZSjhFvEO7swkXwXuBVZQ/pFiq+Rwlu/SpT7dIiK+Zfori0nq2YHnrhh5XPdZvy+Hq19dQlRoIO/OOIEuUce3wC06LIh/XDaCW0/qQ/fodkfdqVBEWgd3Jt051tpv3Hj/FpFTWJ50q6ZbRMR3pDuc7DtUyI0JvY7rPhv3O7jm1SWEBwfw7i0nEN+MHSUGaAG/iE9xZ9I93xjzFPAJUNUjyVq70o3PbHaOQs10i4j4mlV7ytuEj+ze/pjvsWJ3Nrf8ZznBAf68c8s4r2glJyLey51Jd+WuZNWLyS0w1Y3PbHaV5SXakVJExHes2ptNoL9h0DFsob1+Xw7Pzt3C3I0H6BwZwrszTqBHzLFvxCIibYPbkm5r7RR33bslHS4v0UJKERFfsXrPIQZ1jSIksPH10hv3O3h27hbmbEgnKjSQB88YwHUTehLug5vNiEjzc2f3kijgceCkikMLgCettTnueqY7OApLAJWXiIj4kvtPH4DT1bg1/rsz8/n7/zbz1br9RAQH8OtT+3Hjib30CaiINIk7fz1/DVgPXFbx+hrgdeBiNz6z2TmcLvwMhAVpJkNEfI8xJpfy0r9apyhv89r0+otWYGyv6KNeU1Jaxqyfd/Ls3C34G8Ovpvbl5hN7E9VOybaINJ07M8k+1tpLqr3+gzFmtRuf5xY5heW7UR5Pj1QREW9lrW1zLTJ+2HyAQH8/JvbtWO81a1MO8fDH60je7+D0QZ148oIhzbq1uIi0Pe5MuguNMSdaa3+Gqs1yCt34PLdwFLr0EaKI+CxjTINTvtbarJaKpaU88+0WggLqTroLikt45tstvP7LTjqGB/PS1aM4c0gXD0QpIr7GnUn37cCbFbXdBsgCrnfj89yifKZbpSUi4rNWUF5eUtfHeRZoVbsIH83+nELW7cvhN2cm1jq3J7OAK2ctJiW7kKvGdec3ZyVq0kVEmo07u5esBoYbYyIrXjvc9Sx3cjhLtIhSRHyWtfb4dodpZeZtPADAaYPiap175rvNZOUX8+Ft4xnT8+g13yIiTdHsSbcx5mpr7dvGmPuOOA6AtfYfzf1Md3IUuoiLCPd0GCIibmeM6QD0A6qKl621P3ououY3d2M6PWPa0Se25ri+42AeX6xJ5ZZJvZVwi4hbuGOmu3KHAJ9YnJNT6NJMt4j4PGPMzcA9QAKwGjgBWEQr29CsISWlZWxOy+WcoV2qJoIqvTB/O0EBftw8yaeqaUTEizR70m2tnVnxv39o7nt7gsNZ3r1ERMTH3QOMARZba6cYYxKB/+fhmJpVgL8fP/9mKoVH9OfenZnPZ6v3cf2EnsRGBHsoOhHxdX7uurEx5u/GmEhjTKAxZp4x5qAx5mp3Pc8dikpKcbrKiAzRQkoR8XlOa60TwBgTbK3dBAzwcEzNzt/P1NpB8t/zt+PvZ7j1JM1yi4j7uC3pBk6vWDx5LrAL6As86MbnNTvtRikibUiKMaY98BnwnTHmc2C3h2NqNiWlZZz/r5/5dFVKjeN7swr4eGUK08d0Iy5SfbhFxH3cOYVbee9zgA+ttTlH1tB5O4fTBaDyEhHxedbaiyr++oQxZj4QBfzPgyE1q5V7DrE2JYcgf/8ax19csB0/Y7htch8PRSYibYU7Z7q/NMZsAkYD84wxsYDTjc9rdjmFSrpFpG0wxpxgjIkAsNYuAH4ARno0qGY0d2M6gf6Gk/of3hAn9VAhHy7fy6VJCXSJCvVgdCLSFrgt6bbWPgxMAJKstS4gH7jAXc9zB0dl0q3NEUTE970I5FV7nVdxzCfMTU7nhN4xRFQbz2cu2I61cLtmuUWkBbijT/dUa+33xpiLqx2rfsknzf1Md6mc6Y7SjpQi4vuMtdZWvrDWlhljfGLw234wjx0Z+dwwsWfVsXSHk3eX7eWSUQkkdGjnueBEpM1wx4B6MvA9cF4d5yytKOl2OMsXUqq8RETagB3GmLs5PLt9B7DDg/E0q4tHxXPKwE5Vr2cu2EFpmeWOKZrlFpGW4Y4+3Y9X/O8NzX3vlqbyEhFpQ24Dngceo3yCZB4ww6MRNZM+seH847IRVa+z8ot5Z+luLhwRT4+YsAbeKSLSfNzZp/v/VbSfqnzdwRjzJ3c9zx0chS6CA/wICfQ/+sUiIq2YtfaAtfYKa22ctbaTtfZKa+0BT8d1vBxOF5vTcqlWOcMv2zJwusq4ZnwPD0YmIm2NO7uXnGWtPVT5wlqbDZztxuc1O+1GKSJthTGmf8VGZusrXg8zxjzm6biO17cb0jnj2R9J3u+oOrZidzYhgX4M7hrpwchEpK1xZ9Ltb4yp2k/XGBMKtKr9dXMKXdqNUkTaileARwAXgLV2LXCFRyNqBnOT0+kcGcKgLocT7FV7shme0J5Af3f+J1BEpCZ3jjj/pbw/903GmJuA74A33fi8ZucoLNFulCLSVrSz1i494liJRyJpJkUlpfy49SCnDoqr6qLldJWyIdXBqB4dPBydiLQ1bpvGtdb+zRizBji14tAfrbVz3PU8d3A4XUSHBXk6DBGRlpBhjOlD+SJKjDHTgP2eDen4pB5yUlBcyshuhxPstSk5lJRZRndX0i0iLcvdtRMbgRJr7VxjTDtjTIS1NtfNz2w2OYUuenXUynYRaRPuBF4GEo0x+4CdwFWeDen4ZOQVARAbcbiyccXubABGdm9f53tERNzFnd1LbgE+AmZWHIoHPnPX89zBUehSu0ARaROstTustacCsUAi5XsunOjZqI5P/04RvH3TOIYlRFUdW7knm14dw4gJb1VLjETEB7izpvtOYCLgALDWbgXi3Pi8ZmWtxeEsIVK7UYqIDzPGRBpjHjHG/MsYcxpQAFwHbAMu82x0xycqNJAT+3WkfbvyMkFrLSt3Z2uWW0Q8wp0ZZZG1trhy8UrFdsK24bd4j/ziUkrLrBZSioivewvIBhYBtwCPAga4yFq72pOBHa/Vew+RluPkzCGdAdiTVUBmfjGjtYhSRDzAnUn3AmPMb4HQitmTO4Av3Pi8ZqXdKEWkjehtrR0KYIyZRfniye7WWqdnwzp+Hyzfy5z1aVVJd2U99ygtohQRD3BneclvgIPAOuBW4GvKtxduFXIqkm7NdIuIj3NV/sVaWwqk+ELCDZCRW0THarXbK/dkEx4cQP9OER6MSkTaKrfMdBtj/IEN1tpEyjdcaHWqZrqVdIuIbxtujKncrtFQ/umko+Lv1lrbardtzMgromPE4bavK3YfYkS39vj7GQ9GJSJtlVtmuitmSzYbY7q74/4tIUflJSLSBlhr/a21kRV/Iqy1AdX+3moTboCMvOKqme68ohI2p2lTHBHxHHfWdHcANhhjlgL5lQettee78ZnNxuEs34hN5SUiIq1TRt7h8pI1ew9RZmGUOpeIiIe4M+n+nRvv7XaHy0vUMlBEpLWx1jL7rom0Cyofw1dWbYqjmW4R8YxmzyiNMSHAbUBfyhdRvmqtLWnu57hbZXlJhMpLRERaHWMMfeMOL5hcsSebfnHh+vRSRDzGHTXdbwJJlCfcZwHPuOEZbudwuogIDtCCGxGRViglu4DXft7JAYeTsjLLqj2H1J9bRDzKHUn3IGvt1dbamcA0YFJT3myMOdMYs9kYs80Y83AD111ijLHGmKTjDbguOYUudS4RETkKbxmzj7Qh1cGTXyZzILeIHRn55BS61J9bRDzKHUl39Z6vTSorqWg1+ALlM+SDgOnGmEF1XBcB3AMsOb5Q6+coLFHSLSLSAG8as4+UkVcEQEx4UFU9tzqXiIgnuSPpHm6McVT8yQWGVf69Wi/Y+owFtllrd1hri4H3gAvquO6PwN8At23g4HC6iAzRIkoRkQZ4zZh9pIzcYgBiwoJZuSebqNBAencMa6nHi4jU0uxJ93H2fI0H9lZ7nVJxrIoxZhTQzVr7VTOHXoOj0KUFNyIiDfOaMftIGXlFRIUGEhTgx4rd2Yzq3h4/rdEREQ9y5zbwzc4Y4wf8A7i/EdfOMMYsN8YsP3jwYJOf5VBNt4jIcWnJMftI5T26g8gpdLH1QJ7quUXE47ytfmIf0K3a64SKY5UigCHAD8YYgM7AbGPM+dba5dVvZK19GXgZICkpyTY1EIezRLtRiog0zGvG7CM9delw8pwlrNpTXs+tziUi4mneNtO9DOhnjOlljAkCrgBmV5601uZYaztaa3taa3sCi4Fag/fxKiktI6+oROUlIiIN84oxuy7hwQF0jgph5Z5D+BkY3k07UYqIZ3lV0l3R7eQuYA6wEfjAWrvBGPOkMabFto/PrdgCXrtRiojUz1vG7Lo8PWczC7dlsHJ3NomdIwkL1nguIp7ldaOQtfZr4Osjjv2+nmsnuyOGyt0oNdMtItIwbxizj+R0lfKv+dsICvBj9d5DXDQy/uhvEhFxM6+a6fYWDmd50q2abhGR1qeyRzdAXlEJwxKiPBiNiEg5Jd11iAkP5u5T+tE3LtzToYiISBNl5JX36C5ylQLQr1OEJ8MREQG8sLzEG8S3D+W+0/p7OgwRETkGGbnlM92Vn1r2idWmOCLieZrpFhERn5JdUD7TfTC3mE6RwUSoVFBEvICSbhER8SmXJnVj0x/PJDWnUGWCIuI1lHSLiIjPCQ7wY+fBfPrEKukWEe+gmm4REfEpb/yykwO5ReQWlWimW0S8hpJuERHxKd+sT+NQQeUiSiXdIuIdVF4iIiI+JSOvCH8/A6CZbhHxGkq6RUTEp2TkFVNSVkZ4cABxEcGeDkdEBFDSLSIiPqS4pIycQheFrlL6xIVjjPF0SCIigJJuERHxIQ6ni/btAjlU4KKv6rlFxIso6RYREZ/RMTyYnx6aQq6zhD5x2olSRLyHkm4REfEp2w/mA2imW0S8iloGioiIz1iw5SDPfLsZgD7qXCIiXkQz3SIi4jM27newNiWHAD/oHt3O0+GIiFRR0i0iIj4jI7cIPwM9O4YT6K//xImI99CIJCIiPiMjrwg/Y1TPLSJeR0m3iIj4jAO5RZSUWe1EKSJeR0m3iIj4jMrt39UuUES8jZJuERHxGVeN6w5A39gID0ciIlKTkm4REfEZlT26e8dqpltEvIuSbhER8QmZeUW8+vNOotsFEhasbShExLso6RYREZ9wILeIrPxi4iJDPB2KiEgtSrpFRMQnHMx1AtCro0pLRMT7KOkWERGfsO1AeT13/85aRCki3kdJt4iI+IStB3IBGBYf5eFIRERqU9ItIiI+Ia+oBIChSrpFxAsp6RYREZ8QFRpIZEgAsRHBng5FRKQWJd0iIuITth3Io29cOMYYT4ciIlKLkm4REfEJK3cfItdZ4ukwRETqpKRbRERavZxCF8WlZbQL8vd0KCIidVLSLSIirV5l55L4DqEejkREpG5KukVEpNVbl5IDQJ/YcA9HIiJSNyXdIiLS6iWnOgDoG6ekW0S8k5JuERFp9VKyCwgPDtBMt4h4LSXdIiLS6u3PcXJy/1iGaGMcEfFSSrpFRKRVKyopZU9WAX1iwzwdiohIvZR0i4hIqxYc4M9NJ/big+UpWGs9HY6ISJ2UdIuISKt3qMCFxWo3ShHxWl6VdBtjzjTGbDbGbDPGPFzH+fuMMcnGmLXGmHnGmB6eiFNERMp5y7idkVdEx/Bgd9xaRKRZeE3SbYzxB14AzgIGAdONMYOOuGwVkGStHQZ8BPy9ZaMUEZFK3jRuZ+QVK+kWEa/mNUk3MBbYZq3dYa0tBt4DLqh+gbV2vrW2oOLlYiChhWMUEZHDvGbc1ky3iHg7b0q644G91V6nVByrz03AN26NSEREGuI14/aZQzpzYr8Yd9xaRKRZBHg6gGNhjLkaSAJObuCaGcAMgO7du7dQZCIiUpejjdvHO2Y/ft7g4wlPRMTtvGmmex/QrdrrhIpjNRhjTgUeBc631hbVdzNr7cvW2iRrbVJsbGyzBysiIs03bmvMFhFf501J9zKgnzGmlzEmCLgCmF39AmPMSGAm5QP3AQ/EKCIih2ncFhFpJK9Juq21JcBdwBxgI/CBtXaDMeZJY8z5FZc9BYQDHxpjVhtjZtdzOxERcTON2yIijedVNd3W2q+Br4849vtqfz+1xYMSEZF6adwWEWkcr5npFhERERHxVUq6RURERETcTEm3iIiIiIibKekWEREREXEzJd0iIiIiIm6mpFtERERExM2UdIuIiIiIuJlX9eluSS6Xi5SUFJxOp6dDcbuQkBASEhIIDAz0dCgiIiIibVKbTbpTUlKIiIigZ8+eGGM8HY7bWGvJzMwkJSWFXr16eTocERERkTapzZaXOJ1OYmJifDrhBjDGEBMT0yZm9EVERES8VZtNugGfT7grtZWvU0RERMRbtemk25MyMzMZMWIEI0aMoHPnzsTHx1e9Li4ubvC9y5cv5+67726hSEVERETkeLXZmm5Pi4mJYfXq1QA88cQThIeH88ADD1SdLykpISCg7n+epKQkkpKSWiROERERETl+mun2Itdffz233XYb48aN46GHHmLp0qWMHz+ekSNHMmHCBDZv3gzADz/8wLnnnguUJ+w33ngjkydPpnfv3jz//POe/BJEREREpA6a6Qb+8MUGklMdzXrPQV0jefy8wU1+X0pKCgsXLsTf3x+Hw8FPP/1EQEAAc+fO5be//S0ff/xxrfds2rSJ+fPnk5uby4ABA7j99tvVHlBERETEiyjp9jKXXnop/v7+AOTk5HDdddexdetWjDG4XK4633POOecQHBxMcHAwcXFxpKenk5CQ0JJhi4iIiEgDlHTDMc1Iu0tYWFjV33/3u98xZcoUPv30U3bt2sXkyZPrfE9wcHDV3/39/SkpKXF3mCIiIiLSBKrp9mI5OTnEx8cD8MYbb3g2GBERERE5Zkq6vdhDDz3EI488wsiRIzV7LSIiItKKGWutp2Nwu6SkJLt8+fIaxzZu3MjAgQM9FFHLa2tfr4ivMMassNa2qR6hdY3ZIiKtQUNjtma6RURERETcTEm3iIiIiIibKekWEREREXEzJd0iIiIiIm6mpFtERERExM2UdIuIiIiIuJmSbg+ZMmUKc+bMqXHs2Wef5fbbb6/z+smTJ6MWWiIiIiKtk5JuD5k+fTrvvfdejWPvvfce06dP91BEIiIiIuIuSro9ZNq0aXz11VcUFxcDsGvXLlJTU3n33XdJSkpi8ODBPP744x6OUkRERESaQ4CnA/AWl89cVOvYucO6cM34nhQWl3L960trnZ82OoFLk7qRlV/M7W+vqHHu/VvHN/i86Ohoxo4dyzfffMMFF1zAe++9x2WXXcZvf/tboqOjKS0t5ZRTTmHt2rUMGzbs+L44EREREfEozXR7UPUSk8rSkg8++IBRo0YxcuRINmzYQHJysoejFBEREZHjpZnuCg3NTIcG+Td4Pjos6Kgz23W54IILuPfee1m5ciUFBQVER0fz9NNPs2zZMjp06MD111+P0+ls8n1FRERExLtoptuDwsPDmTJlCjfeeCPTp0/H4XAQFhZGVFQU6enpfPPNN54OUURERESagWa6PWz69OlcdNFFvPfeeyQmJjJy5EgSExPp1q0bEydO9HR4IiIiItIMlHR72IUXXoi1tur1G2+8Ued1P/zwQ8sEJCIiIiLNTuUlIiIiIiJupqRbRERERMTNlHSLiIiIiLhZm066q9dS+7K28nWKiIiIeKs2m3SHhISQmZnp8wnPEW+DAAAG+klEQVSptZbMzExCQkI8HYqIiIhIm9Vmu5ckJCSQkpLCwYMHPR2K24WEhJCQkODpMERERETaLK9Luo0xZwLPAf7A/2/v7kPuLus4jr8/zLsUJB+2GMNpWziIRWolYg9/iBGskgx6UDGQEASpMOjJ+qNICqo/0iz/WWqtkEp6HCGWbFJCoVnOhyXSEiNjOi1nCbXa+vbHuWzHe9Odtfvc9znXeb/gcP9+1zmc+/py/+4P1/md6/e7rq+qz897/sXAN4HXAn8BLqiqRw7398zNzbF27doj77AkzbDFymxJmnYTNb0kyTLgOuAtwHrgoiTr573sUuCpqjoVuBr4wuL2UpIEZrYkHY6JGnQDZwE7qurhqvoX8B3g/HmvOR/Y1La/B7wpSRaxj5KkATNbkkY0aYPuk4A/De0/2toO+pqq2gs8DSxflN5JkoaZ2ZI0oomb071QklwGXNZ2n0ny0P/xNiuAJxeuVxNrVuoEa+1Vz7W+bKk7sBjM7MNmrf2ZlTqh71qfN7MnbdD9Z+Dkof3Vre1gr3k0yVHAcQwuznmOqtoIbDySziS5u6rOPJL3mAazUidYa69mqdYJY2YvEWvtz6zUCbNV67BJm17ya2BdkrVJXgRcCGye95rNwCVt+13A1ur9ZtuSNJnMbEka0USd6a6qvUk+APyUwe2nbqyq7UmuAu6uqs3ADcC3kuwA/sog5CVJi8zMlqTRTdSgG6CqbgFumdf2qaHtfwLvXqTuHNFXnVNkVuoEa+3VLNU6UczsJWOt/ZmVOmG2av2f+C2fJEmSNF6TNqdbkiRJ6o6D7oNIsiHJQ0l2JLlyqfuzkJLcmGRXkgeG2k5McluS37efJyxlHxdKkpOT3J7kd0m2J7mitXdXb5Kjk9yV5N5W62da+9okd7Zj+bvtYrepl2RZknuS/KTtd1mnRmNmT3+GgZnd2rvMMjN7wEH3PCMuazzNvgFsmNd2JbClqtYBW9p+D/YCH66q9cDZwPvb37LHevcA51bV6cAZwIYkZzNYcvvqtgT3UwyW5O7BFcCDQ/u91qlDMLO7yTAws83szjnoPtAoyxpPrar6BYM7CAwbXqZ5E/CORe3UmFTVzqr6bdv+O4N/+JPosN4aeKbtzrVHAecyWHobOqk1yWrgbcD1bT90WKdGZmZ3cryb2WZ27xx0H2iUZY17s7Kqdrbtx4CVS9mZcUiyBng1cCed1tu+vtsG7AJuA/4A7G5Lb0M/x/I1wMeA/7T95fRZp0ZjZneSYcPMbKCfY9nMbhx06znaohVd3dImybHA94EPVdXfhp/rqd6q2ldVZzBYFfAs4BVL3KUFl+Q8YFdV/Wap+yJNgp4y7Flmdj/M7OeauPt0T4BRljXuzeNJVlXVziSrGHzq7kKSOQbhfVNV/aA1d1svQFXtTnI78Drg+CRHtTMKPRzLbwDenuStwNHAS4Av01+dGp2Z3VGGmdndZZmZPcQz3QcaZVnj3gwv03wJ8OMl7MuCafPGbgAerKovDT3VXb1JXprk+LZ9DPBmBvMhb2ew9DZ0UGtVfaKqVlfVGgb/m1ur6mI6q1OHxczu5Hg3s83s3rk4zkG0T2TXsH9Z488tcZcWTJJvA+cAK4DHgU8DPwJuBk4B/gi8p6rmX7gzdZK8EbgDuJ/9c8k+yWCOYFf1JjmNwcUoyxh8mL65qq5K8nIGF5adCNwDvLeq9ixdTxdOknOAj1TVeT3XqUMzs6c/w8DMNrP7qPOFOOiWJEmSxszpJZIkSdKYOeiWJEmSxsxBtyRJkjRmDrolSZKkMXPQLUmSJI2Zg26pSbIvybahx5UL+N5rkjywUO8nSbPOzNa0cUVKab9/tCV5JUmTz8zWVPFMt3QISR5J8sUk9ye5K8mprX1Nkq1J7kuyJckprX1lkh8mubc9Xt/ealmSryXZnuRnbRUySdICMrM1qRx0S/sdM++ryguGnnu6ql4FfJXByncAXwE2VdVpwE3Ata39WuDnVXU68Bpge2tfB1xXVa8EdgPvHHM9ktQzM1tTxRUppSbJM1V17EHaHwHOraqHk8wBj1XV8iRPAquq6t+tfWdVrUjyBLB6eEnbJGuA26pqXdv/ODBXVZ8df2WS1B8zW9PGM93SaOp5tg/HnqHtfXhNhSSNi5mtieOgWxrNBUM/f9W2fwlc2LYvBu5o21uAywGSLEty3GJ1UpIEmNmaQH5qk/Y7Jsm2of1bq+rZW1CdkOQ+Bmc+LmptHwS+nuSjwBPA+1r7FcDGJJcyODtyObBz7L2XpNliZmuqOKdbOoQ2P/DMqnpyqfsiSXphZrYmldNLJEmSpDHzTLckSZI0Zp7pliRJksbMQbckSZI0Zg66JUmSpDFz0C1JkiSNmYNuSZIkacwcdEuSJElj9l+EY3qJjF3GdgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_metrics(baseline_history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UCa4iWo6WDKR"
      },
      "source": [
        "Note: That the validation curve generally performs better than the training curve. This is mainly caused by the fact that the dropout layer is not active when evaluating the model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aJC1booryouo"
      },
      "source": [
        "### Evaluate metrics\n",
        "\n",
        "You can use a [confusion matrix](https://developers.google.com/machine-learning/glossary/#confusion_matrix) to summarize the actual vs. predicted labels, where the X axis is the predicted label and the Y axis is the actual label:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aNS796IJKrev",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c4be0499-333a-486c-a18e-7d0aef17c69b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "90/90 [==============================] - 0s 990us/step\n",
            "28/28 [==============================] - 0s 928us/step\n"
          ]
        }
      ],
      "source": [
        "train_predictions_baseline = model.predict(train_features, batch_size=BATCH_SIZE)\n",
        "test_predictions_baseline = model.predict(test_features, batch_size=BATCH_SIZE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MVWBGfADwbWI"
      },
      "outputs": [],
      "source": [
        "def plot_cm(labels, predictions, p=0.5):\n",
        "  cm = confusion_matrix(labels, predictions > p)\n",
        "  plt.figure(figsize=(5,5))\n",
        "  sns.heatmap(cm, annot=True, fmt=\"d\")\n",
        "  plt.title('Confusion matrix @{:.2f}'.format(p))\n",
        "  plt.ylabel('Actual label')\n",
        "  plt.xlabel('Predicted label')\n",
        "\n",
        "  print('Legitimate Transactions Detected (True Negatives): ', cm[0][0])\n",
        "  print('Legitimate Transactions Incorrectly Detected (False Positives): ', cm[0][1])\n",
        "  print('Fraudulent Transactions Missed (False Negatives): ', cm[1][0])\n",
        "  print('Fraudulent Transactions Detected (True Positives): ', cm[1][1])\n",
        "  print('Total Fraudulent Transactions: ', np.sum(cm[1]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nOTjD5Z5Wp1U"
      },
      "source": [
        "Evaluate your model on the test dataset and display the results for the metrics you created above:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "poh_hZngt2_9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 636
        },
        "outputId": "0b5c7a16-2007-4ae0-bbe0-3af75b91d592"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loss :  0.0030375574715435505\n",
            "tp :  75.0\n",
            "fp :  8.0\n",
            "tn :  56856.0\n",
            "fn :  23.0\n",
            "accuracy :  0.9994557499885559\n",
            "precision :  0.9036144614219666\n",
            "recall :  0.7653061151504517\n",
            "auc :  0.9385272860527039\n",
            "prc :  0.8193020820617676\n",
            "\n",
            "Legitimate Transactions Detected (True Negatives):  56856\n",
            "Legitimate Transactions Incorrectly Detected (False Positives):  8\n",
            "Fraudulent Transactions Missed (False Negatives):  23\n",
            "Fraudulent Transactions Detected (True Positives):  75\n",
            "Total Fraudulent Transactions:  98\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ],
            "image/png": 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upo5anL6O08zKo0YnekrhwGlm5eEWp5lZTr4A3swspzpqcVbqOk4zqzOVvABeUndJj0i6Oe0Pk/SApAZJ10rqldLXS/sN6fjWBWV8L6U/I2m/gvQxKa1B0mkdqY8Dp5mVR1OUtnXMN4DCdS5+CpwXEdsAS3nvjsSJwNKUfl7Kh6SRwOHAx4AxwG9TMO4O/AYYC4wExqe8bXLgNLPyqFDglDQE+AJwSdoXsA/wPynL5cCB6fW4tE86vm/KPw64JiLejYjngQay68x3Bhoi4rmIWEm2vsa49urkwGlm5RFNpW3t+yXwXd67XXszYFlENKb9+bx3o81g0t2K6fjrKf+a9BbnFEtvkwOnmZVHiS3OwsV90japuUhJXwQWR8SsKn6y9/GsupmVRZQ4qx4Rk4HJRQ7vBnxJ0v7A+sDGwPlAX0k9UqtyCNkt3aR/hwLzJfUANgFeK0hvVnhOsfSi3OI0s/KowBhnRHwvIoZExNZkkzt3RsSRwF3AISnbBOCm9Hpa2icdvzOyBTmmAYenWfdhwHCytYJnAsPTLH2v9B7T2vuobnGaWXl07i2XpwLXSPoR8AhwaUq/FLhSUgOwhCwQEhGzJU0F5gCNwInNCw5JOgmYDnQHLouI2e29ecVWR1pXXh2pdnl1pNpW6upIb54wtqS/2Y1+e6tXRzKzOuU7h8zMrBi3OM2sLLrqsF8lOHCaWXnUUVfdgdPMysOB08wsn1IvgK9FDpxmVh4OnGZmOdXPAvAOnGZWHu6qm5nl5cBpZpaTu+pmZvm4q25mlpdbnGZm+bjFaWaWl1ucZmb5dOy5ax8MDpxmVh4OnGZm+dRTi9MLGZuZ5eQWp5mVRx21OB04zaws6qmr7sBpZmXhwGlmlpMDJyDpTaD5VoDm5x5Heh0RsXGF62ZmtSRq7vHoJSsaOCNio86siJnVNrc4W5C0OzA8In4vqT+wUUQ8X9mqmVktiSa3ONeQdAawE7At8HugF/BHYLfKVs3MaolbnGs7CNgReBggIl6W5G68ma0lPMa5lpUREZICQFKfCtfJzGqQW5xrmyrpIqCvpOOAY4CLK1stM6s1HuMsEBE/l/R54A1gBHB6RMyoeM3MrKZE/axj3OEL4J8AepNdx/lE5apjZrWqnlqc7a6OJOlY4EHgYOAQ4H5Jx1S6YmZWW6JJJW21qCMtzv8AdoyI1wAkbQb8DbiskhUzs9rirvraXgPeLNh/M6WZma1Rq63HUrR1r/q308sG4AFJN5GNcY4DHu+EupmZdUlttTibL3J/Nm3NbqpcdcysVvkCeCAizurMiphZbfMF8AUkbQ58F/gYsH5zekTsU8F6mVmNaaqjFmdHHtZ2FfA0MAw4C3gBmFnBOplZDYpQSVt7JK0v6UFJj0maLemslD5M0gOSGiRdK6lXSl8v7Tek41sXlPW9lP6MpP0K0sektAZJp7VXp44Ezs0i4lJgVUT8NSKOAdzaNLO1VPA6zneBfSJie2AHYIykUcBPgfMiYhtgKTAx5Z8ILE3p56V8SBoJHE7Wex4D/FZSd0ndgd8AY4GRwPiUt6iOBM5V6d+Fkr4gaUdg0458WjOrHxGlbe2XGxERy9Nuz7QFWQPuf1L65cCB6fW4tE86vq8kpfRrIuLdtJ5wA7Bz2hoi4rmIWAlck/IW1ZHrOH8kaRPgFOBXwMbAtzpwnpnVkVKv45Q0CZhUkDQ5Iia3yNMdmAVsQ9Y6fBZYFhGNKct8YHB6PRh4CSAiGiW9DmyW0u8vKLbwnJdapO/SVp07ssjHzenl68De7eU3s/pU6uRQCpKT28mzGthBUl/gBmC7kt6sTNq6AP5XvPewtveJiJMrUiMzq0mdcR1nRCyTdBfwWbKlLnukVucQYEHKtgAYCsyX1APYhOxux+b0ZoXnFEtvVVstzoc6+FnMzCp2r3q6JHJVCpq9gc+TTfjcRbbw0DXABN67OWda2v97On5nWox9GnC1pF8AWwLDyRYwEjBc0jCygHk4cERbdWrrAvjLix0zM2upgtdxDgIuT+Oc3YCpEXGzpDnANZJ+BDwCXJryXwpcKakBWEIWCImI2ZKmAnOARuDENASApJOA6UB34LKImN1WhRRddEmTVa8+1zUrZu3qveUe1a6CrYPGlQtKioCPbDWupL/ZHefdVHNXznd0IWMzszZ10TZYRXTZwOlWi1ltqadbLj2rbmZl4dWRMp5VN7MOc4sTz6qbmRXT0WXlTiW7+d3LyplZq+pobqjDy8o9hZeVM7M2NIVK2mqRl5Uzs7Ko1HqcXVFHLkdaa1k54GW8rJyZtVBHT87wsnJmVh5BbbYeS+Fl5cysLJrqaHaoI7Pqv6eVCbM01mlmBkCTW5xrubng9frAQWTjnGZma7irXiAi/lS4L2kKcF/FamRmNcmTQ20bDgwod0XMrLa5xVlA0pusPca5iOxOIjOzNdziLBARG3VGRcysttVT4Gz3ziFJd3QkzczqW6CStlrU1nqc6wMbAP0l9YM1n3Bj3nsWsZkZACU+Vr0mtdVV/xrwTbKnwc3ivcD5BvDrCtfLzGqMr+MEIuJ84HxJ/x4Rv+rEOplZDaqjG4c6tDpSk6S+zTuS+kk6oYJ1MjPr0joSOI+LiGXNOxGxFDiuclUys1rUVOJWizpyAXx3SYr0APb0UPhela2WmdWaJnmMs9BtwLWSLkr7X0tpZmZr1NMYZ0cC56nAJOD4tD8DuLhiNTKzmlSr3e5StDvGGRFNEfG7iDgkIg4B5pAtaGxmtkaTSttqUYcW+ZC0IzAeOBR4Hri+kpUys9rj6zgBSSPIguV44FXgWkAR4VXgzex9PMaZeRq4F/hiRDQASPKzhsysVbXa7S5FW2OcBwMLgbskXSxpX6ijtriZ5VJP13EWDZwRcWNEHA5sB9xFdt/6AEkXShrdWRU0s9oQJW61qCOz6isi4uqIOAAYAjyCFzI2sxbqaVa9I7dcrhERSyNickTsW6kKmVltqqeueinPHDIze59aDYKlcOA0s7KIGu12l8KB08zKwi1OM7Oc6ilw5pocMjMrplKXI0kaKukuSXMkzZb0jZS+qaQZkuamf/uldEm6QFKDpMclfaqgrAkp/1xJEwrSPy3piXTOBVLba+Q5cJpZV9cInBIRI4FRwImSRgKnAXdExHDgjrQPMBYYnrZJwIWQBVrgDGAXYGfgjOZgm/IcV3DemLYq5MBpZmVRqes4I2JhRDycXr8JPEX2pN1xwOUp2+XAgen1OOCKyNwP9JU0CNgPmBERS9KTLGYAY9KxjSPi/rRg+xUFZbXKY5xmVhadMcYpaWtgR+ABYGBELEyHFgED0+vBwEsFp81PaW2lz28lvSi3OM2sLEq9AF7SJEkPFWyTWitf0obAn4BvRsQbhcdSS7HT7uB0i9PMyqLUqBURk4HJbeWR1JMsaF4VEc3rAf9T0qCIWJi624tT+gJgaMHpQ1LaAmCvFul3p/QhreQvyi1OMyuLSo1xphnuS4GnIuIXBYemAc0z4xOAmwrSj0qz66OA11OXfjowOj3ivB8wGpiejr0haVR6r6MKymqVW5xmVhYVHOPcDfgq8ISkR1Pa94FzgamSJgIvkj2hAuAWYH+gAXgLOBogIpZIOgeYmfKdHRFL0usTgD8AvYFb01aUA6eZlUWlBhgj4j6KrwX8vgWH0njniUXKugy4rJX0h4CPd7RODpxmVhZNNbu6Zn4OnGZWFvV0y6UDp5mVRf20Nx04zaxM3OI0M8upVh+DUQoHTjMrC08OmZnlVD9h04HTzMrEY5xmZjnVU1fd96qbmeXkFqeZlUX9tDcdOM2sTDzGaWaWUz2NcTpwmllZ1E/YdOA0szJxV93MLKeoozanA6eZlYVbnGZmOXlyyMpmyJAt+cNl5zNgYH8igksuuYpf/fpSzjrzPzjggNE0NQWvLH6VY479FgsX/rPa1bUWRoz4CFdfdeGa/Q8P24ozz/o5fftuzMRjjuCVV7NH1vzwh+dy6213VquaXUL9hE1Q9niOrqdHr8Fds2I5bbHFAAZtMYBHHn2SDTfsw4MP3Ma/HnIM8+cv5M03lwNw0onH8NGPjuDEk06rcm2tLd26dWPeC7PYdfcv8m8TDmP58hX84ryLql2tsmtcuaCkBeK+tvWXS/qbveiF62puQTq3OCts0aLFLFqUPe55+fIVPP30XAZvuQVPPTV3TZ4+fTagq36B2Xv23Wd3nnvuRebNa/OR23WrnsY4fa96J/rQh4aww/Yf54EHHwHgnLNP5flnZzJ+/EGcedbPqlw7a8+hh47jmmtvXLN/wvFH8/CsGVw8+b/p23eTKtasa4gS/6tFnR44JR3d2e/ZFfTpswFTr72Yb3/njDVd9B+e/lOGfeQzTJlyAyeeUJc/lprRs2dPDvjiaP7nTzcD8LuLrmDEdrvy6Z1Gs2jRYn72X6dXuYbV11TiVouq0eI8q9gBSZMkPSTpoaamFZ1Zp4rq0aMH1117MVOm3MCNN77/OfdXT7megw7avwo1s44aM2ZvHnnkCRYvfhWAxYtfpampKZvwu/QqPvOZHapcw+qrpxZnRcY4JT1e7BAwsNh5ETEZmAwfnMkhgIsn/zdPPd3AL8+fvCZtm22G0dDwPABfOmA/nnnm2WpVzzrg8MMOXKubvsUWA9aMXR84biyzZz9Trap1GbXaeixFpSaHBgL7AUtbpAv4W4Xes0vabdfP8NWvHMLjT8zhoZm3A9mlK0cffTgjRnyEpqYm5s1bwAkneka9q9pgg958bt89Of6EU9eknfuTH7D99iOJCF58cf5ax+pVUx1NcFbkciRJlwK/j4j7Wjl2dUQc0V4ZH6QWp1ktKfVypK9+6OCS/mavfPF6X44EEBET2zjWbtA0s9pTTy0dX8dpZmXhWy7NzHKq1RnyUjhwmllZeFbdzCwnd9XNzHJyV93MLCd31c3McqqnFb4cOM2sLDzGaWaWk7vqZmY5eXLIzCyneuqqewV4MyuLiChpa4+kyyQtlvRkQdqmkmZImpv+7ZfSJekCSQ2SHpf0qYJzJqT8cyVNKEj/tKQn0jkXSGp30REHTjMriwquAP8HYEyLtNOAOyJiOHBH2gcYCwxP2yTgQsgCLXAGsAuwM3BGc7BNeY4rOK/le72PA6eZlUWlVoCPiHuAJS2SxwGXp9eXAwcWpF8RmfuBvpIGka0PPCMilkTEUmAGMCYd2zgi7o+s+XtFQVlFeYzTzMqik8c4B0bEwvR6Ee89WWIw8FJBvvkpra30+a2kt8ktTjOrqsJnjaVtUp7zU0uxU6O2W5xmVhal3jlU+KyxHP4paVBELEzd7cUpfQEwtCDfkJS2ANirRfrdKX1IK/nb5BanmZVFE1HSVqJpQPPM+ATgpoL0o9Ls+ijg9dSlnw6MltQvTQqNBqanY29IGpVm048qKKsotzjNrCwqdQG8pClkrcX+kuaTzY6fC0yVNBF4ETg0Zb8F2B9oAN4CjgaIiCWSzgFmpnxnR0TzhNMJZDP3vYFb09Z2nbrqjfl+WJtZdZT6sLY9B+9b0t/sPQvu8MPazKw+1VNLx4HTzMqinm65dOA0s7Jw4DQzy6mrzpdUggOnmZWFW5xmZjl5PU4zs5zcVTczy8lddTOznNziNDPLyS1OM7OcPDlkZpZTUx111b2snJlZTm5xmllZuKtuZpZTPXXVHTjNrCzc4jQzy8ktTjOznNziNDPLyS1OM7Oc3OI0M8spoqnaVeg0DpxmVha+V93MLCevjmRmlpNbnGZmObnFaWaWky9HMjPLyZcjmZnl5K66mVlOnhwyM8upnlqcXgHezCwntzjNrCw8q25mllM9ddUdOM2sLDw5ZGaWk1ucZmY5eYzTzCwn3zlkZpaTW5xmZjl5jNPMLCd31c3McnKL08wsJwdOM7Oc6idsgurpW6IrkTQpIiZXux5WGv/+6ptXR6qeSdWugK0T//7qmAOnmVlODpxmZjk5cFaPx8dqm39/dcyTQ2ZmObnFaWaWkwNnFUgaI+kZSQ2STqt2fazjJF0mabGkJ6tdF6seB85OJqk78BtgLDASGC9pZHVrZTn8ARhT7UpYdTlwdr6dgYaIeC4iVgLXAOOqXCfroIi4B1hS7XpYdTlwdr7BwEsF+/NTmpnVCAdOM7OcHOifCLMAAANwSURBVDg73wJgaMH+kJRmZjXCgbPzzQSGSxomqRdwODCtynUysxwcODtZRDQCJwHTgaeAqRExu7q1so6SNAX4O7CtpPmSJla7Ttb5fOeQmVlObnGameXkwGlmlpMDp5lZTg6cZmY5OXCameXkwPkBIWm1pEclPSnpOkkbrENZf5B0SHp9SVuLkEjaS9KuJbzHC5L6dzS9RZ7lOd/rTEnfyVtHs2IcOD843o6IHSLi48BK4OuFByWV9CjoiDg2Iua0kWUvIHfgNKtlDpwfTPcC26TW4L2SpgFzJHWX9DNJMyU9LulrAMr8Oq0R+hdgQHNBku6WtFN6PUbSw5Iek3SHpK3JAvS3Umt3D0mbS/pTeo+ZknZL524m6XZJsyVdAqi9DyHpRkmz0jmTWhw7L6XfIWnzlPYRSbelc+6VtF05fphmLZXUCrGuK7UsxwK3paRPAR+PiOdT8Hk9Ij4jaT3g/yTdDuwIbEu2PuhAYA5wWYtyNwcuBvZMZW0aEUsk/Q5YHhE/T/muBs6LiPskbUV2h9RHgTOA+yLibElfADpyx80x6T16AzMl/SkiXgP6AA9FxLcknZ7KPonsOUBfj4i5knYBfgvsU8KP0axNDpwfHL0lPZpe3wtcStaFfjAink/po4FPNo9fApsAw4E9gSkRsRp4WdKdrZQ/CrinuayIKLYm5eeAkdKaBuXGkjZM73FwOvfPkpZ24DOdLOmg9HpoqutrQBNwbUr/I3B9eo9dgesK3nu9DryHWW4OnB8cb0fEDoUJKYCsKEwC/j0iprfIt38Z69ENGBUR77RSlw6TtBdZEP5sRLwl6W5g/SLZI73vspY/A7NK8BhnfZkOHC+pJ4CkEZL6APcAh6Ux0EHA3q2cez+wp6Rh6dxNU/qbwEYF+W4H/r15R1JzILsHOCKljQX6tVPXTYClKWhuR9bibdYNaG41H0E2BPAG8LykL6f3kKTt23kPs5I4cNaXS8jGLx9ODxu7iKzXcQMwNx27gmz1n7VExCvAJLJu8WO811X+X+Cg5skh4GRgpzT5NIf3ZvfPIgu8s8m67PPaqettQA9JTwHnkgXuZiuAndNn2Ac4O6UfCUxM9ZuNH0liFeLVkczMcnKL08wsJwdOM7OcHDjNzHJy4DQzy8mB08wsJwdOM7OcHDjNzHJy4DQzy+n/A0XJea/8f49oAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "baseline_results = model.evaluate(test_features, test_labels,\n",
        "                                  batch_size=BATCH_SIZE, verbose=0)\n",
        "for name, value in zip(model.metrics_names, baseline_results):\n",
        "  print(name, ': ', value)\n",
        "print()\n",
        "\n",
        "plot_cm(test_labels, test_predictions_baseline)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PyZtSr1v6L4t"
      },
      "source": [
        "If the model had predicted everything perfectly, this would be a [diagonal matrix](https://en.wikipedia.org/wiki/Diagonal_matrix) where values off the main diagonal, indicating incorrect predictions, would be zero. In this case the matrix shows that you have relatively few false positives, meaning that there were relatively few legitimate transactions that were incorrectly flagged. However, you would likely want to have even fewer false negatives despite the cost of increasing the number of false positives. This trade off may be preferable because false negatives would allow fraudulent transactions to go through, whereas false positives may cause an email to be sent to a customer to ask them to verify their card activity."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P-QpQsip_F2Q"
      },
      "source": [
        "### Plot the ROC\n",
        "\n",
        "Now plot the [ROC](https://developers.google.com/machine-learning/glossary#ROC). This plot is useful because it shows, at a glance, the range of performance the model can reach just by tuning the output threshold."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lhaxsLSvANF9"
      },
      "outputs": [],
      "source": [
        "def plot_roc(name, labels, predictions, **kwargs):\n",
        "  fp, tp, _ = sklearn.metrics.roc_curve(labels, predictions)\n",
        "\n",
        "  plt.plot(100*fp, 100*tp, label=name, linewidth=2, **kwargs)\n",
        "  plt.xlabel('False positives [%]')\n",
        "  plt.ylabel('True positives [%]')\n",
        "  plt.xlim([-0.5,20])\n",
        "  plt.ylim([80,100.5])\n",
        "  plt.grid(True)\n",
        "  ax = plt.gca()\n",
        "  ax.set_aspect('equal')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DfHHspttKJE0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "outputId": "3688edc9-8f83-4390-fd45-673ba4e9c138"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_roc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n",
        "plot_roc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n",
        "plt.legend(loc='lower right');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y5twGRLfNwmO"
      },
      "source": [
        "### Plot the AUPRC\n",
        "\n",
        "Now plot the [AUPRC](https://developers.google.com/machine-learning/glossary?hl=en#PR_AUC). Area under the interpolated precision-recall curve, obtained by plotting (recall, precision) points for different values of the classification threshold. Depending on how it's calculated, PR AUC may be equivalent to the average precision of the model.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XV6JSlFGEqGI"
      },
      "outputs": [],
      "source": [
        "def plot_prc(name, labels, predictions, **kwargs):\n",
        "    precision, recall, _ = sklearn.metrics.precision_recall_curve(labels, predictions)\n",
        "\n",
        "    plt.plot(precision, recall, label=name, linewidth=2, **kwargs)\n",
        "    plt.xlabel('Precision')\n",
        "    plt.ylabel('Recall')\n",
        "    plt.grid(True)\n",
        "    ax = plt.gca()\n",
        "    ax.set_aspect('equal')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FdQs_PcqEsiL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "outputId": "d766dc42-54f8-4e49-954d-519d122216ed"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_prc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n",
        "plot_prc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n",
        "plt.legend(loc='lower right');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gpdsFyp64DhY"
      },
      "source": [
        "It looks like the precision is relatively high, but the recall and the area under the ROC curve (AUC) aren't as high as you might like. Classifiers often face challenges when trying to maximize both precision and recall, which is especially true when working with imbalanced datasets. It is important to consider the costs of different types of errors in the context of the problem you care about. In this example, a false negative (a fraudulent transaction is missed) may have a financial cost, while a false positive (a transaction is incorrectly flagged as fraudulent) may decrease user happiness."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cveQoiMyGQCo"
      },
      "source": [
        "## Class weights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ePGp6GUE1WfH"
      },
      "source": [
        "### Calculate class weights\n",
        "\n",
        "The goal is to identify fraudulent transactions, but you don't have very many of those positive samples to work with, so you would want to have the classifier heavily weight the few examples that are available. You can do this by passing Keras weights for each class through a parameter. These will cause the model to \"pay more attention\" to examples from an under-represented class."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qjGWErngGny7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6e2e36b-e6ee-4972-9363-d31af7c60573"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Weight for class 0: 0.50\n",
            "Weight for class 1: 289.44\n"
          ]
        }
      ],
      "source": [
        "# Scaling by total/2 helps keep the loss to a similar magnitude.\n",
        "# The sum of the weights of all examples stays the same.\n",
        "weight_for_0 = (1 / neg) * (total / 2.0)\n",
        "weight_for_1 = (1 / pos) * (total / 2.0)\n",
        "\n",
        "class_weight = {0: weight_for_0, 1: weight_for_1}\n",
        "\n",
        "print('Weight for class 0: {:.2f}'.format(weight_for_0))\n",
        "print('Weight for class 1: {:.2f}'.format(weight_for_1))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mk1OOE2ZSHzy"
      },
      "source": [
        "### Train a model with class weights\n",
        "\n",
        "Now try re-training and evaluating the model with class weights to see how that affects the predictions.\n",
        "\n",
        "Note: Using `class_weights` changes the range of the loss. This may affect the stability of the training depending on the optimizer. Optimizers whose step size is dependent on the magnitude of the gradient, like `tf.keras.optimizers.SGD`, may fail. The optimizer used here, `tf.keras.optimizers.Adam`, is unaffected by the scaling change. Also note that because of the weighting, the total losses are not comparable between the two models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UJ589fn8ST3x",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1e65a44-e8dc-4867-9747-fb9ca94c37fa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "90/90 [==============================] - 3s 12ms/step - loss: 1.9099 - tp: 161.0000 - fp: 633.0000 - tn: 238177.0000 - fn: 267.0000 - accuracy: 0.9962 - precision: 0.2028 - recall: 0.3762 - auc: 0.8187 - prc: 0.1817 - val_loss: 0.0118 - val_tp: 41.0000 - val_fp: 37.0000 - val_tn: 45468.0000 - val_fn: 23.0000 - val_accuracy: 0.9987 - val_precision: 0.5256 - val_recall: 0.6406 - val_auc: 0.9546 - val_prc: 0.4972\n",
            "Epoch 2/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.7187 - tp: 212.0000 - fp: 1554.0000 - tn: 180392.0000 - fn: 118.0000 - accuracy: 0.9908 - precision: 0.1200 - recall: 0.6424 - auc: 0.9072 - prc: 0.3170 - val_loss: 0.0191 - val_tp: 55.0000 - val_fp: 82.0000 - val_tn: 45423.0000 - val_fn: 9.0000 - val_accuracy: 0.9980 - val_precision: 0.4015 - val_recall: 0.8594 - val_auc: 0.9555 - val_prc: 0.6340\n",
            "Epoch 3/100\n",
            "90/90 [==============================] - 0s 3ms/step - loss: 0.4208 - tp: 252.0000 - fp: 2475.0000 - tn: 179471.0000 - fn: 78.0000 - accuracy: 0.9860 - precision: 0.0924 - recall: 0.7636 - auc: 0.9538 - prc: 0.3165 - val_loss: 0.0284 - val_tp: 56.0000 - val_fp: 292.0000 - val_tn: 45213.0000 - val_fn: 8.0000 - val_accuracy: 0.9934 - val_precision: 0.1609 - val_recall: 0.8750 - val_auc: 0.9604 - val_prc: 0.6295\n",
            "Epoch 4/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.3554 - tp: 261.0000 - fp: 3416.0000 - tn: 178530.0000 - fn: 69.0000 - accuracy: 0.9809 - precision: 0.0710 - recall: 0.7909 - auc: 0.9613 - prc: 0.2963 - val_loss: 0.0399 - val_tp: 56.0000 - val_fp: 491.0000 - val_tn: 45014.0000 - val_fn: 8.0000 - val_accuracy: 0.9890 - val_precision: 0.1024 - val_recall: 0.8750 - val_auc: 0.9644 - val_prc: 0.6223\n",
            "Epoch 5/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.3585 - tp: 267.0000 - fp: 4435.0000 - tn: 177511.0000 - fn: 63.0000 - accuracy: 0.9753 - precision: 0.0568 - recall: 0.8091 - auc: 0.9551 - prc: 0.2496 - val_loss: 0.0508 - val_tp: 56.0000 - val_fp: 640.0000 - val_tn: 44865.0000 - val_fn: 8.0000 - val_accuracy: 0.9858 - val_precision: 0.0805 - val_recall: 0.8750 - val_auc: 0.9642 - val_prc: 0.5909\n",
            "Epoch 6/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.3524 - tp: 276.0000 - fp: 5129.0000 - tn: 176817.0000 - fn: 54.0000 - accuracy: 0.9716 - precision: 0.0511 - recall: 0.8364 - auc: 0.9485 - prc: 0.2396 - val_loss: 0.0602 - val_tp: 56.0000 - val_fp: 769.0000 - val_tn: 44736.0000 - val_fn: 8.0000 - val_accuracy: 0.9829 - val_precision: 0.0679 - val_recall: 0.8750 - val_auc: 0.9636 - val_prc: 0.5765\n",
            "Epoch 7/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.2563 - tp: 288.0000 - fp: 5541.0000 - tn: 176405.0000 - fn: 42.0000 - accuracy: 0.9694 - precision: 0.0494 - recall: 0.8727 - auc: 0.9687 - prc: 0.2447 - val_loss: 0.0654 - val_tp: 56.0000 - val_fp: 816.0000 - val_tn: 44689.0000 - val_fn: 8.0000 - val_accuracy: 0.9819 - val_precision: 0.0642 - val_recall: 0.8750 - val_auc: 0.9639 - val_prc: 0.5735\n",
            "Epoch 8/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.2904 - tp: 287.0000 - fp: 5962.0000 - tn: 175984.0000 - fn: 43.0000 - accuracy: 0.9671 - precision: 0.0459 - recall: 0.8697 - auc: 0.9553 - prc: 0.2233 - val_loss: 0.0698 - val_tp: 56.0000 - val_fp: 845.0000 - val_tn: 44660.0000 - val_fn: 8.0000 - val_accuracy: 0.9813 - val_precision: 0.0622 - val_recall: 0.8750 - val_auc: 0.9668 - val_prc: 0.5538\n",
            "Epoch 9/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.2646 - tp: 289.0000 - fp: 6549.0000 - tn: 175397.0000 - fn: 41.0000 - accuracy: 0.9638 - precision: 0.0423 - recall: 0.8758 - auc: 0.9626 - prc: 0.2052 - val_loss: 0.0805 - val_tp: 56.0000 - val_fp: 985.0000 - val_tn: 44520.0000 - val_fn: 8.0000 - val_accuracy: 0.9782 - val_precision: 0.0538 - val_recall: 0.8750 - val_auc: 0.9665 - val_prc: 0.5125\n",
            "Epoch 10/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.2696 - tp: 287.0000 - fp: 6687.0000 - tn: 175259.0000 - fn: 43.0000 - accuracy: 0.9631 - precision: 0.0412 - recall: 0.8697 - auc: 0.9648 - prc: 0.2014 - val_loss: 0.0834 - val_tp: 58.0000 - val_fp: 1006.0000 - val_tn: 44499.0000 - val_fn: 6.0000 - val_accuracy: 0.9778 - val_precision: 0.0545 - val_recall: 0.9062 - val_auc: 0.9676 - val_prc: 0.5071\n",
            "Epoch 11/100\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.2782 - tp: 286.0000 - fp: 6986.0000 - tn: 174960.0000 - fn: 44.0000 - accuracy: 0.9614 - precision: 0.0393 - recall: 0.8667 - auc: 0.9617 - prc: 0.1935 - val_loss: 0.0853 - val_tp: 58.0000 - val_fp: 1015.0000 - val_tn: 44490.0000 - val_fn: 6.0000 - val_accuracy: 0.9776 - val_precision: 0.0541 - val_recall: 0.9062 - val_auc: 0.9671 - val_prc: 0.5073\n",
            "Epoch 12/100\n",
            "82/90 [==========================>...] - ETA: 0s - loss: 0.2518 - tp: 275.0000 - fp: 6622.0000 - tn: 161004.0000 - fn: 35.0000 - accuracy: 0.9604 - precision: 0.0399 - recall: 0.8871 - auc: 0.9664 - prc: 0.1989Restoring model weights from the end of the best epoch: 2.\n",
            "90/90 [==============================] - 0s 4ms/step - loss: 0.2601 - tp: 292.0000 - fp: 7205.0000 - tn: 174741.0000 - fn: 38.0000 - accuracy: 0.9603 - precision: 0.0389 - recall: 0.8848 - auc: 0.9631 - prc: 0.1924 - val_loss: 0.0842 - val_tp: 58.0000 - val_fp: 988.0000 - val_tn: 44517.0000 - val_fn: 6.0000 - val_accuracy: 0.9782 - val_precision: 0.0554 - val_recall: 0.9062 - val_auc: 0.9676 - val_prc: 0.5183\n",
            "Epoch 12: early stopping\n"
          ]
        }
      ],
      "source": [
        "weighted_model = make_model()\n",
        "weighted_model.load_weights(initial_weights)\n",
        "\n",
        "weighted_history = weighted_model.fit(\n",
        "    train_features,\n",
        "    train_labels,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    epochs=EPOCHS,\n",
        "    callbacks=[early_stopping],\n",
        "    validation_data=(val_features, val_labels),\n",
        "    # The class weights go here\n",
        "    class_weight=class_weight) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R0ynYRO0G3Lx"
      },
      "source": [
        "### Check training history"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BBe9FMO5ucTC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "outputId": "e36725bb-f0ec-45d9-e52a-a8c8c47c69ad"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_metrics(weighted_history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "REy6WClTZIwQ"
      },
      "source": [
        "### Evaluate metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nifqscPGw-5w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63f36338-8e78-4687-ea8c-d41f3ebcbc6b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "90/90 [==============================] - 0s 954us/step\n",
            "28/28 [==============================] - 0s 999us/step\n"
          ]
        }
      ],
      "source": [
        "train_predictions_weighted = weighted_model.predict(train_features, batch_size=BATCH_SIZE)\n",
        "test_predictions_weighted = weighted_model.predict(test_features, batch_size=BATCH_SIZE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "owKL2vdMBJr6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 636
        },
        "outputId": "c5bc6b0e-e4aa-422d-c6fe-a9e2447337f3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loss :  0.019699709489941597\n",
            "tp :  87.0\n",
            "fp :  119.0\n",
            "tn :  56745.0\n",
            "fn :  11.0\n",
            "accuracy :  0.9977177977561951\n",
            "precision :  0.4223301112651825\n",
            "recall :  0.8877550959587097\n",
            "auc :  0.9575821757316589\n",
            "prc :  0.6527646780014038\n",
            "\n",
            "Legitimate Transactions Detected (True Negatives):  56745\n",
            "Legitimate Transactions Incorrectly Detected (False Positives):  119\n",
            "Fraudulent Transactions Missed (False Negatives):  11\n",
            "Fraudulent Transactions Detected (True Positives):  87\n",
            "Total Fraudulent Transactions:  98\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "weighted_results = weighted_model.evaluate(test_features, test_labels,\n",
        "                                           batch_size=BATCH_SIZE, verbose=0)\n",
        "for name, value in zip(weighted_model.metrics_names, weighted_results):\n",
        "  print(name, ': ', value)\n",
        "print()\n",
        "\n",
        "plot_cm(test_labels, test_predictions_weighted)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PTh1rtDn8r4-"
      },
      "source": [
        "Here you can see that with class weights the accuracy and precision are lower because there are more false positives, but conversely the recall and AUC are higher because the model also found more true positives. Despite having lower accuracy, this model has higher recall (and identifies more fraudulent transactions). Of course, there is a cost to both types of error (you wouldn't want to bug users by flagging too many legitimate transactions as fraudulent, either). Carefully consider the trade-offs between these different types of errors for your application."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hXDAwyr0HYdX"
      },
      "source": [
        "### Plot the ROC"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3hzScIVZS1Xm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "outputId": "4b28710a-7518-44be-b646-6a01380dd47b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_roc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n",
        "plot_roc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n",
        "\n",
        "plot_roc(\"Train Weighted\", train_labels, train_predictions_weighted, color=colors[1])\n",
        "plot_roc(\"Test Weighted\", test_labels, test_predictions_weighted, color=colors[1], linestyle='--')\n",
        "\n",
        "\n",
        "plt.legend(loc='lower right');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_0krS8g1OTbD"
      },
      "source": [
        "### Plot the AUPRC"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7jHnmVebOWOC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "outputId": "e945381c-9e35-4b41-e952-8a936098b1f6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_prc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n",
        "plot_prc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n",
        "\n",
        "plot_prc(\"Train Weighted\", train_labels, train_predictions_weighted, color=colors[1])\n",
        "plot_prc(\"Test Weighted\", test_labels, test_predictions_weighted, color=colors[1], linestyle='--')\n",
        "\n",
        "\n",
        "plt.legend(loc='lower right');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ysRtr6xHnXP"
      },
      "source": [
        "## Oversampling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "18VUHNc-UF5w"
      },
      "source": [
        "### Oversample the minority class\n",
        "\n",
        "A related approach would be to resample the dataset by oversampling the minority class."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sHirNp6u7OWp"
      },
      "outputs": [],
      "source": [
        "pos_features = train_features[bool_train_labels]\n",
        "neg_features = train_features[~bool_train_labels]\n",
        "\n",
        "pos_labels = train_labels[bool_train_labels]\n",
        "neg_labels = train_labels[~bool_train_labels]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WgBVbX7P7QrL"
      },
      "source": [
        "#### Using NumPy\n",
        "\n",
        "You can balance the dataset manually by choosing the right number of random \n",
        "indices from the positive examples:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BUzGjSkwqT88",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39b397f1-c8aa-4435-83f7-b02c38bb15fe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(181946, 29)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "ids = np.arange(len(pos_features))\n",
        "choices = np.random.choice(ids, len(neg_features))\n",
        "\n",
        "res_pos_features = pos_features[choices]\n",
        "res_pos_labels = pos_labels[choices]\n",
        "\n",
        "res_pos_features.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7ie_FFet6cep",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0bbd51e5-f0b8-48eb-8b10-6d9804107996"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(363892, 29)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "resampled_features = np.concatenate([res_pos_features, neg_features], axis=0)\n",
        "resampled_labels = np.concatenate([res_pos_labels, neg_labels], axis=0)\n",
        "\n",
        "order = np.arange(len(resampled_labels))\n",
        "np.random.shuffle(order)\n",
        "resampled_features = resampled_features[order]\n",
        "resampled_labels = resampled_labels[order]\n",
        "\n",
        "resampled_features.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IYfJe2Kc-FAz"
      },
      "source": [
        "#### Using `tf.data`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "usyixaST8v5P"
      },
      "source": [
        "If you're using `tf.data` the easiest way to produce balanced examples is to start with a `positive` and a `negative` dataset, and merge them. See [the tf.data guide](../../guide/data.ipynb) for more examples."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yF4OZ-rI6xb6"
      },
      "outputs": [],
      "source": [
        "BUFFER_SIZE = 100000\n",
        "\n",
        "def make_ds(features, labels):\n",
        "  ds = tf.data.Dataset.from_tensor_slices((features, labels))#.cache()\n",
        "  ds = ds.shuffle(BUFFER_SIZE).repeat()\n",
        "  return ds\n",
        "\n",
        "pos_ds = make_ds(pos_features, pos_labels)\n",
        "neg_ds = make_ds(neg_features, neg_labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RNQUx-OA-oJc"
      },
      "source": [
        "Each dataset provides `(feature, label)` pairs:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "llXc9rNH7Fbz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e8060f1-2a62-435a-b351-618bfb197048"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Features:\n",
            " [-5.          5.         -5.          5.         -5.          1.93373045\n",
            " -5.         -5.         -5.         -5.          3.15412161 -5.\n",
            " -1.5283744   0.12136576 -3.38718508 -5.         -5.         -5.\n",
            " -1.5258247   5.         -5.          5.          4.44827287  0.84823809\n",
            " -3.07621607 -0.95345978 -5.         -1.01224308 -1.45701771]\n",
            "\n",
            "Label:  1\n"
          ]
        }
      ],
      "source": [
        "for features, label in pos_ds.take(1):\n",
        "  print(\"Features:\\n\", features.numpy())\n",
        "  print()\n",
        "  print(\"Label: \", label.numpy())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sLEfjZO0-vbN"
      },
      "source": [
        "Merge the two together using `tf.data.Dataset.sample_from_datasets`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e7w9UQPT9wzE"
      },
      "outputs": [],
      "source": [
        "resampled_ds = tf.data.Dataset.sample_from_datasets([pos_ds, neg_ds], weights=[0.5, 0.5])\n",
        "resampled_ds = resampled_ds.batch(BATCH_SIZE).prefetch(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EWXARdTdAuQK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "57a91a39-6efb-4613-810a-ac1f0c6c9e10"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.49169921875\n"
          ]
        }
      ],
      "source": [
        "for features, label in resampled_ds.take(1):\n",
        "  print(label.numpy().mean())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "irgqf3YxAyN0"
      },
      "source": [
        "To use this dataset, you'll need the number of steps per epoch.\n",
        "\n",
        "The definition of \"epoch\" in this case is less clear. Say it's the number of batches required to see each negative example once:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xH-7K46AAxpq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8842a3fb-4a7d-41a0-b13a-bc1c36ffc163"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "278.0"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "resampled_steps_per_epoch = np.ceil(2.0*neg/BATCH_SIZE)\n",
        "resampled_steps_per_epoch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XZ1BvEpcBVHP"
      },
      "source": [
        "### Train on the oversampled data\n",
        "\n",
        "Now try training the model with the resampled data set instead of using class weights to see how these methods compare.\n",
        "\n",
        "Note: Because the data was balanced by replicating the positive examples, the total dataset size is larger, and each epoch runs for more training steps. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "soRQ89JYqd6b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f1fefa5-7e31-4987-ce19-94a4d585e249"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "278/278 [==============================] - 18s 58ms/step - loss: 0.4877 - tp: 250573.0000 - fp: 100739.0000 - tn: 240932.0000 - fn: 34062.0000 - accuracy: 0.7848 - precision: 0.7132 - recall: 0.8803 - auc: 0.9083 - prc: 0.9243 - val_loss: 0.3054 - val_tp: 56.0000 - val_fp: 2631.0000 - val_tn: 42874.0000 - val_fn: 8.0000 - val_accuracy: 0.9421 - val_precision: 0.0208 - val_recall: 0.8750 - val_auc: 0.9636 - val_prc: 0.7582\n",
            "Epoch 2/100\n",
            "278/278 [==============================] - 16s 58ms/step - loss: 0.2275 - tp: 259894.0000 - fp: 22063.0000 - tn: 262829.0000 - fn: 24558.0000 - accuracy: 0.9181 - precision: 0.9218 - recall: 0.9137 - auc: 0.9688 - prc: 0.9761 - val_loss: 0.1678 - val_tp: 57.0000 - val_fp: 1190.0000 - val_tn: 44315.0000 - val_fn: 7.0000 - val_accuracy: 0.9737 - val_precision: 0.0457 - val_recall: 0.8906 - val_auc: 0.9760 - val_prc: 0.7727\n",
            "Epoch 3/100\n",
            "278/278 [==============================] - 16s 58ms/step - loss: 0.1802 - tp: 261140.0000 - fp: 12903.0000 - tn: 271842.0000 - fn: 23459.0000 - accuracy: 0.9361 - precision: 0.9529 - recall: 0.9176 - auc: 0.9796 - prc: 0.9837 - val_loss: 0.1198 - val_tp: 58.0000 - val_fp: 977.0000 - val_tn: 44528.0000 - val_fn: 6.0000 - val_accuracy: 0.9784 - val_precision: 0.0560 - val_recall: 0.9062 - val_auc: 0.9804 - val_prc: 0.7648\n",
            "Epoch 4/100\n",
            "278/278 [==============================] - 17s 62ms/step - loss: 0.1553 - tp: 262766.0000 - fp: 10062.0000 - tn: 274653.0000 - fn: 21863.0000 - accuracy: 0.9439 - precision: 0.9631 - recall: 0.9232 - auc: 0.9848 - prc: 0.9874 - val_loss: 0.0975 - val_tp: 58.0000 - val_fp: 946.0000 - val_tn: 44559.0000 - val_fn: 6.0000 - val_accuracy: 0.9791 - val_precision: 0.0578 - val_recall: 0.9062 - val_auc: 0.9800 - val_prc: 0.7437\n",
            "Epoch 5/100\n",
            "278/278 [==============================] - 16s 58ms/step - loss: 0.1396 - tp: 264369.0000 - fp: 9105.0000 - tn: 275445.0000 - fn: 20425.0000 - accuracy: 0.9481 - precision: 0.9667 - recall: 0.9283 - auc: 0.9880 - prc: 0.9896 - val_loss: 0.0824 - val_tp: 58.0000 - val_fp: 811.0000 - val_tn: 44694.0000 - val_fn: 6.0000 - val_accuracy: 0.9821 - val_precision: 0.0667 - val_recall: 0.9062 - val_auc: 0.9824 - val_prc: 0.7337\n",
            "Epoch 6/100\n",
            "278/278 [==============================] - 16s 58ms/step - loss: 0.1289 - tp: 265424.0000 - fp: 8267.0000 - tn: 276412.0000 - fn: 19241.0000 - accuracy: 0.9517 - precision: 0.9698 - recall: 0.9324 - auc: 0.9899 - prc: 0.9910 - val_loss: 0.0741 - val_tp: 58.0000 - val_fp: 757.0000 - val_tn: 44748.0000 - val_fn: 6.0000 - val_accuracy: 0.9833 - val_precision: 0.0712 - val_recall: 0.9062 - val_auc: 0.9830 - val_prc: 0.7015\n",
            "Epoch 7/100\n",
            "278/278 [==============================] - 17s 60ms/step - loss: 0.1217 - tp: 266675.0000 - fp: 7973.0000 - tn: 276277.0000 - fn: 18419.0000 - accuracy: 0.9536 - precision: 0.9710 - recall: 0.9354 - auc: 0.9911 - prc: 0.9919 - val_loss: 0.0695 - val_tp: 58.0000 - val_fp: 771.0000 - val_tn: 44734.0000 - val_fn: 6.0000 - val_accuracy: 0.9829 - val_precision: 0.0700 - val_recall: 0.9062 - val_auc: 0.9825 - val_prc: 0.6844\n",
            "Epoch 8/100\n",
            "278/278 [==============================] - 16s 59ms/step - loss: 0.1158 - tp: 266707.0000 - fp: 7594.0000 - tn: 277546.0000 - fn: 17497.0000 - accuracy: 0.9559 - precision: 0.9723 - recall: 0.9384 - auc: 0.9921 - prc: 0.9926 - val_loss: 0.0643 - val_tp: 58.0000 - val_fp: 719.0000 - val_tn: 44786.0000 - val_fn: 6.0000 - val_accuracy: 0.9841 - val_precision: 0.0746 - val_recall: 0.9062 - val_auc: 0.9820 - val_prc: 0.6849\n",
            "Epoch 9/100\n",
            "278/278 [==============================] - 16s 57ms/step - loss: 0.1109 - tp: 267739.0000 - fp: 7329.0000 - tn: 277205.0000 - fn: 17071.0000 - accuracy: 0.9571 - precision: 0.9734 - recall: 0.9401 - auc: 0.9929 - prc: 0.9933 - val_loss: 0.0610 - val_tp: 59.0000 - val_fp: 731.0000 - val_tn: 44774.0000 - val_fn: 5.0000 - val_accuracy: 0.9838 - val_precision: 0.0747 - val_recall: 0.9219 - val_auc: 0.9775 - val_prc: 0.6758\n",
            "Epoch 10/100\n",
            "278/278 [==============================] - 15s 56ms/step - loss: 0.1072 - tp: 267584.0000 - fp: 7277.0000 - tn: 277699.0000 - fn: 16784.0000 - accuracy: 0.9577 - precision: 0.9735 - recall: 0.9410 - auc: 0.9934 - prc: 0.9935 - val_loss: 0.0573 - val_tp: 59.0000 - val_fp: 682.0000 - val_tn: 44823.0000 - val_fn: 5.0000 - val_accuracy: 0.9849 - val_precision: 0.0796 - val_recall: 0.9219 - val_auc: 0.9778 - val_prc: 0.6765\n",
            "Epoch 11/100\n",
            "278/278 [==============================] - 16s 56ms/step - loss: 0.1028 - tp: 268128.0000 - fp: 7172.0000 - tn: 277865.0000 - fn: 16179.0000 - accuracy: 0.9590 - precision: 0.9739 - recall: 0.9431 - auc: 0.9941 - prc: 0.9942 - val_loss: 0.0545 - val_tp: 59.0000 - val_fp: 673.0000 - val_tn: 44832.0000 - val_fn: 5.0000 - val_accuracy: 0.9851 - val_precision: 0.0806 - val_recall: 0.9219 - val_auc: 0.9784 - val_prc: 0.6766\n",
            "Epoch 12/100\n",
            "278/278 [==============================] - ETA: 0s - loss: 0.0986 - tp: 269097.0000 - fp: 6990.0000 - tn: 277546.0000 - fn: 15711.0000 - accuracy: 0.9601 - precision: 0.9747 - recall: 0.9448 - auc: 0.9946 - prc: 0.9946Restoring model weights from the end of the best epoch: 2.\n",
            "278/278 [==============================] - 16s 58ms/step - loss: 0.0986 - tp: 269097.0000 - fp: 6990.0000 - tn: 277546.0000 - fn: 15711.0000 - accuracy: 0.9601 - precision: 0.9747 - recall: 0.9448 - auc: 0.9946 - prc: 0.9946 - val_loss: 0.0504 - val_tp: 58.0000 - val_fp: 589.0000 - val_tn: 44916.0000 - val_fn: 6.0000 - val_accuracy: 0.9869 - val_precision: 0.0896 - val_recall: 0.9062 - val_auc: 0.9789 - val_prc: 0.6771\n",
            "Epoch 12: early stopping\n"
          ]
        }
      ],
      "source": [
        "resampled_model = make_model()\n",
        "resampled_model.load_weights(initial_weights)\n",
        "\n",
        "# Reset the bias to zero, since this dataset is balanced.\n",
        "output_layer = resampled_model.layers[-1] \n",
        "output_layer.bias.assign([0])\n",
        "\n",
        "val_ds = tf.data.Dataset.from_tensor_slices((val_features, val_labels)).cache()\n",
        "val_ds = val_ds.batch(BATCH_SIZE).prefetch(2) \n",
        "\n",
        "resampled_history = resampled_model.fit(\n",
        "    resampled_ds,\n",
        "    epochs=EPOCHS,\n",
        "    steps_per_epoch=resampled_steps_per_epoch,\n",
        "    callbacks=[early_stopping],\n",
        "    validation_data=val_ds)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "avALvzUp3T_c"
      },
      "source": [
        "If the training process were considering the whole dataset on each gradient update, this oversampling would be basically identical to the class weighting.\n",
        "\n",
        "But when training the model batch-wise, as you did here, the oversampled data provides a smoother gradient signal: Instead of each positive example being shown in one batch with a large weight, they're shown in many different batches each time with a small weight. \n",
        "\n",
        "This smoother gradient signal makes it easier to train the model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "klHZ0HV76VC5"
      },
      "source": [
        "### Check training history\n",
        "\n",
        "Note that the distributions of metrics will be different here, because the training data has a totally different distribution from the validation and test data. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YoUGfr1vuivl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "outputId": "2c4f2b84-a1f8-49dc-b3f6-a4d1e955dfd6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_metrics(resampled_history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1PuH3A2vnwrh"
      },
      "source": [
        "### Re-train\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KFLxRL8eoDE5"
      },
      "source": [
        "Because training is easier on the balanced data, the above training procedure may overfit quickly. \n",
        "\n",
        "So break up the epochs to give the `tf.keras.callbacks.EarlyStopping` finer control over when to stop training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e_yn9I26qAHU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ad10c68-4d73-4209-8647-91dc7e43f022"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/1000\n",
            "20/20 [==============================] - 3s 95ms/step - loss: 1.2206 - tp: 12496.0000 - fp: 14495.0000 - tn: 51590.0000 - fn: 7948.0000 - accuracy: 0.7406 - precision: 0.4630 - recall: 0.6112 - auc: 0.8062 - prc: 0.6422 - val_loss: 1.0122 - val_tp: 61.0000 - val_fp: 34841.0000 - val_tn: 10664.0000 - val_fn: 3.0000 - val_accuracy: 0.2354 - val_precision: 0.0017 - val_recall: 0.9531 - val_auc: 0.8489 - val_prc: 0.0334\n",
            "Epoch 2/1000\n",
            "20/20 [==============================] - 1s 63ms/step - loss: 0.8277 - tp: 15989.0000 - fp: 13466.0000 - tn: 7202.0000 - fn: 4303.0000 - accuracy: 0.5662 - precision: 0.5428 - recall: 0.7879 - auc: 0.7348 - prc: 0.8128 - val_loss: 0.9382 - val_tp: 62.0000 - val_fp: 32218.0000 - val_tn: 13287.0000 - val_fn: 2.0000 - val_accuracy: 0.2929 - val_precision: 0.0019 - val_recall: 0.9688 - val_auc: 0.9321 - val_prc: 0.2633\n",
            "Epoch 3/1000\n",
            "20/20 [==============================] - 1s 61ms/step - loss: 0.6473 - tp: 17710.0000 - fp: 12206.0000 - tn: 8309.0000 - fn: 2735.0000 - accuracy: 0.6352 - precision: 0.5920 - recall: 0.8662 - auc: 0.8327 - prc: 0.8829 - val_loss: 0.8464 - val_tp: 62.0000 - val_fp: 27746.0000 - val_tn: 17759.0000 - val_fn: 2.0000 - val_accuracy: 0.3911 - val_precision: 0.0022 - val_recall: 0.9688 - val_auc: 0.9385 - val_prc: 0.4402\n",
            "Epoch 4/1000\n",
            "20/20 [==============================] - 1s 61ms/step - loss: 0.5544 - tp: 18412.0000 - fp: 10958.0000 - tn: 9563.0000 - fn: 2027.0000 - accuracy: 0.6830 - precision: 0.6269 - recall: 0.9008 - auc: 0.8811 - prc: 0.9170 - val_loss: 0.7529 - val_tp: 61.0000 - val_fp: 21846.0000 - val_tn: 23659.0000 - val_fn: 3.0000 - val_accuracy: 0.5205 - val_precision: 0.0028 - val_recall: 0.9531 - val_auc: 0.9415 - val_prc: 0.5780\n",
            "Epoch 5/1000\n",
            "20/20 [==============================] - 1s 66ms/step - loss: 0.4857 - tp: 18868.0000 - fp: 9194.0000 - tn: 11226.0000 - fn: 1672.0000 - accuracy: 0.7347 - precision: 0.6724 - recall: 0.9186 - auc: 0.9101 - prc: 0.9363 - val_loss: 0.6678 - val_tp: 60.0000 - val_fp: 15709.0000 - val_tn: 29796.0000 - val_fn: 4.0000 - val_accuracy: 0.6552 - val_precision: 0.0038 - val_recall: 0.9375 - val_auc: 0.9446 - val_prc: 0.6466\n",
            "Epoch 6/1000\n",
            "20/20 [==============================] - 1s 63ms/step - loss: 0.4444 - tp: 18864.0000 - fp: 7907.0000 - tn: 12480.0000 - fn: 1709.0000 - accuracy: 0.7652 - precision: 0.7046 - recall: 0.9169 - auc: 0.9193 - prc: 0.9428 - val_loss: 0.5958 - val_tp: 58.0000 - val_fp: 11120.0000 - val_tn: 34385.0000 - val_fn: 6.0000 - val_accuracy: 0.7558 - val_precision: 0.0052 - val_recall: 0.9062 - val_auc: 0.9477 - val_prc: 0.6954\n",
            "Epoch 7/1000\n",
            "20/20 [==============================] - 1s 61ms/step - loss: 0.4031 - tp: 18870.0000 - fp: 6564.0000 - tn: 13901.0000 - fn: 1625.0000 - accuracy: 0.8001 - precision: 0.7419 - recall: 0.9207 - auc: 0.9313 - prc: 0.9509 - val_loss: 0.5337 - val_tp: 58.0000 - val_fp: 8170.0000 - val_tn: 37335.0000 - val_fn: 6.0000 - val_accuracy: 0.8206 - val_precision: 0.0070 - val_recall: 0.9062 - val_auc: 0.9501 - val_prc: 0.7155\n",
            "Epoch 8/1000\n",
            "20/20 [==============================] - 1s 61ms/step - loss: 0.3746 - tp: 18931.0000 - fp: 5558.0000 - tn: 14768.0000 - fn: 1703.0000 - accuracy: 0.8227 - precision: 0.7730 - recall: 0.9175 - auc: 0.9354 - prc: 0.9546 - val_loss: 0.4823 - val_tp: 57.0000 - val_fp: 6281.0000 - val_tn: 39224.0000 - val_fn: 7.0000 - val_accuracy: 0.8620 - val_precision: 0.0090 - val_recall: 0.8906 - val_auc: 0.9526 - val_prc: 0.7334\n",
            "Epoch 9/1000\n",
            "20/20 [==============================] - 1s 66ms/step - loss: 0.3537 - tp: 18794.0000 - fp: 4814.0000 - tn: 15606.0000 - fn: 1746.0000 - accuracy: 0.8398 - precision: 0.7961 - recall: 0.9150 - auc: 0.9395 - prc: 0.9575 - val_loss: 0.4398 - val_tp: 56.0000 - val_fp: 5080.0000 - val_tn: 40425.0000 - val_fn: 8.0000 - val_accuracy: 0.8883 - val_precision: 0.0109 - val_recall: 0.8750 - val_auc: 0.9556 - val_prc: 0.7405\n",
            "Epoch 10/1000\n",
            "20/20 [==============================] - 1s 56ms/step - loss: 0.3313 - tp: 18621.0000 - fp: 4201.0000 - tn: 16408.0000 - fn: 1730.0000 - accuracy: 0.8552 - precision: 0.8159 - recall: 0.9150 - auc: 0.9458 - prc: 0.9606 - val_loss: 0.4038 - val_tp: 56.0000 - val_fp: 4309.0000 - val_tn: 41196.0000 - val_fn: 8.0000 - val_accuracy: 0.9053 - val_precision: 0.0128 - val_recall: 0.8750 - val_auc: 0.9575 - val_prc: 0.7422\n",
            "Epoch 11/1000\n",
            "20/20 [==============================] - 1s 69ms/step - loss: 0.3138 - tp: 18697.0000 - fp: 3637.0000 - tn: 16917.0000 - fn: 1709.0000 - accuracy: 0.8695 - precision: 0.8372 - recall: 0.9163 - auc: 0.9503 - prc: 0.9637 - val_loss: 0.3724 - val_tp: 56.0000 - val_fp: 3653.0000 - val_tn: 41852.0000 - val_fn: 8.0000 - val_accuracy: 0.9197 - val_precision: 0.0151 - val_recall: 0.8750 - val_auc: 0.9590 - val_prc: 0.7549\n",
            "Epoch 12/1000\n",
            "20/20 [==============================] - 1s 63ms/step - loss: 0.3006 - tp: 18641.0000 - fp: 3245.0000 - tn: 17335.0000 - fn: 1739.0000 - accuracy: 0.8783 - precision: 0.8517 - recall: 0.9147 - auc: 0.9523 - prc: 0.9650 - val_loss: 0.3453 - val_tp: 56.0000 - val_fp: 3160.0000 - val_tn: 42345.0000 - val_fn: 8.0000 - val_accuracy: 0.9305 - val_precision: 0.0174 - val_recall: 0.8750 - val_auc: 0.9606 - val_prc: 0.7683\n",
            "Epoch 13/1000\n",
            "20/20 [==============================] - 1s 65ms/step - loss: 0.2857 - tp: 18754.0000 - fp: 2880.0000 - tn: 17461.0000 - fn: 1865.0000 - accuracy: 0.8842 - precision: 0.8669 - recall: 0.9095 - auc: 0.9538 - prc: 0.9668 - val_loss: 0.3227 - val_tp: 56.0000 - val_fp: 2838.0000 - val_tn: 42667.0000 - val_fn: 8.0000 - val_accuracy: 0.9375 - val_precision: 0.0194 - val_recall: 0.8750 - val_auc: 0.9620 - val_prc: 0.7580\n",
            "Epoch 14/1000\n",
            "20/20 [==============================] - 1s 58ms/step - loss: 0.2797 - tp: 18542.0000 - fp: 2622.0000 - tn: 17914.0000 - fn: 1882.0000 - accuracy: 0.8900 - precision: 0.8761 - recall: 0.9079 - auc: 0.9553 - prc: 0.9669 - val_loss: 0.3035 - val_tp: 56.0000 - val_fp: 2587.0000 - val_tn: 42918.0000 - val_fn: 8.0000 - val_accuracy: 0.9431 - val_precision: 0.0212 - val_recall: 0.8750 - val_auc: 0.9638 - val_prc: 0.7585\n",
            "Epoch 15/1000\n",
            "20/20 [==============================] - 1s 58ms/step - loss: 0.2647 - tp: 18770.0000 - fp: 2401.0000 - tn: 18028.0000 - fn: 1761.0000 - accuracy: 0.8984 - precision: 0.8866 - recall: 0.9142 - auc: 0.9605 - prc: 0.9707 - val_loss: 0.2854 - val_tp: 56.0000 - val_fp: 2329.0000 - val_tn: 43176.0000 - val_fn: 8.0000 - val_accuracy: 0.9487 - val_precision: 0.0235 - val_recall: 0.8750 - val_auc: 0.9652 - val_prc: 0.7585\n",
            "Epoch 16/1000\n",
            "20/20 [==============================] - 1s 60ms/step - loss: 0.2584 - tp: 18723.0000 - fp: 2221.0000 - tn: 18192.0000 - fn: 1824.0000 - accuracy: 0.9012 - precision: 0.8940 - recall: 0.9112 - auc: 0.9612 - prc: 0.9712 - val_loss: 0.2702 - val_tp: 56.0000 - val_fp: 2111.0000 - val_tn: 43394.0000 - val_fn: 8.0000 - val_accuracy: 0.9535 - val_precision: 0.0258 - val_recall: 0.8750 - val_auc: 0.9669 - val_prc: 0.7711\n",
            "Epoch 17/1000\n",
            "20/20 [==============================] - 1s 58ms/step - loss: 0.2492 - tp: 18733.0000 - fp: 1949.0000 - tn: 18428.0000 - fn: 1850.0000 - accuracy: 0.9073 - precision: 0.9058 - recall: 0.9101 - auc: 0.9630 - prc: 0.9723 - val_loss: 0.2575 - val_tp: 56.0000 - val_fp: 1990.0000 - val_tn: 43515.0000 - val_fn: 8.0000 - val_accuracy: 0.9562 - val_precision: 0.0274 - val_recall: 0.8750 - val_auc: 0.9682 - val_prc: 0.7728\n",
            "Epoch 18/1000\n",
            "20/20 [==============================] - 1s 60ms/step - loss: 0.2431 - tp: 18968.0000 - fp: 1825.0000 - tn: 18409.0000 - fn: 1758.0000 - accuracy: 0.9125 - precision: 0.9122 - recall: 0.9152 - auc: 0.9648 - prc: 0.9740 - val_loss: 0.2456 - val_tp: 56.0000 - val_fp: 1874.0000 - val_tn: 43631.0000 - val_fn: 8.0000 - val_accuracy: 0.9587 - val_precision: 0.0290 - val_recall: 0.8750 - val_auc: 0.9690 - val_prc: 0.7719\n",
            "Epoch 19/1000\n",
            "20/20 [==============================] - 1s 64ms/step - loss: 0.2368 - tp: 18651.0000 - fp: 1792.0000 - tn: 18716.0000 - fn: 1801.0000 - accuracy: 0.9123 - precision: 0.9123 - recall: 0.9119 - auc: 0.9664 - prc: 0.9746 - val_loss: 0.2339 - val_tp: 56.0000 - val_fp: 1743.0000 - val_tn: 43762.0000 - val_fn: 8.0000 - val_accuracy: 0.9616 - val_precision: 0.0311 - val_recall: 0.8750 - val_auc: 0.9700 - val_prc: 0.7718\n",
            "Epoch 20/1000\n",
            "20/20 [==============================] - 1s 61ms/step - loss: 0.2317 - tp: 18580.0000 - fp: 1689.0000 - tn: 18941.0000 - fn: 1750.0000 - accuracy: 0.9160 - precision: 0.9167 - recall: 0.9139 - auc: 0.9672 - prc: 0.9751 - val_loss: 0.2230 - val_tp: 56.0000 - val_fp: 1624.0000 - val_tn: 43881.0000 - val_fn: 8.0000 - val_accuracy: 0.9642 - val_precision: 0.0333 - val_recall: 0.8750 - val_auc: 0.9708 - val_prc: 0.7726\n",
            "Epoch 21/1000\n",
            "20/20 [==============================] - 1s 67ms/step - loss: 0.2215 - tp: 18809.0000 - fp: 1441.0000 - tn: 19000.0000 - fn: 1710.0000 - accuracy: 0.9231 - precision: 0.9288 - recall: 0.9167 - auc: 0.9706 - prc: 0.9774 - val_loss: 0.2137 - val_tp: 56.0000 - val_fp: 1533.0000 - val_tn: 43972.0000 - val_fn: 8.0000 - val_accuracy: 0.9662 - val_precision: 0.0352 - val_recall: 0.8750 - val_auc: 0.9718 - val_prc: 0.7728\n",
            "Epoch 22/1000\n",
            "20/20 [==============================] - 1s 62ms/step - loss: 0.2174 - tp: 18765.0000 - fp: 1434.0000 - tn: 19070.0000 - fn: 1691.0000 - accuracy: 0.9237 - precision: 0.9290 - recall: 0.9173 - auc: 0.9721 - prc: 0.9782 - val_loss: 0.2047 - val_tp: 56.0000 - val_fp: 1453.0000 - val_tn: 44052.0000 - val_fn: 8.0000 - val_accuracy: 0.9679 - val_precision: 0.0371 - val_recall: 0.8750 - val_auc: 0.9729 - val_prc: 0.7720\n",
            "Epoch 23/1000\n",
            "20/20 [==============================] - 1s 58ms/step - loss: 0.2177 - tp: 18658.0000 - fp: 1361.0000 - tn: 19162.0000 - fn: 1779.0000 - accuracy: 0.9233 - precision: 0.9320 - recall: 0.9130 - auc: 0.9708 - prc: 0.9774 - val_loss: 0.1963 - val_tp: 56.0000 - val_fp: 1385.0000 - val_tn: 44120.0000 - val_fn: 8.0000 - val_accuracy: 0.9694 - val_precision: 0.0389 - val_recall: 0.8750 - val_auc: 0.9735 - val_prc: 0.7718\n",
            "Epoch 24/1000\n",
            "20/20 [==============================] - 1s 71ms/step - loss: 0.2111 - tp: 18755.0000 - fp: 1289.0000 - tn: 19190.0000 - fn: 1726.0000 - accuracy: 0.9264 - precision: 0.9357 - recall: 0.9157 - auc: 0.9725 - prc: 0.9787 - val_loss: 0.1894 - val_tp: 56.0000 - val_fp: 1338.0000 - val_tn: 44167.0000 - val_fn: 8.0000 - val_accuracy: 0.9705 - val_precision: 0.0402 - val_recall: 0.8750 - val_auc: 0.9742 - val_prc: 0.7719\n",
            "Epoch 25/1000\n",
            "20/20 [==============================] - 1s 58ms/step - loss: 0.2078 - tp: 18632.0000 - fp: 1243.0000 - tn: 19357.0000 - fn: 1728.0000 - accuracy: 0.9275 - precision: 0.9375 - recall: 0.9151 - auc: 0.9734 - prc: 0.9790 - val_loss: 0.1828 - val_tp: 56.0000 - val_fp: 1288.0000 - val_tn: 44217.0000 - val_fn: 8.0000 - val_accuracy: 0.9716 - val_precision: 0.0417 - val_recall: 0.8750 - val_auc: 0.9749 - val_prc: 0.7719\n",
            "Epoch 26/1000\n",
            "20/20 [==============================] - 1s 62ms/step - loss: 0.2066 - tp: 18646.0000 - fp: 1218.0000 - tn: 19331.0000 - fn: 1765.0000 - accuracy: 0.9272 - precision: 0.9387 - recall: 0.9135 - auc: 0.9734 - prc: 0.9792 - val_loss: 0.1763 - val_tp: 57.0000 - val_fp: 1236.0000 - val_tn: 44269.0000 - val_fn: 7.0000 - val_accuracy: 0.9727 - val_precision: 0.0441 - val_recall: 0.8906 - val_auc: 0.9756 - val_prc: 0.7728\n",
            "Epoch 27/1000\n",
            "20/20 [==============================] - 1s 65ms/step - loss: 0.2019 - tp: 18634.0000 - fp: 1206.0000 - tn: 19405.0000 - fn: 1715.0000 - accuracy: 0.9287 - precision: 0.9392 - recall: 0.9157 - auc: 0.9748 - prc: 0.9802 - val_loss: 0.1705 - val_tp: 57.0000 - val_fp: 1196.0000 - val_tn: 44309.0000 - val_fn: 7.0000 - val_accuracy: 0.9736 - val_precision: 0.0455 - val_recall: 0.8906 - val_auc: 0.9761 - val_prc: 0.7727\n",
            "Epoch 28/1000\n",
            "20/20 [==============================] - 1s 64ms/step - loss: 0.1991 - tp: 18543.0000 - fp: 1122.0000 - tn: 19530.0000 - fn: 1765.0000 - accuracy: 0.9295 - precision: 0.9429 - recall: 0.9131 - auc: 0.9751 - prc: 0.9805 - val_loss: 0.1655 - val_tp: 57.0000 - val_fp: 1176.0000 - val_tn: 44329.0000 - val_fn: 7.0000 - val_accuracy: 0.9740 - val_precision: 0.0462 - val_recall: 0.8906 - val_auc: 0.9766 - val_prc: 0.7729\n",
            "Epoch 29/1000\n",
            "20/20 [==============================] - 1s 60ms/step - loss: 0.1923 - tp: 18703.0000 - fp: 996.0000 - tn: 19518.0000 - fn: 1743.0000 - accuracy: 0.9331 - precision: 0.9494 - recall: 0.9148 - auc: 0.9770 - prc: 0.9818 - val_loss: 0.1607 - val_tp: 57.0000 - val_fp: 1152.0000 - val_tn: 44353.0000 - val_fn: 7.0000 - val_accuracy: 0.9746 - val_precision: 0.0471 - val_recall: 0.8906 - val_auc: 0.9771 - val_prc: 0.7728\n",
            "Epoch 30/1000\n",
            "20/20 [==============================] - 1s 58ms/step - loss: 0.1929 - tp: 18448.0000 - fp: 1077.0000 - tn: 19699.0000 - fn: 1736.0000 - accuracy: 0.9313 - precision: 0.9448 - recall: 0.9140 - auc: 0.9765 - prc: 0.9811 - val_loss: 0.1558 - val_tp: 57.0000 - val_fp: 1119.0000 - val_tn: 44386.0000 - val_fn: 7.0000 - val_accuracy: 0.9753 - val_precision: 0.0485 - val_recall: 0.8906 - val_auc: 0.9775 - val_prc: 0.7728\n",
            "Epoch 31/1000\n",
            "20/20 [==============================] - 1s 56ms/step - loss: 0.1877 - tp: 18924.0000 - fp: 1000.0000 - tn: 19264.0000 - fn: 1772.0000 - accuracy: 0.9323 - precision: 0.9498 - recall: 0.9144 - auc: 0.9777 - prc: 0.9826 - val_loss: 0.1525 - val_tp: 58.0000 - val_fp: 1119.0000 - val_tn: 44386.0000 - val_fn: 6.0000 - val_accuracy: 0.9753 - val_precision: 0.0493 - val_recall: 0.9062 - val_auc: 0.9782 - val_prc: 0.7743\n",
            "Epoch 32/1000\n",
            "20/20 [==============================] - 1s 68ms/step - loss: 0.1858 - tp: 18856.0000 - fp: 1030.0000 - tn: 19365.0000 - fn: 1709.0000 - accuracy: 0.9331 - precision: 0.9482 - recall: 0.9169 - auc: 0.9783 - prc: 0.9827 - val_loss: 0.1486 - val_tp: 58.0000 - val_fp: 1096.0000 - val_tn: 44409.0000 - val_fn: 6.0000 - val_accuracy: 0.9758 - val_precision: 0.0503 - val_recall: 0.9062 - val_auc: 0.9785 - val_prc: 0.7760\n",
            "Epoch 33/1000\n",
            "20/20 [==============================] - 1s 65ms/step - loss: 0.1829 - tp: 18624.0000 - fp: 941.0000 - tn: 19714.0000 - fn: 1681.0000 - accuracy: 0.9360 - precision: 0.9519 - recall: 0.9172 - auc: 0.9793 - prc: 0.9832 - val_loss: 0.1442 - val_tp: 58.0000 - val_fp: 1073.0000 - val_tn: 44432.0000 - val_fn: 6.0000 - val_accuracy: 0.9763 - val_precision: 0.0513 - val_recall: 0.9062 - val_auc: 0.9786 - val_prc: 0.7770\n",
            "Epoch 34/1000\n",
            "20/20 [==============================] - 1s 72ms/step - loss: 0.1827 - tp: 18705.0000 - fp: 931.0000 - tn: 19628.0000 - fn: 1696.0000 - accuracy: 0.9359 - precision: 0.9526 - recall: 0.9169 - auc: 0.9789 - prc: 0.9831 - val_loss: 0.1410 - val_tp: 58.0000 - val_fp: 1068.0000 - val_tn: 44437.0000 - val_fn: 6.0000 - val_accuracy: 0.9764 - val_precision: 0.0515 - val_recall: 0.9062 - val_auc: 0.9792 - val_prc: 0.7768\n",
            "Epoch 35/1000\n",
            "20/20 [==============================] - 1s 61ms/step - loss: 0.1802 - tp: 18719.0000 - fp: 962.0000 - tn: 19621.0000 - fn: 1658.0000 - accuracy: 0.9360 - precision: 0.9511 - recall: 0.9186 - auc: 0.9795 - prc: 0.9835 - val_loss: 0.1372 - val_tp: 58.0000 - val_fp: 1043.0000 - val_tn: 44462.0000 - val_fn: 6.0000 - val_accuracy: 0.9770 - val_precision: 0.0527 - val_recall: 0.9062 - val_auc: 0.9793 - val_prc: 0.7771\n",
            "Epoch 36/1000\n",
            "20/20 [==============================] - 1s 59ms/step - loss: 0.1802 - tp: 18922.0000 - fp: 890.0000 - tn: 19473.0000 - fn: 1675.0000 - accuracy: 0.9374 - precision: 0.9551 - recall: 0.9187 - auc: 0.9796 - prc: 0.9837 - val_loss: 0.1341 - val_tp: 58.0000 - val_fp: 1026.0000 - val_tn: 44479.0000 - val_fn: 6.0000 - val_accuracy: 0.9774 - val_precision: 0.0535 - val_recall: 0.9062 - val_auc: 0.9798 - val_prc: 0.7773\n",
            "Epoch 37/1000\n",
            "20/20 [==============================] - 1s 65ms/step - loss: 0.1738 - tp: 18896.0000 - fp: 853.0000 - tn: 19536.0000 - fn: 1675.0000 - accuracy: 0.9383 - precision: 0.9568 - recall: 0.9186 - auc: 0.9807 - prc: 0.9847 - val_loss: 0.1313 - val_tp: 58.0000 - val_fp: 1021.0000 - val_tn: 44484.0000 - val_fn: 6.0000 - val_accuracy: 0.9775 - val_precision: 0.0538 - val_recall: 0.9062 - val_auc: 0.9795 - val_prc: 0.7771\n",
            "Epoch 38/1000\n",
            "20/20 [==============================] - 1s 64ms/step - loss: 0.1726 - tp: 18924.0000 - fp: 833.0000 - tn: 19530.0000 - fn: 1673.0000 - accuracy: 0.9388 - precision: 0.9578 - recall: 0.9188 - auc: 0.9814 - prc: 0.9850 - val_loss: 0.1295 - val_tp: 58.0000 - val_fp: 1040.0000 - val_tn: 44465.0000 - val_fn: 6.0000 - val_accuracy: 0.9770 - val_precision: 0.0528 - val_recall: 0.9062 - val_auc: 0.9798 - val_prc: 0.7640\n",
            "Epoch 39/1000\n",
            "20/20 [==============================] - 1s 61ms/step - loss: 0.1722 - tp: 18804.0000 - fp: 850.0000 - tn: 19648.0000 - fn: 1658.0000 - accuracy: 0.9388 - precision: 0.9568 - recall: 0.9190 - auc: 0.9816 - prc: 0.9852 - val_loss: 0.1266 - val_tp: 58.0000 - val_fp: 1024.0000 - val_tn: 44481.0000 - val_fn: 6.0000 - val_accuracy: 0.9774 - val_precision: 0.0536 - val_recall: 0.9062 - val_auc: 0.9803 - val_prc: 0.7642\n",
            "Epoch 40/1000\n",
            "20/20 [==============================] - 1s 61ms/step - loss: 0.1697 - tp: 18798.0000 - fp: 833.0000 - tn: 19691.0000 - fn: 1638.0000 - accuracy: 0.9397 - precision: 0.9576 - recall: 0.9198 - auc: 0.9817 - prc: 0.9852 - val_loss: 0.1242 - val_tp: 58.0000 - val_fp: 1021.0000 - val_tn: 44484.0000 - val_fn: 6.0000 - val_accuracy: 0.9775 - val_precision: 0.0538 - val_recall: 0.9062 - val_auc: 0.9803 - val_prc: 0.7644\n",
            "Epoch 41/1000\n",
            "20/20 [==============================] - 1s 58ms/step - loss: 0.1679 - tp: 18937.0000 - fp: 815.0000 - tn: 19597.0000 - fn: 1611.0000 - accuracy: 0.9408 - precision: 0.9587 - recall: 0.9216 - auc: 0.9824 - prc: 0.9857 - val_loss: 0.1213 - val_tp: 58.0000 - val_fp: 1004.0000 - val_tn: 44501.0000 - val_fn: 6.0000 - val_accuracy: 0.9778 - val_precision: 0.0546 - val_recall: 0.9062 - val_auc: 0.9808 - val_prc: 0.7645\n",
            "Epoch 42/1000\n",
            "20/20 [==============================] - 1s 60ms/step - loss: 0.1657 - tp: 18922.0000 - fp: 796.0000 - tn: 19619.0000 - fn: 1623.0000 - accuracy: 0.9409 - precision: 0.9596 - recall: 0.9210 - auc: 0.9827 - prc: 0.9862 - val_loss: 0.1192 - val_tp: 58.0000 - val_fp: 1003.0000 - val_tn: 44502.0000 - val_fn: 6.0000 - val_accuracy: 0.9779 - val_precision: 0.0547 - val_recall: 0.9062 - val_auc: 0.9807 - val_prc: 0.7521\n",
            "Epoch 43/1000\n",
            "20/20 [==============================] - 1s 66ms/step - loss: 0.1666 - tp: 18906.0000 - fp: 781.0000 - tn: 19658.0000 - fn: 1615.0000 - accuracy: 0.9415 - precision: 0.9603 - recall: 0.9213 - auc: 0.9824 - prc: 0.9856 - val_loss: 0.1171 - val_tp: 58.0000 - val_fp: 996.0000 - val_tn: 44509.0000 - val_fn: 6.0000 - val_accuracy: 0.9780 - val_precision: 0.0550 - val_recall: 0.9062 - val_auc: 0.9810 - val_prc: 0.7643\n",
            "Epoch 44/1000\n",
            "20/20 [==============================] - 1s 73ms/step - loss: 0.1629 - tp: 18916.0000 - fp: 790.0000 - tn: 19645.0000 - fn: 1609.0000 - accuracy: 0.9414 - precision: 0.9599 - recall: 0.9216 - auc: 0.9835 - prc: 0.9864 - val_loss: 0.1148 - val_tp: 58.0000 - val_fp: 987.0000 - val_tn: 44518.0000 - val_fn: 6.0000 - val_accuracy: 0.9782 - val_precision: 0.0555 - val_recall: 0.9062 - val_auc: 0.9809 - val_prc: 0.7644\n",
            "Epoch 45/1000\n",
            "20/20 [==============================] - 1s 60ms/step - loss: 0.1612 - tp: 18975.0000 - fp: 757.0000 - tn: 19646.0000 - fn: 1582.0000 - accuracy: 0.9429 - precision: 0.9616 - recall: 0.9230 - auc: 0.9833 - prc: 0.9866 - val_loss: 0.1130 - val_tp: 58.0000 - val_fp: 975.0000 - val_tn: 44530.0000 - val_fn: 6.0000 - val_accuracy: 0.9785 - val_precision: 0.0561 - val_recall: 0.9062 - val_auc: 0.9811 - val_prc: 0.7645\n",
            "Epoch 46/1000\n",
            "20/20 [==============================] - ETA: 0s - loss: 0.1580 - tp: 18963.0000 - fp: 767.0000 - tn: 19662.0000 - fn: 1568.0000 - accuracy: 0.9430 - precision: 0.9611 - recall: 0.9236 - auc: 0.9845 - prc: 0.9873Restoring model weights from the end of the best epoch: 36.\n",
            "20/20 [==============================] - 1s 67ms/step - loss: 0.1580 - tp: 18963.0000 - fp: 767.0000 - tn: 19662.0000 - fn: 1568.0000 - accuracy: 0.9430 - precision: 0.9611 - recall: 0.9236 - auc: 0.9845 - prc: 0.9873 - val_loss: 0.1113 - val_tp: 58.0000 - val_fp: 975.0000 - val_tn: 44530.0000 - val_fn: 6.0000 - val_accuracy: 0.9785 - val_precision: 0.0561 - val_recall: 0.9062 - val_auc: 0.9814 - val_prc: 0.7645\n",
            "Epoch 46: early stopping\n"
          ]
        }
      ],
      "source": [
        "resampled_model = make_model()\n",
        "resampled_model.load_weights(initial_weights)\n",
        "\n",
        "# Reset the bias to zero, since this dataset is balanced.\n",
        "output_layer = resampled_model.layers[-1] \n",
        "output_layer.bias.assign([0])\n",
        "\n",
        "resampled_history = resampled_model.fit(\n",
        "    resampled_ds,\n",
        "    # These are not real epochs\n",
        "    steps_per_epoch=20,\n",
        "    epochs=10*EPOCHS,\n",
        "    callbacks=[early_stopping],\n",
        "    validation_data=(val_ds))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UuJYKv0gpBK1"
      },
      "source": [
        "### Re-check training history"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FMycrpJwn39w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "outputId": "6500e9ac-f815-439c-f9e0-d688e37dfa23"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_metrics(resampled_history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bUuE5HOWZiwP"
      },
      "source": [
        "### Evaluate metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C0fmHSgXxFdW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad47f88f-d530-4899-e66e-0de876dd5154"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "90/90 [==============================] - 0s 901us/step\n",
            "28/28 [==============================] - 0s 1ms/step\n"
          ]
        }
      ],
      "source": [
        "train_predictions_resampled = resampled_model.predict(train_features, batch_size=BATCH_SIZE)\n",
        "test_predictions_resampled = resampled_model.predict(test_features, batch_size=BATCH_SIZE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FO0mMOYUDWFk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 636
        },
        "outputId": "ccfd144f-9601-4f5c-bc3a-0578024b88c6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loss :  0.13555769622325897\n",
            "tp :  89.0\n",
            "fp :  1355.0\n",
            "tn :  55509.0\n",
            "fn :  9.0\n",
            "accuracy :  0.9760541915893555\n",
            "precision :  0.06163435056805611\n",
            "recall :  0.9081632494926453\n",
            "auc :  0.9771085381507874\n",
            "prc :  0.732808530330658\n",
            "\n",
            "Legitimate Transactions Detected (True Negatives):  55509\n",
            "Legitimate Transactions Incorrectly Detected (False Positives):  1355\n",
            "Fraudulent Transactions Missed (False Negatives):  9\n",
            "Fraudulent Transactions Detected (True Positives):  89\n",
            "Total Fraudulent Transactions:  98\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "resampled_results = resampled_model.evaluate(test_features, test_labels,\n",
        "                                             batch_size=BATCH_SIZE, verbose=0)\n",
        "for name, value in zip(resampled_model.metrics_names, resampled_results):\n",
        "  print(name, ': ', value)\n",
        "print()\n",
        "\n",
        "plot_cm(test_labels, test_predictions_resampled)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_xYozM1IIITq"
      },
      "source": [
        "### Plot the ROC"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fye_CiuYrZ1U",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "outputId": "2ac4a19f-b25b-4909-b51b-942358c50238"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_roc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n",
        "plot_roc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n",
        "\n",
        "plot_roc(\"Train Weighted\", train_labels, train_predictions_weighted, color=colors[1])\n",
        "plot_roc(\"Test Weighted\", test_labels, test_predictions_weighted, color=colors[1], linestyle='--')\n",
        "\n",
        "plot_roc(\"Train Resampled\", train_labels, train_predictions_resampled, color=colors[2])\n",
        "plot_roc(\"Test Resampled\", test_labels, test_predictions_resampled, color=colors[2], linestyle='--')\n",
        "plt.legend(loc='lower right');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vayGnv0VOe_v"
      },
      "source": [
        "### Plot the AUPRC\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wgWXQ8aeOhCZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "outputId": "5a5cc0bd-08b0-4b30-f929-3db7c436a3ec"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_prc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n",
        "plot_prc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n",
        "\n",
        "plot_prc(\"Train Weighted\", train_labels, train_predictions_weighted, color=colors[1])\n",
        "plot_prc(\"Test Weighted\", test_labels, test_predictions_weighted, color=colors[1], linestyle='--')\n",
        "\n",
        "plot_prc(\"Train Resampled\", train_labels, train_predictions_resampled, color=colors[2])\n",
        "plot_prc(\"Test Resampled\", test_labels, test_predictions_resampled, color=colors[2], linestyle='--')\n",
        "plt.legend(loc='lower right');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3o3f0ywl8uqW"
      },
      "source": [
        "## Applying this tutorial to your problem\n",
        "\n",
        "Imbalanced data classification is an inherently difficult task since there are so few samples to learn from. You should always start with the data first and do your best to collect as many samples as possible and give substantial thought to what features may be relevant so the model can get the most out of your minority class. At some point your model may struggle to improve and yield the results you want, so it is important to keep in mind the context of your problem and the trade offs between different types of errors."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}