deep_learning/structured/Classification_on_Imbalanced_Data.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Classification_on_Imbalanced_Data.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true,"authorship_tag":"ABX9TyOT15vJAQmJFn+M+C5Swbc/"},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"BNyvdA5Ne6dg","colab_type":"text"},"source":["This tutorial demonstrates how to classify a highly imbalanced data in which the number of examples in a one class grealy outnumbers the examples in another. You will work with the [Credit Card Fraud Detection Dataset](https://www.kaggle.com/mlg-ulb/creditcardfraud) hosted on the kaggle.\n","\n","You are trying to detect 492 fraudulent transactions from 284,807 transactions in total. In this tutorial, you are going to build a model with the help of class weights to learn from the imbalanced data.\n","\n","In general, the following are common techniques to deal with the imbalanced data.\n","* Class Weighting\n","* Oversampleing"]},{"cell_type":"code","metadata":{"id":"xiQxQewuRoHO","colab_type":"code","colab":{}},"source":["!pip uninstall -y tensorflow  # remove the default tensorflow\n","!pip install -q tf-nightly sklearn"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"MJViquQ7Ry3B","colab_type":"code","outputId":"06a89823-746f-4088-98a3-5d76ea83b527","executionInfo":{"status":"ok","timestamp":1584595813701,"user_tz":-480,"elapsed":5533,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["import tensorflow as tf\n","import os\n","import tempfile\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","import sklearn\n","from sklearn.metrics import confusion_matrix\n","from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import StandardScaler\n","\n","print(\"Tensorflow Version: {}\".format(tf.__version__))\n","print(\"GPU {} available.\".format(\"is\" if tf.config.experimental.list_physical_devices(\"GPU\") else \"not\"))"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Tensorflow Version: 2.2.0-dev20200318\n","GPU is available.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"BHI9lNTtf5_Z","colab_type":"code","colab":{}},"source":["from google.colab import drive\n","drive.mount(\"/content/gdrive\")"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZeZHqHbCSsjo","colab_type":"code","outputId":"ff64eecb-b15a-4a2e-c946-99793272992e","executionInfo":{"status":"ok","timestamp":1584595904089,"user_tz":-480,"elapsed":1511,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["mpl.rcParams['figure.figsize'] = (12, 10)\n","colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n","print(colors)"],"execution_count":3,"outputs":[{"output_type":"stream","text":["['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"ts4yYg_6gRdk","colab_type":"text"},"source":["# Data Preprocessing and Exploration"]},{"cell_type":"markdown","metadata":{"id":"BiZV8yB3gZhD","colab_type":"text"},"source":["## Download the Kaggle Credit Card Fraud Dataset"]},{"cell_type":"code","metadata":{"id":"GwicDJNif83T","colab_type":"code","outputId":"3eccb542-ef03-4aab-daf7-e69956f0851c","executionInfo":{"status":"ok","timestamp":1584595908375,"user_tz":-480,"elapsed":5777,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":224}},"source":["raw_df = pd.read_csv(\n","  filepath_or_buffer='https://storage.googleapis.com/download.tensorflow.org/data/creditcard.csv')\n","raw_df.head()"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Time</th>\n","      <th>V1</th>\n","      <th>V2</th>\n","      <th>V3</th>\n","      <th>V4</th>\n","      <th>V5</th>\n","      <th>V6</th>\n","      <th>V7</th>\n","      <th>V8</th>\n","      <th>V9</th>\n","      <th>V10</th>\n","      <th>V11</th>\n","      <th>V12</th>\n","      <th>V13</th>\n","      <th>V14</th>\n","      <th>V15</th>\n","      <th>V16</th>\n","      <th>V17</th>\n","      <th>V18</th>\n","      <th>V19</th>\n","      <th>V20</th>\n","      <th>V21</th>\n","      <th>V22</th>\n","      <th>V23</th>\n","      <th>V24</th>\n","      <th>V25</th>\n","      <th>V26</th>\n","      <th>V27</th>\n","      <th>V28</th>\n","      <th>Amount</th>\n","      <th>Class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.0</td>\n","      <td>-1.359807</td>\n","      <td>-0.072781</td>\n","      <td>2.536347</td>\n","      <td>1.378155</td>\n","      <td>-0.338321</td>\n","      <td>0.462388</td>\n","      <td>0.239599</td>\n","      <td>0.098698</td>\n","      <td>0.363787</td>\n","      <td>0.090794</td>\n","      <td>-0.551600</td>\n","      <td>-0.617801</td>\n","      <td>-0.991390</td>\n","      <td>-0.311169</td>\n","      <td>1.468177</td>\n","      <td>-0.470401</td>\n","      <td>0.207971</td>\n","      <td>0.025791</td>\n","      <td>0.403993</td>\n","      <td>0.251412</td>\n","      <td>-0.018307</td>\n","      <td>0.277838</td>\n","      <td>-0.110474</td>\n","      <td>0.066928</td>\n","      <td>0.128539</td>\n","      <td>-0.189115</td>\n","      <td>0.133558</td>\n","      <td>-0.021053</td>\n","      <td>149.62</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.0</td>\n","      <td>1.191857</td>\n","      <td>0.266151</td>\n","      <td>0.166480</td>\n","      <td>0.448154</td>\n","      <td>0.060018</td>\n","      <td>-0.082361</td>\n","      <td>-0.078803</td>\n","      <td>0.085102</td>\n","      <td>-0.255425</td>\n","      <td>-0.166974</td>\n","      <td>1.612727</td>\n","      <td>1.065235</td>\n","      <td>0.489095</td>\n","      <td>-0.143772</td>\n","      <td>0.635558</td>\n","      <td>0.463917</td>\n","      <td>-0.114805</td>\n","      <td>-0.183361</td>\n","      <td>-0.145783</td>\n","      <td>-0.069083</td>\n","      <td>-0.225775</td>\n","      <td>-0.638672</td>\n","      <td>0.101288</td>\n","      <td>-0.339846</td>\n","      <td>0.167170</td>\n","      <td>0.125895</td>\n","      <td>-0.008983</td>\n","      <td>0.014724</td>\n","      <td>2.69</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1.0</td>\n","      <td>-1.358354</td>\n","      <td>-1.340163</td>\n","      <td>1.773209</td>\n","      <td>0.379780</td>\n","      <td>-0.503198</td>\n","      <td>1.800499</td>\n","      <td>0.791461</td>\n","      <td>0.247676</td>\n","      <td>-1.514654</td>\n","      <td>0.207643</td>\n","      <td>0.624501</td>\n","      <td>0.066084</td>\n","      <td>0.717293</td>\n","      <td>-0.165946</td>\n","      <td>2.345865</td>\n","      <td>-2.890083</td>\n","      <td>1.109969</td>\n","      <td>-0.121359</td>\n","      <td>-2.261857</td>\n","      <td>0.524980</td>\n","      <td>0.247998</td>\n","      <td>0.771679</td>\n","      <td>0.909412</td>\n","      <td>-0.689281</td>\n","      <td>-0.327642</td>\n","      <td>-0.139097</td>\n","      <td>-0.055353</td>\n","      <td>-0.059752</td>\n","      <td>378.66</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1.0</td>\n","      <td>-0.966272</td>\n","      <td>-0.185226</td>\n","      <td>1.792993</td>\n","      <td>-0.863291</td>\n","      <td>-0.010309</td>\n","      <td>1.247203</td>\n","      <td>0.237609</td>\n","      <td>0.377436</td>\n","      <td>-1.387024</td>\n","      <td>-0.054952</td>\n","      <td>-0.226487</td>\n","      <td>0.178228</td>\n","      <td>0.507757</td>\n","      <td>-0.287924</td>\n","      <td>-0.631418</td>\n","      <td>-1.059647</td>\n","      <td>-0.684093</td>\n","      <td>1.965775</td>\n","      <td>-1.232622</td>\n","      <td>-0.208038</td>\n","      <td>-0.108300</td>\n","      <td>0.005274</td>\n","      <td>-0.190321</td>\n","      <td>-1.175575</td>\n","      <td>0.647376</td>\n","      <td>-0.221929</td>\n","      <td>0.062723</td>\n","      <td>0.061458</td>\n","      <td>123.50</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2.0</td>\n","      <td>-1.158233</td>\n","      <td>0.877737</td>\n","      <td>1.548718</td>\n","      <td>0.403034</td>\n","      <td>-0.407193</td>\n","      <td>0.095921</td>\n","      <td>0.592941</td>\n","      <td>-0.270533</td>\n","      <td>0.817739</td>\n","      <td>0.753074</td>\n","      <td>-0.822843</td>\n","      <td>0.538196</td>\n","      <td>1.345852</td>\n","      <td>-1.119670</td>\n","      <td>0.175121</td>\n","      <td>-0.451449</td>\n","      <td>-0.237033</td>\n","      <td>-0.038195</td>\n","      <td>0.803487</td>\n","      <td>0.408542</td>\n","      <td>-0.009431</td>\n","      <td>0.798278</td>\n","      <td>-0.137458</td>\n","      <td>0.141267</td>\n","      <td>-0.206010</td>\n","      <td>0.502292</td>\n","      <td>0.219422</td>\n","      <td>0.215153</td>\n","      <td>69.99</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Time        V1        V2        V3  ...       V27       V28  Amount  Class\n","0   0.0 -1.359807 -0.072781  2.536347  ...  0.133558 -0.021053  149.62      0\n","1   0.0  1.191857  0.266151  0.166480  ... -0.008983  0.014724    2.69      0\n","2   1.0 -1.358354 -1.340163  1.773209  ... -0.055353 -0.059752  378.66      0\n","3   1.0 -0.966272 -0.185226  1.792993  ...  0.062723  0.061458  123.50      0\n","4   2.0 -1.158233  0.877737  1.548718  ...  0.219422  0.215153   69.99      0\n","\n","[5 rows x 31 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"m_aRfUK4gqOS","colab_type":"code","outputId":"b584daa3-bc49-4a18-ac98-888619823564","executionInfo":{"status":"ok","timestamp":1584595908731,"user_tz":-480,"elapsed":6117,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":368}},"source":["raw_df[['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V26', 'V27', 'V28', 'Amount', 'Class']].describe()"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>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","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <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>3.919560e-15</td>\n","      <td>5.688174e-16</td>\n","      <td>-8.769071e-15</td>\n","      <td>2.782312e-15</td>\n","      <td>-1.552563e-15</td>\n","      <td>1.699104e-15</td>\n","      <td>-3.660161e-16</td>\n","      <td>-1.206049e-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","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                Time            V1  ...         Amount          Class\n","count  284807.000000  2.848070e+05  ...  284807.000000  284807.000000\n","mean    94813.859575  3.919560e-15  ...      88.349619       0.001727\n","std     47488.145955  1.958696e+00  ...     250.120109       0.041527\n","min         0.000000 -5.640751e+01  ...       0.000000       0.000000\n","25%     54201.500000 -9.203734e-01  ...       5.600000       0.000000\n","50%     84692.000000  1.810880e-02  ...      22.000000       0.000000\n","75%    139320.500000  1.315642e+00  ...      77.165000       0.000000\n","max    172792.000000  2.454930e+00  ...   25691.160000       1.000000\n","\n","[8 rows x 11 columns]"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"8kJkaKG8hH3z","colab_type":"text"},"source":["## Examine the class label imbalance\n","\n","Look at the imbalanced dataset."]},{"cell_type":"code","metadata":{"id":"eH3jY2wlhBWi","colab_type":"code","outputId":"9d13a775-981a-431b-f182-491357c93d6f","executionInfo":{"status":"ok","timestamp":1584595908733,"user_tz":-480,"elapsed":5997,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["neg, pos = np.bincount(raw_df['Class'])\n","total = neg + pos\n","print(\"Total {}, Pos. Count {} and Ratio {:.2f}%\".format(total, pos, (pos / total) * 100))"],"execution_count":6,"outputs":[{"output_type":"stream","text":["Total 284807, Pos. Count 492 and Ratio 0.17%\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"0E_7mEsvleh3","colab_type":"text"},"source":["## Clean, Split, and Normalize the Data"]},{"cell_type":"code","metadata":{"id":"3uSot2n3hoI3","colab_type":"code","colab":{}},"source":["cleaned_df = raw_df.copy()\n","\n","# you don't need the `Time` Column\n","cleaned_df.pop('Time')\n","\n","# The `Amount` column covers a huge range.\n","# Convert to the log space.\n","eps = 1e-3  # avoid 0.0, 0.0 => \n","cleaned_df['Log Amount'] = np.log(cleaned_df.pop('Amount') + eps)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"hRViFjnPnLRe","colab_type":"text"},"source":["Split the dataset into the training, testing, and validation datasets."]},{"cell_type":"code","metadata":{"id":"cx5oYw6JmXHA","colab_type":"code","outputId":"0c2d968e-8e21-41f4-fc95-a3450648e274","executionInfo":{"status":"ok","timestamp":1584595909017,"user_tz":-480,"elapsed":6264,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["train_df, test_df = train_test_split(cleaned_df, test_size=0.2)\n","train_df, val_df = train_test_split(train_df, test_size=0.2)\n","type(val_df)"],"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["pandas.core.frame.DataFrame"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"markdown","metadata":{"id":"i0YttxRHnu3i","colab_type":"text"},"source":["Form numpy arrays of labels and features."]},{"cell_type":"code","metadata":{"id":"Xp4M4jRbnrnT","colab_type":"code","colab":{}},"source":["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)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"mFZDtBJjpfPM","colab_type":"code","outputId":"cc32f17d-4702-4724-d6bc-3c53832059cb","executionInfo":{"status":"ok","timestamp":1584595909019,"user_tz":-480,"elapsed":6240,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":136}},"source":["val_features.shape, val_features[:1,:]"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((45569, 29),\n"," array([[-1.47760136,  1.50485241,  0.65655096,  1.2941012 , -0.63595095,\n","          0.148272  , -0.1740107 ,  1.11203258, -0.63294914, -0.07180714,\n","          0.90290038,  0.96642328, -0.66228655,  0.89185791, -0.49129594,\n","         -0.8183953 ,  0.80817772, -0.12908092,  0.89952487, -0.00676966,\n","          0.06827329,  0.32398818, -0.0555326 ,  0.22367308, -0.07600722,\n","         -0.24167707,  0.32194851,  0.08200606,  3.66512453]]))"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"markdown","metadata":{"id":"Na4n_T8focZ4","colab_type":"text"},"source":["Normalize the input features using the Sklearn StandardScaler to normalize the value that the mean is `0` and the standard deviation is `1`. Normalize along the feature dimension space (by columns).\n","\n","Second, remove the outliner using `clip`."]},{"cell_type":"code","metadata":{"id":"NwNO3rTBoYv0","colab_type":"code","colab":{}},"source":["scaler = StandardScaler()\n","\n","# normalize the features\n","train_features = scaler.fit_transform(X=train_features)\n","val_features = scaler.fit_transform(X=val_features)\n","test_features = scaler.fit_transform(X=test_features)\n","\n","# remove the outliner\n","train_features = np.clip(train_features, a_min=-5, a_max=5)\n","val_features = np.clip(val_features, a_min=-5, a_max=5)\n","test_features = np.clip(test_features, a_min=-5, a_max=5)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"yKtwrn4jpOCH","colab_type":"code","outputId":"4cb75649-c342-4134-dd06-34e3f0fcd573","executionInfo":{"status":"ok","timestamp":1584595909282,"user_tz":-480,"elapsed":6460,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":119}},"source":["val_features[:1, :]"],"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.76204124,  0.91410177,  0.42841297,  0.91374309, -0.47351179,\n","         0.11256248, -0.14492558,  0.97274352, -0.57862367, -0.0657021 ,\n","         0.89001582,  0.96416777, -0.66755172,  0.93261873, -0.54136969,\n","        -0.9342987 ,  0.94073267, -0.14994867,  1.09915188, -0.00607635,\n","         0.10268388,  0.45219409, -0.07043574,  0.37323665, -0.14665639,\n","        -0.49961872,  0.79244208,  0.24026357,  0.36334872]])"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"code","metadata":{"id":"q0llExeW9R9G","colab_type":"code","outputId":"765c85e0-d7a8-461a-e8aa-7bda3a7af0e8","executionInfo":{"status":"ok","timestamp":1584595909283,"user_tz":-480,"elapsed":6386,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":119}},"source":["print('Training labels shape: {}'.format(train_labels.shape))\n","print(\"Validation labels shape: {}\".format(val_labels.shape))\n","print(\"Testing labels shape: {}\".format(test_labels.shape))\n","\n","print(\"Training features shape: {}\".format(train_features.shape))\n","print(\"Validation features shape: {}\".format(val_features.shape))\n","print(\"Testing features shape: {}\".format(test_features.shape))"],"execution_count":13,"outputs":[{"output_type":"stream","text":["Training labels shape: (182276,)\n","Validation labels shape: (45569,)\n","Testing labels shape: (56962,)\n","Training features shape: (182276, 29)\n","Validation features shape: (45569, 29)\n","Testing features shape: (56962, 29)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"4CxKQlOIKE5_","colab_type":"text"},"source":["## Look at the Data Distribution\n","\n","Next, compare the distribution between the positive and the negative examples over a few features. Following are few viewpoints:\n","\n","* Do these distributions make senses?\n","* Can you see the difference between the distributions?"]},{"cell_type":"code","metadata":{"id":"AYnioDNc-uvX","colab_type":"code","colab":{}},"source":["pos_df = pd.DataFrame(data=train_features[bool_train_labels], columns=train_df.columns)\n","neg_df = pd.DataFrame(data=train_features[~bool_train_labels], columns=train_df.columns)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"zO7TmXgMNry9","colab_type":"code","outputId":"64d07231-40c2-4f7d-fd4c-4ee3b58de52d","executionInfo":{"status":"ok","timestamp":1584595910065,"user_tz":-480,"elapsed":7138,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":460}},"source":["sns.jointplot(x=pos_df['V5'], y=pos_df['V6'], \n","              kind='hex', xlim=[-5,5], ylim=[-5,5])\n","plt.suptitle('Positive Distribution')"],"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 0.98, 'Positive Distribution')"]},"metadata":{"tags":[]},"execution_count":15},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAagAAAGqCAYAAABeetDLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYoJpsSioNvXYjOVt/EBjyBGTEEK0pasCCpJz1LQcQylQ4TWnbl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UZO/xQnk3jaLxA4wea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size 432x432 with 3 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"YmEl5cbvOCXO","colab_type":"code","outputId":"55153299-faa2-4b0d-a29d-810bea464184","executionInfo":{"status":"ok","timestamp":1584595911558,"user_tz":-480,"elapsed":8617,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":460}},"source":["sns.jointplot(x=neg_df['V5'], y=neg_df['V6'], kind='hex', \n","              xlim=[-5,5], ylim=[-5,5])\n","plt.suptitle('Negative Distribution')"],"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 0.98, 'Negative Distribution')"]},"metadata":{"tags":[]},"execution_count":16},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAagAAAGqCAYAAABeetDLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQRCGEglQgiAIwlAyek4Sg+6AIAiCsCGMXoCSCCUIgrAlkCk+QR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KPBCchyVCIJIjoUgDX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zZYTgCunK8WjIFU82ml4hErBDt6qQxiv5ssdMtLyorqqBYGpV9sKKNwMbO9lPRR9sidqzaz6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\nEVAAgCz9H5dPNOcZJgyyAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x432 with 3 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"kPd8kAsDQXxF","colab_type":"text"},"source":["# Define the Model and the Metrics"]},{"cell_type":"markdown","metadata":{"id":"90gMGWiWSnmc","colab_type":"text"},"source":["About the metrics:\n","\n","* `True Positives` and `True Negatives` are examples that were correctly classified\n","* `False Positives` and `False Negatives` are examples that were incorrectly classified\n","* `Accuracy` is the precentage of examples that were correctly classified. $\\frac{True\\ Positives+True\\ Negatives}{Total\\ samples}$\n","* `Precision` is the percentage of predicted examples that were correctly classified. $\\frac{True\\ Positives}{True\\ Positives+False\\ Positives}$\n","* `Recall`, a.k.a. `Sensitivity`, is the precentage of true examples that were correctly predicted. $\\frac{True\\ Positives}{True\\ Positives+False\\ Negatives}$\n","* `AUC` is the area under the curve of 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."]},{"cell_type":"code","metadata":{"id":"nxdjnH4hOdSv","colab_type":"code","colab":{}},"source":["METRICS = [\n","  tf.keras.metrics.TruePositives(name='tp'),\n","  tf.keras.metrics.FalsePositives(name='fp'),\n","  tf.keras.metrics.TrueNegatives(name='tn'),\n","  tf.keras.metrics.FalseNegatives(name='fn'),\n","  tf.keras.metrics.BinaryAccuracy(name='accuracy'),\n","  tf.keras.metrics.Precision(name='percision'),\n","  tf.keras.metrics.Recall(name='recall'),\n","  tf.keras.metrics.AUC(name='auc')\n","]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"tHw2JLuVRCjt","colab_type":"code","colab":{}},"source":["def build_model(metrics=METRICS, output_bias=None):\n","  if output_bias is not None:\n","    output_bias = tf.keras.initializers.Constant(value=output_bias)\n","  \n","  def _model(inputs):\n","    x = tf.keras.layers.Dense(units=16, activation='relu')(inputs)\n","    x = tf.keras.layers.Dropout(rate=0.5)(x)\n","    prob = tf.keras.layers.Dense(units=1, activation='sigmoid', \n","                                 bias_initializer=output_bias)(x)\n","    return prob\n","  \n","  inputs = tf.keras.Input(shape=(train_features.shape[-1], ))\n","  outputs = _model(inputs)\n","  model = tf.keras.Model(inputs=inputs, outputs=outputs)\n","\n","  model.compile(\n","    loss=tf.keras.losses.BinaryCrossentropy(),\n","    optimizer=tf.keras.optimizers.Adam(),\n","    metrics=metrics\n","  )\n","  return model"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"l_fLQ_xke4Oc","colab_type":"text"},"source":["# Baseline Model"]},{"cell_type":"markdown","metadata":{"id":"Fx-wF6D9g5yw","colab_type":"text"},"source":["## Build the Model\n","\n","Notice each batch must have a decent chance of containing a few positive samples. If the batch size is too small, the batch would probably have no fraudulent transactions to learn from."]},{"cell_type":"code","metadata":{"id":"ocSORyxnSg6f","colab_type":"code","colab":{}},"source":["EPOCHS = 100\n","BATCH_SIZE = 2048\n","\n","early_stopping = tf.keras.callbacks.EarlyStopping(\n","  monitor='val_auc', verbose=1, patience=10, mode='max', restore_best_weights=True\n",")"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"JiBT9zQah3KZ","colab_type":"code","outputId":"7c78c18e-20e8-43c3-a085-c3d58efebc89","executionInfo":{"status":"ok","timestamp":1584595912436,"user_tz":-480,"elapsed":9365,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":289}},"source":["model = build_model()\n","model.summary()"],"execution_count":20,"outputs":[{"output_type":"stream","text":["Model: \"model\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","input_1 (InputLayer)         [(None, 29)]              0         \n","_________________________________________________________________\n","dense (Dense)                (None, 16)                480       \n","_________________________________________________________________\n","dropout (Dropout)            (None, 16)                0         \n","_________________________________________________________________\n","dense_1 (Dense)              (None, 1)                 17        \n","=================================================================\n","Total params: 497\n","Trainable params: 497\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"6wvBuk7gmEiB","colab_type":"text"},"source":["Run the model."]},{"cell_type":"code","metadata":{"id":"IMJhX6Gyh8lH","colab_type":"code","outputId":"1d5f5c09-68a3-4fb3-8b24-08499a471818","executionInfo":{"status":"ok","timestamp":1584595914109,"user_tz":-480,"elapsed":11017,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["model.predict(train_features[:10])"],"execution_count":21,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.20420463],\n","       [0.27226916],\n","       [0.17816898],\n","       [0.32714817],\n","       [0.69555146],\n","       [0.03711061],\n","       [0.4150433 ],\n","       [0.10481083],\n","       [0.12358614],\n","       [0.26342157]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"markdown","metadata":{"id":"RMgbU1udn3Oi","colab_type":"text"},"source":["## Set the correct initial bias (Optional)\n","\n","These initial predictions are understandable. However, they are not great. You know these are imbalanced datasets. The predictions should be gathered to the subset that the most elements belong to. Now, this outcome is different from the prediction.\n","\n","We are going to set the output layer's bias to reflact the outcome. This can help with the initial convergence."]},{"cell_type":"code","metadata":{"id":"Gl8gmf5UmPM-","colab_type":"code","outputId":"86445eda-2244-4849-caf0-dcb7976180de","executionInfo":{"status":"ok","timestamp":1584595915226,"user_tz":-480,"elapsed":12099,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["results = model.evaluate(x=train_features, y=train_labels, batch_size=BATCH_SIZE, verbose=0)\n","print(\"Loss: {:.4f}\".format(results[0]))"],"execution_count":22,"outputs":[{"output_type":"stream","text":["Loss: 0.3398\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"-ohxZ7D2zzz2","colab_type":"text"},"source":["The correct bias to be set is derived from:\n","\n","$$p_0 = \\frac{pos}{pos + neg} = \\frac{1}{1 + e^{-b_0}}$$\n","$$p_0 + p_0e^{-b_0} = 1$$\n","$$e^{-b_0} = \\frac{1 - p_0}{p_0}$$\n","$$log_e{e^{-b_0}}=log_e{\\frac{1 - p_0}{p_0}}$$\n","$$-b_0=log_e\\frac{neg}{pos}$$\n","$$b_0=log_e{\\frac{pos}{neg}}$$"]},{"cell_type":"code","metadata":{"id":"q_h6O3VEyf-3","colab_type":"code","outputId":"3f8800f2-9e06-4b81-b821-e8a1c9f502bf","executionInfo":{"status":"ok","timestamp":1584595915227,"user_tz":-480,"elapsed":12080,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["initial_bias = np.log([pos / neg])\n","initial_bias, pos / (pos + neg)"],"execution_count":23,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([-6.35935934]), 0.001727485630620034)"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"markdown","metadata":{"id":"A8GLwrKQ1hdP","colab_type":"text"},"source":["Set this value as the initial bias to see whether the output of the model is more reasonable. It should be near `pos/total = 0.0018`. "]},{"cell_type":"code","metadata":{"id":"H2JdZfJq1G8Y","colab_type":"code","colab":{}},"source":["model = build_model(output_bias=initial_bias)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"gLsxU4_SGn0F","colab_type":"code","outputId":"1eb14edf-fe24-42ca-a975-04d0a4184e0f","executionInfo":{"status":"ok","timestamp":1584595915228,"user_tz":-480,"elapsed":12056,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["model.predict(train_features[:10])"],"execution_count":25,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.00150714],\n","       [0.00260496],\n","       [0.00104932],\n","       [0.00092553],\n","       [0.00046384],\n","       [0.00017974],\n","       [0.00260226],\n","       [0.00061101],\n","       [0.00056102],\n","       [0.00114533]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":25}]},{"cell_type":"markdown","metadata":{"id":"1ExYWVDiHJJR","colab_type":"text"},"source":["With this initialization the initial binary cross entropy loss should be near:\n","\n","$$-p_0log(p_0) - (1-p_0)log(1-p_0) \\approx 0.013174$$"]},{"cell_type":"code","metadata":{"id":"XpyYpP3-GtS9","colab_type":"code","outputId":"93b3a600-0b4f-4aed-cab9-e59c2f4b569e","executionInfo":{"status":"ok","timestamp":1584595915229,"user_tz":-480,"elapsed":12042,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["-0.0018 * np.log(0.0018) - (1-0.0018)*np.log(1-0.0018)"],"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.01317432253246831"]},"metadata":{"tags":[]},"execution_count":26}]},{"cell_type":"code","metadata":{"id":"hW_osXUmHm8H","colab_type":"code","outputId":"754f31fb-5b77-44bf-e0a3-8fa44f01b734","executionInfo":{"status":"ok","timestamp":1584595917042,"user_tz":-480,"elapsed":13840,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["results = model.evaluate(x=train_features, y=train_labels, batch_size=BATCH_SIZE, verbose=0)\n","print(\"Adjusted Loss: {:.4f}\".format(results[0]))"],"execution_count":27,"outputs":[{"output_type":"stream","text":["Adjusted Loss: 0.0189\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Ml6_ogZxIvML","colab_type":"text"},"source":["Such a loss value is about 50 times less than the naive initialization."]},{"cell_type":"markdown","metadata":{"id":"Y-Hk6XGLJMPi","colab_type":"text"},"source":["## Checkpoint the initial weights"]},{"cell_type":"code","metadata":{"id":"9hPDm0xRI1jW","colab_type":"code","colab":{}},"source":["initial_weights = os.path.join(tempfile.mkdtemp(), \"initial_weights\")\n","model.save_weights(initial_weights)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"AagJ4OgLLFjB","colab_type":"text"},"source":["## Confirm that the bias fix helps\n","\n","Let's train the model for 20 epochs and compare the model between the zero bias and the initialization bias."]},{"cell_type":"code","metadata":{"id":"CZVrIv-gKVia","colab_type":"code","colab":{}},"source":["model = build_model()\n","model.load_weights(initial_weights)\n","\n","# reassign the bias of the last layer\n","model.layers[-1].bias.assign([0.0])\n","\n","zero_bias_history = model.fit(x=train_features, \n","          y=train_labels, \n","          batch_size=BATCH_SIZE,\n","          epochs=20, \n","          validation_data=(val_features, val_labels),\n","          verbose=0)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"yWSUm207MyLZ","colab_type":"code","colab":{}},"source":["model = build_model()\n","model.load_weights(initial_weights)\n","\n","init_bias_history = model.fit(x=train_features, \n","          y=train_labels, \n","          batch_size=BATCH_SIZE, \n","          epochs=20, \n","          verbose=0, \n","          validation_data=(val_features, val_labels))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"U3trJFgfNvh0","colab_type":"code","outputId":"141dd3f4-9f59-44a2-8602-ed89dbcd28b7","executionInfo":{"status":"ok","timestamp":1584595943649,"user_tz":-480,"elapsed":40321,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":54}},"source":["init_bias_history.history.keys()"],"execution_count":31,"outputs":[{"output_type":"execute_result","data":{"text/plain":["dict_keys(['loss', 'tp', 'fp', 'tn', 'fn', 'accuracy', 'percision', 'recall', 'auc', 'val_loss', 'val_tp', 'val_fp', 'val_tn', 'val_fn', 'val_accuracy', 'val_percision', 'val_recall', 'val_auc'])"]},"metadata":{"tags":[]},"execution_count":31}]},{"cell_type":"code","metadata":{"id":"p8lXPIMaNNVa","colab_type":"code","colab":{}},"source":["def plot_history(history, label, col):\n","  plt.semilogy(history.epoch, history.history['loss'], \n","               color=colors[col], label=\"Train \" + label)\n","  plt.semilogy(history.epoch, history.history['val_loss'],\n","               color=colors[col], label=\"Val \" + label, linestyle=\"--\")\n","  plt.xlabel(\"Epochs\")\n","  plt.ylabel(\"Loss\")\n","\n","  plt.legend()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"qSLsoT2hOwIJ","colab_type":"code","outputId":"e60f93ec-2721-4b2c-c932-b56694b5298a","executionInfo":{"status":"ok","timestamp":1584595944400,"user_tz":-480,"elapsed":41046,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":606}},"source":["plot_history(zero_bias_history, \"Zero Bias\", 0)\n","plot_history(init_bias_history, \"Init Bias\", 1)\n","plt.show()"],"execution_count":33,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAtkAAAJNCAYAAADkumnLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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mooimIQ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\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sLNUY7t8GY0M8EeDZkneVJCIyAizZpLcWbjuDhdvS8M3UMPTp6Cw7DpHOKYqCxAtXsO5Q\nJjYdu4TKWi183ewxtpcnHg10h5OtheyIRETUSCZTsoUQwwEM9/HxmZqWliY7DjVAVa0Wjy89gOkD\nO2Joj9ay4xA1qdKqWmw8egnrEjNxNPMqLFRmGNzdDY+FeKKfjzPMzLi6TURkSEymZF/HlWzDoigK\nPzonk3P6cgnWHcrEhiPZuFpRC/eW1hgT4oExIZ5w5yhAIiKDcN8lWwhhC6BSUZQ6IURnAF0AbFYU\npVa3UXWDJdvwaOsUfJNwAYO7tUZrByvZcYiaTbVGi60nc7E+MRN7zxYAAPp3csFjIZ54oJsrLNW8\njTsRkb7SRclOAtAfgCOAfQAOAahRFCVWl0F1hSXb8GQWVSBq/i482L01PhoXKDsOkRSZRRX4NikL\n3yVmIqe4Ck62Fng00B2P9fJEZzd72fGIiOhPdFGyDyuKEiSEmAnAWlGUfwshkhVFCdB1WF1gyTZM\n8389g0XbeREkkbZOwZ60fKxPzMSvKbmo1SoIbNcSj4V4Isa/Le+YSkSkJ3RRso8AeAbAAgCTFUU5\nKYQ4riiKn26j6gZLtmGqqtXigfm7YG2uws/P94e5infMIyosq8aGI9lYdygTaXllsLFQIaZnGzzW\nyxNB7Rx5PQMRkUR3KtkNbTEvAPgrgA2/F2xvADt0FZAIAKzMVXhreHek5ZXhq/0ZsuMQ6YVWdpaY\n0t8bW2cPwH+f6YPhPdvip2OXMGpxPAYv2I1lu9NRUFYtOyYREf3JPU8XEUKYAbBTFKWkaSLdP65k\nGy5FUfD3jSkY0t2NW0aIbqO8WoNNxy5h7aGLOHzxKtRmAg90dcOIgLaI8HWFtQUvliQiag662C7y\nDYDpALS4dtFjCwD/URTlA10G1RWWbCIyFWm5pVh3KBM/JGejoKwGNhYqDOriipiebRDh6worcxZu\nIqKmoouSnawoSoAQIhZAEIBXASQpitJTt1F1gyXb8FXUaLDg1zOI6uqGcO9WsuMQ6T2Ntg4Hzxdh\n0/FL+OXEZRSWXyvcUV3dEO3XBhG+LizcREQ6dqeS3dBL1M2FEOYAHgHwsaIotUII47uLDekNAYGf\nj1/G7jMF+GlWP14ESXQXapUZ+vg4o4+PM/7+cHcknC/CT8cuYcvJy9h4NAe21wt3zzYY2JmFm4io\nqTW0ZH8GIAPAUQC7hRDtAejtnmwyfNYWKrwR0w3TVyXh6/gLmNyvg+xIRAZDrTJDXx9n9PVxxj9H\ndMeB9CJsOp6DX05cxv8dzYGdpRpRXV0R7dcGA1i4iYiaRKNvqy6EUCuKotFxHp3gdhHjoCgK4pYf\nwuELV7D9pYFwteedIInuR622DgfSC7Hp2CX8cvIyrlbUws5SjcHd3DDMrw0GdHbmHSaJiO6BLvZk\nOwB4C8CA3w/tAvAPRVGKdZZSh1iyjcf5gnI8uGA3hvu3xYdj/WXHITIatdo6xJ/7X+EurqyF/R8K\nd38WbiKiu9JFyf4ewAkAX/1+6AkA/oqijNRZSh1iyTYu/z2cBX/PlujoYic7CpFRqtXWYd/ZAvx8\n/BK2nMz9X+Hufu2iyX6dWLiJiG5FZ9NF7nZMX7BkGy9FUXiHO6ImVKOpw75zBfj594smS6o0sLdS\nY0i31ojp2QZ9fZxhoeaFyEREgG6mi1QKIfopirL39zfsC6BSVwGJ7qayRovZ65IR5u2Ep/ryIkii\npmKhNkOkrysifV3xzqN+2He2AD8du4StKZfx/eEstLBSY0j31oju2QZ9O7JwExHdTkNL9nQAX/++\nNxsArgCY2DSRiG5mZW6G8hoN5m89g5iebeFibyk7EpHRs1CbIbKLKyK7uKJa06O+cG85cRnfJV0r\n3A9eL9w+zhy1SUT0B/c0XUQI0QIAFEUpEUK8oCjKwiZLdh+4XcQ4peeX4cGF1y6CnD9WL3cqEZmE\nao0We9MKsOnYJfyakovSag0crM0xsLMLBnVxxYDOLnCytZAdk4ioyd33nuzbvOlFRVHa3VeyJsKS\nbbze/+U0Fu88h++m90aIl5PsOEQmr1qjxZ4zBfj5xCXsSs1HYXkNhAACPVte23bSxRXd27bgtRRE\nZJSaqmRnKorieV/JmghLtvGqqNEg6sNd8GplizXTwmXHIaI/qKtTcDy7GL+dzsPO1Dwczbo25dXV\n3hIRvtdWufv6OMPeylxyUiIi3eBKNhmVo5lX0c7JBo78OJpIr+WXVmPXmXzsSM3D7jP5KK3SQG0m\n0MvLCYO6uCKyiws6uthxlZuIDFajS7YQohTArZ4gAFgritLQCyebFUu2adDWKaiq1cLWUi//GxLR\nH9Rq63D4whX8lpqHnafzkZpbCgDwdLKun2bSu2Mr3uKdiAxKk6xk6zOWbOOnrVMw9rN4eDvb4oMx\nvBMkkaHJvlqJHb9vK9l3thCVtdr/Z+++w+Mqz/SPf88UjXqvtuQq2XKVjQ24YNN7KAFCIBAgARJC\nSEiyCSmbtknYH8tuNoENCSUBEgIhhE6optvGNrh3W+6WZBXL6mWkmTm/P95Rc8OOJR1Jc3+u61wz\nmjMjPxLYuvXOc54Xn8fFnLFpnFWYyRnjM8lLjXW6TBGRo+qNOdkiA4o7/Jbzgx9s55pTRjBjZIrT\nJYnIcRieHMP1s0Zyut+YrQAAIABJREFU/ayRtLYHWbbzAO9truS9LZW899IGYAP5mfHhwJ3BzJGp\nmsktIoOKVrJl0GryBzjnfz8gNS6Kl+84DbdLfZ0iQ8GOqkbe21LFe5srWbazmvagTbzPw7yCdM4c\nb0J3ZmK002WKiKhdRIauf64t446nVvHLyybxxdmjnC5HRHpZkz/A4m37O0N3eX0rAJOHJ3aOCCzK\nTdYv2SLiCIVsGbJs2+a6Py6juS3IC7fP0ZQCkSHMtm02lzd0jghcsbuGkA2pcVHML0jn9PEZzCvI\nID1eO8KKSP9QyJYhrbKhlaQYLz6PphKIRJLa5jY+LN7P+5sreX9rFQea2gCYNCyReQUZzB+Xrl5u\nEelTEROyLcu6BLgkPz//1uLiYqfLkX7W6A+wp7qZicMSnS5FRPpZKGSzoayeD4ur+HBrFSt21xAI\n2cRGuZk9Jo15BenMH5fB6PQ4veMlIr0mYkJ2B61kR6YvP/4JG8rqePNb80mO1UY1IpGs0R9gyfZq\nPtxaxcLiKnZVNwOQmxLD/HEZzC9IZ05+OonafVJEToBCtkSE9aV1XP7AYs6fnM3vrp2u1SoR6bSn\nupkPwqvcS7ZX0+gP4HZZTM9LZv64DOYVpDNVF1CKyHFSyJaI8cB72/jvN7dw3zXTuGzacKfLEZEB\nqD0YYtWeWj7cWsWHxVWsK63DtiE51svc/HROL8hg3rh0cpJinC5VRAY4hWyJGIFgiM8/vJStFQ28\n+a35DEvWD0kROboDTW0s2rbfhO6tVVQ2+AEoyIw3rSXjMjh1dKq2fBeRQyhkS0TZU93Mz1/ZwK8u\nn6yQLSLHxbZttlQ0sHDrfj4srmLZzgO0BUJEeVycOjqV+QUmdI/LildLmogoZIuIiPwrWtqCLNtZ\nzcJis9JdXNkIQFaiLzwmMIN5+emkxOlia5FIdLSQ7envYkT6S1WDn39/YR3fOW8chdka6ycixy8m\nys0Z4zM5Y3wmAGW1LSwsruLD4v0s2FjBsytKsCyYOjyJ2WPTmTUmlZmjUon36cerSKTTSrYMWfsb\n/Vzw2w9Jj/fx0h1ztVmNiPSqYMhmbUktH27dz6JtVazeW0t70MbtspgyPInZY9OYNSaNmSNTiFPo\nFhmS1C4iEeudTRXc/OflfPX0MfzwwglOlyMiQ1hzW4CVu2tZuqOapTuqWb23lkDIhO6puUnMGqPQ\nLTLUKGRLRPvh8+t4+pM9PH3rLE4dk+Z0OSISITpC95Id+1m64wBrwqHbc1DonqHQLTJoKWRLRGvy\nB7j4/oWkxft49rbZmgggIo5obguwYndNeKX7yKF75qgUYqMUukUGA4VsiXjFFQ1kJPi03bqIDBhN\n/u6hu5q1JXWdobsoL5lZY1I7V7oVukUGJoVskbD2YIgdVU2Mz05wuhQRkR66h+4l4dAdPCh0zx6T\nzkkjkxW6RQYIhWyRsB88t5Y3N5Tz5rfmk5kY7XQ5IiJH1OQPsPygle5gyMbrtijKTe7R0x0TpelJ\nIk5QyBYJ217VyMX3L2TWmDQeu+lk9WeLyKDR6A+wfNcBlu44wNId1awr7Rm6Z4xMoSgvmWl5yeQk\nRevfN5F+oJAt0s1fluzipy9t4JeXT+aLs0Y6XY6IyL/k4NC9sayetmAIgIwEH9PCgXtaXjJTcpNI\njPY6XLHI0KMdH0W6+eKskby9qZK7X93I3LFpjMmId7okEZHjFu/z9NiN0h8IsnlfA6v31rJmby2r\n99ayYGMFAJYFYzPimZaXTFFeMtPzkhmfnYDX7XLySxAZ0hSyJeJYlsV/XzWVbzy1qnPVR0RksPN5\n3BSFQ3SHuuZ21pR0he73Nlfy7IqS8PNdTB6eRFFuMtNGJDMtN5m81Bi1mYj0ErWLiIiIRAjbtimp\naWFNSS2r99SypqSWdaV1tLabBYfUuCiKcpOYlpfCtBHJFOUmafSpyFGoXUTkCJr8AX716iaunpnL\n9BEpTpcjItKnLMsiLzWWvNRYPjN1GGBGm24pb+gRvN/fWkXHGtzo9Lhw8Dar5BOHJeLzaJqJyKfR\nSrZEtPrWdi787UKiPC5e/eZpmj0rIgI0tLazrrSuR393Rb0fAK/bYmJOYmfonpaXzKi0OFwutZlI\n5NF0EZGjWLqjmmsfWcoXThnB3Z+d4nQ5IiIDUnldK6v31rB6bx2r99awrqSOprYgAInRHiYNS2JC\nTiITchKYkJNIQVa8VrxlyFO7iMhRzBqTxq3zxvDwhzs4Z0IWZxZmOl2SiMiAk50UzQVJOVwwOQeA\nYMhmW2WjWekuqWVjWT1/+3gPLe0meHtcFvmZ8T2C94ScRNLjfU5+GSL9RivZIpjRV5f9bjH+QIi3\nv3M6br3tKSJy3IIhm13VTWzaVx8+Gti0r559da2dz8lI8DExHLgn5CQwMSeR0elxeDROUAYhtYuI\nHINtlQ34PG7yUmOdLkVEZEipaWpj0756NoaD98Z99WyrbKA9aDKIz+NifHYCE7K7Vr0LcxJJitEG\nOjKwKWSLHAfbttm5v0mb1IiI9KG2QIjtVY2dq94dAfxAU1vnc4YnxzBxmFn1nhgO33kpsbrIUgYM\n9WSLHIffLNjKo4t38fqd87SqLSLSR6I8rs4+7Q62bVPZ4Gfjvno2ltV3BvB3NlUQCq8Jxvs8jM9O\n6NFyMj47QdOhZMDRSrbIQUpqmrnwtwspzEng6a/MVn+2iIjDWtqCbK1oCK92d/V7N/oDALgsOGlE\nCudMzOLciVmM1TuR0k/ULiJynJ5fWcJ3nlnD9y8o5GtnjHW6HBEROUjH7pUbyupZX1rHe1sq2VBW\nD8CYjDjOnZjFeROzmJaXosUS6TMK2SLHybZtvv7UShZsrOCF2+cyeXiS0yWJiMinKK1t4e2NFby9\nqYIl26sJhGzS46M4qzCTcydmc1p+OjFRmt0tvUc92SLHybIs7r58CnsPtNDQGnC6HBEROQbDk2O4\ncc4obpwzivrWdt7fUsWCjRW8vq6cZ5aXEO11Ma8gg3MnZnFWYaZmdkuf0kq2yFHYto1l6W1GEZHB\nrC0Q4uOdB1iwsZwFGysoq2vFsmDGiBTODfdxa6KU/CvULiJyAtqDIR54bxsnj0plbn660+WIiMgJ\nsG2bDWX1LAi3laiPW06EQrbICWhpC3Lx/y2kpS3IG3fOJylWmyOIiAwVHX3cCzZWsHRHVx/32YVm\nhfu0gnSiverjlsNTyBY5QWtLarni9x9x8dQc7rtmutPliIhIH+jex/3+5koa/IEefdxnF2aSpj5u\n6UYXPoqcoKm5ydx5dgG/XrCVsydkcWnRMKdLEhGRXpYY7eXSomFcWjSMtkCIZTurO1e5F2ysUB+3\nHBetZIsco0AwxOceWsKe6mYWff8sjYESEYkQ3fu4F2ysYOM+08c9NiOOc8J93FNzk/G6XQ5XKv1N\n7SIivWR3dRN1Le1MzU12uhQREXHI4fq4o9wuxmbGMyE7gcKcBAqzEynMSSAj3qcpVUNYxIRsy7Iu\nAS7Jz8+/tbi42OlyZIgrq21hWHKM02WIiIiD6lraWVhcxfrSejaX17N5XwPl9a2d59PiorpCd7a5\nLciK18WUQ0TEhOwOWsmWvvbkst384pWN/PMbp1GQleB0OSIiMoDUNLWxubyhM3RvLq9nS0UDre0h\nAFwWjE6PozAn0ax8h1e9hyfHaNV7kFHIFullVQ1+zv/th+QkRfPC7XOJ8qgPT0REjiwYstld3WTC\n9756NpU3sKW8gT0Hmjufk+Dz9Gg1KcxOZHx2AvE+zakYqBSyRfrAWxvK+coTK/j6mWP53vmFTpcj\nIiKDUKM/wJaDVr0372ugwR/ofE5eagyF2eFV7xzTdjIyLU4b5gwAGuEn0gfOm5TN52fm8Yf3t3Pm\n+Exmjkp1uiQRERlk4n0eZoxMYcbIlM7HbNumtLalM3RvCq9+v7OpglB4bTTa62J8Vtdq9+j0OIYl\nxzAsOZqEaG2aNhAoZIucgJ9cMpE1JbVU1PudLkVERIYIy7LITYklNyWWcyZmdT7e2h5kW2Ujm/bV\nd/Z8L9hUwd+X7+3x+oRoD8OSTOA2wTuG4eHbYcnRZCVGa9xgP1C7iMgJCoZsvWUnIiKOsG2bqkY/\nJTUtlNV2HK2U1nZ9XNPc3uM1LguyEk0Az0mK7hbATQgfnhxDUoxXF2EeA7WLiPShjoD99Md7CNnw\nhVNHOFyRiIhECsuyyEyIJjMhmpNGpBz2Oc1tAfbVtXaG7tLarvvrS+t4a2MFbYFQj9fERrm7rYJH\nk5PUM4RnJ0Xj82gM4dEoZIv0Atu2eWtjBR9srSIvNYZ5BRlOlyQiIgJAbJSHsRnxjD3CNvC2bVPd\n1HbYEF5W28LGsnr2Nx7aFpmR4OsM4WMz4pk0LJFJw5LITdEoQlC7iEivafQHuOoPH1Fa28ILt88h\nP1Pzs0VEZGjwB4KU13W0oXRfFTfH7upmguGrMhOjPUwMB+6O4D02Iw7PEOwD1wg/kX5SWtvCZb9b\nTEyUixdvn0tavM/pkkRERPpca3uQLeUNbCirZ0NZHRvKzA6YHRvw+DwuCrMTmNgZvBMpzE4kJmpw\nt5woZIv0o9V7a7nm4SX88rLJfG5mntPliIiIOCIQDLFzf1OP4L2hrJ66FnMhpsuCMZ1tJl0r38mx\nUQ5XfuwUskX62b66FnKSYpwuQ0REZEDpmAHeEbg3hsP3vrrWzucMT44Jt5t0Be+cpOgB2eet6SIi\n/awjYH+y6wBr9tZyy7wxDlckIiLivO4zwM+flN35+IGmth6r3RvK6nh7UwUda8Epsd7OwN3R7z06\nfWDveqmQLdKHnl1ewt+X7yUrMZpLioY5XY6IiMiAlBoXxbyCjB7TuZr8ATaXh0N3aT0b9tXx2OJd\ntAVNn3eM182EnAQmDUvizMIMzirMOtKnd4RCtkgf+sXlk9i5v4nv/mMNuSkxTD/CDFMRERHpKc7n\nYcbIVGaMTO18rD0YoriisXPVe2NZPS+sKsXtsgZcyFZPtkgfO9DUxmd/v5gmf4AXvz6X3JRYp0sS\nEREZMkIhm9ZAkNio/l87PlpP9tAbWCgywKTGRfGnG0+mLRDiiSW7nS5HRERkSHG5LEcC9qcZeBWJ\nDEH5mfG8+PW5jEqLc7oUERER6QdayRbpJ2My4nG5LEprW3jog+1OlyMiIiJ9SCvZIv3smU/2ct87\nxUR73dw4Z5TT5YiIiEgf0Eq2SD/75tkFnDMhi/94ZQPvbal0uhwRERHpAwrZIv3M7bK475ppFGYn\n8o2nVrGlvMHpkkRERKSXKWSLOCDO5+FPN80kNsrNf7+52elyREREpJepJ1vEITlJMTx5y6nkJMc4\nXYqIiIj0Mq1kizioICuBeJ+H5rYAjy7aSSg09DaHEhERiUQK2SIDwD/X7uMX/9zIb98pdroUERER\n6QUK2SIDwOdm5HL1zFzuf6eYF1eVOl2OiIiInCCFbJEBwLIsfnX5FGaNSeWuZ9eyfNcBp0sSERGR\nE6CQLTJARHlcPHj9DIanxPCD59cRVH+2iIjIoKXpIiIDSHJsFI/edDJuy8LtspwuR0RERP5FWskW\nGWBGp8cxIi0W27Z5dkUJ7cGQ0yWJiIjIcVLIFhmgluyo5rv/WMPPX96Abat1REREZDBRyBYZoOaM\nTee208fy5LI9PLp4l9PliIiIyHFQT7bIAHbX+ePZtb+JX726kVFpsZw9IcvpkkREROQYaCVbZABz\nuSz+9/NFTB6WxHeeWUNDa7vTJYmIiMgx0Ep2b9nxPpSvhzl3OF2JDDGxUR7+eONMdlQ1kRDtdboc\nEREROQZaye4tG16EBT+Fio1OVyJDUFZiNLPHpgHwwdYqWtqCDlckIiIiR6OQ3VvO/in4EuD1u0CT\nIKSP7K5u4suPf8J3nllNSJvViIiIDFgK2b0lNhXO+jHsWggbX3S6GhmiRqbF8cMLC3l9fTn/89YW\np8sRERGRI1DI7k0zvwxZU+DNH0Nbs9PVyBB182mjufaUEfz+/e38Y/lep8sRERGRw1DI7k0uN1x0\nL9SXwKLfOF2NDFGWZfGLyyZxWn46P3phHbv2NzldkoiIiBxEIbu3jZwDk6+CxffBgZ1OVyNDlNft\n4oHrTuKeK6YyKj3O6XJERETkIArZfeG8X4LLA2/+u9OVyBCWFOPlyhm5AGwur6e2uc3hikRERKSD\nQnZfSBwG878LW16FbW87XY0McY3+ANc8vJTb/rqC1naN9hMRERkIFLL7yuyvQ+pYeP37ENAKo/Sd\neJ+Hn10ykWU7D3DDox9Tr10hRUREHKeQ3Vc8PrjgHqjeBsv+4HQ1MsR9dnou910znZW7a/jCI0up\nbvQ7XZKIiEhEU8juS+POg3EXwAf3Qv0+p6uRIe7SomE8csNMiisaeXjhDqfLERERiWgK2X3t/P+E\nYBu8/TOnK5EIcGZhJi/cPpfvnjceAFu7j4qIiDhCIbuvpY2F2XfA2r/DnqVOVyMRYOKwRLxuF/sb\n/Vz90BLWl9Y5XZKIiEjEUcjuD/P+DRKGwWvfg5CmP0j/aGwNUFbbyrUPL2XZjmqnyxEREYkoCtn9\nwRdvZmeXr4WVf3a6GokQo9LjePZrs8lM9HHDox/z7uYKp0sSERGJGArZ/WXylTDyNHjnl9B8wOlq\nJELkJMXwzFdnMy4rga/8ZQXvbFLQFhER6Q8K2f3FsuDC/4LWWnjvbqerkQiSFu/jqVtP5YqThlOU\nl+x0OSIiIhFBIbs/ZU+Gk2+B5Y9C+Tqnq5EIkhDt5d6rikiP99EeDPHymjJNHhEREelDCtn97cwf\nQUwKvHYXKOSIA55Zvpdv/m0V//naJgVtERGRPqKQ3d9iUuDsn8Kej2Dds05XIxHo2pNHcOPskTyy\ncCfff24tgWDI6ZJERESGHIVsJ0z/IuQUwYKfgL/R6WokwrhcFj+/dBLfPLuAZ5aXcMdTq/AHNFpS\nRESkNylkO8Hlhov+Bxr2wcL/cboaiUCWZfGdc8fxk89MZPH2/eypbna6JBERkSFFIdspeadA0bXw\n0e+gervT1UiEuvm00bz/3TMoyEoAoLVdK9oiIiK9QSHbSef8B3ii4Y0fOF2JRLC0eB8Af1q0k8t+\nt5jK+laHKxIRERn8FLKdlJAFZ3wfit+CLW84XY1EuMLsBPbWNHPVg0vUPiIiInKCFLKddspXIX2c\nWc1u1wqiOGdufjpP3TqL+tZ2rnrwI7aUNzhdkoiIyKClkO00T5TZCbJmJyz5ndPVSISblpfMM1+d\njWXBNQ8voa653emSREREBiWP0wV8GsuyLgcuBhKBP9m2/ZbDJfW+sWdB4Wdg4a+h6BpIynW6Iolg\n47ISePa2OXyy6wBJsV6nyxERERmU+nQl27KsRy3LqrQsa/1Bj19gWdYWy7K2WZZ11Kv+bNt+0bbt\nW4HbgM/3Zb2OOv9usEPw1k+crkSEvNRYrjjJ/LK3qHg/b6zf53BFIiIig0tft4s8DlzQ/QHLstzA\nA8CFwETgWsuyJlqWNcWyrH8edGR2e+mPw68bmlJGwdw7YcPzsGuR09WIAGDbNg99uJ3bn1zJM8v3\nOl2OiIjIoNGnIdu27Q+BAwc9fAqwzbbtHbZttwFPA5fZtr3Otu3PHHRUWsZ/Aa/btr2yL+t13Nxv\nQVIevHYXBANOVyOCZVk89MUZzM1P565n1/LHhTucLklERGRQcOLCx+FA9yWxkvBjR/IN4BzgKsuy\nbuvLwhwXFWvaRio3wPJHna5GBIDYKA9/vHEmF0/J4VevbuLXb23Btm2nyxIRERnQBvyFj7Zt3w/c\n/2nPsyzrK8BXAEaMGNHXZfWdCZfC6NPhvV/B5CsgLt3pikTwedzcf+104n0eyus0alJEROTTOLGS\nXQrkdfs4N/zYCbFt+2Hbtmfatj0zIyPjRD+dcywLLrwX2prgnV84XY1IJ7fL4p4rp3DPlVOxLIvy\nulbagyGnyxIRERmQnAjZnwAFlmWNtiwrCrgGeNmBOgauzEKzSc3Kv0Dp0G5Dl8HFsizcLouWtiBX\nP7SErz6xgpa2oNNliYiIDDh9PcLvb8ASYLxlWSWWZd1s23YAuAN4E9gEPGPb9oa+rGNQOuP7plXk\n9bsgpNVCGVhiotx8Zf4Y3ttSyY2Pfkx9qzatERER6a6vp4tca9t2jm3bXtu2c23b/lP48dds2x5n\n2/ZY27bv7ssaBq3oJDjn51DyCax92ulqRA5x/ayR3HfNdFbuqeHah5dS3eh3uiQREZEBQ9uqD2RF\nX4DhM2HBz6C13ulqRA5xadEwHrlhJturGvnvN7c4XY6IiMiAoZA9kLlccNG90FQFH/yX09WIHNaZ\nhZn89eZT+c554wBo9Ac04k9ERCKeQvZAN3wGTL8elj0IVVoplIFp5qhUMhOiCYZsbvnzJ3z1iRXU\nNrc5XZaIiIhjFLIHg7N/Bt44eP37oBVCGcAs4OzCLN7bUsmF9y1k6Y5qp0sSERFxhEL2YBCfAWf+\nCHa8B5v/6XQ1IkfkclncOn8Mz39tLj6Piy88spT/fWsLAc3TFhGRCDOkQrZlWZdYlvVwXV2d06X0\nvpNvgYwJ8OaPoL3F6WpEjmpKbhL//OY8Pjs9lxdWl9LcrlnaIiISWYZUyLZt+xXbtr+SlJTkdCm9\nz+0xF0HW7oHF9zldjcinivd5+PXVRbxyx2kkRnvxB4K8t7nS6bJERET6xZAK2UPe6Pkw8XJY9Buo\n2e10NSLHJDk2CoAnluzmS49/wg+eW0tzW8DhqkRERPqWQvZgc96vAAve+nenKxE5LjfOGcXXzxzL\n35fv5ZL/W8TGMs1+FxGRoUshe7BJzoN5/wabXoHt7zldjcgx87pdfO/8Qp68+VQaWgNc/sBiXl5T\n5nRZIiIifUIhezCa8w1IGWVG+gXbna5G5LjMyU/njW/N59yJWUzMSXC6HBERkT6hkD0YeaPh/P8H\n+7fAxw87XY3IcUuNi+KB604iPzMB27b5xSsbWVS83+myREREeo1C9mA1/kLIPwfevwcaNbFBBq/6\nlgAfFlfxxUeXcc/rm2nXTG0RERkCFLIHK8uCC+4xM7Pf/rnT1Yj8y5Jivbxyx2lcc/IIHvxgO1c9\nuITd1U1OlyUiInJCFLIHs/QCmPU1WP0k7P3E6WpE/mUxUW7+3xVT+P11J7GzqpEvPLJMK9oiIjKo\neZwuQE7Q6XfB2mfg9e/BLe+CS783yeB10ZQcivKS2VPdjNftIhSyaWkPEufTP1UiIjK4DKlENqS3\nVT8SXwKc+wsoWwWrnnC6GpETNjw5htlj0wD406KdXHz/QtaW1DpclYiIyPEZUiF7SG+rfjRTr4a8\nWfDad+GlO6Bqq9MVifSKorxk2gIhrvzDRzz84XZCIdvpkkRERI7JkArZEcuy4Oq/wEk3wLp/wAOn\nwNPXwd6Pna5M5IScMjqV1+6cx9mFWfzna5u58bGPqWxodbosERGRT6WQPVQkZMHFv4Zvb4D534Nd\ni+BP58KjF8KWNyCki8hkcEqOjeIP15/E3Z+dzMrdNeys0uQREREZ+CzbHnpvv86cOdNevny502U4\ny99oerSXPAB1eyFjAsy9EyZfCZ4op6sT+ZfUNreRHGv+/313cwVz89PxedwOVyUiIpHKsqwVtm3P\nPNw5rWT3lqZqeOE2aCh3uhLDF2/G+31zFXz2YbBc8OJtcP80E7z9DU5XKHLcOgL2jqpGbv7zcq74\n/Udsr2p0uCoREZFDKWT3lqYq2PAiPP8VCAWdrqaL2wtFn4evLYbrnoWU0fDmj+A3k+CdX2q3SBmU\nxmTE89D1MyitbeEz9y/imeV7GYrvyomIyOClkN1bMgvhonth5wew6H+druZQlgUF58KXXoVb3oHR\n82Hhr+E3k+Gf34bq7U5XKHJczpuUzRt3zqcoL4m7nl3Lj15Y73RJIiIindST3ZtsG567BTY8Dze9\nBiNn938Nx2P/NvjofljzNwgFYMKlpm97+ElOVyZyzIIhmwc/2I7P4+KWeWMIhWya2gIkRHudLk1E\nRIa4o/VkK2T3ttZ6eGg+xKaaFWPLcqaO49FQDssehE8eBX+dWeWeeyeMPXtw1C/SzUurS/mPVzby\njbPyue7UkUR59IadiIj0DYXs/la5GWLTID7DuRr+Fa31sOJxWPp7aNgHWVNM2J70WXBrW2sZHDaU\n1fGfr21i8bZq8lJj+O5547lk6jBcLv3CKCIivUsh2ynBAJSthLxTnK7k+AT8ZlObxffB/q2QPAJm\n3wHTr4eoOKerE/lUtm2zsHg//+/1zWzaV89FU7L5/XUznC5LRESGGIVsp7zzS9PzfPMCGDbN6WqO\nXygEW9+Axb+FvcsgJhVO+Yo54tKcrk7kU4VCNi+tKSUpxstZhVk0twXYub+JScOSnC5NRESGAIVs\npzQfgAdPA48Pvvoh+BKcruhft2cpLPotbH0dPDFmC/fZX4eUkU5XJnLMHvxgO/e8vpnLpw3j384b\nT15qrNMliYjIIBYxIduyrEuAS/Lz828tLi52uhxj90fw+MUw+Sq44uHBfyFh5WazOr/2GbBDMPkK\n07edPcXpykQ+VV1LOw9+sJ1HF+3EtuGLs0dyx5n5pMRpF1QRETl+EROyOwyYlewOH9wL790Nlz1g\n+pqHgrpSc4HkisehrdFMIpn9dRhzBri0zbUMbPvqWvjtgmL+sWIvZ4zP5NGbTna6JBERGYQUsp0W\nCsIrd8LJN8Ow6U5X07taamD5o7D0QWiqhPgsM41k8pWQe/LgX7mXIW1rRQMA47ISKK9r5f0tlVw1\nIxePW2P/RETk0ylkDzShELiG2A/x9lZzkeT6Z2HrWxD0Q9II004y+UrTTqLALQPYA+9t47/f3EJ+\nZjx3nT+ecydmYen/WREROQqF7IHCtuG175pe5s/8xulq+k5rPWx+FdY/B9vfBTsI6eNM2J58FaTn\nO12hyCFs2+bNDeXc+8YWduxvYubIFH54USEzRqY6XZqIiAxQCtkDyYKfmvnTn3vctFUMdU3VsOkl\nWP887FoE2JAsmuYDAAAgAElEQVQ9FaZcBZOugOQ8pysU6SEQDPH35Xv57dvFzCtI53+vHoTjN0VE\npF8oZA8kwXZ49AKzycttCyFllNMV9Z/6MtjwomkpKV1hHss71axuT7oc4jOdrU+km+a2AK3tIVLj\nolhfWseTy/bw7XMKyEyMdro0EREZIBSyB5qa3fDgPNM28aU3wBOB48MO7DTtJOufh8oNYLlg9HzT\nUjLhEohJcbpCkU5PLdvDz15ej8fl4pZ5o/nK/DEkRHudLktERBymkD0QbXgRXvo63PgKDD/J6Wqc\nVbnJBO51z0LNTnB5If8cE7jHXwi+eKcrFGF3dRP/89ZWXllTRmpcFN87fzzXnjLC6bJERMRBCtkD\nVWMVxGc4XcXAYdtQtqprhbuhzOwuOf4C01KSfw549Va9OGttSS33vL6ZmaNS+c6547BtG9sGl0uT\nSEREIo1C9kBm27DqryZAJuY4Xc3AEQrB3qUmcG94EZr3gy/RtJJMvgJGnwFuj9NVSoSybZtgyMbj\ndvHmhnL+791ivn9BIaflp2vsn4hIBDlayFZKcVp9Gbx+FwyfATe8pN0SO7hcMHKOOS74L9j5gQnc\nm16B1U9CbBpMvNy0lIyYPfTmjsuAZlkWHrcJ0x6XRU1TO1/808dMGpbITXNGcUnRMKK9+rssIhLJ\ntJI9EKx6El66Hc74EZzxfaerGdjaW2Hb2yZwb3kdAi2QMMysbk/5HAzTuDXpf63tQZ5bWcKfP9rF\n1opGZo5M4dmvzXG6LBER6WNqFxnobBte+Cqs+4e5EHLUaU5XNDj4G8O7TD4HxQsg1G62rT/5FjOD\nOyrW6Qolwti2zZLt1fgDIc4szKSlLciPX1zPtafkMWNkilpJRESGGIXswcDfAA+dDoFW+MYK8MY4\nXdHg0lJjppN88keo2gzRSTDtepj5Ze0wKY5ZtaeGGx/9mPrWAJOHJ3LTnNF8ZmqOWklERIaIiAnZ\nlmVdAlySn59/a3FxsdPlHL99a6ChHMad73Qlg5dtw+6PTNje9DKEAjDmDLO6Pe5CXSwp/a65LcAL\nq0p5fPEuiisbSY2L4o0752lTGxGRISBiQnaHQbmSfbCm/RCX7nQVg1tDBaz6Cyx/HOpLTO/2jJtg\nxo2QkO10dRJhbNvmo+3VfLC1ih9dNAGApz/eQ0FWPCeNUCuJiMhgpJA92Gx6BZ7/Ctz0qjaq6Q3B\nABS/ZVa3t78DLg8UXmxWt0fNA4UbcUBbIMRp//UulQ1+pgxP4qY5o/hMUQ4+j1pJREQGC4Xswab5\ngNl23e2Bry6E6ESnKxo6qrfDisfMbPKWGkgfBzNvhqJrICbZ6eokwjT5w60kH+1iW2Uj6fFR3HvV\nVM4qzHK6NBEROQYK2YPRnqXw2EUw6XK48k9abe1t7S1mk5tP/gily8EbC1OuMqvbOUVOVycRxrZt\nFm+r5vGPdvKDCyeQnxnPlvIGGv0BThqRrFYSEZEBSiF7sPrwf+DdX8Il95s+YukbZath+Z9g7T/M\n3O3ck83q9qTPaht3ccy3/76aF1aVMjXXtJJcPFWtJCIiA41C9mAVCsFTn4PRp8PcbzpdzdDXUgtr\nnjar29XFEJMK068zYwBTxzhdnUSYJn+A51eW8PhHu9he1UR6fBS3nT6WW+bp/0URkYFCIXswC4W0\nZXh/s23YtTA8BvCfYAch/xyzuj3ufHBpNVH6Tyhks2jbfv780S4mDUvkO+eNJxiyWV9aR1GeriMQ\nEXGSQvZQUPy2CX7n/ofTlUSW+n2w8i/mYsmGfZCUZ8YAnnQDxGc6XZ0E/FC5EfathcZKmHgpZIx3\nuqo+Y9s2lmWxYGMFt/5lOUW5Sdw0dxQXTVEriYiIExSyh4K3fw6LfgNXPQqTr3S6msgTDMDW183q\n9o73weU1gW7mzTByji5M7Q+tdVC+zgTq8rXmdv8Ws+FQdyNPg5lfggmXgMfnTK19rNEf4IWVJTz2\n0S52VDWRHu/jC6eO4OtnjlXYFhHpRwrZQ0Gw3UwbqdwEt32oHmEn7d8Gyx+F1X81wS9jApx8M0z9\nvMYt9gbbNjufdgTp8jUmXNfs6npOfBZkT4XsKZAz1dz3JcDqp8y7DjW7IDYNpl1n3nlIG+vQF9O3\nOlpJHv9oF1vKG1j0/TOxLItX1+4jI8HHjJEpuF36BVBEpK8oZA8VtXvgwdNMwP7yW+CJcrqiyNbW\nDBueN6vbZavM6nb2FBg+A3JnwvCZ5r+VeuqPLBSCAztMkN631oTp8rXQVNX1nNQx3QJ1kbmfcJQ5\n0qEQ7Hzf/CK0+TXTUz/mDHMB6/iLwO3t4y/KGU3+AHE+D7ZtM/eedymrayU1LoqzCzM5d2IW8woy\niInSKreISG9SyB5KNv0T/n4dXPZ7M/lCBobSFbDxZXNbtgraGs3j0UkmdA+f2RW+49KdrdUpAb95\nJ6Z8bVfbR8X6ru+VywuZhZBd1LVCnTX5xN4dqN9nNh5a8TjUl5gV8OlfNCMxk0f0ypc1EDW0tvPB\n1ioWbKzgvc2V1LcGuGH2SH5x2WRCIZsDzW2kxw/NVhoRkf6kkD3U7P4IRsxWH/BAFQpC1RazyU3J\ncihdCZUbwA6Z88kje65250wFb4yzNfe21vrwqvS6rraPqs0Qajfno+JNkO7e8pFR2Hc91KEgFC8w\nrSRb3zSPFZxnercLzhvSE2PagyE+3nmAjAQf47ISWLWnhiv+8BEzRqRw7sQszp2YxZiMeKfLFBEZ\nlBSyh6rq7SacJQ5zuhL5NG1NsG9NOHSHg3fdXnPO5YGsSV0r3rkzIa1g4LeZ+BugocJMXWkoh9pd\nXSvUNTu7nheXGe6bDofqnCJIGe3c11e710yMWfkXaCyHxFwzLeakL0bE36Wy2haeWb6XtzZUsHFf\nPQBjM+J45IaZCtsiIsdJIXsoam+F+4ogLR9ufHlIr8QNWQ3lpr2kdIUJ32WrwG9CD75EGDY9vNod\nDt9H60PuTW3NJnw2lHcF6M7bbvc72jy6SxndLVAXmfsJ2f1T9/EKtsOW183q9vZ3wXLD+AvN6vaY\nswb+Lzm9oKSmmXc2VfLB1ip+f91JRHvd/HHhDrZVNnLuxCzm5qcT7dW/LSIiR6KQPVSt/hu8eBuc\n/gM484dOVyMnKhQyO02WLA+H7+VQsaFrRF1SHgw/qWu1O6cIouKO/fMH/IcG5cOFaH/doa/1RJuw\nnJBz+Nv4bEjMMRM+BqMDO0zf9qonoXm/aemZcRNMvz7i5qHf+8Zm/rJkN43+ADFeN/PHpfOZqcO4\npGjor/KLiBwvheyh7IXbYO3f4YaXYfQ8p6uR3tbeYtovSpd3rXjX7jbnLDdkToTcGWa1Oy0fmquP\nHKJbag79/C5vt8B8cHjO6vo4OjkyrgEI+GHTK7D8Mdi9yHx/JnzGTCYZNS8yvgeAPxBk6Y4DLNhY\nztsbK5mam8TDN5ifIc+uKOGUUamMSIt1uEoREedFTMi2LOsS4JL8/Pxbi4uLnS6nf/gb4eEzTH/s\n1xZH7uSKSNJYBWUru614r4DW2p7PsdyHCc7hFefuQTo2NWKC43Gr2mJWt1c/Zb6/afkw40sw7Qvm\n+xYhbNumvjVAUoyXivpWTv3PdwAYn5XQeeHklOFJuDSPW0QiUMSE7A4RtZIN5mKz5Y/Beb8yF0La\nIfVoRxLbNhfB1uyC+IxweE7T/wO9pb0FNrxo5m6XfAxuH0y63Kxu550acb+k7KluZsGmChZsLOeT\nXTUEQza//lwRV87IpbrRT01zG6PT47UJjohEBIXsSLJ7CTx3MxRdY3a7G6I73Yk4ony9uVByzd+h\nrcG068z4EhR93sxEjzA1TW28t6WS08dlkBbv44mlu/nJi+uJi3IzaXgSU4cnMSU3iXMnZhEb5XG6\nXBGRXqeQHUlKV8D798C2t82K9ojZJmxPvbrvZhCLRBp/I6x/zqxu71sN3lizm2R0ovl713nYZkZ3\nj8dCZhdK2+752CHPs8PPCx3mefahj1uWGUGYPMJcuJk8ElLCt/GZ/bLiXlrbwpLt1awrqWVtaR0b\ny+rxB0Ks/fl5JEZ7eX5lCVsqGpg6PJmpuUnkpsRgRdg7ASIytChkR6L6fbD2abPbXfMB+LctZhv2\nulLzg1g/2ER6R+lKs7q95Y1w2HV1HS63+bvW/THLfdBzXAed7/4cq9vnOdzzuh2hANSXQu2entvS\ng5kOkzyiK4CnjOwZxvuoN789GGLX/iYKsszUmZ+/vIEnl+2mPWh+7qTEepk1Jo0/XD8DMDtVxvs8\nCt4iMmgoZEcy2zabniSPMCPi7p9mfiBPuw6mXQtJuU5XKCK9ra3JhO3aPVCz20ykqd0dvr/n0Atl\no+LDgXtEzwDecb8XW2H8gSBbyhtYW1LHupI6bGzuvaoIgEv+bxH76lqZmpvUeUwZnkxGgt6FE5GB\nSSFbjGAA1j9rVrd3LQQsGHMGzP8ejJrrcHEi0m9a6w4K4Hu6Anjt7kM3GopO7hbARx60Gj7i+Oa1\nH8WTy3azYncN60rq2FbViG3DRVOy+f11ZqX7jwt3UJCVwJThSaTGRfXKnykiciIUsuVQNbvMaLLV\nf4ML74HCi02LSX2Z2fBEb9eKRCbbNi1mtUcI4LV7INDa8zVxGSZsJ+SY/u/4bDNnPT58JGSb57i9\nx1xGkz/AhrJ6fB4XRXnJHGhq46RfLug8n5sSw9TcJK49ZQTzCjJ666sXETkuCtlyZKEQYJuez/f+\nEz74L8goNO0kRddE3G53IvIpbBsaK7sF8F1d9xsrzeZHLQcO80LLjJZMyD5yEO+474s/7B9d39rO\n+lLTZrK2pI61pbXcefY4rpqRy/rSOm7583LyM+PJz4xnbGY8+RnxTMlNIt6nySYi0jcUsuXYtNbB\nhhfM1tIlH5uLrwovhqv/opVtETl2gTZoqoSGCmgsh8aKbvfDQbyxwhyhwKGvj4o/ShDveDwbYlKx\nLQvLsiiuaOAP729nW1Uj2ysbaWoLAvDEzacwryCD5bsO8PdP9naG8PzMeHJTYjXPW0ROyNFCtn69\nly7RSTDjJnNUbYXVT5rezI6AvfQPMHo+ZE1yskoRGeg8Ueai6k+7sDoUgpaaowfx8nXQ8LaZS34w\nlwcrLhMSsiiIz+J/Y1JgbBL2xEQarDj2+aPJbQ7AzlQadvvZvLmSN1d4aCAGGxdRHhcLvj2fkWlx\nrNlby+4DzeRnxDMmI45orzZzEpETo5VsOTaNlfCbSRBsg5xpMP16mHxlRG0vLSIOams6+op4YwW0\n1Jl35Px1R/1UNhbtngSaXXEkpqTjiklmU43FumqLemKpJw53TDJxSancdPY0PLEpVLZH44tPISkl\n3ay06909EUHtItJbmqph3T9g9V/N6pI7Cq75GxSc43RlIiJdQkHw15vAffDRUnvYx0MtNQSba7H8\ndXgCzUf99EFctLoTCPkSSUhKM+8CxiRDymhIL4D0cZCWr0UIkQigdhHpHXFpMOs2c+xba6aTDD/J\nnFv/nNmUY9Q8yDtFP1xExDkuN8SkmONYXxI+ADPu1F9vWlnCIbx4TwllFRU01O6ntf4AgeYacoJt\nnB4fBa11lG1dQUbgVbx09Zi3+VKJyhoP6QW0p+TjzTT3SR4Jbv34FRnq9Ldc/jU5U83RYd8aWPYQ\nLPmd+ThjAoyeBxfeq7dVRWRwcXvMQkG3xYKCsVDQ7Sm2bdPcFoTw5JKn3tzC9opaQjW7ianbQYZ/\nN6cn1nIaNbD5NbzN+ztfG8BDtS8XO62A7DFTIL2Afd48UkZMIjpBCxQiQ4XaRaT3tLeY1ew9S2DP\nUsCG658z5565wew0OWK2ObImmdUmEZEhqC0QoqUtSFKsF9u2eeztVQQqt+Kt2UZ8407SW3czObqS\njLbSHhNW9pPEPk8eB2JGkjlqMhOmzKQ9ZSwr6hLISYkjOykan0f/dvartuZw339lt2sAKs0Enc7H\nqsDlMv36UfFmg6aouK77vvieHx/ueR3P8caZzxWpbNtcg9HZ8tWt9ctf1/Ox7s8Zd57ZXK+fqSdb\nnGXb8NLXYcf7UF9qHotKgNm3w5k/Mh+3t4I32rESRUT6Wyhk47ID+Kt2sGLFMqjeRlTtNpKadpHZ\ntocku2uiit/2ssvOYrs9jHJPLjVxo5h76mxmnXwqtaEYPthaRWZCNBkJPjISfCRGe7D0LuKRBQPQ\nVHX48Hzw7eEm22BBXHp4vGSm2WypIxy2NR7+Nth27PV5Y48QxuPAl9Dz447zbp9ZvHJ7weUJH+5u\n972Hecxj3rnp/vEhr3Ef3zvSHe1WrbWHD8Odgfmg6ya6P8cOHv3PcHnNtRDRiebWlwjjLzLtrP1M\nIVsGjtq9ZpV7zxIYPgOmX2d2l/v1eMieCiNmhVe7Z5l/wEREIlVTNVQX4y/fTNWu9biqi4mp30Fi\nSyluukJIW0wGq5rSaLW7tpq3LItJwxJJi4ui0R+grLaFKI+LKLdFlMeF1+0izufB0zEn/FOzwDFk\nBZfXXBDviTKBz+0Fj++g+1Hh5xzpfvjW7et2/wjPP/jdUNsOj4Q8OCgfJjw3Vx/+a/IlhWexZx3m\nttv92LTj76sPtHUL3h1HQ7f74XP+xoOed/DHTeBvMI8dbs58X7COFszD59pbTEBub/r0zxeVcGhI\nPurHST0/9kQPmFZUhWwZ2BqrYOnvTfguXQFBv3n8ikdg6tVmGkBzNaSOGTB/qUREHBNoMztt7t8K\n1cUEKrfQXrWdQHsb7cEQgaBNezBEWnwU0R43dS3t7DnQTHuo58/7cVkJxPs8HGhqY19dKx63C6/b\nCt+6SI/34XVZBEI2IdvG43bhOtK/wbZtAl+wDQJ+c9t5v938u348K7nHwnKHA3c43LfUQqj90Oe5\nfV2bGsVlHiU8Z4I3pndr7Gudwb3RfJ9DgYOO4EGPBw/znMDhzwfbj/D8oPk+H/xneGPCQfhTQrMv\ncUi1i2q6iAxs8Rlwzs/M/YAfylable68U8xjW16DF79m3o7rvtKdXaQr9EUk8niiIGOcOTA/yI/2\nL2ESMAUIBEMcaG6jqsFPVYMfe2QKRHvZtG0/f/t4D/sb/Z3n6lsDfPTlsxiWHMND723jv9/cAkBK\nrLezJeUP188gMdrLit0H2F3dTFKMl4RoLwnRHhKiPQxPjunZsmLbXYE7EA7hB98Pth8mpB/ufltX\ncO94fUzy4cOzL3HoLtB4osCTqoleA5QSigwsHh+MONUcHUbPh8/8tqvNZNMr5vF/22K2Vt77sfkt\nfvgM85uyiIgcwuN2kZkQTWZCz+tf5uanMze/Z3tea3uQKLe5+O7sCZmkxEZR1eDvCuKNfmLDu2I+\nv7KUJ5ft6flnuSyK774QgJ+9tJ4FGyt6BPCsxGjuudJMqHprQzkVDX4So1PC572kxEeRnxkPmEku\n6i+XwUghWwa+pFyY+SVzANSXQdkqE7ABPvo/2PSyuR+dDEl5kDkBrnzEPLZnWdfnScgeUm9TiYj0\nhe7byhdmJ1KYnXjE5/7oogncMm8M9S3tNLQGqG9txx8IdgbjScOTaGoL0tBqzu9vbKPJ39VT/uSy\nPXywtarH5xyTHse73z0DgC88soz1ZXUkdgvpk4Yl8fNLJwHwxJJdNPgDxHjdRHlc+DxuhiVHM2es\n+cVh5Z4abNvG53GH+9JdJMZ4SY0zPeztwRAel6UgL71uSIVsy7IuAS7Jz893uhTpS4nDzNHhsgdM\nAN+3FupKzNG992/BT2BvOGhbbkgcbmZ4X/5789imf5p+vqRcc0Qf+YeJiIj0FOfzMNp35Dhx9cw8\nrp6Zd8TzD31xBvXhAG6O9h693xdPzWF8dkLnuYbWAP5AV0h/YulutlY09vic88dldIbsO55cSVld\na4/zF07O5g/XzwBg5q/epr61HV84gPu8bi6fNox/v3giAFf94SNclhUO8C6iPC7OnpDFVTNyaQ+G\nuOf1zfg8LqK97s7baXnJFOUl0xYIsXj7fqI9bqK95ly0101afBSJ0Wa8o22Dy6WAPxQNqZBt2/Yr\nwCszZ8681elapB9FJ8LYs8xxOJc9YC4SqtsbDuGlpk+vw+vfh/qSro99STD1c3Dxr83HHz9ido7r\nCOHx2eoFFxHpJR3BMzPh8OevnzXyqK9/81vz8QdCtLYH8QdCtAVCuLuF1v/7wnQa/UH87UHaguZ8\ndlJXy8zXzhhLkz9AWyCEP3yMyzLF2LZNnM+DPxCkuS1ATbN5/eThpjXRHwjx9Md78AdCBLpdWHrn\n2QUU5SVT09zGlx775JCaf3hhIV89fSy7qps583/eD4f7jhDu4nvnF3Jp0TB27m/ipy+tx9ctpPs8\nLj5/ch5Tc5MprW3htbX78HnNxapul4XHZTFnbDrZSdFU1reytqQOt9s8bs67KMxJIDHaS21z+KLX\nbufcbouMeB9RHhet4e9Z9/MuC636HyMlBRn60gvMcSS3LAiH771dK+EZ4825UNCE8O4zOy03zPkG\nnPsf5vy7vwwH8DxzJOeZOaYiItLnLMvqDOqHM2Pk0S8KvO30sUf93H/+8ilHPB/v87DhFxcAEAzZ\ntLYHTT+7x/SzJ8d6ef72OeYXgHbzi0BrIMikYSakJ0Z7uPPsAloD5rw/EKS1PUR6fFcrS6PftNj4\nw5+7NRBi/rgMpubCjqpG7n5t0yF1Pfalk8lOimblnlpu++uKQ84/89XZnDI6lXc3V/KdZ9Yccv7V\nb57GpGFJ/GP5Xn7y0oZDzr//3TMYlR7Hnxbt5Ldvb8XjMlNposITap6/fS6pcVE8uWw3z68sxeu2\n8Ian1njdFvddM51or5tX1pSxZEd15+s6nvPtc81FvYuK97NzfyNet6tz+k2M1815k0y76JbyBupa\n2onzuTu/pwOJQrZIR/tJ3mH+IXW54Qd7zCY6HSG8dq+5yBKgab/pCT94Vuk5/wGnfcvMuf3w3q7w\n3RHE49KH7tXuIiIRyO2yiPN5iOvWOuPzuDlpRMoRX5MW7+sMlIczLiuBF26fe8Tzc8ams+7n59Ha\nHiIYsgmEzG1Ggg+A2WPTeOWO0zofD4RsgiGb8dlmIejUMWk8eP1JnY8HguZ2eLIZZThjZCo/vnhC\nj9cGQjYpseaXgMLsBK6akUsgaP7s9vD4yI5fMrxuF9FeF+1Bm0Z/gPZgiPaA3dkOtKW8gbc2lNMW\nfiegPRgC6PyevLCqlOdWdnunGUiK8XaG7N8s2MobG8oZlRbL+98784jfJ6doTrbIiQoFzQYHdXuh\ndo8J4qNOg9yZpk/8sQvN9JPuLn8Qpl0LVVthye/CAXwEJI8w9xNydIGmiIhEnO7TZJr8AVrag53h\nvD0UIhSyKQi382ytaKCqwY/HZXHqmDRH6tWcbJG+5HJDYo45Dl4Nz5kKPywxu5DV7TWr4HV7u55X\nXwKbX4Xm/T1fd8PLMOZ02LUYVj3RcyU8eQQkj1RfuIiIDDnd+70PfmfgYOOyEjr75wci/ZQW6WuW\nZTYKiE2FnKKe58aeBXdth7bmcD/4HhPEs8xoKhr2wc6F0FAGdqjrdd9YCWljYf1zsPHlrgCekG0u\nzBw+QyFcRETEQfopLDIQRMX22MGt05SrzBFsN/PBO1pSknLN+eYDUL4OtrzetR09wL9XmJD97q/M\n5j3xWeEAnmVaUWZ9zYT/5gNmA6CouP77WkVERCKAQrbIYOD2QspIc3R3yq3mCIVMy0lDOTRVgTc8\nnipllJms0lAOu5dAY7mZfDL7dnP+lTvNRj5RCZCQZVbBMwu7xhfuWmwu6uwI6NFJumBTRETkGChk\niwwFLpeZ/d19/jfA9OvN0cG2wV/f9fFJN8Lwk0wIbyiHxgozR7zDgp9CabeLiD0xMPZMuPZv5uPF\n94VDeE7XanlCjmmNERERiWAK2SKRxLLManSHgnPMcSRXPmJ6xRsqzCp4Q7kJ0x1W/RX2b+35mvEX\nw7VPmft/vdLsptnZrpJp+tI7RiAGA+odFxGRIUk/3UTkyFLHmONI7vjEXLTZWN4VxGPCq9i2DVhQ\nsxv2ftw1QeXkW03IDrTB3VlmN8347K52lYmXwvgLTR96yXITzBOy1TcuIiKDikK2iJyYqNjDh3HL\nguuf7fo42G7miXfM/w4F4PTvh9tUKk1Ar9oKWRPN+YZ98NgF3f6ccN/4GT80F4M27Tcr6R0r5PFZ\nJuDHpoEnqm+/ZhERkU+hkC0i/cPthaThXR9HxcIZPzjy82PT4frnTZ949yDe0e9dvQ3e/tmhr7vq\nMZh8BexZai7sjEmB6GRzG5MMM79sLgat3wcV63uei05W+4qIiPQK/TQRkYEpKhbyzz7y+RGz4Iel\n3UJ4BbTWmgs5wYwmTB9nNgKqLzGBuqUWJlxiQvauhfD8rYd+3lvfM59j0z9h2YNdAbwjrM+4yQT9\nulIzyaXjvC9Rk1dERKSTQraIDF6+eHOkjT303LDp8Pknjvza/HPg5rdNCG+tNQG8pcbsqAlm859g\nu7mws6XGHME2mPp5c371k/De3V2fz3KZEP7NVSZ0r3oStr3dM6DHJMO06800mPp9EGo356LiFdBF\nRIYYhWwRiUwdu3AeycRLzdHBtqG9BTzhGeSTr4TMieGAXmNCemutCcwATZWwb01XgLeD4PLC9C+a\n8+/+0gR1AJfHTH1JyoWvfmgeW/aQCfgd4Tw62fSfF5xrzjcfMJNbouIU0EVEBiCFbBGRY2FZpoWl\nQ9rYw6+gdzjt2+YAE9DbGqG1risQz7jJtLx0hPOW2p5hed9a2PKaOWeHwn9mQVfI/vv1sHtxOKCH\nV8uHz4ArHjLnF99vZqJ3X0VPyoOcqeZ8wG9CugK6iEifUMgWEelrlmV22vQldD2Wd4o5juTyB8xt\nKARtDSaEB9u7zs/6Gow7v+cqenxG1/kNz0PZasDueiz/HLj+OXP//pPMantHAI9JMQF+/vfM+UW/\nAbevZ7tLcp5ZbRcRkU+lkC0iMpC5XKaVpPsmQmAu4Dyar7xvArq/riuEu31d52d/3Uxr6b6SHgqv\nmNs2vCi2fq8AAA/OSURBVHu36Rnv7uRb4OJfm02E/l+uqan7ZJYpV5mjvQU++WNX3R1H8kjtBioi\nEUMhW0RkqHK5wiE45dBzs28/8ussC35cYdpbuvecJ2Sb86F2OOWWngG9rsT0iQM0V8NbPz708573\nK5jzDajeDg+edmgIn/U1GHuWmRaz5unw44nh22RIyzeB3rbV5iIiA55CtoiIHMrlPvLFod4YE5iP\nJHE4/GBvOKTXmd7w1jozUhHAG2vmlXecb60zc9ADfnO+evvhZ6Bf/YS5GHXbO/D0F7oF9EQzQvG8\nX0L2FNj7Cax4zMxmd0d1HafcConDoGKjGeHY47zXtNP4EswvDLV7zaZG3c8njTBz1AN+CAXNmMiO\nzZVERA6ikC0iIr3LssIr0IlA3qHnE3Pg/LsPfbzDyDnwozJore8ZxHOKzPnkPJh1W7dz4eeFguZ8\nUyXs/NCMXAz4TS97sA0mXW5C9p6P4PW7Dv1zv7HShOz1z8GCnx56/t+2/P/27j1ayuq84/jvx7kg\nHIiAKMUrRoxdar2eiIkki2pERY2XdinGWmNMI9QYrb1omyZ1maxVY9RaE1eMNmbZ1ls1YjT1RmOq\nxhYjWkREo8QeWxRFpQoHkMOBp3/sd5j3DDNHcF7OnMv3s9as85732TPs2ew588yevfebRvMfv0p6\n/MrsuQ4rJ+IXL073n3eD9NLP0k4zrW1pm8nWUemDiZ0ulPT+0p7x4R8rL6RlpB4YFEiyAQD9i52S\nz9a2lJBX2nEf6ejLa9//t49Pt1oOPkva79SUeG/okrqzn6VFnfudkkbES8l5KVHfbkyK73Vk2mmm\nFC89Rku2+4yHpYR/5VJpXafUtTqVKX2wmP9jaeEdPes0Yqx0SUc6vuvsNFrf2pYS8eGjpLF7Sqfd\nkuLzbpDe+59y8t7alr492OfYFH9zkbSxO9WnZUT62ZodA+gzjogPLzXAtLe3x/z58xtdDQAANrf6\n3TRvvWtVSsDXdaZ91EuLWZ+7I23h2NWZbus6UxJe2p7x9i9I//1YipVMPEg677F0/MPPpj3a8/aY\nKp3zr+n4pqPSvPeWEeUkfNJU6ahvpPjDX08fIFpHlhP1nfYtX4F1yb+lkftSrHm7NK2oNPefkXgM\nIbafiYj2ajFGsgEA6EttO6RbLQfOTLdazrgt/dy4UVqfS9JLjvuutOadtMvL+jXS+g96bu+41+9K\nK9/IYlmZ0l7sUkqiVy1LsQ1dWZ2+UE6yb5vZy84z66Vv75QS8ObtskR8uzQH//DZ0rpV0j3npXMt\nI6TmLNH/xDEp0V+3Slp0T24UPruN20saPSE9/tr30v2bR6Q58kA/Re8EAGAgGjZs8/3XJWn3Kb3f\n78gqO7/knf9U+XhDt9S9tmf83Iez5HxtOVEf9/EUi5CmXpzOda8tlxs5PsW7u6T3XiufL5Vp2zEl\n2Z3Lpfu/tnmdjr86JfLLF6eR+k1t0JIS8hOvlfY/NY3g/+ziimkyI6Up56U5/e/+Rnrx/jTFJj+S\nv9uUNBr/wcr0AWVTkt9GIo+PjJ4DAACqa2qWmiqS+F0OrV2+ubU87aSath2k2U/Wjo/ZQ/qTF3JJ\nfJaI7zA5xUfvLM24Sur+oGeZcXtmD5Atul2/Nu0Dv36t1LVGOuC0FF7+YvWda774gDTpCOnXD0pz\nvtIzNqxF+qNH09VSn787Xaipcr77MX+bRtpffaw8naZ5eHkHm0PPSeWWLZTeebnnrjVNrWmx77Am\naeWyNJpfufPNiDG12wz91qBKsm2fKOnEyZMnN7oqAABgazU1935V0VE7pq0Ya5l4gHTWnNrxfWak\nnWvWr03z4Uuj8eP3TvHdDpNOvqHnVJr1a6RRE1K8dFGl0vm1K1K50nSbZc9Jv7ox244yt+btwDNS\nkr3oJ9KT125er79+OyXZT1wtPX1TRZu0St94Ox3/9PyU6G9K0IdLbeOlWU+k+Nxvpt1r8gn86Ilp\npF+S5v0gjebn4x+bmKbzSNLi+9K++PkPCG07lb8dKS2qzX8AaB1Vnv7EfPweWPgIAABQtI0byrvP\ntI5O03tKi15L50s71EyampLTNxZI7y7Jzme72sTGNN1FSvPV3/ivnjvbNG8nHX9Viv/7d9IWlfn4\nqAnSmXel+J1nSR2/zMXXpZ10Zv0yxW+clh4/b/dPSV96KB1//5NpJD5v8tHSH9ydjv9u/3RRqtaR\n2ZScNukT06XPXZbi91+YEvHWtvK3ADsfnHbskaSXH0nfELSOTMl7y8i0oHb4qIL+U4rX28JHkmwA\nAIChKCJ9GCjNO1+zIo3Q5z8ANA2XdswuJNXxZNqTPh8fPSFdyElKI/Gr300Lcruy0f5dPylNvSjF\nrz88jf53rU43hdR+rnTCNakel1e5+NWnL0h7zK9bJV2zbzk5LyXx7ef0vlB4G2N3EQAAAPRk91zY\nOXKcpCqJbsmkI3p/vM/8ae/x8+eVjyPS3PpNg72WvvxoLkHPEvGd9i3HDzqzHO9anY7Vf6enkGQD\nAACgb9k9L5A0bJi0ay+LaoePko67YtvXq0DDGl0BAAAAYLAhyQYAAAAKRpINAAAAFIwkGwAAACgY\nSTYAAABQMJJsAAAAoGAk2QAAAEDBSLIBAACAgpFkAwAAAAUjyQYAAAAKRpINAAAAFIwkGwAAACgY\nSTYAAABQMJJsAAAAoGAk2QAAAEDBSLIBAACAgpFkAwAAAAUjyQYAAAAKRpINAAAAFIwkGwAAACgY\nSTYAAABQMJJsAAAAoGAk2QAAAEDBHBGNrkPhbL8t6bUG/NPjJb3TgH93sKD96kP71Yf2qx9tWB/a\nrz60X31ov49mj4jYsVpgUCbZjWJ7fkS0N7oeAxXtVx/arz60X/1ow/rQfvWh/epD+xWP6SIAAABA\nwUiyAQAAgIKRZBfrxkZXYICj/epD+9WH9qsfbVgf2q8+tF99aL+CMScbAAAAKBgj2QAAAEDBSLI/\nAtvH2v617SW2L60SH277ziz+lO1JfV/L/sn2brZ/YXux7RdsX1ilzDTb79tekN2+2Yi69le2O2w/\nn7XN/Cpx274u638LbR/SiHr2R7b3yfWrBbZX2r6oogz9r4Ltm20vt70od26c7bm2X8l+jq1x37Oz\nMq/YPrvvat1/1Gi/79p+KXuNzrE9psZ9e329DwU12u8y26/nXqczaty31/froaBG+92Za7sO2wtq\n3HfI9796MF1kK9lukvSypKMlLZX0tKQzImJxrswfSzogImbZninplIg4vSEV7mdsT5Q0MSKetT1a\n0jOSTq5ov2mS/iwiTmhQNfs12x2S2iOi6n6m2ZvNBZJmSJoi6e8jYkrf1XBgyF7Lr0uaEhGv5c5P\nE/2vB9ufldQp6R8jYv/s3JWSVkTEFVnyMjYiLqm43zhJ8yW1Swql1/uhEfF/ffoEGqxG+02X9GhE\ndNv+jiRVtl9WrkO9vN6Hghrtd5mkzoi4qpf7fej79VBQrf0q4ldLej8iLq8S69AQ73/1YCR76x0m\naUlEvBoRXZLukHRSRZmTJN2SHd8t6Sjb7sM69lsRsSwins2OV0l6UdIuja3VoHOS0h/TiIh5ksZk\nH27Q01GSfpNPsFFdRDwuaUXF6fzfuVsknVzlrsdImhsRK7LEeq6kY7dZRfupau0XEY9ERHf26zxJ\nu/Z5xQaIGv1vS2zJ+/Wg11v7ZbnJaZJu79NKDREk2VtvF0n/m/t9qTZPEjeVyf6Ivi9phz6p3QCS\nTaM5WNJTVcKfsv2c7Qdt79enFev/QtIjtp+x/ZUq8S3po5BmqvYbC/3vw02IiGXZ8ZuSJlQpQ1/c\nMl+S9GCN2Ie93oeyr2bTbW6uMV2J/vfhPiPprYh4pUac/lcHkmw0hO1Rkn4i6aKIWFkRflbpMqUH\nSvqepHv7un793NSIOETScZLOz74KxFaw3Srp85LuqhKm/22lSPMOmXv4Edj+uqRuSbfWKMLrvbof\nSNpL0kGSlkm6urHVGbDOUO+j2PS/OpBkb73XJe2W+33X7FzVMrabJW0v6d0+qd0AYLtFKcG+NSLu\nqYxHxMqI6MyOH5DUYnt8H1ez34qI17OfyyXNUfpKNG9L+uhQd5ykZyPircoA/W+LvVWahpT9XF6l\nDH2xF7a/KOkESWdGjQVSW/B6H5Ii4q2I2BARGyXdpOrtQv/rRZafnCrpzlpl6H/1Icneek9L2tv2\nntlo2ExJ91WUuU9SaRX97ystbmGUR5vmf/1I0osRcU2NMr9VmsNu+zClfsqHFEm227IFo7LdJmm6\npEUVxe6T9IdODlda0LJMyKs5ekP/22L5v3NnS/pplTIPS5pue2z2df707NyQZ/tYSX8h6fMRsaZG\nmS15vQ9JFetMTlH1dtmS9+uh7HOSXoqIpdWC9L/6NTe6AgNNthL8q0pvFE2Sbo6IF2xfLml+RNyn\nlET+k+0lSosNZjauxv3OEZLOkvR8bsugv5K0uyRFxA1KH0xm2+6WtFbSTD6kbDJB0pwsB2yWdFtE\nPGR7lrSp/R5Q2llkiaQ1ks5pUF37pezN4mhJ5+XO5duP/lfB9u2Spkkab3uppL+RdIWkf7F9rqTX\nlBZPyXa7pFkR8eWIWGH7W0rJjiRdHhEfZQHbgFaj/f5S0nBJc7PX87xsR6qdJf1DRMxQjdd7A55C\nQ9Vov2m2D1KaptSh7PWcb79a79cNeAoNVa39IuJHqrIuhf5XLLbwAwAAAArGdBEAAACgYCTZAAAA\nQMFIsgEAAICCkWQDAAAABSPJBgAAAApGkg0Ag4DtDbYX5G6XFvjYk2yzPy4AbAX2yQaAwWFtRBzU\n6EoAABJGsgFgELPdYftK28/b/pXtydn5SbYftb3Q9s9t756dn2B7ju3nstuns4dqsn2T7RdsP2J7\nRFb+a7YXZ49zR4OeJgD0OyTZADA4jKiYLnJ6LvZ+RPyOpO9LujY79z1Jt0TEAZJulXRddv46SY9F\nxIGSDpFUukLe3pKuj4j9JL0n6fey85dKOjh7nFnb6skBwEDDFR8BYBCw3RkRo6qc75B0ZES8artF\n0psRsYPtdyRNjIj12fllETHe9tuSdo2IdbnHmCRpbkTsnf1+iaSWiPi27YckdUq6V9K9EdG5jZ8q\nAAwIjGQDwOAXNY63xrrc8QaV1/QcL+l6pVHvp22z1gcARJINAEPB6bmf/5kd/4ekmdnxmZKeyI5/\nLmm2JNlusr19rQe1PUzSbhHxC0mXSNpe0maj6QAwFDHiAACDwwjbC3K/PxQRpW38xtpeqDQafUZ2\n7gJJP7b955LelnROdv5CSTfaPldpxHq2pGU1/s0mSf+cJeKWdF1EvFfYMwKAAYw52QAwiGVzstsj\n4p1G1wUAhhKmiwAAAAAFYyQbAAAAKBgj2QAAAEDBSLIBAACAgpFkAwAAAAUjyQYAAAAKRpINAAAA\nFIwkGwAAACjY/wOzAAYcNQMJ7AAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 864x720 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"8plwjrKyjHZ6","colab_type":"text"},"source":["## Train the Model"]},{"cell_type":"code","metadata":{"id":"F6oJ__-0O6yG","colab_type":"code","outputId":"80776825-3d15-4f74-d564-e12b2922b847","executionInfo":{"status":"ok","timestamp":1584595959717,"user_tz":-480,"elapsed":56348,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":649}},"source":["model = build_model()\n","model.load_weights(initial_weights)\n","\n","baseline_history = model.fit(x=train_features, \n","                             y=train_labels, \n","                             batch_size=BATCH_SIZE, \n","                             epochs=EPOCHS, \n","                             callbacks=[early_stopping], \n","                             validation_data=(val_features, val_labels))"],"execution_count":34,"outputs":[{"output_type":"stream","text":["Epoch 1/100\n","90/90 [==============================] - 1s 17ms/step - loss: 0.0136 - tp: 72.0000 - fp: 10.0000 - tn: 227439.0000 - fn: 324.0000 - accuracy: 0.9985 - percision: 0.8780 - recall: 0.1818 - auc: 0.6865 - val_loss: 0.0085 - val_tp: 0.0000e+00 - val_fp: 0.0000e+00 - val_tn: 45489.0000 - val_fn: 80.0000 - val_accuracy: 0.9982 - val_percision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.8741\n","Epoch 2/100\n","90/90 [==============================] - 1s 9ms/step - loss: 0.0083 - tp: 90.0000 - fp: 24.0000 - tn: 181936.0000 - fn: 226.0000 - accuracy: 0.9986 - percision: 0.7895 - recall: 0.2848 - auc: 0.8124 - val_loss: 0.0050 - val_tp: 33.0000 - val_fp: 5.0000 - val_tn: 45484.0000 - val_fn: 47.0000 - val_accuracy: 0.9989 - val_percision: 0.8684 - val_recall: 0.4125 - val_auc: 0.9123\n","Epoch 3/100\n","90/90 [==============================] - 1s 8ms/step - loss: 0.0062 - tp: 144.0000 - fp: 20.0000 - tn: 181940.0000 - fn: 172.0000 - accuracy: 0.9989 - percision: 0.8780 - recall: 0.4557 - auc: 0.8764 - val_loss: 0.0043 - val_tp: 48.0000 - val_fp: 6.0000 - val_tn: 45483.0000 - val_fn: 32.0000 - val_accuracy: 0.9992 - val_percision: 0.8889 - val_recall: 0.6000 - val_auc: 0.9124\n","Epoch 4/100\n","90/90 [==============================] - 1s 9ms/step - loss: 0.0062 - tp: 169.0000 - fp: 31.0000 - tn: 181929.0000 - fn: 147.0000 - accuracy: 0.9990 - percision: 0.8450 - recall: 0.5348 - auc: 0.8996 - val_loss: 0.0040 - val_tp: 52.0000 - val_fp: 6.0000 - val_tn: 45483.0000 - val_fn: 28.0000 - val_accuracy: 0.9993 - val_percision: 0.8966 - val_recall: 0.6500 - val_auc: 0.9186\n","Epoch 5/100\n","90/90 [==============================] - 1s 8ms/step - loss: 0.0054 - tp: 173.0000 - fp: 27.0000 - tn: 181933.0000 - fn: 143.0000 - accuracy: 0.9991 - percision: 0.8650 - recall: 0.5475 - auc: 0.9031 - val_loss: 0.0038 - val_tp: 55.0000 - val_fp: 6.0000 - val_tn: 45483.0000 - val_fn: 25.0000 - val_accuracy: 0.9993 - val_percision: 0.9016 - val_recall: 0.6875 - val_auc: 0.9186\n","Epoch 6/100\n","90/90 [==============================] - 1s 8ms/step - loss: 0.0052 - tp: 175.0000 - fp: 25.0000 - tn: 181935.0000 - fn: 141.0000 - accuracy: 0.9991 - percision: 0.8750 - recall: 0.5538 - auc: 0.9020 - val_loss: 0.0037 - val_tp: 59.0000 - val_fp: 6.0000 - val_tn: 45483.0000 - val_fn: 21.0000 - val_accuracy: 0.9994 - val_percision: 0.9077 - val_recall: 0.7375 - val_auc: 0.9186\n","Epoch 7/100\n","90/90 [==============================] - 1s 8ms/step - loss: 0.0048 - tp: 181.0000 - fp: 27.0000 - tn: 181933.0000 - fn: 135.0000 - accuracy: 0.9991 - percision: 0.8702 - recall: 0.5728 - auc: 0.9102 - val_loss: 0.0036 - val_tp: 58.0000 - val_fp: 6.0000 - val_tn: 45483.0000 - val_fn: 22.0000 - val_accuracy: 0.9994 - val_percision: 0.9062 - val_recall: 0.7250 - val_auc: 0.9249\n","Epoch 8/100\n","90/90 [==============================] - 1s 9ms/step - loss: 0.0050 - tp: 186.0000 - fp: 35.0000 - tn: 181925.0000 - fn: 130.0000 - accuracy: 0.9991 - percision: 0.8416 - recall: 0.5886 - auc: 0.9117 - val_loss: 0.0035 - val_tp: 58.0000 - val_fp: 6.0000 - val_tn: 45483.0000 - val_fn: 22.0000 - val_accuracy: 0.9994 - val_percision: 0.9062 - val_recall: 0.7250 - val_auc: 0.9248\n","Epoch 9/100\n","90/90 [==============================] - 1s 8ms/step - loss: 0.0049 - tp: 184.0000 - fp: 34.0000 - tn: 181926.0000 - fn: 132.0000 - accuracy: 0.9991 - percision: 0.8440 - recall: 0.5823 - auc: 0.9086 - val_loss: 0.0034 - val_tp: 58.0000 - val_fp: 6.0000 - val_tn: 45483.0000 - val_fn: 22.0000 - val_accuracy: 0.9994 - val_percision: 0.9062 - val_recall: 0.7250 - val_auc: 0.9248\n","Epoch 10/100\n","90/90 [==============================] - 1s 8ms/step - loss: 0.0047 - tp: 184.0000 - fp: 29.0000 - tn: 181931.0000 - fn: 132.0000 - accuracy: 0.9991 - percision: 0.8638 - recall: 0.5823 - auc: 0.9182 - val_loss: 0.0033 - val_tp: 59.0000 - val_fp: 6.0000 - val_tn: 45483.0000 - val_fn: 21.0000 - val_accuracy: 0.9994 - val_percision: 0.9077 - val_recall: 0.7375 - val_auc: 0.9248\n","Epoch 11/100\n","90/90 [==============================] - 1s 8ms/step - loss: 0.0049 - tp: 173.0000 - fp: 35.0000 - tn: 181925.0000 - fn: 143.0000 - accuracy: 0.9990 - percision: 0.8317 - recall: 0.5475 - auc: 0.9149 - val_loss: 0.0032 - val_tp: 60.0000 - val_fp: 6.0000 - val_tn: 45483.0000 - val_fn: 20.0000 - val_accuracy: 0.9994 - val_percision: 0.9091 - val_recall: 0.7500 - val_auc: 0.9248\n","Epoch 12/100\n","90/90 [==============================] - 1s 8ms/step - loss: 0.0046 - tp: 182.0000 - fp: 27.0000 - tn: 181933.0000 - fn: 134.0000 - accuracy: 0.9991 - percision: 0.8708 - recall: 0.5759 - auc: 0.9103 - val_loss: 0.0032 - val_tp: 60.0000 - val_fp: 6.0000 - val_tn: 45483.0000 - val_fn: 20.0000 - val_accuracy: 0.9994 - val_percision: 0.9091 - val_recall: 0.7500 - val_auc: 0.9248\n","Epoch 13/100\n","90/90 [==============================] - 1s 8ms/step - loss: 0.0045 - tp: 187.0000 - fp: 30.0000 - tn: 181930.0000 - fn: 129.0000 - accuracy: 0.9991 - percision: 0.8618 - recall: 0.5918 - auc: 0.9183 - val_loss: 0.0031 - val_tp: 60.0000 - val_fp: 6.0000 - val_tn: 45483.0000 - val_fn: 20.0000 - val_accuracy: 0.9994 - val_percision: 0.9091 - val_recall: 0.7500 - val_auc: 0.9248\n","Epoch 14/100\n","90/90 [==============================] - 1s 9ms/step - loss: 0.0045 - tp: 182.0000 - fp: 30.0000 - tn: 181930.0000 - fn: 134.0000 - accuracy: 0.9991 - percision: 0.8585 - recall: 0.5759 - auc: 0.9291 - val_loss: 0.0031 - val_tp: 60.0000 - val_fp: 6.0000 - val_tn: 45483.0000 - val_fn: 20.0000 - val_accuracy: 0.9994 - val_percision: 0.9091 - val_recall: 0.7500 - val_auc: 0.9248\n","Epoch 15/100\n","90/90 [==============================] - 1s 8ms/step - loss: 0.0041 - tp: 198.0000 - fp: 31.0000 - tn: 181929.0000 - fn: 118.0000 - accuracy: 0.9992 - percision: 0.8646 - recall: 0.6266 - auc: 0.9247 - val_loss: 0.0030 - val_tp: 62.0000 - val_fp: 7.0000 - val_tn: 45482.0000 - val_fn: 18.0000 - val_accuracy: 0.9995 - val_percision: 0.8986 - val_recall: 0.7750 - val_auc: 0.9248\n","Epoch 16/100\n","90/90 [==============================] - 1s 9ms/step - loss: 0.0044 - tp: 185.0000 - fp: 32.0000 - tn: 181928.0000 - fn: 131.0000 - accuracy: 0.9991 - percision: 0.8525 - recall: 0.5854 - auc: 0.9263 - val_loss: 0.0030 - val_tp: 62.0000 - val_fp: 7.0000 - val_tn: 45482.0000 - val_fn: 18.0000 - val_accuracy: 0.9995 - val_percision: 0.8986 - val_recall: 0.7750 - val_auc: 0.9248\n","Epoch 17/100\n","87/90 [============================>.] - ETA: 0s - loss: 0.0042 - tp: 183.0000 - fp: 28.0000 - tn: 177842.0000 - fn: 123.0000 - accuracy: 0.9992 - percision: 0.8673 - recall: 0.5980 - auc: 0.9222Restoring model weights from the end of the best epoch.\n","90/90 [==============================] - 1s 9ms/step - loss: 0.0042 - tp: 193.0000 - fp: 31.0000 - tn: 181929.0000 - fn: 123.0000 - accuracy: 0.9992 - percision: 0.8616 - recall: 0.6108 - auc: 0.9247 - val_loss: 0.0030 - val_tp: 63.0000 - val_fp: 7.0000 - val_tn: 45482.0000 - val_fn: 17.0000 - val_accuracy: 0.9995 - val_percision: 0.9000 - val_recall: 0.7875 - val_auc: 0.9248\n","Epoch 00017: early stopping\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"vzb0D7O8kH1b","colab_type":"text"},"source":["## Check training history"]},{"cell_type":"code","metadata":{"id":"UghT0s6cj9ce","colab_type":"code","outputId":"ce7986ee-419a-433c-ea76-f80a3a4e873e","executionInfo":{"status":"ok","timestamp":1584595959717,"user_tz":-480,"elapsed":56332,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":54}},"source":["baseline_history.history.keys()"],"execution_count":35,"outputs":[{"output_type":"execute_result","data":{"text/plain":["dict_keys(['loss', 'tp', 'fp', 'tn', 'fn', 'accuracy', 'percision', 'recall', 'auc', 'val_loss', 'val_tp', 'val_fp', 'val_tn', 'val_fn', 'val_accuracy', 'val_percision', 'val_recall', 'val_auc'])"]},"metadata":{"tags":[]},"execution_count":35}]},{"cell_type":"code","metadata":{"id":"jJfM3gKvkrxT","colab_type":"code","colab":{}},"source":["def plot_metrics(history):\n","  metrics = [\"loss\", \"auc\", \"percision\", \"recall\"]\n","  for n, met in enumerate(metrics):\n","    plt.subplot(2, 2, n+1)\n","    plt.plot(history.epoch, history.history[met], \n","             color=colors[0], label='Train')\n","    plt.plot(history.epoch, history.history[\"val_\" + met], \n","             color=colors[0], label=\"Val\", linestyle=\"--\")\n","    \n","    name = met.capitalize()\n","    plt.xlabel('Epochs')\n","    plt.ylabel(name)\n","\n","    if met == \"loss\":\n","      plt.ylim([0, history.history[\"loss\"][0]])\n","    elif met == \"auc\":\n","      plt.ylim([0.8, 1.0])\n","    else:\n","      plt.ylim([0, 1])\n","    \n","    plt.legend()\n","  plt.show()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"-Sb4M5wTl7QR","colab_type":"code","outputId":"62ef32e6-ad2f-4fdd-aef2-2318506ab226","executionInfo":{"status":"ok","timestamp":1584595960896,"user_tz":-480,"elapsed":57488,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":609}},"source":["plot_metrics(baseline_history)"],"execution_count":37,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAt0AAAJQCAYAAABSGdj0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKuJEWEIuJCSE1NRjssGu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Qfckq2qpOCvBC4H1Hn8Sc0y0dyjvbizj0aU7ySuoJD4iiIsm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Yv6kPVNEXn6J4/cXC8QxERiZkTpc4WkRNXfWsbG8sbKBiUQn6mn/e2VvPlPyzn/7d35/FR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\nCT7Nleimb5w+lPqWNqfDEBGRHthe2c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ZijJmFd3fG8dbaMcC9QYwnYilnH5FydgfK20eM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\nAFPRLSIiIiISYCq6RUREREQC7P8Db+wa4EReO5YAAAAASUVORK5CYII=\n","text/plain":["<Figure size 864x720 with 4 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"EVOb2WIfn6No","colab_type":"text"},"source":["## Evaluate Metrics\n","\n","You can use a confusion matrix to summarize the true vs. predicted label where the Y axis is the actual label and the X axis is the predicted label."]},{"cell_type":"code","metadata":{"id":"70lseHz-q7QY","colab_type":"code","colab":{}},"source":["def plot_cm(labels, predictions, p=0.5):\n","  # cm[0]: Predicted Negatives\n","  # cm[1]: Predicted Positives\n","  cm = confusion_matrix(y_true=labels, y_pred=(predictions > p))\n","  \n","  plt.figure(figsize=(5, 5))\n","  sns.heatmap(data=cm, annot=True, fmt='d')\n","  plt.title('Confusion Matrix @{:.1f}'.format(p))\n","  plt.xlabel('Predicted label')\n","  plt.ylabel('True label')\n","  plt.show()\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]))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"hTcRyVg0mfSp","colab_type":"code","outputId":"c2f83915-71c0-4a5a-f83d-4f98def8c3fe","executionInfo":{"status":"ok","timestamp":1584595960897,"user_tz":-480,"elapsed":57464,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":435}},"source":["test_prediction_baseline = model.predict(x=test_features, batch_size=BATCH_SIZE)\n","plot_cm(labels=test_labels, predictions=test_prediction_baseline)"],"execution_count":39,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAU4AAAFNCAYAAABvx4bHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXLmSM39yIYd86Wt0796Nn5x5GpI4/LOf5syfXMCYI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+Rv/YCJE/6PHj2689JL/+TYr57K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\n","text/plain":["<Figure size 360x360 with 2 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Legitimate Transactions Detected (True Negatives): 56851\n","Legitimate Transactions Incorrectly Detected (False Positives): 15\n","Fraudulent Transactions Missed (False Negatives): 30\n","Fraudulent Transactions Detected (True Positives): 66\n","Total Fraudulent Transactions: 96\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"UdEzKGeyzBrM","colab_type":"text"},"source":["False negatives and false positives are a trade-off problem. If you try to minimize the false negatives, you might encounter the increase of false positives. False negatives would allow fraudulent transactions to continue. False positives cause more operations took to ask the customers for more verification."]},{"cell_type":"markdown","metadata":{"id":"s0cKABt5zD6T","colab_type":"text"},"source":["## Plot the ROC"]},{"cell_type":"code","metadata":{"id":"NBTYZhpAsmKk","colab_type":"code","colab":{}},"source":["def plot_roc(name, labels, predictions, **kwargs):\n","  fp, tp, _ = sklearn.metrics.roc_curve(labels, predictions)\n","\n","  plt.plot(fp*100, tp*100, 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., 100.0])\n","  plt.grid(True)\n","  ax = plt.gca()  # gca: get current axes\n","  ax.set_aspect('equal')"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"apQqCxBi0bRJ","colab_type":"code","colab":{}},"source":["train_predictions_baseline = model.predict(train_features, batch_size=BATCH_SIZE)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"17ta_b7B0Uhv","colab_type":"code","outputId":"b30274c3-5f81-4f14-ca66-d01f4deeb431","executionInfo":{"status":"ok","timestamp":1584595961219,"user_tz":-480,"elapsed":57749,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":610}},"source":["plot_roc('Train Baseline', train_labels, train_predictions_baseline, color=colors[0])\n","plot_roc('Test Baseline', test_labels, test_prediction_baseline, color=colors[0], linestyle='--')\n","plt.legend(loc='lower right')\n","plt.show()"],"execution_count":42,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAngAAAJRCAYAAADWNMrEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndsTgrCNIkqQKYsGrAHfPODrrCJIkqYJ4Dp4kSVLOWPAkSZJyxoJXAUbP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e3xYvz6x3fuQ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/9u5bdeuutPPHEE9x1111cfvnlLFrU+gziO0UE06ZNY/bs2cycOZMZM2bwxS9+kY9+9KPU1NRw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\n9a4K4QsAAAAASUVORK5CYII=\n","text/plain":["<Figure size 864x720 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"-VfimkDpyZ2g","colab_type":"text"},"source":["# Class Weights"]},{"cell_type":"markdown","metadata":{"id":"MHdvRsBjyqHZ","colab_type":"text"},"source":["## Calculate Class Weights\n","\n","The goal is to find the fraudulent transactions, but the datasets contain not many positive examples. You would want to have the classifier heavily weight the few examples that are positive. You can pass the weights for each class to tell the model pay more attentions to the examples from an under-represented class."]},{"cell_type":"code","metadata":{"id":"Fj1f7UTN02_O","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":51},"outputId":"3c127807-da4c-4613-f51b-b36db43168e6","executionInfo":{"status":"ok","timestamp":1584599204656,"user_tz":-480,"elapsed":717,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}}},"source":["# scaling by total/2.0 helps keep the loss to a similar magnitude\n","# the sum of the weights of all examples stays the same\n","weight_for_neg = (1.0 / neg) * (total / 2.0)\n","weight_for_pos = (1.0 / pos) * (total / 2.0)\n","\n","class_weight = {0: weight_for_neg, 1: weight_for_pos}\n","\n","print('Weight for Neg. Samples: {:.2f}'.format(weight_for_neg))\n","print('Weight for Pos. Samples: {:.2f}'.format(weight_for_pos))"],"execution_count":49,"outputs":[{"output_type":"stream","text":["Weight for Neg. Samples: 0.50\n","Weight for Pos. Samples: 289.44\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"6sZb7RhB2laL","colab_type":"text"},"source":["## Train a Model with Class Weights\n","\n","Next, try training a new model with the help of class weights, and look at the effects on the predictions.\n","\n","**Using `class weights` changes the range of the loss. This may cause the instability of the training depending on the optimizer. Optimizers whose step size is dependent on the magnitude of the gradient, like optimizers, `SGD`, may fail. We suggest using `Adam` that is not affected by the scaling change. Remember, the total losses are not comparable between models above because of the weighting.**"]},{"cell_type":"code","metadata":{"id":"80OLkU6bZAWD","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":785},"outputId":"a0e97ef8-34b0-4e28-866f-07f1e6696681","executionInfo":{"status":"ok","timestamp":1584599234287,"user_tz":-480,"elapsed":20844,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}}},"source":["weighted_model = build_model()\n","weighted_model.load_weights(initial_weights)\n","\n","weighted_history = weighted_model.fit(\n","  x=train_features, \n","  y=train_labels, \n","  batch_size=BATCH_SIZE, \n","  epochs=EPOCHS, \n","  callbacks=[early_stopping], \n","  validation_data=(val_features, val_labels), \n","  # use the class weights\n","  class_weight=class_weight)"],"execution_count":50,"outputs":[{"output_type":"stream","text":["Epoch 1/100\n","90/90 [==============================] - 2s 18ms/step - loss: 3.1340 - tp: 107.0000 - fp: 13483.0000 - tn: 213966.0000 - fn: 289.0000 - accuracy: 0.9396 - percision: 0.0079 - recall: 0.2702 - auc: 0.6356 - val_loss: 0.0074 - val_tp: 20.0000 - val_fp: 4.0000 - val_tn: 45485.0000 - val_fn: 60.0000 - val_accuracy: 0.9986 - val_percision: 0.8333 - val_recall: 0.2500 - val_auc: 0.9024\n","Epoch 2/100\n","90/90 [==============================] - 1s 9ms/step - loss: 1.2077 - tp: 141.0000 - fp: 142.0000 - tn: 181818.0000 - fn: 175.0000 - accuracy: 0.9983 - percision: 0.4982 - recall: 0.4462 - auc: 0.8593 - val_loss: 0.0074 - val_tp: 62.0000 - val_fp: 8.0000 - val_tn: 45481.0000 - val_fn: 18.0000 - val_accuracy: 0.9994 - val_percision: 0.8857 - val_recall: 0.7750 - val_auc: 0.9293\n","Epoch 3/100\n","90/90 [==============================] - 1s 9ms/step - loss: 0.7384 - tp: 207.0000 - fp: 378.0000 - tn: 181582.0000 - fn: 109.0000 - accuracy: 0.9973 - percision: 0.3538 - recall: 0.6551 - auc: 0.8950 - val_loss: 0.0098 - val_tp: 66.0000 - val_fp: 13.0000 - val_tn: 45476.0000 - val_fn: 14.0000 - val_accuracy: 0.9994 - val_percision: 0.8354 - val_recall: 0.8250 - val_auc: 0.9244\n","Epoch 4/100\n","90/90 [==============================] - 1s 9ms/step - loss: 0.5799 - tp: 232.0000 - fp: 820.0000 - tn: 181140.0000 - fn: 84.0000 - accuracy: 0.9950 - percision: 0.2205 - recall: 0.7342 - auc: 0.9163 - val_loss: 0.0132 - val_tp: 66.0000 - val_fp: 15.0000 - val_tn: 45474.0000 - val_fn: 14.0000 - val_accuracy: 0.9994 - val_percision: 0.8148 - val_recall: 0.8250 - val_auc: 0.9415\n","Epoch 5/100\n","90/90 [==============================] - 1s 9ms/step - loss: 0.4416 - tp: 242.0000 - fp: 1617.0000 - tn: 180343.0000 - fn: 74.0000 - accuracy: 0.9907 - percision: 0.1302 - recall: 0.7658 - auc: 0.9280 - val_loss: 0.0183 - val_tp: 66.0000 - val_fp: 36.0000 - val_tn: 45453.0000 - val_fn: 14.0000 - val_accuracy: 0.9989 - val_percision: 0.6471 - val_recall: 0.8250 - val_auc: 0.9446\n","Epoch 6/100\n","90/90 [==============================] - 1s 9ms/step - loss: 0.3683 - tp: 253.0000 - fp: 2847.0000 - tn: 179113.0000 - fn: 63.0000 - accuracy: 0.9840 - percision: 0.0816 - recall: 0.8006 - auc: 0.9397 - val_loss: 0.0268 - val_tp: 67.0000 - val_fp: 82.0000 - val_tn: 45407.0000 - val_fn: 13.0000 - val_accuracy: 0.9979 - val_percision: 0.4497 - val_recall: 0.8375 - val_auc: 0.9516\n","Epoch 7/100\n","90/90 [==============================] - 1s 9ms/step - loss: 0.3571 - tp: 255.0000 - fp: 4594.0000 - tn: 177366.0000 - fn: 61.0000 - accuracy: 0.9745 - percision: 0.0526 - recall: 0.8070 - auc: 0.9312 - val_loss: 0.0394 - val_tp: 68.0000 - val_fp: 244.0000 - val_tn: 45245.0000 - val_fn: 12.0000 - val_accuracy: 0.9944 - val_percision: 0.2179 - val_recall: 0.8500 - val_auc: 0.9613\n","Epoch 8/100\n","90/90 [==============================] - 1s 9ms/step - loss: 0.3286 - tp: 259.0000 - fp: 6552.0000 - tn: 175408.0000 - fn: 57.0000 - accuracy: 0.9637 - percision: 0.0380 - recall: 0.8196 - auc: 0.9332 - val_loss: 0.0592 - val_tp: 69.0000 - val_fp: 601.0000 - val_tn: 44888.0000 - val_fn: 11.0000 - val_accuracy: 0.9866 - val_percision: 0.1030 - val_recall: 0.8625 - val_auc: 0.9692\n","Epoch 9/100\n","90/90 [==============================] - 1s 9ms/step - loss: 0.2962 - tp: 261.0000 - fp: 9346.0000 - tn: 172614.0000 - fn: 55.0000 - accuracy: 0.9484 - percision: 0.0272 - recall: 0.8259 - auc: 0.9332 - val_loss: 0.0918 - val_tp: 69.0000 - val_fp: 1127.0000 - val_tn: 44362.0000 - val_fn: 11.0000 - val_accuracy: 0.9750 - val_percision: 0.0577 - val_recall: 0.8625 - val_auc: 0.9730\n","Epoch 10/100\n","90/90 [==============================] - 1s 9ms/step - loss: 0.2140 - tp: 271.0000 - fp: 12562.0000 - tn: 169398.0000 - fn: 45.0000 - accuracy: 0.9308 - percision: 0.0211 - recall: 0.8576 - auc: 0.9493 - val_loss: 0.1296 - val_tp: 69.0000 - val_fp: 1714.0000 - val_tn: 43775.0000 - val_fn: 11.0000 - val_accuracy: 0.9621 - val_percision: 0.0387 - val_recall: 0.8625 - val_auc: 0.9750\n","Epoch 11/100\n","90/90 [==============================] - 1s 9ms/step - loss: 0.2365 - tp: 276.0000 - fp: 15867.0000 - tn: 166093.0000 - fn: 40.0000 - accuracy: 0.9127 - percision: 0.0171 - recall: 0.8734 - auc: 0.9342 - val_loss: 0.1709 - val_tp: 71.0000 - val_fp: 2429.0000 - val_tn: 43060.0000 - val_fn: 9.0000 - val_accuracy: 0.9465 - val_percision: 0.0284 - val_recall: 0.8875 - val_auc: 0.9755\n","Epoch 12/100\n","90/90 [==============================] - 1s 9ms/step - loss: 0.1653 - tp: 285.0000 - fp: 18868.0000 - tn: 163092.0000 - fn: 31.0000 - accuracy: 0.8963 - percision: 0.0149 - recall: 0.9019 - auc: 0.9485 - val_loss: 0.2144 - val_tp: 72.0000 - val_fp: 3134.0000 - val_tn: 42355.0000 - val_fn: 8.0000 - val_accuracy: 0.9310 - val_percision: 0.0225 - val_recall: 0.9000 - val_auc: 0.9750\n","Epoch 13/100\n","90/90 [==============================] - 1s 9ms/step - loss: 0.1648 - tp: 285.0000 - fp: 21907.0000 - tn: 160053.0000 - fn: 31.0000 - accuracy: 0.8796 - percision: 0.0128 - recall: 0.9019 - auc: 0.9441 - val_loss: 0.2679 - val_tp: 73.0000 - val_fp: 3978.0000 - val_tn: 41511.0000 - val_fn: 7.0000 - val_accuracy: 0.9126 - val_percision: 0.0180 - val_recall: 0.9125 - val_auc: 0.9749\n","Epoch 14/100\n","90/90 [==============================] - 1s 9ms/step - loss: 0.1346 - tp: 285.0000 - fp: 25673.0000 - tn: 156287.0000 - fn: 31.0000 - accuracy: 0.8590 - percision: 0.0110 - recall: 0.9019 - auc: 0.9484 - val_loss: 0.3360 - val_tp: 74.0000 - val_fp: 5098.0000 - val_tn: 40391.0000 - val_fn: 6.0000 - val_accuracy: 0.8880 - val_percision: 0.0143 - val_recall: 0.9250 - val_auc: 0.9740\n","Epoch 15/100\n","90/90 [==============================] - 1s 9ms/step - loss: 0.1284 - tp: 290.0000 - fp: 29172.0000 - tn: 152788.0000 - fn: 26.0000 - accuracy: 0.8398 - percision: 0.0098 - recall: 0.9177 - auc: 0.9450 - val_loss: 0.3930 - val_tp: 75.0000 - val_fp: 6014.0000 - val_tn: 39475.0000 - val_fn: 5.0000 - val_accuracy: 0.8679 - val_percision: 0.0123 - val_recall: 0.9375 - val_auc: 0.9733\n","Epoch 16/100\n","90/90 [==============================] - 1s 9ms/step - loss: 0.1256 - tp: 289.0000 - fp: 32315.0000 - tn: 149645.0000 - fn: 27.0000 - accuracy: 0.8226 - percision: 0.0089 - recall: 0.9146 - auc: 0.9426 - val_loss: 0.4695 - val_tp: 78.0000 - val_fp: 7108.0000 - val_tn: 38381.0000 - val_fn: 2.0000 - val_accuracy: 0.8440 - val_percision: 0.0109 - val_recall: 0.9750 - val_auc: 0.9722\n","Epoch 17/100\n","90/90 [==============================] - 1s 9ms/step - loss: 0.0821 - tp: 297.0000 - fp: 35812.0000 - tn: 146148.0000 - fn: 19.0000 - accuracy: 0.8034 - percision: 0.0082 - recall: 0.9399 - auc: 0.9505 - val_loss: 0.5314 - val_tp: 78.0000 - val_fp: 7930.0000 - val_tn: 37559.0000 - val_fn: 2.0000 - val_accuracy: 0.8259 - val_percision: 0.0097 - val_recall: 0.9750 - val_auc: 0.9715\n","Epoch 18/100\n","90/90 [==============================] - 1s 9ms/step - loss: 0.0723 - tp: 300.0000 - fp: 39276.0000 - tn: 142684.0000 - fn: 16.0000 - accuracy: 0.7844 - percision: 0.0076 - recall: 0.9494 - auc: 0.9501 - val_loss: 0.6008 - val_tp: 78.0000 - val_fp: 8788.0000 - val_tn: 36701.0000 - val_fn: 2.0000 - val_accuracy: 0.8071 - val_percision: 0.0088 - val_recall: 0.9750 - val_auc: 0.9707\n","Epoch 19/100\n","90/90 [==============================] - 1s 9ms/step - loss: 0.0731 - tp: 298.0000 - fp: 42112.0000 - tn: 139848.0000 - fn: 18.0000 - accuracy: 0.7689 - percision: 0.0070 - recall: 0.9430 - auc: 0.9451 - val_loss: 0.6769 - val_tp: 78.0000 - val_fp: 9729.0000 - val_tn: 35760.0000 - val_fn: 2.0000 - val_accuracy: 0.7865 - val_percision: 0.0080 - val_recall: 0.9750 - val_auc: 0.9691\n","Epoch 20/100\n","90/90 [==============================] - 1s 9ms/step - loss: 0.0770 - tp: 299.0000 - fp: 45478.0000 - tn: 136482.0000 - fn: 17.0000 - accuracy: 0.7504 - percision: 0.0065 - recall: 0.9462 - auc: 0.9414 - val_loss: 0.7545 - val_tp: 78.0000 - val_fp: 10580.0000 - val_tn: 34909.0000 - val_fn: 2.0000 - val_accuracy: 0.7678 - val_percision: 0.0073 - val_recall: 0.9750 - val_auc: 0.9674\n","Epoch 21/100\n","87/90 [============================>.] - ETA: 0s - loss: 0.0641 - tp: 290.0000 - fp: 47518.0000 - tn: 130352.0000 - fn: 16.0000 - accuracy: 0.7332 - percision: 0.0061 - recall: 0.9477 - auc: 0.9394Restoring model weights from the end of the best epoch.\n","90/90 [==============================] - 1s 9ms/step - loss: 0.0620 - tp: 300.0000 - fp: 48669.0000 - tn: 133291.0000 - fn: 16.0000 - accuracy: 0.7329 - percision: 0.0061 - recall: 0.9494 - auc: 0.9403 - val_loss: 0.8465 - val_tp: 78.0000 - val_fp: 11546.0000 - val_tn: 33943.0000 - val_fn: 2.0000 - val_accuracy: 0.7466 - val_percision: 0.0067 - val_recall: 0.9750 - val_auc: 0.9658\n","Epoch 00021: early stopping\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"c9izjzpCdYsM","colab_type":"text"},"source":["## Check training history"]},{"cell_type":"code","metadata":{"id":"DktA3DBiZm5l","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":609},"outputId":"a9ecfcb6-9979-48dc-f051-47422d6d66ae","executionInfo":{"status":"ok","timestamp":1584599254807,"user_tz":-480,"elapsed":1568,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}}},"source":["plot_metrics(weighted_history)"],"execution_count":51,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAtAAAAJQCAYAAACn51NEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbN/HvpIKKqsd3sgILhzrP+bv317LS4t3ABAZYXSIjSK9YyyvXn8CAI9/v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CuxXg+bsov4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xPaUpJzazLrVTwJrZuEAsuU05ZnPxz0aomwhFJKTOBXYB88zs72Y2FdC80s3s3dW76RTvJaN3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\nEJRAi4iIiIgMghJoEREREZFB+P8qsJ+E2J+ZNwAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 864x720 with 4 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"-RL6ShSqd9OV","colab_type":"text"},"source":["## Evaluate Metrics"]},{"cell_type":"code","metadata":{"id":"f7Txioe4dhI3","colab_type":"code","colab":{}},"source":["test_prediction_weighted = weighted_model.predict(test_features, batch_size=BATCH_SIZE)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"frDJ7zRid8fE","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":435},"outputId":"b0bc33cd-2111-43e1-e1f2-3c5d43702d3f","executionInfo":{"status":"ok","timestamp":1584599262190,"user_tz":-480,"elapsed":1754,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}}},"source":["plot_cm(labels=test_labels, predictions=test_prediction_weighted)"],"execution_count":53,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAU4AAAFNCAYAAABvx4bHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+3/pMHbefl923fXzfHrbrZuse/+9E9h9x/3p98VD+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WNjXn99HgCvvz6Pnj02rnBLKy+K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\ne+kKLJGeowAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 360x360 with 2 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Legitimate Transactions Detected (True Negatives): 53814\n","Legitimate Transactions Incorrectly Detected (False Positives): 3052\n","Fraudulent Transactions Missed (False Negatives): 6\n","Fraudulent Transactions Detected (True Positives): 90\n","Total Fraudulent Transactions: 96\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"MkzH3c30em7d","colab_type":"text"},"source":["## Plot the ROC"]},{"cell_type":"code","metadata":{"id":"Zb3GUz6Bex97","colab_type":"code","colab":{}},"source":["train_predictions_weighted = weighted_model.predict(x=train_features, batch_size=BATCH_SIZE)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"9IShlmzteD1_","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":610},"outputId":"1fb41ff0-515e-4bc3-e8be-4d87982d88d2","executionInfo":{"status":"ok","timestamp":1584599438696,"user_tz":-480,"elapsed":1388,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}}},"source":["plot_roc('Train Baseline', train_labels, train_predictions_baseline, color=colors[0])\n","plot_roc('Test Baseline', test_labels, test_prediction_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_prediction_weighted, color=colors[1], linestyle=\"--\")\n","\n","plt.legend(loc='lower right')\n","plt.show()"],"execution_count":55,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAngAAAJRCAYAAADWNMrEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8JMfI3inSFrknPvaOXdQ0luSRiW5bz9JnzjnPnXO7Zf0pKTzPaoTAAAg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sqdoJ0nqp+gY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GhunbtWu1jvvnmmzruuOO0atUqDRkyJKUBr7w77rjDk3bTgUeVJbD3oHSc7Yxd2GVI/M8AAKDU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\nIowleMYYY4wxEeb/A+7i0KKW8PQIAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 864x720 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"OV6QL4U0hZqc","colab_type":"text"},"source":["# Oversampling"]},{"cell_type":"markdown","metadata":{"id":"PoNVeRCbh477","colab_type":"text"},"source":["## Oversample the minority class\n","\n","A related approach is to resample the dataset by oversampling the minority class."]},{"cell_type":"code","metadata":{"id":"_t3-SZ5nfI97","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":697},"outputId":"d505bf13-d916-4335-e3b4-23113a06b84d","executionInfo":{"status":"ok","timestamp":1584600277648,"user_tz":-480,"elapsed":710,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}}},"source":["# the positive samples\n","np.where(bool_train_labels)"],"execution_count":58,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([   652,    879,   2977,   4923,   5485,   5628,   6587,   7897,\n","          7959,   8123,   9080,  12075,  12338,  13187,  13912,  15715,\n","         16036,  16792,  17219,  17746,  17881,  18519,  18823,  19109,\n","         21105,  21756,  22039,  22361,  22439,  22504,  22647,  23231,\n","         23620,  23751,  23924,  24666,  25080,  25442,  25765,  25829,\n","         26169,  26425,  26863,  27027,  27449,  27844,  27964,  28094,\n","         28506,  28932,  29688,  30192,  30875,  31278,  31377,  31686,\n","         32224,  32335,  32987,  33113,  33289,  33619,  33977,  34781,\n","         34961,  38501,  39092,  39224,  39665,  40199,  40618,  41185,\n","         42189,  42328,  42457,  43791,  44170,  44409,  44539,  45009,\n","         47783,  48722,  48857,  50296,  52087,  52228,  52938,  53148,\n","         53476,  54411,  55421,  55927,  56695,  57526,  58079,  58624,\n","         59107,  59460,  59553,  60377,  60402,  60490,  60555,  60622,\n","         60626,  60730,  60832,  61135,  61252,  61319,  61817,  61864,\n","         62798,  63963,  64321,  64490,  65088,  66079,  66454,  66499,\n","         66809,  66981,  68414,  68765,  68801,  71467,  71529,  72880,\n","         73750,  74164,  74733,  74908,  74951,  75158,  75352,  75747,\n","         76244,  76325,  76336,  76815,  77054,  78156,  78819,  79728,\n","         80375,  80741,  80749,  81063,  81656,  81973,  82747,  83406,\n","         84344,  86610,  87248,  88017,  88158,  89050,  90658,  91060,\n","         91272,  92083,  94195,  94686,  95038,  95858,  95950,  96320,\n","         96521,  96774,  97511,  97729,  98098,  98835, 100592, 100840,\n","        102124, 102765, 103477, 103911, 103916, 104966, 105515, 107619,\n","        108630, 108742, 108828, 109027, 109727, 110932, 111475, 112708,\n","        113218, 113781, 113801, 113925, 114384, 114414, 115338, 115612,\n","        115985, 117107, 117490, 117848, 118021, 118137, 119255, 119643,\n","        119734, 119788, 120537, 120560, 120849, 120926, 121712, 122145,\n","        123928, 124095, 125412, 125543, 125589, 127173, 127395, 127646,\n","        127858, 128055, 128791, 128845, 129060, 129560, 131159, 131562,\n","        132797, 133432, 133436, 133668, 133770, 134006, 134446, 135740,\n","        135992, 136211, 136451, 136832, 137008, 137280, 137541, 138033,\n","        139118, 139575, 139931, 141463, 141589, 142118, 143256, 143751,\n","        144028, 144256, 145089, 146063, 146116, 146334, 146544, 146823,\n","        147904, 148864, 149341, 150566, 151522, 151531, 151776, 151852,\n","        152737, 153255, 154094, 154263, 155178, 155376, 156208, 156362,\n","        156405, 156930, 158064, 158734, 158773, 160128, 161676, 163339,\n","        163930, 164599, 165019, 167145, 167382, 167638, 169701, 170528,\n","        170728, 171166, 171884, 172499, 173803, 173980, 174138, 175078,\n","        176703, 176889, 177159, 177161, 177354, 178144, 178355, 178843,\n","        179566, 179992, 180584, 181778]),)"]},"metadata":{"tags":[]},"execution_count":58}]},{"cell_type":"code","metadata":{"id":"YnW31JwAiPnu","colab_type":"code","colab":{}},"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]"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"d6bejKMXkBdr","colab_type":"text"},"source":["You can balance the dataset manually by generating the same size with negative samples."]},{"cell_type":"code","metadata":{"id":"2enAWxLXigf1","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"0f761458-8110-4197-b09e-667f6d4a30b6","executionInfo":{"status":"ok","timestamp":1584600856927,"user_tz":-480,"elapsed":789,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}}},"source":["ids = np.arange(len(pos_features))\n","choices = np.random.choice(a=ids, size=len(neg_features))\n","\n","res_pos_features = pos_features[choices]\n","res_pos_labels = pos_labels[choices]\n","\n","res_pos_features.shape"],"execution_count":62,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(181960, 29)"]},"metadata":{"tags":[]},"execution_count":62}]},{"cell_type":"markdown","metadata":{"id":"e3bakYBnkrxM","colab_type":"text"},"source":["Concatenate the positive and negative samples and then shuffle them for a new dataset."]},{"cell_type":"code","metadata":{"id":"lzTs9gZPklzX","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"320a8715-1d75-41c2-f3b8-096cfc0cfc62","executionInfo":{"status":"ok","timestamp":1584601552211,"user_tz":-480,"elapsed":709,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}}},"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)  # shuffle the order\n","resampled_features = resampled_features[order]\n","resampled_labels = resampled_labels[order]\n","\n","resampled_features.shape"],"execution_count":65,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(363920, 29)"]},"metadata":{"tags":[]},"execution_count":65}]},{"cell_type":"markdown","metadata":{"id":"9CszJfvsnT3L","colab_type":"text"},"source":["## Create a new data input pipeline using `tf.data`.\n","\n","You can also use the `tf.data` APIs to generate the dataset pipeline with a proper ratio of imbalanced datasets."]},{"cell_type":"code","metadata":{"id":"1Bwkj3z0m7Zc","colab_type":"code","colab":{}},"source":["BUFFER_SIZE = 100000\n","\n","def make_ds(features, labels):\n","  ds = tf.data.Dataset.from_tensor_slices((features, labels))\n","  ds = ds.shuffle(BUFFER_SIZE).repeat()\n","  return ds\n","\n","pos_ds = make_ds(features=pos_features, labels=pos_labels)\n","neg_ds = make_ds(features=neg_features, labels=neg_labels)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"uSXBEyUOq291","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"51b71487-7293-49d4-d858-9f1737db24f5","executionInfo":{"status":"ok","timestamp":1584602548796,"user_tz":-480,"elapsed":1021,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}}},"source":["for features, labels in pos_ds.take(1):\n","  print(features.shape, labels.numpy())"],"execution_count":68,"outputs":[{"output_type":"stream","text":["(29,) 1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"lHX-wfwnrEfT","colab_type":"text"},"source":["Merge these two datasets use `tf.data.experimental.sample_from_datasets` and pass a weighted list."]},{"cell_type":"code","metadata":{"id":"2wSEJWNeq-BQ","colab_type":"code","colab":{}},"source":["resampled_ds = tf.data.experimental.sample_from_datasets([pos_ds, neg_ds], weights=[0.5, 0.5])\n","resampled_ds = resampled_ds.batch(BATCH_SIZE).prefetch(2)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"GAelKVqrrnDW","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"af1f44e6-3247-4faa-bfc4-9187f5ee5440","executionInfo":{"status":"ok","timestamp":1584602768037,"user_tz":-480,"elapsed":896,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}}},"source":["for features, labels in resampled_ds.take(1):\n","  print(np.mean(labels.numpy()))"],"execution_count":71,"outputs":[{"output_type":"stream","text":["0.5087890625\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"XYGRZpRTsJnG","colab_type":"text"},"source":["To use this resampled dataset, you'll need the number of steps per epoch (You have set the `repeat()` to the dataset). The number of batches is to see each negative example once."]},{"cell_type":"code","metadata":{"id":"jb0x9Vbushtb","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"efcd2592-b1ed-4613-c0be-efd120d8d4cc","executionInfo":{"status":"ok","timestamp":1584603089878,"user_tz":-480,"elapsed":1047,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}}},"source":["resampled_steps_per_epoch = np.ceil(2 * neg / BATCH_SIZE)\n","resampled_steps_per_epoch"],"execution_count":73,"outputs":[{"output_type":"execute_result","data":{"text/plain":["278.0"]},"metadata":{"tags":[]},"execution_count":73}]},{"cell_type":"markdown","metadata":{"id":"n8XCeI4QtFwM","colab_type":"text"},"source":["## Train on the Oversampled Data"]},{"cell_type":"code","metadata":{"id":"jWwOhiDitaVC","colab_type":"code","colab":{}},"source":["val_ds = tf.data.Dataset.from_tensor_slices((val_features, val_labels)).cache()\n","val_ds = val_ds.batch(BATCH_SIZE).prefetch(2)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"H-Lmv4kqtEdU","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":615},"outputId":"e3e0d6d8-2480-4ea0-ff60-f5d284fa4603","executionInfo":{"status":"ok","timestamp":1584603621553,"user_tz":-480,"elapsed":95725,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}}},"source":["resampled_model = build_model()\n","resampled_model.load_weights(filepath=initial_weights)\n","\n","# no imblanced data existing\n","resampled_model.layers[-1].bias.assign([0.0])\n","\n","resampled_history = resampled_model.fit(\n","  resampled_ds, \n","  epochs=EPOCHS, \n","  callbacks=[early_stopping], \n","  validation_data=val_ds, \n","  steps_per_epoch=resampled_steps_per_epoch\n",")"],"execution_count":76,"outputs":[{"output_type":"stream","text":["Epoch 1/100\n","278/278 [==============================] - 7s 23ms/step - loss: 0.5722 - tp: 218341.0000 - fp: 85495.0000 - tn: 245882.0000 - fn: 65195.0000 - accuracy: 0.7549 - percision: 0.7186 - recall: 0.7701 - auc: 0.8251 - val_loss: 0.2468 - val_tp: 68.0000 - val_fp: 914.0000 - val_tn: 44575.0000 - val_fn: 12.0000 - val_accuracy: 0.9797 - val_percision: 0.0692 - val_recall: 0.8500 - val_auc: 0.9377\n","Epoch 2/100\n","278/278 [==============================] - 6s 20ms/step - loss: 0.2415 - tp: 251927.0000 - fp: 20810.0000 - tn: 264112.0000 - fn: 32495.0000 - accuracy: 0.9064 - percision: 0.9237 - recall: 0.8858 - auc: 0.9587 - val_loss: 0.1392 - val_tp: 68.0000 - val_fp: 760.0000 - val_tn: 44729.0000 - val_fn: 12.0000 - val_accuracy: 0.9831 - val_percision: 0.0821 - val_recall: 0.8500 - val_auc: 0.9670\n","Epoch 3/100\n","278/278 [==============================] - 6s 21ms/step - loss: 0.1882 - tp: 256308.0000 - fp: 13001.0000 - tn: 271673.0000 - fn: 28362.0000 - accuracy: 0.9273 - percision: 0.9517 - recall: 0.9004 - auc: 0.9754 - val_loss: 0.1028 - val_tp: 69.0000 - val_fp: 743.0000 - val_tn: 44746.0000 - val_fn: 11.0000 - val_accuracy: 0.9835 - val_percision: 0.0850 - val_recall: 0.8625 - val_auc: 0.9721\n","Epoch 4/100\n","278/278 [==============================] - 6s 21ms/step - loss: 0.1617 - tp: 259869.0000 - fp: 10611.0000 - tn: 274000.0000 - fn: 24864.0000 - accuracy: 0.9377 - percision: 0.9608 - recall: 0.9127 - auc: 0.9822 - val_loss: 0.0862 - val_tp: 69.0000 - val_fp: 705.0000 - val_tn: 44784.0000 - val_fn: 11.0000 - val_accuracy: 0.9843 - val_percision: 0.0891 - val_recall: 0.8625 - val_auc: 0.9733\n","Epoch 5/100\n","278/278 [==============================] - 6s 21ms/step - loss: 0.1467 - tp: 261286.0000 - fp: 9107.0000 - tn: 275740.0000 - fn: 23211.0000 - accuracy: 0.9432 - percision: 0.9663 - recall: 0.9184 - auc: 0.9856 - val_loss: 0.0774 - val_tp: 70.0000 - val_fp: 724.0000 - val_tn: 44765.0000 - val_fn: 10.0000 - val_accuracy: 0.9839 - val_percision: 0.0882 - val_recall: 0.8750 - val_auc: 0.9730\n","Epoch 6/100\n","278/278 [==============================] - 6s 21ms/step - loss: 0.1347 - tp: 262260.0000 - fp: 8224.0000 - tn: 277080.0000 - fn: 21780.0000 - accuracy: 0.9473 - percision: 0.9696 - recall: 0.9233 - auc: 0.9882 - val_loss: 0.0694 - val_tp: 70.0000 - val_fp: 662.0000 - val_tn: 44827.0000 - val_fn: 10.0000 - val_accuracy: 0.9853 - val_percision: 0.0956 - val_recall: 0.8750 - val_auc: 0.9737\n","Epoch 7/100\n","278/278 [==============================] - 6s 21ms/step - loss: 0.1245 - tp: 264045.0000 - fp: 7492.0000 - tn: 277282.0000 - fn: 20525.0000 - accuracy: 0.9508 - percision: 0.9724 - recall: 0.9279 - auc: 0.9903 - val_loss: 0.0637 - val_tp: 71.0000 - val_fp: 657.0000 - val_tn: 44832.0000 - val_fn: 9.0000 - val_accuracy: 0.9854 - val_percision: 0.0975 - val_recall: 0.8875 - val_auc: 0.9719\n","Epoch 8/100\n","278/278 [==============================] - 6s 21ms/step - loss: 0.1170 - tp: 265443.0000 - fp: 7030.0000 - tn: 277485.0000 - fn: 19386.0000 - accuracy: 0.9536 - percision: 0.9742 - recall: 0.9319 - auc: 0.9917 - val_loss: 0.0594 - val_tp: 70.0000 - val_fp: 613.0000 - val_tn: 44876.0000 - val_fn: 10.0000 - val_accuracy: 0.9863 - val_percision: 0.1025 - val_recall: 0.8750 - val_auc: 0.9720\n","Epoch 9/100\n","278/278 [==============================] - 6s 20ms/step - loss: 0.1114 - tp: 266906.0000 - fp: 6809.0000 - tn: 276942.0000 - fn: 18687.0000 - accuracy: 0.9552 - percision: 0.9751 - recall: 0.9346 - auc: 0.9927 - val_loss: 0.0553 - val_tp: 70.0000 - val_fp: 582.0000 - val_tn: 44907.0000 - val_fn: 10.0000 - val_accuracy: 0.9870 - val_percision: 0.1074 - val_recall: 0.8750 - val_auc: 0.9721\n","Epoch 10/100\n","278/278 [==============================] - 6s 21ms/step - loss: 0.1062 - tp: 266202.0000 - fp: 6570.0000 - tn: 278472.0000 - fn: 18100.0000 - accuracy: 0.9567 - percision: 0.9759 - recall: 0.9363 - auc: 0.9934 - val_loss: 0.0513 - val_tp: 70.0000 - val_fp: 552.0000 - val_tn: 44937.0000 - val_fn: 10.0000 - val_accuracy: 0.9877 - val_percision: 0.1125 - val_recall: 0.8750 - val_auc: 0.9721\n","Epoch 11/100\n","278/278 [==============================] - 6s 21ms/step - loss: 0.1021 - tp: 266903.0000 - fp: 6322.0000 - tn: 278390.0000 - fn: 17729.0000 - accuracy: 0.9578 - percision: 0.9769 - recall: 0.9377 - auc: 0.9940 - val_loss: 0.0490 - val_tp: 70.0000 - val_fp: 553.0000 - val_tn: 44936.0000 - val_fn: 10.0000 - val_accuracy: 0.9876 - val_percision: 0.1124 - val_recall: 0.8750 - val_auc: 0.9718\n","Epoch 12/100\n","278/278 [==============================] - 6s 21ms/step - loss: 0.0982 - tp: 268113.0000 - fp: 6139.0000 - tn: 278002.0000 - fn: 17090.0000 - accuracy: 0.9592 - percision: 0.9776 - recall: 0.9401 - auc: 0.9946 - val_loss: 0.0465 - val_tp: 70.0000 - val_fp: 530.0000 - val_tn: 44959.0000 - val_fn: 10.0000 - val_accuracy: 0.9881 - val_percision: 0.1167 - val_recall: 0.8750 - val_auc: 0.9677\n","Epoch 13/100\n","278/278 [==============================] - 6s 21ms/step - loss: 0.0957 - tp: 268292.0000 - fp: 6035.0000 - tn: 278374.0000 - fn: 16643.0000 - accuracy: 0.9602 - percision: 0.9780 - recall: 0.9416 - auc: 0.9949 - val_loss: 0.0429 - val_tp: 70.0000 - val_fp: 492.0000 - val_tn: 44997.0000 - val_fn: 10.0000 - val_accuracy: 0.9890 - val_percision: 0.1246 - val_recall: 0.8750 - val_auc: 0.9680\n","Epoch 14/100\n","278/278 [==============================] - 6s 20ms/step - loss: 0.0922 - tp: 268484.0000 - fp: 5907.0000 - tn: 278833.0000 - fn: 16120.0000 - accuracy: 0.9613 - percision: 0.9785 - recall: 0.9434 - auc: 0.9952 - val_loss: 0.0413 - val_tp: 70.0000 - val_fp: 484.0000 - val_tn: 45005.0000 - val_fn: 10.0000 - val_accuracy: 0.9892 - val_percision: 0.1264 - val_recall: 0.8750 - val_auc: 0.9685\n","Epoch 15/100\n","278/278 [==============================] - 6s 21ms/step - loss: 0.0900 - tp: 268587.0000 - fp: 5861.0000 - tn: 279176.0000 - fn: 15720.0000 - accuracy: 0.9621 - percision: 0.9786 - recall: 0.9447 - auc: 0.9956 - val_loss: 0.0393 - val_tp: 70.0000 - val_fp: 471.0000 - val_tn: 45018.0000 - val_fn: 10.0000 - val_accuracy: 0.9894 - val_percision: 0.1294 - val_recall: 0.8750 - val_auc: 0.9689\n","Epoch 16/100\n","278/278 [==============================] - ETA: 0s - loss: 0.0874 - tp: 269789.0000 - fp: 5857.0000 - tn: 278400.0000 - fn: 15298.0000 - accuracy: 0.9628 - percision: 0.9788 - recall: 0.9463 - auc: 0.9958Restoring model weights from the end of the best epoch.\n","278/278 [==============================] - 6s 21ms/step - loss: 0.0874 - tp: 269789.0000 - fp: 5857.0000 - tn: 278400.0000 - fn: 15298.0000 - accuracy: 0.9628 - percision: 0.9788 - recall: 0.9463 - auc: 0.9958 - val_loss: 0.0380 - val_tp: 70.0000 - val_fp: 479.0000 - val_tn: 45010.0000 - val_fn: 10.0000 - val_accuracy: 0.9893 - val_percision: 0.1275 - val_recall: 0.8750 - val_auc: 0.9689\n","Epoch 00016: early stopping\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"ytCE0tnM0pf8","colab_type":"text"},"source":["## Check training history\n","\n","Note that the distributions of the metrics will be different here because the training data has a totally different distribution from the validation and test data."]},{"cell_type":"code","metadata":{"id":"yJaub-pjt2v4","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":609},"outputId":"165ea422-4dd9-40c0-81a0-7cd61af98a32","executionInfo":{"status":"ok","timestamp":1584605236820,"user_tz":-480,"elapsed":2686,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}}},"source":["plot_metrics(resampled_history)"],"execution_count":77,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAtAAAAJQCAYAAACn51NEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzIiFgwOWZz+mfvcL7AQD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+oluaSTklwUpDfvH+XWBgJJYERERGZ2UQBeptniSpl1dNO3s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\nDbQkSZJUhP8CS0MYk6Epzr0AAAAASUVORK5CYII=\n","text/plain":["<Figure size 864x720 with 4 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"eqGx6Vvo1dSq","colab_type":"text"},"source":["## Retrain\n","\n","Because training is much easier on the balanced data, the above training procedure may overfit quickly."]},{"cell_type":"code","metadata":{"id":"cKRjpJAr1QNO","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"06180b97-8657-484f-c096-121ee996682c","executionInfo":{"status":"ok","timestamp":1584605566347,"user_tz":-480,"elapsed":43184,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}}},"source":["resampled_model = build_model()\n","resampled_model.load_weights(filepath=initial_weights)\n","\n","resampled_model.layers[-1].bias.assign([0.0])\n","\n","resampled_history = resampled_model.fit(\n","  x = resampled_ds, \n","  epochs = 10 * EPOCHS,\n","  steps_per_epoch = 20,\n","  callbacks=[early_stopping],\n","  validation_data=(val_ds)\n",")"],"execution_count":78,"outputs":[{"output_type":"stream","text":["Epoch 1/1000\n","20/20 [==============================] - 1s 67ms/step - loss: 1.8661 - tp: 5315.0000 - fp: 8492.0000 - tn: 57462.0000 - fn: 15260.0000 - accuracy: 0.7255 - percision: 0.3849 - recall: 0.2583 - auc: 0.6707 - val_loss: 0.5907 - val_tp: 13.0000 - val_fp: 14119.0000 - val_tn: 31370.0000 - val_fn: 67.0000 - val_accuracy: 0.6887 - val_percision: 9.1990e-04 - val_recall: 0.1625 - val_auc: 0.2805\n","Epoch 2/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 1.0681 - tp: 10146.0000 - fp: 8414.0000 - tn: 11957.0000 - fn: 10443.0000 - accuracy: 0.5396 - percision: 0.5467 - recall: 0.4928 - auc: 0.5336 - val_loss: 0.6105 - val_tp: 63.0000 - val_fp: 15394.0000 - val_tn: 30095.0000 - val_fn: 17.0000 - val_accuracy: 0.6618 - val_percision: 0.0041 - val_recall: 0.7875 - val_auc: 0.8071\n","Epoch 3/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 0.7058 - tp: 14344.0000 - fp: 8437.0000 - tn: 11983.0000 - fn: 6196.0000 - accuracy: 0.6427 - percision: 0.6296 - recall: 0.6983 - auc: 0.7212 - val_loss: 0.5957 - val_tp: 69.0000 - val_fp: 14926.0000 - val_tn: 30563.0000 - val_fn: 11.0000 - val_accuracy: 0.6722 - val_percision: 0.0046 - val_recall: 0.8625 - val_auc: 0.8877\n","Epoch 4/1000\n","20/20 [==============================] - 0s 25ms/step - loss: 0.5690 - tp: 16196.0000 - fp: 8101.0000 - tn: 12401.0000 - fn: 4262.0000 - accuracy: 0.6982 - percision: 0.6666 - recall: 0.7917 - auc: 0.8117 - val_loss: 0.5536 - val_tp: 72.0000 - val_fp: 12555.0000 - val_tn: 32934.0000 - val_fn: 8.0000 - val_accuracy: 0.7243 - val_percision: 0.0057 - val_recall: 0.9000 - val_auc: 0.9052\n","Epoch 5/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 0.5035 - tp: 16778.0000 - fp: 7094.0000 - tn: 13496.0000 - fn: 3592.0000 - accuracy: 0.7391 - percision: 0.7028 - recall: 0.8237 - auc: 0.8521 - val_loss: 0.5040 - val_tp: 71.0000 - val_fp: 9472.0000 - val_tn: 36017.0000 - val_fn: 9.0000 - val_accuracy: 0.7919 - val_percision: 0.0074 - val_recall: 0.8875 - val_auc: 0.9117\n","Epoch 6/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 0.4579 - tp: 17050.0000 - fp: 6051.0000 - tn: 14489.0000 - fn: 3370.0000 - accuracy: 0.7700 - percision: 0.7381 - recall: 0.8350 - auc: 0.8740 - val_loss: 0.4583 - val_tp: 69.0000 - val_fp: 6719.0000 - val_tn: 38770.0000 - val_fn: 11.0000 - val_accuracy: 0.8523 - val_percision: 0.0102 - val_recall: 0.8625 - val_auc: 0.9145\n","Epoch 7/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 0.4241 - tp: 17298.0000 - fp: 5278.0000 - tn: 15259.0000 - fn: 3125.0000 - accuracy: 0.7948 - percision: 0.7662 - recall: 0.8470 - auc: 0.8893 - val_loss: 0.4185 - val_tp: 69.0000 - val_fp: 4618.0000 - val_tn: 40871.0000 - val_fn: 11.0000 - val_accuracy: 0.8984 - val_percision: 0.0147 - val_recall: 0.8625 - val_auc: 0.9173\n","Epoch 8/1000\n","20/20 [==============================] - 1s 25ms/step - loss: 0.3973 - tp: 17447.0000 - fp: 4609.0000 - tn: 15892.0000 - fn: 3012.0000 - accuracy: 0.8139 - percision: 0.7910 - recall: 0.8528 - auc: 0.9012 - val_loss: 0.3837 - val_tp: 69.0000 - val_fp: 3212.0000 - val_tn: 42277.0000 - val_fn: 11.0000 - val_accuracy: 0.9293 - val_percision: 0.0210 - val_recall: 0.8625 - val_auc: 0.9204\n","Epoch 9/1000\n","20/20 [==============================] - 1s 30ms/step - loss: 0.3733 - tp: 17460.0000 - fp: 4021.0000 - tn: 16614.0000 - fn: 2865.0000 - accuracy: 0.8319 - percision: 0.8128 - recall: 0.8590 - auc: 0.9106 - val_loss: 0.3535 - val_tp: 69.0000 - val_fp: 2342.0000 - val_tn: 43147.0000 - val_fn: 11.0000 - val_accuracy: 0.9484 - val_percision: 0.0286 - val_recall: 0.8625 - val_auc: 0.9236\n","Epoch 10/1000\n","20/20 [==============================] - 1s 27ms/step - loss: 0.3574 - tp: 17493.0000 - fp: 3569.0000 - tn: 17067.0000 - fn: 2831.0000 - accuracy: 0.8438 - percision: 0.8305 - recall: 0.8607 - auc: 0.9173 - val_loss: 0.3263 - val_tp: 69.0000 - val_fp: 1767.0000 - val_tn: 43722.0000 - val_fn: 11.0000 - val_accuracy: 0.9610 - val_percision: 0.0376 - val_recall: 0.8625 - val_auc: 0.9269\n","Epoch 11/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 0.3405 - tp: 17733.0000 - fp: 3143.0000 - tn: 17332.0000 - fn: 2752.0000 - accuracy: 0.8561 - percision: 0.8494 - recall: 0.8657 - auc: 0.9236 - val_loss: 0.3030 - val_tp: 69.0000 - val_fp: 1414.0000 - val_tn: 44075.0000 - val_fn: 11.0000 - val_accuracy: 0.9687 - val_percision: 0.0465 - val_recall: 0.8625 - val_auc: 0.9310\n","Epoch 12/1000\n","20/20 [==============================] - 1s 28ms/step - loss: 0.3241 - tp: 17881.0000 - fp: 2836.0000 - tn: 17560.0000 - fn: 2683.0000 - accuracy: 0.8653 - percision: 0.8631 - recall: 0.8695 - auc: 0.9291 - val_loss: 0.2828 - val_tp: 68.0000 - val_fp: 1193.0000 - val_tn: 44296.0000 - val_fn: 12.0000 - val_accuracy: 0.9736 - val_percision: 0.0539 - val_recall: 0.8500 - val_auc: 0.9333\n","Epoch 13/1000\n","20/20 [==============================] - 1s 25ms/step - loss: 0.3104 - tp: 17793.0000 - fp: 2619.0000 - tn: 17960.0000 - fn: 2588.0000 - accuracy: 0.8729 - percision: 0.8717 - recall: 0.8730 - auc: 0.9349 - val_loss: 0.2636 - val_tp: 68.0000 - val_fp: 1028.0000 - val_tn: 44461.0000 - val_fn: 12.0000 - val_accuracy: 0.9772 - val_percision: 0.0620 - val_recall: 0.8500 - val_auc: 0.9357\n","Epoch 14/1000\n","20/20 [==============================] - 1s 27ms/step - loss: 0.2992 - tp: 17837.0000 - fp: 2271.0000 - tn: 18243.0000 - fn: 2609.0000 - accuracy: 0.8809 - percision: 0.8871 - recall: 0.8724 - auc: 0.9382 - val_loss: 0.2463 - val_tp: 68.0000 - val_fp: 902.0000 - val_tn: 44587.0000 - val_fn: 12.0000 - val_accuracy: 0.9799 - val_percision: 0.0701 - val_recall: 0.8500 - val_auc: 0.9377\n","Epoch 15/1000\n","20/20 [==============================] - 1s 25ms/step - loss: 0.2869 - tp: 17961.0000 - fp: 2057.0000 - tn: 18397.0000 - fn: 2545.0000 - accuracy: 0.8876 - percision: 0.8972 - recall: 0.8759 - auc: 0.9424 - val_loss: 0.2330 - val_tp: 68.0000 - val_fp: 854.0000 - val_tn: 44635.0000 - val_fn: 12.0000 - val_accuracy: 0.9810 - val_percision: 0.0738 - val_recall: 0.8500 - val_auc: 0.9406\n","Epoch 16/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 0.2770 - tp: 17911.0000 - fp: 2003.0000 - tn: 18550.0000 - fn: 2496.0000 - accuracy: 0.8902 - percision: 0.8994 - recall: 0.8777 - auc: 0.9463 - val_loss: 0.2209 - val_tp: 68.0000 - val_fp: 819.0000 - val_tn: 44670.0000 - val_fn: 12.0000 - val_accuracy: 0.9818 - val_percision: 0.0767 - val_recall: 0.8500 - val_auc: 0.9429\n","Epoch 17/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 0.2690 - tp: 18024.0000 - fp: 1826.0000 - tn: 18656.0000 - fn: 2454.0000 - accuracy: 0.8955 - percision: 0.9080 - recall: 0.8802 - auc: 0.9492 - val_loss: 0.2097 - val_tp: 68.0000 - val_fp: 801.0000 - val_tn: 44688.0000 - val_fn: 12.0000 - val_accuracy: 0.9822 - val_percision: 0.0783 - val_recall: 0.8500 - val_auc: 0.9454\n","Epoch 18/1000\n","20/20 [==============================] - 1s 29ms/step - loss: 0.2583 - tp: 17965.0000 - fp: 1712.0000 - tn: 18874.0000 - fn: 2409.0000 - accuracy: 0.8994 - percision: 0.9130 - recall: 0.8818 - auc: 0.9533 - val_loss: 0.1996 - val_tp: 68.0000 - val_fp: 786.0000 - val_tn: 44703.0000 - val_fn: 12.0000 - val_accuracy: 0.9825 - val_percision: 0.0796 - val_recall: 0.8500 - val_auc: 0.9477\n","Epoch 19/1000\n","20/20 [==============================] - 1s 25ms/step - loss: 0.2527 - tp: 18180.0000 - fp: 1653.0000 - tn: 18748.0000 - fn: 2379.0000 - accuracy: 0.9016 - percision: 0.9167 - recall: 0.8843 - auc: 0.9546 - val_loss: 0.1904 - val_tp: 68.0000 - val_fp: 778.0000 - val_tn: 44711.0000 - val_fn: 12.0000 - val_accuracy: 0.9827 - val_percision: 0.0804 - val_recall: 0.8500 - val_auc: 0.9503\n","Epoch 20/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 0.2433 - tp: 18184.0000 - fp: 1538.0000 - tn: 18911.0000 - fn: 2327.0000 - accuracy: 0.9056 - percision: 0.9220 - recall: 0.8865 - auc: 0.9582 - val_loss: 0.1817 - val_tp: 68.0000 - val_fp: 754.0000 - val_tn: 44735.0000 - val_fn: 12.0000 - val_accuracy: 0.9832 - val_percision: 0.0827 - val_recall: 0.8500 - val_auc: 0.9525\n","Epoch 21/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 0.2388 - tp: 18193.0000 - fp: 1382.0000 - tn: 19052.0000 - fn: 2333.0000 - accuracy: 0.9093 - percision: 0.9294 - recall: 0.8863 - auc: 0.9591 - val_loss: 0.1750 - val_tp: 68.0000 - val_fp: 773.0000 - val_tn: 44716.0000 - val_fn: 12.0000 - val_accuracy: 0.9828 - val_percision: 0.0809 - val_recall: 0.8500 - val_auc: 0.9551\n","Epoch 22/1000\n","20/20 [==============================] - 1s 27ms/step - loss: 0.2389 - tp: 18283.0000 - fp: 1403.0000 - tn: 18887.0000 - fn: 2387.0000 - accuracy: 0.9075 - percision: 0.9287 - recall: 0.8845 - auc: 0.9595 - val_loss: 0.1686 - val_tp: 68.0000 - val_fp: 763.0000 - val_tn: 44726.0000 - val_fn: 12.0000 - val_accuracy: 0.9830 - val_percision: 0.0818 - val_recall: 0.8500 - val_auc: 0.9572\n","Epoch 23/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 0.2320 - tp: 18217.0000 - fp: 1387.0000 - tn: 19070.0000 - fn: 2286.0000 - accuracy: 0.9103 - percision: 0.9292 - recall: 0.8885 - auc: 0.9621 - val_loss: 0.1628 - val_tp: 68.0000 - val_fp: 776.0000 - val_tn: 44713.0000 - val_fn: 12.0000 - val_accuracy: 0.9827 - val_percision: 0.0806 - val_recall: 0.8500 - val_auc: 0.9598\n","Epoch 24/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 0.2242 - tp: 18191.0000 - fp: 1307.0000 - tn: 19233.0000 - fn: 2229.0000 - accuracy: 0.9137 - percision: 0.9330 - recall: 0.8908 - auc: 0.9645 - val_loss: 0.1570 - val_tp: 68.0000 - val_fp: 765.0000 - val_tn: 44724.0000 - val_fn: 12.0000 - val_accuracy: 0.9829 - val_percision: 0.0816 - val_recall: 0.8500 - val_auc: 0.9622\n","Epoch 25/1000\n","20/20 [==============================] - 1s 27ms/step - loss: 0.2200 - tp: 18252.0000 - fp: 1222.0000 - tn: 19226.0000 - fn: 2260.0000 - accuracy: 0.9150 - percision: 0.9372 - recall: 0.8898 - auc: 0.9656 - val_loss: 0.1518 - val_tp: 68.0000 - val_fp: 769.0000 - val_tn: 44720.0000 - val_fn: 12.0000 - val_accuracy: 0.9829 - val_percision: 0.0812 - val_recall: 0.8500 - val_auc: 0.9639\n","Epoch 26/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 0.2144 - tp: 18355.0000 - fp: 1155.0000 - tn: 19245.0000 - fn: 2205.0000 - accuracy: 0.9180 - percision: 0.9408 - recall: 0.8928 - auc: 0.9674 - val_loss: 0.1480 - val_tp: 68.0000 - val_fp: 793.0000 - val_tn: 44696.0000 - val_fn: 12.0000 - val_accuracy: 0.9823 - val_percision: 0.0790 - val_recall: 0.8500 - val_auc: 0.9656\n","Epoch 27/1000\n","20/20 [==============================] - 1s 30ms/step - loss: 0.2137 - tp: 18162.0000 - fp: 1152.0000 - tn: 19470.0000 - fn: 2176.0000 - accuracy: 0.9187 - percision: 0.9404 - recall: 0.8930 - auc: 0.9680 - val_loss: 0.1434 - val_tp: 68.0000 - val_fp: 786.0000 - val_tn: 44703.0000 - val_fn: 12.0000 - val_accuracy: 0.9825 - val_percision: 0.0796 - val_recall: 0.8500 - val_auc: 0.9667\n","Epoch 28/1000\n","20/20 [==============================] - 1s 27ms/step - loss: 0.2123 - tp: 18206.0000 - fp: 1141.0000 - tn: 19391.0000 - fn: 2222.0000 - accuracy: 0.9179 - percision: 0.9410 - recall: 0.8912 - auc: 0.9683 - val_loss: 0.1389 - val_tp: 68.0000 - val_fp: 765.0000 - val_tn: 44724.0000 - val_fn: 12.0000 - val_accuracy: 0.9829 - val_percision: 0.0816 - val_recall: 0.8500 - val_auc: 0.9674\n","Epoch 29/1000\n","20/20 [==============================] - 1s 28ms/step - loss: 0.2061 - tp: 18197.0000 - fp: 1120.0000 - tn: 19493.0000 - fn: 2150.0000 - accuracy: 0.9202 - percision: 0.9420 - recall: 0.8943 - auc: 0.9705 - val_loss: 0.1345 - val_tp: 68.0000 - val_fp: 750.0000 - val_tn: 44739.0000 - val_fn: 12.0000 - val_accuracy: 0.9833 - val_percision: 0.0831 - val_recall: 0.8500 - val_auc: 0.9679\n","Epoch 30/1000\n","20/20 [==============================] - 1s 27ms/step - loss: 0.2016 - tp: 18305.0000 - fp: 1019.0000 - tn: 19425.0000 - fn: 2211.0000 - accuracy: 0.9211 - percision: 0.9473 - recall: 0.8922 - auc: 0.9711 - val_loss: 0.1321 - val_tp: 69.0000 - val_fp: 785.0000 - val_tn: 44704.0000 - val_fn: 11.0000 - val_accuracy: 0.9825 - val_percision: 0.0808 - val_recall: 0.8625 - val_auc: 0.9687\n","Epoch 31/1000\n","20/20 [==============================] - 1s 27ms/step - loss: 0.1976 - tp: 18490.0000 - fp: 978.0000 - tn: 19381.0000 - fn: 2111.0000 - accuracy: 0.9246 - percision: 0.9498 - recall: 0.8975 - auc: 0.9725 - val_loss: 0.1293 - val_tp: 69.0000 - val_fp: 794.0000 - val_tn: 44695.0000 - val_fn: 11.0000 - val_accuracy: 0.9823 - val_percision: 0.0800 - val_recall: 0.8625 - val_auc: 0.9691\n","Epoch 32/1000\n","20/20 [==============================] - 0s 24ms/step - loss: 0.1960 - tp: 18424.0000 - fp: 1000.0000 - tn: 19439.0000 - fn: 2097.0000 - accuracy: 0.9244 - percision: 0.9485 - recall: 0.8978 - auc: 0.9732 - val_loss: 0.1266 - val_tp: 69.0000 - val_fp: 803.0000 - val_tn: 44686.0000 - val_fn: 11.0000 - val_accuracy: 0.9821 - val_percision: 0.0791 - val_recall: 0.8625 - val_auc: 0.9696\n","Epoch 33/1000\n","20/20 [==============================] - 1s 28ms/step - loss: 0.1939 - tp: 18298.0000 - fp: 965.0000 - tn: 19621.0000 - fn: 2076.0000 - accuracy: 0.9258 - percision: 0.9499 - recall: 0.8981 - auc: 0.9734 - val_loss: 0.1237 - val_tp: 69.0000 - val_fp: 796.0000 - val_tn: 44693.0000 - val_fn: 11.0000 - val_accuracy: 0.9823 - val_percision: 0.0798 - val_recall: 0.8625 - val_auc: 0.9702\n","Epoch 34/1000\n","20/20 [==============================] - 1s 28ms/step - loss: 0.1884 - tp: 18416.0000 - fp: 935.0000 - tn: 19568.0000 - fn: 2041.0000 - accuracy: 0.9273 - percision: 0.9517 - recall: 0.9002 - auc: 0.9753 - val_loss: 0.1211 - val_tp: 69.0000 - val_fp: 794.0000 - val_tn: 44695.0000 - val_fn: 11.0000 - val_accuracy: 0.9823 - val_percision: 0.0800 - val_recall: 0.8625 - val_auc: 0.9699\n","Epoch 35/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 0.1877 - tp: 18398.0000 - fp: 970.0000 - tn: 19594.0000 - fn: 1998.0000 - accuracy: 0.9275 - percision: 0.9499 - recall: 0.9020 - auc: 0.9758 - val_loss: 0.1183 - val_tp: 69.0000 - val_fp: 790.0000 - val_tn: 44699.0000 - val_fn: 11.0000 - val_accuracy: 0.9824 - val_percision: 0.0803 - val_recall: 0.8625 - val_auc: 0.9702\n","Epoch 36/1000\n","20/20 [==============================] - 1s 30ms/step - loss: 0.1869 - tp: 18510.0000 - fp: 942.0000 - tn: 19484.0000 - fn: 2024.0000 - accuracy: 0.9276 - percision: 0.9516 - recall: 0.9014 - auc: 0.9757 - val_loss: 0.1156 - val_tp: 69.0000 - val_fp: 779.0000 - val_tn: 44710.0000 - val_fn: 11.0000 - val_accuracy: 0.9827 - val_percision: 0.0814 - val_recall: 0.8625 - val_auc: 0.9700\n","Epoch 37/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 0.1852 - tp: 18466.0000 - fp: 909.0000 - tn: 19549.0000 - fn: 2036.0000 - accuracy: 0.9281 - percision: 0.9531 - recall: 0.9007 - auc: 0.9763 - val_loss: 0.1129 - val_tp: 69.0000 - val_fp: 774.0000 - val_tn: 44715.0000 - val_fn: 11.0000 - val_accuracy: 0.9828 - val_percision: 0.0819 - val_recall: 0.8625 - val_auc: 0.9706\n","Epoch 38/1000\n","20/20 [==============================] - 1s 27ms/step - loss: 0.1811 - tp: 18373.0000 - fp: 883.0000 - tn: 19759.0000 - fn: 1945.0000 - accuracy: 0.9310 - percision: 0.9541 - recall: 0.9043 - auc: 0.9772 - val_loss: 0.1108 - val_tp: 69.0000 - val_fp: 766.0000 - val_tn: 44723.0000 - val_fn: 11.0000 - val_accuracy: 0.9829 - val_percision: 0.0826 - val_recall: 0.8625 - val_auc: 0.9713\n","Epoch 39/1000\n","20/20 [==============================] - 1s 27ms/step - loss: 0.1798 - tp: 18509.0000 - fp: 901.0000 - tn: 19622.0000 - fn: 1928.0000 - accuracy: 0.9309 - percision: 0.9536 - recall: 0.9057 - auc: 0.9779 - val_loss: 0.1088 - val_tp: 69.0000 - val_fp: 761.0000 - val_tn: 44728.0000 - val_fn: 11.0000 - val_accuracy: 0.9831 - val_percision: 0.0831 - val_recall: 0.8625 - val_auc: 0.9710\n","Epoch 40/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 0.1756 - tp: 18485.0000 - fp: 796.0000 - tn: 19742.0000 - fn: 1937.0000 - accuracy: 0.9333 - percision: 0.9587 - recall: 0.9052 - auc: 0.9789 - val_loss: 0.1072 - val_tp: 69.0000 - val_fp: 768.0000 - val_tn: 44721.0000 - val_fn: 11.0000 - val_accuracy: 0.9829 - val_percision: 0.0824 - val_recall: 0.8625 - val_auc: 0.9713\n","Epoch 41/1000\n","20/20 [==============================] - 0s 25ms/step - loss: 0.1729 - tp: 18716.0000 - fp: 806.0000 - tn: 19562.0000 - fn: 1876.0000 - accuracy: 0.9345 - percision: 0.9587 - recall: 0.9089 - auc: 0.9793 - val_loss: 0.1055 - val_tp: 69.0000 - val_fp: 767.0000 - val_tn: 44722.0000 - val_fn: 11.0000 - val_accuracy: 0.9829 - val_percision: 0.0825 - val_recall: 0.8625 - val_auc: 0.9719\n","Epoch 42/1000\n","20/20 [==============================] - 1s 27ms/step - loss: 0.1735 - tp: 18430.0000 - fp: 848.0000 - tn: 19795.0000 - fn: 1887.0000 - accuracy: 0.9332 - percision: 0.9560 - recall: 0.9071 - auc: 0.9794 - val_loss: 0.1037 - val_tp: 69.0000 - val_fp: 767.0000 - val_tn: 44722.0000 - val_fn: 11.0000 - val_accuracy: 0.9829 - val_percision: 0.0825 - val_recall: 0.8625 - val_auc: 0.9712\n","Epoch 43/1000\n","20/20 [==============================] - 0s 25ms/step - loss: 0.1700 - tp: 18605.0000 - fp: 833.0000 - tn: 19685.0000 - fn: 1837.0000 - accuracy: 0.9348 - percision: 0.9571 - recall: 0.9101 - auc: 0.9801 - val_loss: 0.1024 - val_tp: 69.0000 - val_fp: 777.0000 - val_tn: 44712.0000 - val_fn: 11.0000 - val_accuracy: 0.9827 - val_percision: 0.0816 - val_recall: 0.8625 - val_auc: 0.9718\n","Epoch 44/1000\n","20/20 [==============================] - 0s 25ms/step - loss: 0.1684 - tp: 18728.0000 - fp: 774.0000 - tn: 19625.0000 - fn: 1833.0000 - accuracy: 0.9364 - percision: 0.9603 - recall: 0.9109 - auc: 0.9808 - val_loss: 0.1009 - val_tp: 69.0000 - val_fp: 778.0000 - val_tn: 44711.0000 - val_fn: 11.0000 - val_accuracy: 0.9827 - val_percision: 0.0815 - val_recall: 0.8625 - val_auc: 0.9717\n","Epoch 45/1000\n","20/20 [==============================] - 1s 31ms/step - loss: 0.1695 - tp: 18630.0000 - fp: 829.0000 - tn: 19667.0000 - fn: 1834.0000 - accuracy: 0.9350 - percision: 0.9574 - recall: 0.9104 - auc: 0.9806 - val_loss: 0.0989 - val_tp: 69.0000 - val_fp: 756.0000 - val_tn: 44733.0000 - val_fn: 11.0000 - val_accuracy: 0.9832 - val_percision: 0.0836 - val_recall: 0.8625 - val_auc: 0.9724\n","Epoch 46/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 0.1691 - tp: 18566.0000 - fp: 811.0000 - tn: 19725.0000 - fn: 1858.0000 - accuracy: 0.9348 - percision: 0.9581 - recall: 0.9090 - auc: 0.9806 - val_loss: 0.0970 - val_tp: 69.0000 - val_fp: 741.0000 - val_tn: 44748.0000 - val_fn: 11.0000 - val_accuracy: 0.9835 - val_percision: 0.0852 - val_recall: 0.8625 - val_auc: 0.9719\n","Epoch 47/1000\n","20/20 [==============================] - 1s 27ms/step - loss: 0.1627 - tp: 18652.0000 - fp: 735.0000 - tn: 19788.0000 - fn: 1785.0000 - accuracy: 0.9385 - percision: 0.9621 - recall: 0.9127 - auc: 0.9820 - val_loss: 0.0955 - val_tp: 69.0000 - val_fp: 733.0000 - val_tn: 44756.0000 - val_fn: 11.0000 - val_accuracy: 0.9837 - val_percision: 0.0860 - val_recall: 0.8625 - val_auc: 0.9719\n","Epoch 48/1000\n","20/20 [==============================] - 1s 27ms/step - loss: 0.1642 - tp: 18645.0000 - fp: 796.0000 - tn: 19699.0000 - fn: 1820.0000 - accuracy: 0.9361 - percision: 0.9591 - recall: 0.9111 - auc: 0.9815 - val_loss: 0.0946 - val_tp: 69.0000 - val_fp: 746.0000 - val_tn: 44743.0000 - val_fn: 11.0000 - val_accuracy: 0.9834 - val_percision: 0.0847 - val_recall: 0.8625 - val_auc: 0.9722\n","Epoch 49/1000\n","20/20 [==============================] - 1s 27ms/step - loss: 0.1614 - tp: 18738.0000 - fp: 773.0000 - tn: 19719.0000 - fn: 1730.0000 - accuracy: 0.9389 - percision: 0.9604 - recall: 0.9155 - auc: 0.9824 - val_loss: 0.0933 - val_tp: 69.0000 - val_fp: 740.0000 - val_tn: 44749.0000 - val_fn: 11.0000 - val_accuracy: 0.9835 - val_percision: 0.0853 - val_recall: 0.8625 - val_auc: 0.9727\n","Epoch 50/1000\n","20/20 [==============================] - 1s 27ms/step - loss: 0.1599 - tp: 18611.0000 - fp: 735.0000 - tn: 19873.0000 - fn: 1741.0000 - accuracy: 0.9396 - percision: 0.9620 - recall: 0.9145 - auc: 0.9827 - val_loss: 0.0926 - val_tp: 69.0000 - val_fp: 750.0000 - val_tn: 44739.0000 - val_fn: 11.0000 - val_accuracy: 0.9833 - val_percision: 0.0842 - val_recall: 0.8625 - val_auc: 0.9730\n","Epoch 51/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 0.1582 - tp: 18729.0000 - fp: 720.0000 - tn: 19768.0000 - fn: 1743.0000 - accuracy: 0.9399 - percision: 0.9630 - recall: 0.9149 - auc: 0.9830 - val_loss: 0.0915 - val_tp: 69.0000 - val_fp: 750.0000 - val_tn: 44739.0000 - val_fn: 11.0000 - val_accuracy: 0.9833 - val_percision: 0.0842 - val_recall: 0.8625 - val_auc: 0.9727\n","Epoch 52/1000\n","20/20 [==============================] - 1s 27ms/step - loss: 0.1564 - tp: 18840.0000 - fp: 683.0000 - tn: 19740.0000 - fn: 1697.0000 - accuracy: 0.9419 - percision: 0.9650 - recall: 0.9174 - auc: 0.9836 - val_loss: 0.0904 - val_tp: 69.0000 - val_fp: 751.0000 - val_tn: 44738.0000 - val_fn: 11.0000 - val_accuracy: 0.9833 - val_percision: 0.0841 - val_recall: 0.8625 - val_auc: 0.9721\n","Epoch 53/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 0.1575 - tp: 18766.0000 - fp: 734.0000 - tn: 19755.0000 - fn: 1705.0000 - accuracy: 0.9405 - percision: 0.9624 - recall: 0.9167 - auc: 0.9831 - val_loss: 0.0896 - val_tp: 69.0000 - val_fp: 751.0000 - val_tn: 44738.0000 - val_fn: 11.0000 - val_accuracy: 0.9833 - val_percision: 0.0841 - val_recall: 0.8625 - val_auc: 0.9725\n","Epoch 54/1000\n","20/20 [==============================] - 1s 30ms/step - loss: 0.1538 - tp: 18839.0000 - fp: 697.0000 - tn: 19697.0000 - fn: 1727.0000 - accuracy: 0.9408 - percision: 0.9643 - recall: 0.9160 - auc: 0.9838 - val_loss: 0.0886 - val_tp: 69.0000 - val_fp: 744.0000 - val_tn: 44745.0000 - val_fn: 11.0000 - val_accuracy: 0.9834 - val_percision: 0.0849 - val_recall: 0.8625 - val_auc: 0.9727\n","Epoch 55/1000\n","20/20 [==============================] - 1s 27ms/step - loss: 0.1532 - tp: 18714.0000 - fp: 694.0000 - tn: 19851.0000 - fn: 1701.0000 - accuracy: 0.9415 - percision: 0.9642 - recall: 0.9167 - auc: 0.9841 - val_loss: 0.0873 - val_tp: 69.0000 - val_fp: 732.0000 - val_tn: 44757.0000 - val_fn: 11.0000 - val_accuracy: 0.9837 - val_percision: 0.0861 - val_recall: 0.8625 - val_auc: 0.9730\n","Epoch 56/1000\n","20/20 [==============================] - 1s 27ms/step - loss: 0.1533 - tp: 18862.0000 - fp: 688.0000 - tn: 19673.0000 - fn: 1737.0000 - accuracy: 0.9408 - percision: 0.9648 - recall: 0.9157 - auc: 0.9842 - val_loss: 0.0864 - val_tp: 69.0000 - val_fp: 720.0000 - val_tn: 44769.0000 - val_fn: 11.0000 - val_accuracy: 0.9840 - val_percision: 0.0875 - val_recall: 0.8625 - val_auc: 0.9728\n","Epoch 57/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 0.1496 - tp: 18945.0000 - fp: 655.0000 - tn: 19678.0000 - fn: 1682.0000 - accuracy: 0.9429 - percision: 0.9666 - recall: 0.9185 - auc: 0.9849 - val_loss: 0.0859 - val_tp: 69.0000 - val_fp: 726.0000 - val_tn: 44763.0000 - val_fn: 11.0000 - val_accuracy: 0.9838 - val_percision: 0.0868 - val_recall: 0.8625 - val_auc: 0.9731\n","Epoch 58/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 0.1510 - tp: 18856.0000 - fp: 726.0000 - tn: 19715.0000 - fn: 1663.0000 - accuracy: 0.9417 - percision: 0.9629 - recall: 0.9190 - auc: 0.9847 - val_loss: 0.0848 - val_tp: 69.0000 - val_fp: 722.0000 - val_tn: 44767.0000 - val_fn: 11.0000 - val_accuracy: 0.9839 - val_percision: 0.0872 - val_recall: 0.8625 - val_auc: 0.9732\n","Epoch 59/1000\n","20/20 [==============================] - 1s 25ms/step - loss: 0.1503 - tp: 18774.0000 - fp: 664.0000 - tn: 19838.0000 - fn: 1684.0000 - accuracy: 0.9427 - percision: 0.9658 - recall: 0.9177 - auc: 0.9849 - val_loss: 0.0845 - val_tp: 69.0000 - val_fp: 734.0000 - val_tn: 44755.0000 - val_fn: 11.0000 - val_accuracy: 0.9837 - val_percision: 0.0859 - val_recall: 0.8625 - val_auc: 0.9734\n","Epoch 60/1000\n","20/20 [==============================] - 0s 25ms/step - loss: 0.1509 - tp: 18690.0000 - fp: 722.0000 - tn: 19924.0000 - fn: 1624.0000 - accuracy: 0.9427 - percision: 0.9628 - recall: 0.9201 - auc: 0.9848 - val_loss: 0.0831 - val_tp: 69.0000 - val_fp: 715.0000 - val_tn: 44774.0000 - val_fn: 11.0000 - val_accuracy: 0.9841 - val_percision: 0.0880 - val_recall: 0.8625 - val_auc: 0.9738\n","Epoch 61/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 0.1492 - tp: 18763.0000 - fp: 669.0000 - tn: 19832.0000 - fn: 1696.0000 - accuracy: 0.9423 - percision: 0.9656 - recall: 0.9171 - auc: 0.9853 - val_loss: 0.0819 - val_tp: 69.0000 - val_fp: 693.0000 - val_tn: 44796.0000 - val_fn: 11.0000 - val_accuracy: 0.9846 - val_percision: 0.0906 - val_recall: 0.8625 - val_auc: 0.9741\n","Epoch 62/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 0.1473 - tp: 18756.0000 - fp: 702.0000 - tn: 19806.0000 - fn: 1696.0000 - accuracy: 0.9415 - percision: 0.9639 - recall: 0.9171 - auc: 0.9855 - val_loss: 0.0811 - val_tp: 69.0000 - val_fp: 688.0000 - val_tn: 44801.0000 - val_fn: 11.0000 - val_accuracy: 0.9847 - val_percision: 0.0911 - val_recall: 0.8625 - val_auc: 0.9742\n","Epoch 63/1000\n","20/20 [==============================] - 1s 30ms/step - loss: 0.1459 - tp: 18870.0000 - fp: 625.0000 - tn: 19814.0000 - fn: 1651.0000 - accuracy: 0.9444 - percision: 0.9679 - recall: 0.9195 - auc: 0.9858 - val_loss: 0.0807 - val_tp: 69.0000 - val_fp: 697.0000 - val_tn: 44792.0000 - val_fn: 11.0000 - val_accuracy: 0.9845 - val_percision: 0.0901 - val_recall: 0.8625 - val_auc: 0.9714\n","Epoch 64/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 0.1445 - tp: 18835.0000 - fp: 630.0000 - tn: 19824.0000 - fn: 1671.0000 - accuracy: 0.9438 - percision: 0.9676 - recall: 0.9185 - auc: 0.9860 - val_loss: 0.0805 - val_tp: 69.0000 - val_fp: 704.0000 - val_tn: 44785.0000 - val_fn: 11.0000 - val_accuracy: 0.9843 - val_percision: 0.0893 - val_recall: 0.8625 - val_auc: 0.9717\n","Epoch 65/1000\n","20/20 [==============================] - 1s 26ms/step - loss: 0.1438 - tp: 18903.0000 - fp: 666.0000 - tn: 19735.0000 - fn: 1656.0000 - accuracy: 0.9433 - percision: 0.9660 - recall: 0.9195 - auc: 0.9864 - val_loss: 0.0796 - val_tp: 69.0000 - val_fp: 701.0000 - val_tn: 44788.0000 - val_fn: 11.0000 - val_accuracy: 0.9844 - val_percision: 0.0896 - val_recall: 0.8625 - val_auc: 0.9720\n","Epoch 66/1000\n","20/20 [==============================] - 1s 28ms/step - loss: 0.1444 - tp: 18921.0000 - fp: 628.0000 - tn: 19771.0000 - fn: 1640.0000 - accuracy: 0.9446 - percision: 0.9679 - recall: 0.9202 - auc: 0.9860 - val_loss: 0.0793 - val_tp: 69.0000 - val_fp: 698.0000 - val_tn: 44791.0000 - val_fn: 11.0000 - val_accuracy: 0.9844 - val_percision: 0.0900 - val_recall: 0.8625 - val_auc: 0.9721\n","Epoch 67/1000\n","20/20 [==============================] - 1s 27ms/step - loss: 0.1435 - tp: 18927.0000 - fp: 638.0000 - tn: 19747.0000 - fn: 1648.0000 - accuracy: 0.9442 - percision: 0.9674 - recall: 0.9199 - auc: 0.9866 - val_loss: 0.0790 - val_tp: 70.0000 - val_fp: 712.0000 - val_tn: 44777.0000 - val_fn: 10.0000 - val_accuracy: 0.9842 - val_percision: 0.0895 - val_recall: 0.8750 - val_auc: 0.9724\n","Epoch 68/1000\n","20/20 [==============================] - 0s 24ms/step - loss: 0.1422 - tp: 18883.0000 - fp: 626.0000 - tn: 19816.0000 - fn: 1635.0000 - accuracy: 0.9448 - percision: 0.9679 - recall: 0.9203 - auc: 0.9863 - val_loss: 0.0787 - val_tp: 70.0000 - val_fp: 713.0000 - val_tn: 44776.0000 - val_fn: 10.0000 - val_accuracy: 0.9841 - val_percision: 0.0894 - val_recall: 0.8750 - val_auc: 0.9725\n","Epoch 69/1000\n","20/20 [==============================] - 0s 24ms/step - loss: 0.1419 - tp: 18777.0000 - fp: 635.0000 - tn: 19930.0000 - fn: 1618.0000 - accuracy: 0.9450 - percision: 0.9673 - recall: 0.9207 - auc: 0.9869 - val_loss: 0.0780 - val_tp: 70.0000 - val_fp: 707.0000 - val_tn: 44782.0000 - val_fn: 10.0000 - val_accuracy: 0.9843 - val_percision: 0.0901 - val_recall: 0.8750 - val_auc: 0.9727\n","Epoch 70/1000\n","20/20 [==============================] - 1s 27ms/step - loss: 0.1395 - tp: 19038.0000 - fp: 638.0000 - tn: 19669.0000 - fn: 1615.0000 - accuracy: 0.9450 - percision: 0.9676 - recall: 0.9218 - auc: 0.9871 - val_loss: 0.0769 - val_tp: 70.0000 - val_fp: 697.0000 - val_tn: 44792.0000 - val_fn: 10.0000 - val_accuracy: 0.9845 - val_percision: 0.0913 - val_recall: 0.8750 - val_auc: 0.9730\n","Epoch 71/1000\n","20/20 [==============================] - 1s 27ms/step - loss: 0.1403 - tp: 18908.0000 - fp: 628.0000 - tn: 19801.0000 - fn: 1623.0000 - accuracy: 0.9450 - percision: 0.9679 - recall: 0.9209 - auc: 0.9871 - val_loss: 0.0762 - val_tp: 70.0000 - val_fp: 688.0000 - val_tn: 44801.0000 - val_fn: 10.0000 - val_accuracy: 0.9847 - val_percision: 0.0923 - val_recall: 0.8750 - val_auc: 0.9729\n","Epoch 72/1000\n","19/20 [===========================>..] - ETA: 0s - loss: 0.1371 - tp: 17837.0000 - fp: 621.0000 - tn: 18944.0000 - fn: 1510.0000 - accuracy: 0.9452 - percision: 0.9664 - recall: 0.9220 - auc: 0.9877Restoring model weights from the end of the best epoch.\n","20/20 [==============================] - 1s 26ms/step - loss: 0.1370 - tp: 18781.0000 - fp: 643.0000 - tn: 19940.0000 - fn: 1596.0000 - accuracy: 0.9453 - percision: 0.9669 - recall: 0.9217 - auc: 0.9877 - val_loss: 0.0753 - val_tp: 70.0000 - val_fp: 678.0000 - val_tn: 44811.0000 - val_fn: 10.0000 - val_accuracy: 0.9849 - val_percision: 0.0936 - val_recall: 0.8750 - val_auc: 0.9719\n","Epoch 00072: early stopping\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"1FYO8Jrg2jLE","colab_type":"text"},"source":["## Re-Check the training history"]},{"cell_type":"code","metadata":{"id":"MiInKcxZ2Www","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":609},"outputId":"0f659710-f3e2-4d08-87d1-80272256605c","executionInfo":{"status":"ok","timestamp":1584605593448,"user_tz":-480,"elapsed":1283,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}}},"source":["plot_metrics(resampled_history)"],"execution_count":79,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAtcAAAJQCAYAAABFO0g9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTnJzcwmHw5xxxhmsXr2amTNnHt6b6wfMbD7wE8ALPOCc+/4+58cCi4AC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6LhLi2LHEibV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\nkRRRcS0iIiIikiIqrkVEREREUuT/AyyVFHAFBl90AAAAAElFTkSuQmCC\n","text/plain":["<Figure size 864x720 with 4 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"fWW8YKXl2pj5","colab_type":"text"},"source":["## Re-Evaluate the metrics"]},{"cell_type":"code","metadata":{"id":"LpFbKDjb2nnt","colab_type":"code","colab":{}},"source":["test_prediction_resampled = resampled_model.predict(x=test_features, batch_size=BATCH_SIZE)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"pxKNPKkO218X","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":207},"outputId":"26b76c7e-3f89-4be2-e509-a4e653c16a0f","executionInfo":{"status":"ok","timestamp":1584605780355,"user_tz":-480,"elapsed":1579,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}}},"source":["resampled_result = resampled_model.evaluate(x=test_features, y=test_labels, batch_size=BATCH_SIZE)\n","\n","for name, value in zip(resampled_model.metrics_names, resampled_result):\n","  print(name, \":\", value)"],"execution_count":81,"outputs":[{"output_type":"stream","text":["28/28 [==============================] - 0s 6ms/step - loss: 0.0827 - tp: 88.0000 - fp: 894.0000 - tn: 55972.0000 - fn: 8.0000 - accuracy: 0.9842 - percision: 0.0896 - recall: 0.9167 - auc: 0.9948\n","loss : 0.0826992616057396\n","tp : 88.0\n","fp : 894.0\n","tn : 55972.0\n","fn : 8.0\n","accuracy : 0.9841648936271667\n","percision : 0.08961303532123566\n","recall : 0.9166666865348816\n","auc : 0.9947949647903442\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"dj7IPY9Y3VLC","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":435},"outputId":"f0a10506-c1d8-4f64-9327-72b04ca927ba","executionInfo":{"status":"ok","timestamp":1584605844254,"user_tz":-480,"elapsed":916,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}}},"source":["plot_cm(labels=test_labels, predictions=test_prediction_resampled)"],"execution_count":82,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAU4AAAFNCAYAAABvx4bHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n80qn73rrAlnXe8ieu/LUM8+z0w7brVj/1a8M54Tv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9fX1TPjDXewyaKdKN8ta0Nz3tesu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\nnGZmOTlwmpnl9P8u7OE90JKwGwAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 360x360 with 2 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Legitimate Transactions Detected (True Negatives): 55972\n","Legitimate Transactions Incorrectly Detected (False Positives): 894\n","Fraudulent Transactions Missed (False Negatives): 8\n","Fraudulent Transactions Detected (True Positives): 88\n","Total Fraudulent Transactions: 96\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f0P9F_gr3oqv","colab_type":"text"},"source":["## Plot the ROC"]},{"cell_type":"code","metadata":{"id":"Rz0ShwXP3y27","colab_type":"code","colab":{}},"source":["train_predictions_resampled = resampled_model.predict(x=train_features, batch_size=BATCH_SIZE)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"O-Tfb4qm3k8H","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":610},"outputId":"5a501d40-2416-483b-ae1c-92601efb206a","executionInfo":{"status":"ok","timestamp":1584605993582,"user_tz":-480,"elapsed":1373,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXVAgB9A4OE1KXeAfp5b-xUS2OSsbqSRVEe_UETw=s64","userId":"04300517850278510646"}}},"source":["plot_roc('Train Baseline', train_labels, train_predictions_baseline, color=colors[0])\n","plot_roc('Test Baseline', test_labels, test_prediction_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_prediction_weighted, color=colors[1], linestyle=\"--\")\n","\n","plot_roc('Train Resampled', train_labels, train_predictions_resampled, color=colors[2])\n","plot_roc('Test Weighted', test_labels, test_prediction_resampled, color=colors[2], linestyle=\"--\")\n","\n","plt.legend(loc='lower right')\n","plt.show()"],"execution_count":85,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAngAAAJRCAYAAADWNMrEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZgAodkE9oh0W5/jXYhzbJG8ihpxzH0oa7GtwAAAAeY43WQAAABQY3kUL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JotZfvbI2X6H\nhEKTZIFjAEBuIsHLYx/t/qj+8+nHnS5J6t6uuwadMChmeRY4RpM1TO5YuBgA8gYJXgH46bCfasSp\nI4IOA4Ws0F69BgAFjgQvD9xzbeweuaaqWFLhbXkXLQAABY0ELw/ceEGfjNQzb808SSR4vhp5X9AR\nAABAggdkVPmEoCMAAIB18PLBo6+u06Ovrgs6DAAAkCfowcsDdz1RKSlzj2rho2UzvC09eQCAAJHg\n5ZHdh3brkbcfUdXhKknS2zvfDjgiNLLwdm8bRILHmnUAgAgSvDyy8MOFmr5ieqPjHVt1DCAa5By/\nkjvWvwOAvEOCl0cO1RySJF1w4gUa3me4JKlz6866sMeFKV3fv3N/32JDDmHNOgAoeiR4eWhAlwH6\nUv8vNfm6OaPm+BANAADINcyiBQAAKDAkeAAAAAWGBC8PrJ16ldZOvarZ9QyaOUiDZmbmtWcAACB3\nMQYPyCQmOAAAcgA9eAAAAAWGHrwc9PGej3XTMzdp276dqnVOkmRmKimpjXvNpOcnafHGxZKkyRdN\n1pgzxkiS5r43V1NemeJ/0Pmi4WLA546XRk/zPm96Q5oein/dxLDUc4j3ef5tCr0+UwrHKNdjsHTz\nooyFGxMLGgMAkiDBy0Ertq3Q1v1bJZPMjh6vdVJZSZmGdBvS6Jq65C6ZYb2GZSrM/FQIiVGi78Ci\nxAAAkeDltCO7B+vg5jF678efrz9WYiVqURL/j61yfGXU/pgzxtT35qGBY8fK9RyS+vi50dMU7nid\nQqFQxsNqEsb7AQDiCGQMnpl9x8xWmtkqM7s9cuznZvaOma0wsyfM7Lg41641s0oze9PMlmU38mwz\nybVQWWlZ/U+i5A4AAEAKIMEzs7MkfVPSUEmDJY00s9MlPSfpLOfc2ZLek3RngmqGO+fOcc6V+x4w\nAABAngmiO6i/pFedc/slycxeknSdc+5nDcr8Q9IXAogtp/Tt0k5DTuoddBiFpcfgoCMAAMB3QSR4\nKyX9xMy6SDogaYSkYx+13iTpf+Nc7yQ9a2ZO0oPOuem+RRqwc/ocp3uHnZ1S2WPH3iEOv2e4AgCQ\nA8xFluHI6k3Nvi5pkqR9klZJOuScqxuL9wNJ5fJ69RoFZ2a9nHMbzaybvMe6tzrnGv2rbWYTJU2U\npO7du583e/bsJsVYVVWl9u3bN+2LZchrVa/pkR2PqLxducafMD6QGI4VZHvkoiDbIxS+WpIUDj0Z\nyP1j4fcjGu0RjfaIRntEoz2iDR8+fHkmhqAFMmLfOfd7Sb+XJDO7R9KGyOevSRop6bJYyV3k2o2R\n7VYze0LeWL5GCV6kZ2+6JJWXl7umzngMh8NZnyW5Ye8GLf9kufZv2y/tkFp16Kwupw/RoJM6ZTWO\nWIJoj1wWaHuEvU0u/Xnw+xGN9ohGe0SjPaLRHv4IJMEzs26RBK2PpOskXWhmV0r6D0mfrhufF+O6\ndpJKnHN7I58vl1Qwq/je+rCih44AACAASURBVLdb9f6u9+v3X3xnp55+8eWU3kM7dsFYSdKcUXN8\ni68gVESS5VxcYoQFjAEAGRLUmhvzImPwjkj6tnNul5k9IKmVpOfMW933H865b5lZT0kPOedGSOou\n6YnI+RaSHnXOPR3MV8i8XYd2SZI+d/Ln1LGsox55uk/K167eudqvsJAtTUnuWNAYAJBAUI9oG71O\nwTl3epyym+RNxJBz7kN5S6sUtDuH3qmubbvqD/OfCjoUBCEXexcBAHklkIWOAQAA4B8SPAAAgAJD\nggcAAFBgSPAAAAAKDG+uz2ELbrkk5bLX97vex0gKyMj7go4AAADfkeAF7M2tb+r+N+7X4ZrD2nVw\nV9S5pixwXHFxRYYjK1DlE4KOILZZY4KOAACQBe9v3av/t+Bt7T9c4+t9SPAC9viax7V0y9L6/Q4t\nO6hDWYcAI0Ig6tbAY307AChoC97arMVrtvt+HxK8gNW6WknSNwZ9Q5eedKl6d+it1i1aS5LufHyF\nJOne685OWs+qHaskSQO7DPQp0gKxbIa3zdWevC/NDToCAICP6t7E+sXy3hpTflKj8+f/NDP3IcHL\nEX069NGQbkOijj22dL2k1BK8cQvHSZIqx1dmPrhCsvB2b5urCR4AoCj0PK6Nyvt29q1+ZtECAAAU\nGBI8AACAAkOCBwAAUGBI8AAAAAoMkywC5uSCDgEAAF9U19Tqo+37Ev5Lt3Fvrd77ZG/WYgra9n2H\ns3IfEryALdqwSNLR5VIaOqtXx2yHg2yaNebo+ncAUIC+9afX9fzqT5IX/Psi/4PJMWb+1k+CF7DT\njjtNyz9ZrlYtWjU6t/DWYSnXM3vk7EyGVbgqdgcdwVHHJncscgygwHy4rUqSdHKXtiorjT0qbN/+\nfWrXtl02wwpcu1YtdPnA7r7egwQvR3Rv27w/aBY4zmO5lHQCgA8e/tr5Oq1r+5jnwuGwQqFPZzmi\nwkeCl6MmzFiqs086Tt/93Blxy0x6fpIWb1wsSbq+3/X5+T7aRI8pzx0vjZ7mfd70hjQ9FL+eiWGp\nZ2Sh6Pm3Sa/PjF2ux2Dp5uJ7FAAAKC7Mos1RL767Tf/9whpNmLE0bpm65C6vVaUwNgMAADQJPXg5\nbsaEoUnL5PXryVLtTes5JPVHmaOnHe35AwCgCCVM8MwslX8l9zjn7s5QPAAAAGimZD14V0uanKTM\nHZJI8AAAAHJEsgTvV865OKPVPWZ2fAbjKQrbD2zXrNWzdKD6gNbvWZ92Pf07989gVAGp6BTZBjiT\nNI316EKSFPYhFqAI7T5wRL9/+SPtOXAk6FCyYuPGQwrvWRV0GFmxrepQ0CEUrYQJnnPuvmQVpFIG\n0ea+O1cPVT4UdaxjWdMXNZ4zak6mQipuQS42zNp3gJ5asVnTXlgTdBjZ9fHaoCPIGjOpQ2uG/Gdb\nk1rczEZJ+p6k1pIecc79xpeoCtzBmoOSpE+f9Gld2ONC9WjXQ2ccH70cyj3XDgoitOLWhF5Eb92m\nkH+xAEXk4JEaSdLQvp115VknBhyN/95//32dfvrpQYeRNad3a69uHVoHHUbRSTbJ4hzn3JsNDn1F\n0nBJJuktSSR4zXBOt3P05QFfjnnuxgv6ZDkaAAjWgJ4dddMlpwQdhu/C1R8rVATfE8FK1oP3r2ZW\nIuk/nXNbJK2XN6GiVtImv4NDYoNmer18eb1MCgAAyLhkY/BuNrPBkh40s+XyZtReJKmtpF9kIb6i\n9eir6yTRkwcAAJou6ZssnHNvOeeulvSGpCcl9XTOzXfOMTXGR3c9Uam7nqBnDgAANF3CBM/MvmVm\nS8xsiaR2kq6UdJyZPWNml2YlQhS2kfd5PwAAIGOSjcGb5Jw728xaSVrinJstaZqZ/VHSf0rire1o\nnvIJQUcAAEDBSZbgbTSzu+SNuXun7qBz7p+S/s3PwArVbX+7TYs2kBcHJo1FjZEdb2/ao6/NWKrd\nBbDYbW1trUqe/2vQYeSMZO1RU+uyGA1QHFJ5VdkVko5I+qH/4RS2mtoavbj+RUlS69LWOvuEswOO\nKAcsm+Fts9WTFy+5Y8HhwC37eKe27i2gob21tUFHkFuStEfLUtP5fTtnKRig8CVL8Do75xYkKmBm\nJ0aWUEGKTKYlNy5Ry5KWzapn8kXJXhOcBxbe7m2z/ag2yFejIaEbhvbRD0cNCDqMZlm0aJEuvZRh\nynVSaY8SM5W1SDrvD0CKkiV4f5F0bgbKoAEza3ZyJ0ljzhiTgWiA3NKixNS6ZWnQYTRLWWn+f4dM\noj2A7EuW4A02sz0JzpukROeRprVTrwo6BAAAkKeSLXTM/3LlsLnvzZVETx4AAIiWrAcPOWzKK1Mk\nkeABAIBojGjNUSPvX6yR9y8OOgwAAJCH6MHLUSs3MrQRzbd+5369tnan7/dZvfGIdr6+oVl1vLlu\nV4aiAQCklOCZ2WmSNjjnDplZSNLZkh5xzvE3chNs2e+tJlPrWB+rnh/LlbCYcb2b/vCa1mytys7N\nKt/KSDUslQEAzZdqD948SeVmdrqk6ZKelPSopBF+BVaIdh0iH86KZMldES1qvHPfYUnSiEEnqlUL\n/+ZMbflki07sfmKz62ndskRfufDkDEQEAMUt1QSv1jlXbWbXSrrfOXe/mb3hZ2CFyDnvdTwDuuT3\nIq55g8WM6025+iyd0L6Vb/WHw2GFQuf4Vj8AoGlSfRZyxMxukDRe0sLIseav1Ftk6hK8Eua2HPXg\npd4PAADImFR78CZI+paknzjnPjKzUyT90b+wClOtvLF3ZpaR+irHV2aknkBtzsy4LQAAcFRKCZ5z\n7m0z+/8k9YnsfyTpp34GVojqevBSSfBuGNpbKw4+pEEz74h5vn/n/pozak7ym84aI31qxNF3vS6b\ncfT9r7E0fKz54KX1CVhIksINyp07Xho9zfu86Q1peih+nRPDUs8h3uf5t0mvz0weNwAASFtKzwrN\nbJSkNyU9Hdk/x8zm+xlYIXKKJHiKn+BVbtityg27de91Z+v8vp2bf9M1z0rhqc2vx09FNOkBAIBs\nSPURbYWkoYr04Tjn3jSzU32KqWDVj8Gz+Hn1qAdeluS9i7bi4gpVXFzR/BtXbTn6uXzC0d68ZG5e\nVP/RG0Qfil2u55DUJzSMnna05w8AAPgi1QTviHNu9zGPFlnMrYnq1r9L1INXZ9WOVZKkgV0G+hpT\n3mvmmnc7qg7pricq65cTSdXu3Qf063eWpH3fbNl94EjQIQAAApBqgrfKzG6UVGpm/STdJin3/3XL\nMfWPaFMYgzdu4ThJBTKRwk/xkrsUH/suXrNdz6z6JL17//Of6V2XZR1bt1D7Vry0BgCKSap/698q\n6QeSDslb4PgZST/2K6hCVT/JIoUePDRRmmve1Ub+TIb1O0G3fqZfyte98cYbGjJkSFr3zLZTTmin\n1i39W+QYAJB7Uk3wznTO/UBekoc01fXgJRqDh2Cc0L6Vhp6S+qSW/R+XNqk8AADZlGqm8UszW21m\nPzKzs3yNqIA1ZQweAABAulJdB2+4mZ0oaaykB82so6T/dc7xmLYJmrIOXsbwui4AAIpOys8KnXNb\nnHPT5L3R4k1Jk32LqkClsg7eglsu0YJbLslWSAAAoACl1INnZv0lfVHS9ZJ2SPpfSd/zMa6CVPeI\nNtEYvEEndcpWOAAAoEClOsniYXlJ3RXOuU0+xlMQ9h/Zry37tjQ6vnnfZu9DCk9oZ4+cnZlgHrzU\n2zZYtNhPW3YfVNWh7K29dnpk+/7WvWldv2XPwcwFAwBAjkh1DN5FfgdSKKprqzX6/0brk/3x11Yr\nSfBk/M7HV0iS7r3u7MwEFHmXbDY8//Yn+sYjy7J2v4db/kynR1b/+Ox/NS+BZdoLAKCQJEzwzGyO\nc26smVVKkQFkkVOSnHMuQ1lI4dhfvV+f7P9EJlPfTn0bnS+1Ul3b79q41z+2dL2kDCZ4WfTBtipJ\nUqc2LXVC+zLf7/eZvW9Kkv5Rep5O69ou7XrKWpTq6iG9MhUWAACBS9aD953IdmQmb2pm35H0TXmJ\n4u+cc/eZWWd5j4H7SloraaxzrtGrAsxsvKS7I7s/ds7NzGRsmdK+ZXvNv2Z+2tdXLKnwtpl4F22W\nffH83rprRH//b1ThbS78z7/pBf/vBgBA3kg4i9Y5Fxk0pknOuY8b/kialM4NI+vofVPSUEmDJY00\ns9Ml3SHpBedcP0kvRPaPvbazpB9KuiBy/Q/N7Ph04sh189bM07w184IOAwAA5KFUl0n5XIxjn0/z\nnv0lveqc2++cq5b0kqTrJF0tqa43bqaka2Jce4Wk55xzOyO9e89JujLNOAAAAApSwgTPzP41Mv7u\nU2a2osHPR5JWpHnPlZKGmVkXM2sraYSk3pK6N+gx3CKpe4xre0la32B/Q+QYAAAAIpKNwXtU0l8l\n3avoR6Z7nXM707mhc261mf1U0rOS9slbNLnmmDLOzFys61NlZhMlTZSk7t27KxwON+n6qqqqJl8j\nSftr90uSqqur07r+WM2t44wel0uS3mtmPam0xwcfHZYkrVu3XuFw/FnEmRKKbDPRzk2V7u9HoaI9\notEe0WiPaLRHNNrDH8kSPOecW2tm3z72hJl1bkaS93tJv4/Uc4+8nrhPzKyHc26zmfWQtDXGpRt1\n9N91STpJUjjOPaZLmi5J5eXlLhQKxSoWVzgcVlOvkaQ9h/dIj0ktWrRI6/qzKhdLkj6O7KdTR5TI\n9T2bV0tK7fGufSC9+4769OmtUCgLkyzC3qbZbZTOrdP8/ShUtEc02iMa7RGN9ohGe/gjlR68kZKW\ny1smpeFyYU7Sqenc1My6Oee2mlkfeePvLpR0iqTxkqZGtk/GuPQZSfc0mFhxuaQ704nBD3/f+He9\nuP7FZtWx8NZhkqRBOTk3OL6Nuw7oZ8+8e/TArDHSmmeDCwgAgCKWMMFzzo2MbE/J8H3nmVkXSUck\nfds5t8vMpkqaY2Zfl9eBNVaSzKxc0recc99wzu00sx9Jei1Sz5R0exH9cPff79b2A9slSR3KOjSr\nrv6dM9QDtukNb9tzSGbqi+Phlz9STa33VL1j6xbZS+76XZ6d+wAAkEdSfRftv0h60zm3z8y+LOlc\nSfc559alc1Pn3LAYx3ZIuizG8WWSvtFg/2F5r07LOQervdde3TbkNg07qdFXbJI5o+ZkIiRpesjb\nVuzOTH1xHDjiDaMc2LOjxl/cV6p7sYTP9wUAAI2lukzK/0jab2aDJX1P0geS/uhbVHlu3JnjdGbn\nM9O6tu8dT6nvHU9lOKLsuWFoH3Vo3TLoMAAAKGqpJnjVzjknb626B5xzv5bUvGeQAAAA8EVKj2gl\n7TWzOyV9Rd4adiWS6Kbx0aCZgyRJleMrA44EAADkm1R78L4o6ZCkm5xzW+QtT/Jz36ICAABA2lJK\n8CJJ3SxJncxspKSDzrlHfI0MAAAAaUl1Fu1YeT12YXlr4d1vZt93zv3Zx9gKRt3j1lgmXzRZY84Y\nI0kaOePn6tA/w3nzGVdK7z2d2ToBAEBOS3UM3g8kne+c2ypJZtZV0vOSSPAkOec06YVJqjpSJUma\n8PQElVhJk5c66d+zoz7ecnR/WK80l1qZf5sk6e3yH+unH16mm9wn+tZ/Ni/Jq6mtUekLT+u3JVP1\naXuj0fl7JN3TWtLTkR8AABCYVBO8krrkLmKHUh+/V/AOVB/QyxtfluQtULx65+qo86lOlPj5FTfr\n57q5+QG97r0G47UT/k0vVfXSS/q+jnndb3pqavTp1o2Tu7hYhBgAgECkmuA9bWbPSHossv9FSX/x\nJ6T8NnvkbA1+ZHDQYUT5wnknacrVA5tVx+JFizXs0mFeV52k/XftaFSmRUmJylqQ9wMAELSUEjzn\n3PfN7DpJl0QOTXfOPeFfWPmpTYs2KrH0E5xHX/VeDHLjBX0yFZIkqW1ZqdqWpZrLx9aqhUXV0dz6\nAACAfxL+K21m/ST9QtJpkiol/btzbmM2AitGdz3hPcrNdIIHAACKS7LupoclLZR0vaTlku73PSIA\nAAA0S7LnbB2cc7+LfH7XzF73OyAAAAA0T7IEr7WZDZG39p0ktWm475wj4Yvh+n7XBxtAj9ya5AEA\nALIrWYK3WdJ/Ndjf0mDfSfqMH0Hlmz2H90TtV1xckbV719Y6/f2D7dpRdfjowaHeZOe31mzLWhwA\nACB3JEzwnHPDsxVIPvvZaz+TJB2qOZT1e//9g+36yu+XJizTsjTFmb2zxkhrno15KiR57zEBAAA5\nj7UuMmDfkX2SpJGnjpQkrdqxSpI0sEvz1p5Lxc59Xs9dr+PaqLzv8Y3Ot25Rqi9feHJqlcVJ7hph\nAWMAAHIaCV4GjThlhCRp3MJxklJ/g0WdtVOvSvve5518vP573BBvp6JTZLs7vcpiXBcOhxUKhdKr\nDwAAZBWvHQAAACgwKSV45vmymU2O7Pcxs6H+hgYAAIB0pNqD9xtJF0m6IbK/V9KvfYmoiI28f7FG\n3r846DAAAECeS3UM3gXOuXPN7A1Jcs7908zKfIyrKK3cuCd5IQAAgCRS7cE7Ymal8ta+k5l1lVTr\nW1QAAABIW6o9eNMkPSGpm5n9RNIXJN3tW1R55KkPn9KSTUt8vccrH+zQAy+u0ZEa1+jc9qo0195L\nsOYdAADIbykleM65WWa2XNJl8l5Tdo1zbrWvkeWJh1c+XP/5xHYnSpJmj5yd0Xv86dWP9ff3dyQs\n0/O4Nkd3Rt6XvNJ4yR1r3AEAkPdSSvDMrI+k/ZIWNDzmnFvnV2D5otZ5T6rvC92n0447TVLmFziu\nrfV67m7/bD9deGqXRudblpZo8Emdjh4on5B65emulQcAAHJWqo9on5I3/s4ktZZ0iqR3Jfn/qoY8\n0adjH9/vcUb3DjETPAAAgIZSfUQ7qOG+mZ0raZIvERWAiiUV3vbiiiZdd8PQ3pkJYNkMb9uUnjwA\nAFAw0npVmXPudTO7INPBFIp5a+ZJanqCd+91Z2cmgIW3e1sSPAAAilKqY/D+rcFuiaRzJW3yJSIA\nAAA0S6o9eB0afK6WNyZvXubDKW6VG7wJD4MaTpgAAABooqQJXmSB4w7OuX/PQjx5ZdfBXTpcczhj\n9Y164GVJ0tqpV0mS9h48ok/2HFLVoeqM3QMAABS+hAmembVwzlWb2b9kK6B8sX7Peo1+crSqa73k\ny2QZrX/foWpd8tMXtfvAkfpjTb5DBT2BAAAUo2Q9eEvljbd708zmS5oraV/dSefc4z7GltPW712v\n6tpqtS5trQt7Xqi+nfpmtP7tVYfqk7tTu7ZT1/atNPSUzhm9B4saAwBQmFIdg9da0g5Jn9HR9fCc\npKJN8OoM6TZENbU1GvLHIaocXylJ6t+5f8bq79O5rf72vZC3M/826fWZsQv2GCzdvCj6GIsYAwBQ\nlJIleN0iM2hX6mhiV6fxi1GL1OKNi6P254yaE1AkAAAAyRO8UkntFXv4Fwletmx6w9uOnub9AAAA\nJJAswdvsnJuSlUgQ3/SQt+WRKwAASEGyBC+zU0OR0IJbLgk6BAAAUACSJXiXZSUKSGKBYwAAkBkJ\nEzzn3M5sBZJv3vnnO77Wv70qzgLKs8ZIa5719d4AACC/pbpMCo7x1IdPSZIO1hzU5IsmZ6TOOx9f\nIUm697qz9ezbWyR56+GppEGhVJM71rgDAKBokeClqW2LtpKkG8+8UVeecmVG6nxs6XpJXoJXat7w\nxysGniitjlGYCRcAACCOkuRFkEj3dt19rf/0bu19rR8AABQeevAyYO57cyVJY84Y488NJob9qRcA\nABQkErwMmPKKt1SgbwlezyH+1AsAAAoSj2gBAAAKDAlePph/m/cDAACQAh7R5pCzenWMfeL1md6W\n99ACAIAUkOClYVPVJr257c2M1PX2pj36+szXtPvAkfpjAyY/rcPVtY0Lz/JpjB8AACgoJHhpWLFt\nRf3nvh37NquupR/t0ObdB2Oea9WiRINPOu7ogbpFjlnEGAAAJECC1wyf7fNZHd/6+IzU9YXzTtL/\nGz0w6liLUlOrFqWNC39pbkbuCQAAChMJXhqcnCSptMRLvirHVza7znZlpRr4w2ckSWunXtXs+gAA\nQPFiFm0zmCzoEAAAABohwUuDcy67N+wx2PsBAABIAY9om6GuB2/sgrGSpDmj5vhzo5sXeduKTv7U\nDwAACgoJXhrqxuDVWb1zdUCRAAAANMYj2ubIwBC8TXGWSAEAAEgXPXgBm77oQ0lSwlF9PJoFAABN\nEEiCZ2bflfQNeXlNpaQJkp6T1CFSpJukpc65a2JcWxO5RpLWOedG+x9xtLpHtJmYRdu1Qytt23tI\nVw3qoTNPjPOqsoZY5BgAACSR9QTPzHpJuk3SAOfcATObI2mcc25YgzLzJD0Zp4oDzrlzshBqVp1y\nQjtdcGqXxIUqdmcnGAAAkNeCGoPXQlIbM2shqa2kTXUnzKyjpM9I+r+AYkuqbpkUM9bBAwAAuSfr\nPXjOuY1m9gtJ6yQdkPSsc+7ZBkWukfSCc25PnCpam9kySdWSpjrnAksEdx/yetSu73d945Ozxhx9\nd2yPwUeXOpGixtS9JkmtJf2Xt//qwMm6YMz3vJ1lM6SFt2c8bgAAUNgs24v2mtnxkuZJ+qKkXZLm\nSvqzc+5PkfN/lfSQc25enOt7RZLEUyX9TdJlzrkPYpSbKGmiJHXv3v282bNnNynOqqoqtW/fPua5\npVVL9ccdf1Sn0k768Uk/jlkmFL66/vPe9qdpefl/xTx3rF+3+qYGXjRSktRj0zP61Hu/qT8XDsV7\nau2/RO1RjGiPaLRHNNojGu0RjfaIRntEGz58+HLnXHlz6wkiwRsj6Urn3Ncj+1+VdKFzbpKZnSDp\nXUm9nHNJ1w8xsz9IWuic+3OicuXl5W7ZsmVNijMcDisUCsU8t+CDBbrr5bskJXgPbV0vXZJxc33v\neEqStPSuy9StY+tm1eWnRO1RjGiPaLRHNNojGu0RjfaIRntEM7OMJHhBjMFbJ+lCM2tr3iC2yyTV\nrRT8BXkJW8zkzsyON7NWkc8nSPoXSW9nIWYAAIC8EcQYvFfN7M+SXpc3ju4NSdMjp8dJmtqwvJmV\nS/qWc+4bkvpLetDMauUlp1Odc1lL8DZWbdTUpVO1dvfatOv47+fXaMkH22OfbDhuDwAAIE2BrIPn\nnPuhpB/GOB6KcWyZvDXz5JxbImmQ3/HF8/zHzyu8Ppxa4XPHNzp0uLpWv3r+vUbH27dqoQ6tWyZO\n7lj/DgAApIg3WTRBrauVJF3R9wo9s/aZxIVHT2t0qG6B5BYlpj9+/YL646d2bac2ZaVHC7LeHQAA\naAYSvDT0aNejWdeXmOmi05IsagwAAJAmErw0zR6ZZNmVTW94255DEhYbef9iSdLCW4clLAcAAJAq\nErw0DewyMHGB6SFvm+Rx68qN8dZzBgAASE9QryoDAACAT0jw0lSxpEIVSyqCDgMAAKARErwm+GT/\nJ/Wf562Zp3lrYr5NLa4svzQEAAAUKRK8FC34YIFmrZ4lSTJZWnX86jlvDbzaYzK9h1v+7OjryAAA\nAJqJBC9F7+96v/7zZSdfllYdH23fJ0nq06Vt1PHPlL55dIcFjQEAQDMxi7aJvnPudzS46+Bm1fEf\nV5xZ//mGob2lFZEdFjgGAAAZQILnl4nhlIrde93ZRxM8AACADCDB80uSBY4BAAD8QoKXpv6d+2ek\nnsoNuzUoIzUBAAB4SPDSNGfUnMQF5t/mbUdPS1hs1AMva23rDAUFAAAgZtH65/WZ3g8AAECWkeCl\n4EjtET3y9iPNqmPr3oN69u1PkhcEAABoJhK8FLy25TVV11ZLkjqWdZQkDZo5SINmpj567o+vfFz/\nuWMbnowDAAD/kOCl4GD1wfrPI08dmVYdBw7XSJIG9OioC07pkpG4AAAAYiHBa4JQ75DatmybvGAC\n1w7ppdKS9F51BgAAkAoSPAAAgAJDgueXHoO9nyQW3HJJFoIBAADFhNH+frl5UUrFBp3UyedAAABA\nsSHBS1PXNl217cC22CdnjZG+NLd+d8KMpXrxXa/sT/6yWj/5y+r6czcM7a17fY0UAAAUGx7Rpulf\nz/lXDes1LPbJNc9KLx5N27ZVHcpSVAAAAPTgpWTa60dfNzb3Pa9nbswZYzTmjDHxL3ppqjT8TknS\n9eeepJUb35Yk/WBEf33z0lOjy67IbLwAAKC4keClYO/hvZKk3h16a8orUyQpcXJ3jGdWban/zJg7\nAADgNx7RpiKybN1XB3y1WdXMmHC+LjyVRY4BAIC/SPCy4B8f7pQktWpBcwMAAP+RcQAAABQYEjwA\nAIACwyQLP4y8L+gIAABAESPB80P5hKAjAAAARYwELwVb92+t/1w5vrJJ1368Y1+mwwEAAEiIBC+J\nLfuOrmHXsqRlahctm+FtyydozG9fqT9cVnrMkMdZY7y3XgAAAGQQCV4S/zz4z/rPXdqkuIbdwtu9\nbfkE7dh3WJJ08WlddE7v46LLNUzu+l3enDABAADqkeCl6MzOZ0qSxi4YK0maM2pOk65/5KahanFs\nD16dit3Nig0AAKAhErwmWr1zddAhAAAAJMQ6eFkye+m6oEMAAABFggQvS+5+clXQIQAAgCJBggcA\nAFBgSPAAAAAKDJMskthzeE+Tr3lsRKXmv7lJmv4P1dQ6PdzyZ/pM6ZtSRebjAwAAOBYJXhJ1b7H4\ncNeHkqTr+12f9JppL6zR5t0H6/c/U/pm/MKsfwcAADKMBC+JurdXXNTzIklSxcUVSa+pqXWSpAdu\nHKLObcukP0VOsN4dAADIAhK8FLVu0TrlsjOPfF/VZbXq1vdVde+Y+nUAAACZQILXRKt2eMudDOwy\nMG6Z/vpQKpE+yVZQAAAADZDgNdG4heMkSZXjKwOOBAAAIDaWSQEAACgwJHgAAAAFhgQviUM1h4IO\nAQAAoEkYg5dAravV3X+/W5JkspSuOfiH61Q3b7b7f3X3KTIAAID46MFL4HDN4frPI04ZoUnPT0p6\nTeu1L8Q8/reaczIW7cSR5AAAIABJREFUFwAAQCL04CXg5C1Y3Kq0lYb3Ga5u7bqpxEpU62qTXjuu\n59OaPfEi9b3jKUnS8E911Wd8jRYAAMBDgpeAc16CV2JeR+fALgP1wGUPpFXXjAlDMxYXAABAIiR4\nCdT14DXHDUN7ZyASAACA1JHgJVDXg1c3waJiSYW3TeF9tHXuve7sTIcFAACQEJMsEqjrwat7RDtv\nzTzNWzMvyJAAAACSIsFLoG4yRapLpMRSuWG3KjfszlRIAAAASfGINhUp5Hf7Hr5W7db9rdHxUQ+8\nLElaO/WqTEcFAAAQUyA9eGb2XTNbZWYrzewxM2ttZn8ws4/M7M3IT8yF48xsvJmtifyM9zPOY2fR\nJnJsctehdUtfYgIAAEgm6z14ZtZL0m2SBjjnDpjZHEnjIqe/75z7c4JrO0v6oaRySU7ScjOb75z7\npx+x1ir9R7STRw7IdDgAAAApCWoMXgtJbcyshaS2kjaleN0Vkp5zzu2MJHXPSbrSpxgbzaJNxbIr\nnpAmhtW7c1ufogIAAEgs6wmec26jpF9IWidps6TdzrlnI6d/YmYrzOxXZtYqxuW9JK1vsL8hcsyf\nWCOzaM28BK9/5/7q37l/wmuqOp8l9RziV0gAAABJWV0vVdZuaHa8pHmSvihpl6S5kv4s6QVJWySV\nSZou6QPn3JRjrv13Sa2dcz+O7P+npAPOuV/EuM9ESRMlqXv37ufNnj27SXFWVVWppnWN7t54tzqU\ndNA9ve9JWD4UvlqSNG3gPJ3d9eiT7689vU+S9Icr2zXp/rmmqqpK7du3DzqMnEF7RKM9otEe0WiP\naLRHNNoj2vDhw5c758qbW08Qs2g/K+kj59w2STKzxyVd7Jz7U+T8ITObIenfY1y7UVKowf5JksKx\nbuKcmy4vUVR5ebkLhUKxisUVDoc1YOgAaa7UqlUrJb0+EsUXDs5Rzz2tpdHTvANPe++iber9c004\nHM7775BJtEc02iMa7RGN9ohGe0SjPfwRRIK3TtKFZtZW0gFJl0laZmY9nHObzXseeo2klTGufUbS\nPZFeQEm6XNKdfgVaP4u2CU+ye37wv96HSIK34JZLMh4XAABAIllP8Jxzr5rZnyW9Lqla0hvyetr+\namZd5a0696akb0mSmZVL+pZz7hvOuZ1m9iNJr0Wqm+Kc+//bu/fwqKp7/+PvlYBBDAJCEIRWwAKS\nkDAJCRCCSEQSKxeRS1WwghyPoC0o1iLo4aLHC1ZaEaqlCpT6UyuVS9GCNaUSA0SIASMgFwMSEcwB\njBIIcsll/f6YyZghMyGBJJPL5/U8eZg9e+21v7Mymfmy9tprfVdlsRavReu6xyL8r+EA7Bi7o9x1\nhLdrWtlhiYiIiJTJLxMdW2tn4ZzupKSbfJRNB+4rsb0EWFJ10f3o4ImDQBl30b45CjKTvO8TERER\n8RMtVVaGb045Z2858sMR7wXOS+4+LCw9N/P0lduZvnJ7pccmIiIi4osSvDIU99wN6TikzHL/GrmX\n9mfe4v91fKHUvr+lfc3f0r72cpSIiIhI1VCCVw7F8+BdSMNANaeIiIj4n1/G4NVpbbr7OwIRERGp\n55TgVbYJKf6OQEREROo5JXgVMDN2pr9DEBEREbkgJXgVMKrzKH+HICIiInJBSvDKcLbwbLnK/XCu\n4MeN2a6JjWfnAtCt7ZWVHZaIiIhImZTglSH1m1QAzhScAeCdL94BSvfkrdx22FWuqFQd/5x0Q1WG\nKCIiIlKKErwyXNXoKo9/n/r4KaB0gte0cUMArm/dxLnSroiIiIgfaeK2cujUvFO5yoW31bqzIiIi\n4n/qwaskdwX+hyGrRru3209b47E/a86g6g5JRERE6in14FWB89ekje8S4qdIREREpD5SD14l+Vvh\nAPr84lGGdL+Gm4AsfwckIiIi9ZYSvEoQd/w9rgzMBSL9HYqIiIiILtFWhtFH/8BzDRezad+3/g5F\nRERERAleWT7P+dxje8fYHewYuwOA00uHlyr/9idfV0tcIiIiImVRgleGZkHNADhx7kSpfZdn/Qco\nfUOFiIiIiL8pwStDoAkEoFMz3/Pgrez6YnWFIyIiIlIuSvDKwRgDwC/e+wW/eO8XHvvaNW/sj5BE\nREREfNJdtGWwWI/t3d/t9lMkIiIiIuWnHrwynJ/giYiIiNQGSvDKwWDKLjA7l/Zn3qqeYEREREQu\nQJdoy1KBDjytNSsiIiI1hXrwylB8ibb4JotiX+Wc8kc4IiIiIuWiBK8czr9Eu3/erT/uM8Cf+zl/\nRERERGoAJXhlsNbzGu3g9rdz7vue3BSYAUBag2gSQq+G7M8g+zMGL9jgjzBFREREPCjBK4P7Eq2r\nB+/RHk9w9v9+XKKs5//8h8ifNndv7zxcesULERERkeqmBK88LnATrYiIiEhNogSvDOfPg7f3+90E\nNDrkp2hEREREykfTpJTFld8VX6Kd8OEvuaIDcMB/IYmIiIhciHrwyuH8aVJEREREajIleGUo91Jl\nUWN5qyC+aoMRERERKSddoi1DcYKXui+H9Rm7fRccOp8dBdu5q5riEhGR+iU/P59Dhw5x5swZf4dS\n6Zo2bcru3WV8x9ZRjRo1ol27djRs2LBK6leCVw4vr99P4Q/QpGuJJzsleJR5bnhE9QYlIiL1xqFD\nh2jSpAnt27evc8OGTp48SZMmTfwdRrWy1pKTk8OhQ4fo0KFDlZxDCV4ZSk503KTRj021878P0q1t\n0x8LfvOp899rIqsrNBERqUfOnDlTJ5O7+soYQ4sWLTh27FiVnUNj8MpQcgxek6AfEzyP5A7g1f7w\nan92HMqtpshERKS+UXJXt1T171MJXrk4fwlvH87m7cPZPksN+ePG6gpIRESkWuTk5OBwOHA4HLRu\n3Zq2bdu6t8+dO1fmsenp6UyePLlC52vfvj3h4eE4HA7Cw8NZvXr1pYRfyuzZs5k7dy4AM2fOZN26\ndZVaf02hS7RlOH8t2rBz+X6KRERExD9atGhBRoZzDfbZs2cTHBzMo48+6t5fUFBAgwbe04no6Gii\no6MrfM7169fTsmVL9u7dS0JCArfddtvFBX8BTz31VJXUWxOoB68cGl/7KieveZjZLa5yP5eZsQFm\nN3X+iIiI1CPjxo1j4sSJ9OrVi6lTp5KWlkZsbCyRkZH06dOHvXv3ApCcnMzgwYMBZ3I4fvx4+vfv\nT8eOHZk/f/4Fz3PixAmaN/9xzfdhw4bRo0cPwsLCePXVVwEoLCxk3LhxdOvWjfDwcF588UUA9u/f\nzy233EKPHj244YYb2LNnj9fXsXz5csDZczhr1iyioqIIDw93lz916hTjx4+nZ8+eREZGVnqPYlVR\nD14l+bDQQXyXEH+HISIidVz7aWuqpN6sOYMqVP7QoUOkpqYSGBjIiRMn2LBhAw0aNGDdunU8/vjj\nrFixotQxe/bsYf369Zw8eZIuXbrwwAMPeK07Pj4eay1ffvklf//7393PL1myhKuuuorTp08TExPD\niBEjyMrK4vDhw+zcuROA48ePA3D//fezcOFCOnXqxJYtW3jwwQf58MMPy3xNLVu2ZNu2bbzyyivM\nnTuXRYsW8cwzz3DTTTexZMkSjh8/Ts+ePbn55pu54oorKtRe1U0JXhlK3mRxcvccZjca7d7u5LgB\nHD/eVHGT60dERKQ+GDVqFIGBgQDk5uYyduxYMjMzMcaQn+99SNOgQYMICgoiKCiIVq1aceTIEZo2\nLX0lrPgS7f79+xkwYAD9+/cnODiY+fPns2rVKgC+/vprMjMz6dKlC19++SWTJk1i0KBBJCQkkJeX\nR2pqKqNGjXLXefbs2Qu+puHDhwPQo0cPVq5cCUBSUhLvvvuue9zemTNnOHjwIF27dvVZT02gBK8M\n54/Bc3vXNWB06IW7l0VERCpTRXvaqkrJHqwZM2YQHx/PqlWryMrKon///l6PCQoKcj8ODAykoKCg\nzHNcd911XH311ezatYsffviBdevW8fHHH9O4cWP69+/PmTNnaN68OZ999hkffPABCxcu5O9//zvz\n5s2jWbNm7rGD5VUcX8nYrLWsWLGCLl26VKguf9MYvIux7a/OHxERESE3N5e2bdsCsHTp0kqr9+jR\noxw4cIBrr72W3NxcmjdvTuPGjdmzZw+bN28G4Ntvv6WoqIgRI0bw9NNPs23bNq688ko6dOjAO++8\nAziTtM8+++yiYkhMTGTBggXuTp9PP/20cl5cFVOCV4biS7RFZ1v6ORIREZGaa+rUqUyfPp3IyMgL\n9sqVR3x8PA6Hg/j4eObMmcPVV1/NLbfcQkFBAV27dmXatGn07t0bgMOHD9O/f38cDgd33303zz33\nHABvvvkmixcvpnv37oSFhV30zREzZswgPz+fiIgIwsLCmDFjxiW/vupgfF6GrEOio6Ntenp6hY5J\nTk7mtR9eY/ux7ZzKeoCi09eSVWIMHgCz68/ExsnJyT673OsjtYcntYcntYcntYeni2mP3bt31/gx\nXxerPi5VVszb79UYs9VaW/G5Zc6jHrxyui6kZt8tIyIiIlJMCV5ZSnRuzrtD68yKiIhI7aAErwzF\nY/CuaP8nP0ciIiIiUn6aJuVitOnu7whEREREfFKCV4Zv8r5xPx7yx41kNXJtTEjxT0AiIiIi5aBL\ntGXIOZPjfryk4e/8GImIiIhI+SnBK4PBuB/fFOiaDbtTgp+iERERqX45OTk4HA4cDgetW7embdu2\n7u1z585d8Pjk5GRSU1O97lu6dCkdOnTA4XAQFhbGyJEj+eGHHyo1/uDgYAC++eYbRo4cWal112RK\n8MpQci1atzHvwOymzh8REZE6rkWLFmRkZJCRkcHEiROZMmWKe/uyyy674PFlJXjgXP81IyODzz//\nnMsuu4xly5ZVZvhu11xzDcuXL6+SumsiJXg+1IcJoEVERC7G1q1bufHGG+nRoweJiYlkZ2cDMH/+\nfEJDQ4mIiODOO+8kKyuLhQsX8uKLL+JwONiwYYPPOgsKCjh16hTNmzcH4L333qNXr15ERkZy8803\nc+TIEQA++ugjdw9iZGQkJ0+eBOCFF14gJiaGiIgIZs2aVar+rKwsunXrBjh7DocPH84tt9xCp06d\nmDp1qrtcUlISsbGxREVFMWrUKPLy8iqn0aqZbrLwobj3zlooOjYCeNG/AYmIiLi0n7bG575nbw9n\ndK+fAvDWloM8vmqHz7JZcwZV+NzWWiZNmsTq1asJCQlh2bJlPPHEEyxZsoQ5c+Zw4MABgoKCOH78\nOM2aNWPixIkEBwfz6KOPeq1v5cqVpKWlkZ2dTefOnRkyZAgAffv2ZfPmzRhjWLRoEb/73e/4/e9/\nz9y5c3n55ZeJi4sjLy+PRo0akZSURGZmJmlpaVhrGTp0KCkpKfTr18/n68jIyODTTz8lKCiILl26\nMGnSJC6//HKefvpp1q1bxxVXXMHzzz/PH/7wB2bOnFnhdvI3JXg+/Hh5NgB7ojcE+jUcERGRGuHs\n2bPs3LmTgQMHAlBYWEibNm0AiIiIYMyYMQwbNoxhw4aVq77hw4fz6quvYq3lV7/6FS+88ALTpk3j\n0KFD3HHHHWRnZ3Pu3Dk6dOgAQFxcHI888ghjxoxh+PDhtGvXjqSkJJKSkoiMdC5KkJeXR2ZmZpkJ\n3oABA2ja1DncKjQ0lK+++orjx4+za9cu4uLiADh37hyxsbEX11B+pgTPB6/j70RERGqA8va8je71\nU3dvXmWx1hIWFsbHH39cat+aNWtISUnhvffe45lnnmHHDt+9h+czxjBkyBAWLFjAtGnTmDRpEo88\n8ghDhw4lOTmZ2bNnAzBt2jQGDRrE2rVriYuL44MPPsBay/Tp05kwYUK5zxcUFOR+HBgYSEFBAdZa\nBg4cyN/+9rdy11NTaQyeD+4Ez1rMlZv9G4yIiEgNERQUxLFjx9wJXn5+Pp9//jlFRUV8/fXXxMfH\n8/zzz5Obm0teXh5NmjRxj5O7kI0bN3LdddcBkJubS9u2bQH461//6i6zf/9+wsPDeeyxx4iJiWHP\nnj0kJiayZMkS93i5w4cPc/To0Qq/tt69e7Np0yb27dsHwKlTp/jiiy8qXE9N4JcePGPMFOA+nKu9\n7gDuBRYD0UA+kAZMsNbmezm20HUMwEFr7dCqiLH4JgsTYAlstQIOVMVZREREapeAgACWL1/O5MmT\nyc3NpaCggIcffpjOnTtz9913k5ubi7WWyZMn06xZM4YMGcLIkSNZvXo1CxYs4IYbbvCor3gMXlFR\nEe3atWPp0qUAzJ49m1GjRtG8eXNuuukmDhxwfhHPmzeP9evXExAQQFhYGD//+c8JCgpi9+7d7sup\nwcHBvPHGG7Rq1apCry0kJISlS5dy1113cfbsWQCefvppOnfufImtVv1Mdd8taoxpC2wEQq21p40x\nfwfWAkeB913F3gJSrLWlFoE1xuRZa4Mrcs7o6Gibnp5eoTiTPkziN1//xr2948BB54PZuZD+F1fF\n91aoztosOTmZ/v37+zuMGkPt4Unt4Unt4Unt4eli2mP37t107dq1agLys5MnT9KkSRN/h+EX3n6v\nxpit1troS63bX2PwGgCXG2PygcbAN9bapOKdxpg0oJ2fYgMuMAavHiV2IiIiUvtU+xg8a+1hYC5w\nEMgGcs9L7hoCvwT+5aOKRsaYdGPMZmNM+W7RuZg4dZOFiIiI1FL+uETbHFgB3AEcB94Blltr33Dt\nfw04Za192Mfxba21h40xHYEPgQHW2v1eyt0P3A9w9dVX93j77bcrFOe3J77lye+fdG8XX6JN7r+a\nNt98AED2NYkVqrM2y8vLcy/3ImqP86k9PKk9PKk9PF1MezRt2pSf/exnVRSRfxUWFhIYWD/nItu3\nbx+5ubkez8XHx9faS7Q3AwestccAjDErgT7AG8aYWUAI4PM+Z1cPINbaL40xyUAkUCrBs9a+CrwK\nzjF4FR3vsPbDtfB96ef79+8Ps28DoMvo5ypUZ22mMTSe1B6e1B6e1B6e1B6eLnYMXl0dp1afx+A1\natTIPXdfZfPHNCkHgd7GmMbGGAMMAHYbY+4DEoG7rLVF3g40xjQ3xgS5HrcE4oBdVRGklioTERGR\n2sofY/C2AMuBbTinOwnA2dO2ELga+NgYk2GMmQlgjIk2xixyHd4VSDfGfAasB+ZYa6skwXPHW9CY\nwU3erMpTiIiIiFQqv0x0bK2dZa293lrbzVr7S2vtWWttA2vtddZah+vnKVfZdGvtfa7HqdbacGtt\nd9e/i6sqxiKcnYjqxxMRkfosJycHh8OBw+GgdevWtG3b1r197ty5Mo9NT09n8uTJ5T7XSy+9xMMP\n/zgEf8KECdx8883u7QULFpRZ38KFC3n99dfLPMfSpUv59a9/7XXfs88+W+5Yy1OfP2mpMh82nUwF\noGHgKZ7bXmJSxtlN/RSRiIhI9WvRogUZGRmAc/Lh4OBgHn30Uff+goICGjTwnk5ER0cTHV3++wXi\n4uJ4880fr5p99tlnFBYWum/ESE1N5bbbbvN5/MSJE8t9Lm+effZZHn/88Uuqo6bQUmU+fJvvvMOi\n0Jgfn+yU4P2xiIhIPTJu3DgmTpxIr169mDp1KmlpacTGxhIZGUmfPn3Yu3cv4LyhZPDgwYAzORw/\nfjz9+/enY8eOzJ8/v1S9DoeDL774gtOnT5Obm8vll1+Ow+Fwr2mbmppKXFwc+/fv55ZbbqFHjx7c\ncMMN7Nmzx32OuXPnAvDJJ58QERGBw+Hgt7/9Ld26dXOf55tvvuGWW26hU6dOTJ06FXCucXv69Gkc\nDgdjxowB4I033qBnz544HA4mTJhAYWEhAH/5y1/o3LkzPXv2ZNOmTVXRxJdMPXg+eFyanZ3rq5iI\niEj1qqorSRX8rjt06BCpqakEBgZy4sQJNmzYQIMGDVi3bh2PP/44K1asKHXMnj17WL9+PSdPnqRL\nly488MADHvsbNGhAZGQkn3zyCadPn6ZXr1506tSJ1NRUQkJCsNbyk5/8hAEDBrBw4UI6derEli1b\nePDBB/nwww896rr33nt57bXXiI2NZdq0aR77MjIy+PTTTwkKCqJLly5MmjSJOXPm8Mc//tHdW7l7\n926WLVvGpk2baNiwIQ8++CBvvvkmAwcOZNasWWzdupWmTZsSHx9fZXfCXgoleD7oLloRERHfRo0a\n5Z6/Ljc3l7Fjx5KZmYkxhvz8UkvJAzBo0CCCgoIICgqiVatWHDlyhKZNPRPWPn36kJqayunTp4mN\njaVTp048++yzhISE0KdPH/Ly8khNTWXUqFHuY4rXjS12/PhxTp486V6bdvTo0fzzn/907x8wYID7\nvKGhoXz11Vf85Cc/8ajjP//5D1u3biUmJgaA06dP06pVK7Zs2UL//v0JCQkB4I477uCLL76ocPtV\nNSV4PinBExGRGqiGXFW64oor3I9nzJhBfHw8q1atIisry+c8f0FBQe7HgYGBFBQUlCoTFxfHwoUL\nOXPmDL/61a8ICQlh165d7gSvqKiIZs2auXvaLkZ54rDWMnbsWJ57znPO23/84x8Xfd7qpDF4Pmip\nMhERkfLJzc2lbdu2gPOu0ksRGxvL5s2bOXbsGK1atcIYQ0hICKtXryYuLo4rr7ySDh068M477wDO\nROyzzz7zqKNZs2Y0adKELVu2AFDe1awaNmzo7n0cMGAAy5cv5+jRowB89913fPXVV/Tq1YuPPvqI\nnJwc8vPz3XHUNErwfFCCJyIiUj5Tp05l+vTpREZGeu0Nq4jmzZsTEhJCWFiY+7nY2FiOHj1K9+7d\nAXjzzTdZvHgx3bt3JywsjNWrV5eqZ/Hixfz3f/83DoeDU6dOlboU7M39999PREQEY8aMITQ0lKef\nfpqEhAQiIiIYOHAg2dnZtGnThtmzZxMbG0tcXBxdu3a9pNdbVap9LVp/iI6Otunp6RU6Zuzf/4tt\np9PocfoMSydmVlFktYeWGvKk9vCk9vCk9vCk9vB0sUuV1dRE4lJV1VJlJdf8nTNnDtnZ2bz00kuV\nfp5L4e33aoyptWvR1gr5Rc7E9/a8U36ORERERCpqzZo1PPfccxQUFHDttdde8qXj2kYJng/HThdB\nAJgLFxUREZEa5o477uCOO+7wdxh+ozF4PhS5Ll1/E6gmEhERkdpF2YtPzgTv5aua+zkOERERkYpR\ngueD7qIVERGR2koJng9K8ERERKS2UoInIiIiPuXk5OBwOHA4HLRu3Zq2bdu6t8+dO3fB45OTk0lN\nTS31vLWWli1b8v333wOQnZ2NMYaNGze6y4SEhJCTk+Oz7j59+lzw/O3bt+fbb78td1wXW19NowTP\nB/XgiYiIQIsWLcjIyCAjI4OJEycyZcoU9/Zll112weN9JVLGGHr37k1aWhoAqampREZGusvu3buX\nFi1a0KJFC591X0yCdqG46goleD4owRMREfFu69at3HjjjfTo0YPExESys7MBmD9/PqGhoURERHDn\nnXeSlZXFwoULefHFF3E4HGzYsMGjnj59+riXE0tNTWXKlCl8/PHH7u24uDgAXnjhBWJiYoiIiGDW\nrFnu44snMi4qKuLBBx/k+uuvZ+DAgdx6660sX77cXW7BggVERUURHh7Onj17vMZ17NgxRowYQUxM\nDDExMWzatAlw9mAmJCQQFhbGfffdR21ZIELz4PnwXeAOf4cgIiLi3ewylt0aPA+i73U+Tv8L/PPh\nMurJrfCprbVMmjSJ1atXExISwrJly3jiiSdYsmQJc+bM4cCBAwQFBXH8+HGaNWvGxIkTCQ4O5tFH\nHy1VV1xcHDNnzgQgLS2NJ5980r3aRGpqKn369CEpKYnMzEzS0tKw1jJ06FBSUlLo16+fu56VK1eS\nlZXFrl27OHr0KF27dmX8+PHu/S1btmTbtm288sorzJ07l0WLFpWKa/To0UyZMoW+ffty8OBBEhMT\n2b17N08++SR9+/Zl5syZrFmzhsWLF1e4zfxBCZ4X5wp/HFPwRO7lfoxERESkZjl79iw7d+5k4MCB\nABQWFtKmTRsA9zquw4YNY9iwYResKyYmhu3bt3Pq1Cny8/MJDg6mY8eO7Nu3j9TUVH7zm9+waNEi\nkpKSiIyMBJxLkGVmZnokeBs3bmTUqFEEBATQunVr4uPjPc4zfPhwAHr06MHKlSu9xrJu3Tp27drl\n3j5x4gR5eXmkpKS4jxk0aBDNm9eO6dOU4F3AD1HL/B2CiIiIp/L2vEXf+2NvXiWx1hIWFua+lFrS\nmjVrSElJ4b333uOZZ55hx46yr4Y1btyYjh07smTJEqKiogDo3bs3a9eu5ejRo3Tp0gVrLdOnT2fC\nhAkXHXNQUBAAgYGBFBQUeC1TVFTE5s2badSo0UWfpybRGLwyNLSWAK1VJiIi4hYUFMSxY8fcCV5+\nfj6ff/45RUVFfP3118THx/P888+Tm5tLXl4eTZo04eTJkz7r69WrF/PmzSM2NhaA2NhYXnrpJXr3\n7o0xhsTERJYsWUJeXh4Ahw8f5ujRox51xMXFsWLFCoqKijhy5AjJyckXfB3nx5WQkMCCBQvc2xkZ\nGQD069ePt956C4D333/ffddvTacErwwFwKbt9/g7DBERkRojICCA5cuX89hjj9G9e3ccDgepqakU\nFhZy9913Ex4eTmRkJJMnT6ZZs2YMGTKEVatWeb3JApw9dl9++aU7wYuKiuLQoUPuKVASEhIYPXo0\nsbGxhIeHM3LkyFIJ44gRI2jXrh2hoaHcfffdREVF0bRpGeMUoVRc8+fPJz09nYiICEJDQ1m4cCEA\ns2bNIiUlhbCwMFauXMlPf/rTymjGKmdqy90glyI6Otqmp6eXu/y5wnP0eKOHe3vHWN1wkZycTP/+\n/f0dRo2h9vCk9vCk9vCk9vB0Me2xe/duunbtWjUB+dnJkydp0qTJJdeTl5dHcHAwOTk59OzZk02b\nNtG6detKiLDqePu9GmO2WmujL7VujcETERGRWm/w4MEcP36cc+fOMWPGjBqf3FU1JXgiIiJS65Vn\n3F19ojF4IiJPEbBHAAAXd0lEQVQiInWMEjwv8ovy/R2CiIiIyEVTgufF2cKz/g5BRERE5KJpDJ4X\nxXcWB1rLtec0EZ6IiIjULurB88LiTPCaFhXx2ID1fo5GRETEf3JycnA4HDgcDlq3bk3btm3d2+fO\nnSvz2PT0dCZPnlyh87Vv357w8HAiIiK48cYb+eqrry4l/EqVlZVFt27dKnTMuHHjWL58eRVF5Jt6\n8ERERMSnFi1auFd1mD17NsHBwTz66KPu/QUFBTRo4D2diI6OJjq64lO6rV+/npYtWzJr1iyefvpp\nXnvttYsLvh5TD54XxZdovwsM9HMkIiIiNc+4ceOYOHEivXr1YurUqaSlpREbG0tkZCR9+vRh7969\ngHPqksGDBwPO5HD8+PH079+fjh07Mn/+/AueJzY2lsOHDwNw7NgxRowYQUxMDDExMWzatAmAjz76\nyN2jGBkZycmTJ8nLy2PAgAFERUURHh7O6tWrAWcP3PXXX8+4cePo3LkzY8aMYd26dcTFxdGpUyfS\n0tLcsf7yl78kNjaWTp06eU0wCwsL+e1vf0tMTAwRERH8+c9/Bpw5xK9//Wu6dOnCzTffXGpZteqi\nHjwvii/RioiI1DThfw2vknorumrToUOHSE1NJTAwkBMnTrBhwwYaNGjAunXrePzxx1mxYkWpY/bs\n2cP69es5efIkXbp04YEHHijzHP/6178YNmwYAA899BBTpkyhb9++HDx4kMTERHbv3s3cuXN5+eWX\niYuLIy8vj0aNGgGwatUqrrzySr799lt69+7N0KFDAdi3bx/vvPMOS5YsISYmhrfeeouNGzfy7rvv\n8uyzz/KPf/wDgO3bt7N582ZOnTpFZGQkgwYN8oht8eLFNG3alE8++YSzZ88SFxdHQkICn376KXv3\n7mXXrl0cOXKE0NBQxo8fX6G2rQxK8LyoD8u3iYiIXIpRo0YR6LrSlZuby9ixY8nMzMQYQ36+9+nG\nBg0aRFBQEEFBQbRq1YojR454XTM2Pj6e7777juDgYP73f/8XgHXr1rFr1y53mRMnTpCXl0dcXByP\nPPIIY8aMYfjw4bRr1478/Hwef/xxUlJSCAgI4PDhwxw5cgSADh06EB7uTJLDwsIYMGAAxhjCw8PJ\nyspy13/bbbdx+eWXc/nllxMfH09aWhoOh8O9Pykpie3bt7vH1+Xm5pKZmUlKSgp33XUXgYGBXHPN\nNdx0002X0MoXTwmeF0W2yN8hiIiIeFVT1ke/4oor3I9nzJhBfHw8q1atIisry+dau0FBQe7HgYGB\nFBQUeC23fv16mjVrxpgxY5g1axZ/+MMfKCoqYvPmze4eumLTpk1j0KBBrF27lri4OD744AM2b97M\nsWPH2Lp1Kw0bNqR9+/acOXOmVAwBAQHu7YCAAI94jPGcReP8bWstCxYsIDEx0eP5tWvXen1N1U1j\n8LzIzj3tftz6ykZllBQREZHc3Fzatm0LwNKlSyulzgYNGjBv3jxef/11vvvuOxISEliwYIF7f/GN\nH/v37yc8PJzHHnuMmJgY9uzZQ25uLq1ataJhw4asX7/+ou7EXb16NWfOnCEnJ4fk5GRiYmI89icm\nJvKnP/3J3Vv5xRdfcOrUKfr168eyZcsoLCwkOzub9ev9MxuHEjwvCot+7MHrGBLsx0hERERqvqlT\npzJ9+nQiIyN99spdjDZt2nDXXXfx8ssvM3/+fNLT04mIiCA0NJSFCxcCMG/ePLp160ZERAQNGzbk\n5z//OWPGjCE9PZ3w8HBef/11rr/++gqfOyIigvj4eHr37s2MGTO45pprPPbfd999hIaGEhUVRbdu\n3ZgwYQIFBQXcfvvtdOrUidDQUO655x5iY2MrpS0qytSH8WbR0dE2PT293OXTvs7kvz4cDtScrnB/\nS05O9tnlXh+pPTypPTypPTypPTxdTHvs3r2brl27Vk1Afnby5EmaNGni7zA8eJsOpip4+70aY7Za\nays+t8x51IPnRXHK27Sg0K9xiIiIiFwM3WThRXGvZnA96N0UERERT7Nnz/Z3CJdMPXhe1IfL1iIi\nIlJ3KcHzonii41PmAgVFREREaiAleF45E7zjPtbWExEREanJlOB5kV/kfQZuERERkdpACZ5XujYr\nIiICkJOTg8PhwOFw0Lp1a9q2bevePnfu3AWPT05OJjU1tdTz1lpatmzJ999/D0B2djbGGDZu3Ogu\nExISQk5Ojs+6+/Tpc8Hzt2/fnm+//bbccV1sfTWNEjwRERHxqUWLFmRkZJCRkcHEiROZMmWKe/uy\nyy674PG+EiljDL179yYtLQ2A1NRUIiMj3WX37t1LixYtaNGihc+6LyZBu1BcdYUSPC8suotWRETE\nl61bt3LjjTfSo0cPEhMTyc7OBmD+/PmEhoYSERHBnXfeSVZWFgsXLuTFF1/E4XCwYcMGj3r69OnD\nli1bAGeyNmXKFD7++GP3dlxcHAAvvPACMTExREREMGvWLPfxwcHO1aaKiop48MEHuf766xk4cCC3\n3nory5cvd5dbsGABUVFRhIeHs2fPHq9xHTt2jBEjRhATE0NMTAybNm0CnD2YCQkJhIWFcd9999Wa\nmTZ0F4GIiEgtE/7XcJ/7ZsbOZFTnUQC888U7PPXxUz7LXsxqTdZaJk2axOrVqwkJCWHZsmU88cQT\nLFmyhDlz5nDgwAGCgoI4fvw4zZo1Y+LEiT5XhYiLi2PmzJkApKWl8eSTT/LSSy8BzgSvT58+JCUl\nkZmZSVpaGtZahg4dSkpKCv369XPXs3LlSrKysti1axdHjx6la9eujB8/3r2/ZcuWbNu2jVdeeYW5\nc+eyaNGiUnGNHj2aKVOm0LdvXw4ePEhiYiK7d+/mySefpG/fvsycOZM1a9awePHiCreZPyjB86K2\nZOciIiLV7ezZs+zcuZOBAwcCUFhYSJs2bQDn+q1jxoxh2LBhDBs27IJ1xcTEsH37dk6dOkV+fj7B\nwcF07NiRffv2kZqaym9+8xsWLVpEUlISkZGRAOTl5ZGZmemR4G3cuJFRo0YREBBA69atiY+P9zjP\n8OHO5Ud79OjBypUrvcaybt06du3a5d4+ceIEeXl5pKSkuI8ZNGgQzZs3L29T+ZUSPC+KE7yflWPw\nqIiISHUrb8/bqM6j3L15lcVaS1hYmPtSaklr1qwhJSWF9957j2eeeYYdO8qOs3HjxnTs2JElS5YQ\nFRUFQO/evVm7di1Hjx6lS5cuWGuZPn06EyZMuOiYg4KCAAgMDKSgoMBrmaKiIjZv3kyjRo0u+jw1\nicbgiYiISLkFBQVx7Ngxd4KXn5/P559/TlFREV9//TXx8fE8//zz5ObmkpeXR5MmTTh58qTP+nr1\n6sW8efOIjY0FIDY2lpdeeonevXtjjCExMZElS5aQl5cHwOHDhzl69KhHHXFxcaxYsYKioiKOHDlC\ncnLyBV/H+XElJCSwYMEC93ZGRgYA/fr146233gLg/fffd9/1W9MpwRMREZFyCwgIYPny5Tz22GN0\n794dh8NBamoqhYWF3H333YSHhxMZGcnkyZNp1qwZQ4YMYdWqVV5vsgBnj92XX37pTvCioqI4dOiQ\newqUhIQERo8eTWxsLOHh4YwcObJUwjhixAjatWtHaGgod999N1FRUTRt2rTM13F+XPPnzyc9PZ2I\niAhCQ0NZuHAhALNmzSIlJYWwsDBWrlzJT3/608poxiqnS7Rl+ForWYiIiLjNnj3b/TglJaXU/pJz\n2BXr3Lkz27dv91nn7bffzj333OPeDgoK4uzZsx5lHnroIR566KFSxxb36gUEBDB37lyCg4PJycmh\nZ8+ehIc7b0TJyspyl4+Ojnb37nmLa9myZaXO0aJFC5KSknzGX1Mpg/GiyDVNytkAdXCKiIjUBoMH\nD+b48eOcO3eOGTNm0Lp1a3+H5FdK8ERERKTWK8+4u/pEXVReaKJjERERqc2U4ImIiNQCmqO1bqnq\n36cSPBERkRquUaNG5OTkKMmrI6y15OTkVOmcexqD54Ut0h+QiIjUHO3atePQoUMcO3bM36FUujNn\nztSZyYUrolGjRrRr167K6vdLgmeMmQLcB1hgB3Av0AZ4G2gBbAV+aa0ttZSEMWY68F9AITDZWvtB\nVcXZtLCwqqoWEREpt4YNG9KhQwd/h1ElkpOT3cuQSeWp9ku0xpi2wGQg2lrbDQgE7gSeB1601v4M\n+B5nEnf+saGusmHALcArxpjAyo6xuP+uTYESPBEREal9/DUGrwFwuTGmAdAYyAZuApa79v8V8LZK\n8W3A29bas9baA8A+oGdVBWmqqmIRERGRKlTtCZ619jAwFziIM7HLxXlJ9ri1tngF4ENAWy+HtwW+\nLrHtq1ylOG2U4omIiEjtU+1j8IwxzXH2xHUAjgPv4LzcWtnnuR+437WZZ4zZW8EqWu6Eb839SvJc\nWgLf+juIGkTt4Unt4Unt4Unt4Unt4Unt4alLZVTij5ssbgYOWGuPARhjVgJxQDNjTANXL1474LCX\nYw8DPymx7asc1tpXgVcvNkhjTLq1Nvpij69r1B6e1B6e1B6e1B6e1B6e1B6e1B6ejDHplVGPP8bg\nHQR6G2MaG2MMMADYBawHRrrKjAVWezn2XeBOY0yQMaYD0AlIq4aYRURERGoNf4zB24LzZoptOKdI\nCcDZ0/YY8IgxZh/OqVIWAxhjhhpjnnId+znwd5wJ4b+AX1lrdauriIiISAl+mQfPWjsLmHXe01/i\n5Y5Ya+27OHvuirefAZ6p0gCdLvrybh2l9vCk9vCk9vCk9vCk9vCk9vCk9vBUKe1htOyJiIiISN2i\ntWhFRERE6ph6n+AZY24xxuw1xuwzxkzzsj/IGLPMtX+LMaZ99UdZPYwxPzHGrDfG7DLGfG6MechL\nmf7GmFxjTIbrZ6Y/Yq0uxpgsY8wO12stdWeTcZrven9sN8ZE+SPO6mCM6VLi955hjDlhjHn4vDJ1\n+v1hjFlijDlqjNlZ4rmrjDH/NsZkuv5t7uPYsa4ymcaYsdUXddXx0R4vGGP2uP4eVhljmvk4tsy/\nrdrIR3vMNsYcLvE3cauPY8v8LqqNfLTHshJtkWWMyfBxbJ16f/j6fq3Szw9rbb39wblM2n6gI3AZ\n8BkQel6ZB4GFrsd3Asv8HXcVtkcbIMr1uAnwhZf26A/809+xVmObZAEty9h/K/A+zoVPegNb/B1z\nNbVLIPB/wLX16f0B9AOigJ0lnvsdMM31eBrwvJfjrsI5zvgqoLnrcXN/v54qao8EoIHr8fPe2sO1\nr8y/rdr446M9ZgOPXuC4C34X1cYfb+1x3v7fAzPrw/vD1/drVX5+1PcevJ7APmvtl9bac8DbOCdh\nLuk2nEungfPu3wGu6V3qHGtttrV2m+vxSWA3VbhSSB1xG/C6ddqMcz7HNv4OqhoMAPZba7/ydyDV\nyVqbAnx33tMlPyN8LbOYCPzbWvudtfZ74N9UwQTv1c1be1hrk+yPqxJtxjlfab3g4/1RHuX5Lqp1\nymoP1/foL4C/VWtQflLG92uVfX7U9wSvPEufucu4PrRycU7jUqe5LkVHAlu87I41xnxmjHnfGBNW\nrYFVPwskGWO2GufqKOer1uXzapA78f3BXJ/eHwBXW2uzXY//D7jaS5n6+j4Zj7OH25sL/W3VJb92\nXbJe4uMSXH18f9wAHLHWZvrYX2ffH+d9v1bZ50d9T/DEC2NMMLACeNhae+K83dtwXpbrDiwA/lHd\n8VWzvtbaKODnwK+MMf38HZC/GWMuA4biXGbwfPXt/eHBOq+naGoCwBjzBFAAvOmjSH352/oTcB3g\nwLn++u/9G06NcRdl997VyfdHWd+vlf35Ud8TvPIsfeYuY4xpADQFcqolOj8wxjTE+eZ701q78vz9\n1toT1to81+O1QENjTMtqDrPaWGsPu/49Cqyi9FyN5V4+rw75ObDNWnvk/B317f3hcqT4srzr36Ne\nytSr94kxZhwwGBjj+tIqpRx/W3WCtfaItbbQWlsEvIb311nf3h8NgOHAMl9l6uL7w8f3a5V9ftT3\nBO8ToJMxpoOrV+JOSkyq7PIuzqXTwLmU2oe+PrBqO9eYiMXAbmvtH3yUaV08BtEY0xPne6hOJrzG\nmCuMMU2KH+McPL7zvGLvAvcYp95Abonu9rrK5/+869P7o4SSnxG+lln8AEgwxjR3XaJLcD1X5xhj\nbgGmAkOttT/4KFOev6064bwxubfj/XWW57uoLrkZ2GOtPeRtZ118f5Tx/Vp1nx/+vrPE3z8474L8\nAucdTE+4nnsK54cTQCOcl6L24Vz3tqO/Y67CtuiLs3t4O5Dh+rkVmAhMdJX5NfA5zru8NgN9/B13\nFbZHR9fr/Mz1movfHyXbwwAvu94/O4Bof8ddxW1yBc6ErWmJ5+rN+wNnYpsN5OMcB/NfOMfk/gfI\nBNYBV7nKRgOLShw73vU5sg+419+vpQrbYx/O8ULFnyHFsxBcA6x1Pfb6t1Xbf3y0x/9zfTZsx/ll\n3ub89nBtl/ouqu0/3trD9fzS4s+MEmXr9PujjO/XKvv80EoWIiIiInVMfb9EKyIiIlLnKMETERER\nqWOU4ImIiIjUMUrwREREROoYJXgiIiIidYwSPBEREZE6RgmeiPidMabQGJNR4qd9GWXzKuF8S40x\nB1zn2maMib2IOhYZY0Jdjx8/b1/qpcZYxnmzjDE7jDHRru03XeucPluizP8YY4aV2L7DGLPPGPPP\nqopLRGoWJXgiUhOcttY6SvxkVcM5f2utdQDTgD9X9GBr7X3W2l2uzcfP29enEuIrS7y1Nt0YE4Gz\n7SKAGGNMU9fKCb2ste51gK21y4D7qjgmEalBlOCJSI1jjAk2xvzH1bu2wxhzm5cybYwxKa5euJ3G\nmBtczycYYz52HfuOa3HvsqQAP3Md+4irrp3GmIddz11hjFljjPnM9fwdrueTjTHRxpg5wOWuON50\n7ctz/fu2MWZQiZiXGmNGGmMCjTEvGGM+cfW+TSjrNZUh33XuAKAhUIhzJZ5ZFzhOROq4Bv4OQEQE\nV4LkenwAGAXcbq09YYxpCWw2xrxrPZfeGQ18YK19xhgTCDR2lf0f4GZr7SljzGPAIziTHl+GADuM\nMT2Ae4FeOJeg22KM+QjnsknfWGsHARhjmpY82Fo7zRjza1dv4PmWAb8A1rjWGB0APIBzCatca22M\nMSYI2GSMScK5ALvHayqr0ay1u40xx4BtOJfE+hkQYK3dVtZxIlL3KcETkZrgdMkEyRjTEHjWGNMP\nKALaAlcD/1fimE+AJa6y/7DWZhhjbgRCcSZMAJcBH/s45wvGmP8BjuFMuAYAq6y1p1wxrARuAP4F\n/N4Y8zzwT2vthgq8rveBl1xJ3C1AirX2tDEmAYgwxox0lWsKdPL2mi50Amvtw8WPjTHvAROMMU8A\n3YF/W2tfq0C8IlJH6BKtiNREY4AQoIcr8TsCNCpZwFqbAvQDDgNLjTH34Ox5+3eJsXyh1tr/8nGO\n37rKDLTW7vQViLX2CyAK54LxTxtjZpb3RVhrzwDJQCJwB84ePVxxTioRZwdrbZKP11QursvYW4Fg\n4Dpr7S+AkcaYMnsBRaRuUoInIjVRU+CotTbfGBMPXHt+AWPMtcARVw/VIpxJ2GYgzhhTPKbuCmNM\n53KecwMwzBjT2BhzBXA7sMEYcw3wg7X2DeAF13nOl+/qdfNmGc5Lv8W9gQAfAA8UH2OM6eyK1dtr\nuiBXPQ8DvwMuB4ovZQfi7MUUkXpGl2hFpCZ6E3jPGLMDSAf2eCnTH/itMSYfyAPusdYeM8aMA/7m\nuiwKzjF5X1zohNbabcaYpUCa66lF1tpPjTGJOC/nFuG8qeEBL4e/Cmw3xmyz1o45b18SzvFxq621\n54rrBtoD24zzWvIxYJi313ShuF1+BfzVWvuDMWY7zvGIO4C11trj5axDROoQ4zlmWUREajJjTBYQ\nba39toLH9QcetdYOroq4RKRm0SVaEZHa5Rjwn+KJjsvDNbXLK8D3VRaViNQo6sETERERqWPUgyci\nIiJSxyjBExEREaljlOCJiIiI1DFK8ERERETqGCV4IiIiInXM/wc9YNNErswZAQAAAABJRU5ErkJg\ngg==\n","text/plain":["<Figure size 864x720 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"LUrXUnMk4HkF","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}