Nueral_Network_with_PSO-TF2.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Nueral_Network_with_PSO_and_Keras_on_KDD.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"QsFmsqafu6co","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608012485432,"user_tz":-330,"elapsed":6442,"user":{"displayName":"shiv ganesh","photoUrl":"","userId":"04377884227294419716"}},"outputId":"dbc25f08-3990-4dca-8b1d-9fbb675e2645"},"source":["!pip install --upgrade pyswarm\n","# !pip install shap==0.37.0"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting pyswarm\n","  Downloading https://files.pythonhosted.org/packages/79/1e/254c108b5e65c65d57a83a9a448405ea8b6a6c5c10dada8bcab4e9d9a831/pyswarm-0.6.tar.gz\n","Requirement already satisfied, skipping upgrade: numpy in /usr/local/lib/python3.6/dist-packages (from pyswarm) (1.18.5)\n","Building wheels for collected packages: pyswarm\n","  Building wheel for pyswarm (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for pyswarm: filename=pyswarm-0.6-cp36-none-any.whl size=4481 sha256=93611800b7cfc7a2e3db808bf8b51d2ab239a76ffa5056fbb8a3104490586906\n","  Stored in directory: /root/.cache/pip/wheels/37/c5/f6/b33b9ac00040cb95c1f00af982a4197334a672d6de43f4699f\n","Successfully built pyswarm\n","Installing collected packages: pyswarm\n","Successfully installed pyswarm-0.6\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"xzET-UbHHij9"},"source":["### Import Libraries"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SD-hXPczA1gF","executionInfo":{"status":"ok","timestamp":1608012539341,"user_tz":-330,"elapsed":48825,"user":{"displayName":"shiv ganesh","photoUrl":"","userId":"04377884227294419716"}},"outputId":"f23b00a7-15b1-46c4-c888-69bc1229adf4"},"source":["# from google.colab import drive\n","# drive.mount('/content/drive')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"IZIdcD_BbiIi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608012582905,"user_tz":-330,"elapsed":2356,"user":{"displayName":"shiv ganesh","photoUrl":"","userId":"04377884227294419716"}},"outputId":"bf4c370c-69fa-4d3b-e12a-f69c5c4c167f"},"source":["# !pip install pyswarms\n","\n","import pandas as pd # Data Management Framework\n","import numpy as np # Mathematical Computation\n","from matplotlib import pyplot as plt # Graphs\n","from sklearn.metrics import confusion_matrix, classification_report # Evalution Perameter\n","import seaborn as sns # Graph\n","%matplotlib inline\n","from sklearn.preprocessing import * # Data Preprocessing\n","from sklearn.model_selection import train_test_split # Training and testing Split \n","import gc # Memory Management\n","import warnings # warnings ignore\n","warnings.filterwarnings('ignore')\n","\n","# Natural Inspired Algorithms\n","from pyswarm import pso\n","# import shap\n","\n","# Deep learning Framework\n","from tensorflow.keras.models import Sequential\n","from tensorflow.keras.layers import Dense\n","from tensorflow.keras.layers import Dropout\n","from tensorflow.keras.layers import LSTM\n","from tensorflow.keras.layers import Conv1D\n","from tensorflow.keras.layers import Flatten\n","from tensorflow.keras.layers import Activation\n","from tensorflow.keras.callbacks import EarlyStopping\n","from tensorflow.keras import layers\n","from tensorflow import keras\n","import tensorflow\n","from tensorflow.keras import datasets, layers, models\n","from keras.utils import np_utils\n","from keras.utils import to_categorical\n","from google.colab import drive \n","drive.mount('/content/drive')\n","\n","tensorflow.random.set_seed(42)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"vCTqKYO4ItYV"},"source":["import os\n","os.chdir('/content/drive/MyDrive/pso')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"hJkOR7GBEZWW"},"source":["# import os\n","# os.getcwd() \n","# os.chdir('/content/drive/My Drive/Colab Notebooks/PSOKeras/')\n","# !pwd"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"OCN19ZUiIGru","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608012590223,"user_tz":-330,"elapsed":1215,"user":{"displayName":"shiv ganesh","photoUrl":"","userId":"04377884227294419716"}},"outputId":"d7bff8bb-ff6d-42c2-827a-3dbcf6344eee"},"source":["!ls"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" attack_type.csv    KDDTrain.csv\n"," CICIDS2017.rar     model.png\n","'Field Names.csv'   Nueral_Network_with_PSO_and_Keras_on_KDD.ipynb\n"," KDDTest.csv\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"AbbUghRuHe4k"},"source":["### Data Reading"]},{"cell_type":"code","metadata":{"id":"G4avFesRVM_i"},"source":["### Read the dataset\n","cols = ['duration', 'protocol_type', 'service', 'flag', 'src_bytes','dst_bytes', 'land', 'wrong_fragment', 'urgent', 'hot',\n","'num_failed_logins', 'logged_in', 'num_compromised', 'root_shell', 'su_attempted', 'num_root', 'num_file_creations', 'num_shells',\n","'num_access_files', 'num_outbound_cmds', 'is_host_login', 'is_guest_login', 'count', 'srv_count', 'serror_rate',\n","'srv_serror_rate', 'rerror_rate', 'srv_rerror_rate', 'same_srv_rate', 'diff_srv_rate', 'srv_diff_host_rate', 'dst_host_count',\n","'dst_host_srv_count', 'dst_host_same_srv_rate', 'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate', 'dst_host_srv_diff_host_rate', 'dst_host_serror_rate',\n","'dst_host_srv_serror_rate', 'dst_host_rerror_rate', 'dst_host_srv_rerror_rate', 'attack_type', 'difficulty_level']\n","\n","train = pd.read_csv(\"KDDTrain.csv\", names = cols)\n","test = pd.read_csv(\"KDDTest.csv\", names = cols)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"MQFz04BRlV9K","colab":{"base_uri":"https://localhost:8080/","height":235},"executionInfo":{"status":"ok","timestamp":1608012600004,"user_tz":-330,"elapsed":1091,"user":{"displayName":"shiv ganesh","photoUrl":"","userId":"04377884227294419716"}},"outputId":"4a102fec-7153-4051-8841-a858ec5a3c34"},"source":["# Train dataset display\n","print(f\"Training dataset: {train.shape}\")\n","train.head()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training dataset: (125973, 43)\n"],"name":"stdout"},{"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>duration</th>\n","      <th>protocol_type</th>\n","      <th>service</th>\n","      <th>flag</th>\n","      <th>src_bytes</th>\n","      <th>dst_bytes</th>\n","      <th>land</th>\n","      <th>wrong_fragment</th>\n","      <th>urgent</th>\n","      <th>hot</th>\n","      <th>num_failed_logins</th>\n","      <th>logged_in</th>\n","      <th>num_compromised</th>\n","      <th>root_shell</th>\n","      <th>su_attempted</th>\n","      <th>num_root</th>\n","      <th>num_file_creations</th>\n","      <th>num_shells</th>\n","      <th>num_access_files</th>\n","      <th>num_outbound_cmds</th>\n","      <th>is_host_login</th>\n","      <th>is_guest_login</th>\n","      <th>count</th>\n","      <th>srv_count</th>\n","      <th>serror_rate</th>\n","      <th>srv_serror_rate</th>\n","      <th>rerror_rate</th>\n","      <th>srv_rerror_rate</th>\n","      <th>same_srv_rate</th>\n","      <th>diff_srv_rate</th>\n","      <th>srv_diff_host_rate</th>\n","      <th>dst_host_count</th>\n","      <th>dst_host_srv_count</th>\n","      <th>dst_host_same_srv_rate</th>\n","      <th>dst_host_diff_srv_rate</th>\n","      <th>dst_host_same_src_port_rate</th>\n","      <th>dst_host_srv_diff_host_rate</th>\n","      <th>dst_host_serror_rate</th>\n","      <th>dst_host_srv_serror_rate</th>\n","      <th>dst_host_rerror_rate</th>\n","      <th>dst_host_srv_rerror_rate</th>\n","      <th>attack_type</th>\n","      <th>difficulty_level</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>tcp</td>\n","      <td>ftp_data</td>\n","      <td>SF</td>\n","      <td>491</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>150</td>\n","      <td>25</td>\n","      <td>0.17</td>\n","      <td>0.03</td>\n","      <td>0.17</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.05</td>\n","      <td>0.00</td>\n","      <td>normal</td>\n","      <td>20</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>udp</td>\n","      <td>other</td>\n","      <td>SF</td>\n","      <td>146</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>13</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.08</td>\n","      <td>0.15</td>\n","      <td>0.00</td>\n","      <td>255</td>\n","      <td>1</td>\n","      <td>0.00</td>\n","      <td>0.60</td>\n","      <td>0.88</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>normal</td>\n","      <td>15</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>tcp</td>\n","      <td>private</td>\n","      <td>S0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>123</td>\n","      <td>6</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.05</td>\n","      <td>0.07</td>\n","      <td>0.00</td>\n","      <td>255</td>\n","      <td>26</td>\n","      <td>0.10</td>\n","      <td>0.05</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>1.00</td>\n","      <td>1.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>neptune</td>\n","      <td>19</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>tcp</td>\n","      <td>http</td>\n","      <td>SF</td>\n","      <td>232</td>\n","      <td>8153</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>0.2</td>\n","      <td>0.2</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>30</td>\n","      <td>255</td>\n","      <td>1.00</td>\n","      <td>0.00</td>\n","      <td>0.03</td>\n","      <td>0.04</td>\n","      <td>0.03</td>\n","      <td>0.01</td>\n","      <td>0.00</td>\n","      <td>0.01</td>\n","      <td>normal</td>\n","      <td>21</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>tcp</td>\n","      <td>http</td>\n","      <td>SF</td>\n","      <td>199</td>\n","      <td>420</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>30</td>\n","      <td>32</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.00</td>\n","      <td>0.00</td>\n","      <td>0.09</td>\n","      <td>255</td>\n","      <td>255</td>\n","      <td>1.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>normal</td>\n","      <td>21</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   duration protocol_type  ... attack_type difficulty_level\n","0         0           tcp  ...      normal               20\n","1         0           udp  ...      normal               15\n","2         0           tcp  ...     neptune               19\n","3         0           tcp  ...      normal               21\n","4         0           tcp  ...      normal               21\n","\n","[5 rows x 43 columns]"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"id":"sLNMXpdxlhFH","colab":{"base_uri":"https://localhost:8080/","height":235},"executionInfo":{"status":"ok","timestamp":1608012604961,"user_tz":-330,"elapsed":1541,"user":{"displayName":"shiv ganesh","photoUrl":"","userId":"04377884227294419716"}},"outputId":"11a371a0-d1a7-4190-fb5f-2dabd50fbd6d"},"source":["# Test dataset display\n","print(f\"Testing dataset: {test.shape}\")\n","test.head()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Testing dataset: (22543, 43)\n"],"name":"stdout"},{"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>duration</th>\n","      <th>protocol_type</th>\n","      <th>service</th>\n","      <th>flag</th>\n","      <th>src_bytes</th>\n","      <th>dst_bytes</th>\n","      <th>land</th>\n","      <th>wrong_fragment</th>\n","      <th>urgent</th>\n","      <th>hot</th>\n","      <th>num_failed_logins</th>\n","      <th>logged_in</th>\n","      <th>num_compromised</th>\n","      <th>root_shell</th>\n","      <th>su_attempted</th>\n","      <th>num_root</th>\n","      <th>num_file_creations</th>\n","      <th>num_shells</th>\n","      <th>num_access_files</th>\n","      <th>num_outbound_cmds</th>\n","      <th>is_host_login</th>\n","      <th>is_guest_login</th>\n","      <th>count</th>\n","      <th>srv_count</th>\n","      <th>serror_rate</th>\n","      <th>srv_serror_rate</th>\n","      <th>rerror_rate</th>\n","      <th>srv_rerror_rate</th>\n","      <th>same_srv_rate</th>\n","      <th>diff_srv_rate</th>\n","      <th>srv_diff_host_rate</th>\n","      <th>dst_host_count</th>\n","      <th>dst_host_srv_count</th>\n","      <th>dst_host_same_srv_rate</th>\n","      <th>dst_host_diff_srv_rate</th>\n","      <th>dst_host_same_src_port_rate</th>\n","      <th>dst_host_srv_diff_host_rate</th>\n","      <th>dst_host_serror_rate</th>\n","      <th>dst_host_srv_serror_rate</th>\n","      <th>dst_host_rerror_rate</th>\n","      <th>dst_host_srv_rerror_rate</th>\n","      <th>attack_type</th>\n","      <th>difficulty_level</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>tcp</td>\n","      <td>private</td>\n","      <td>REJ</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>229</td>\n","      <td>10</td>\n","      <td>0.0</td>\n","      <td>0.00</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.04</td>\n","      <td>0.06</td>\n","      <td>0.00</td>\n","      <td>255</td>\n","      <td>10</td>\n","      <td>0.04</td>\n","      <td>0.06</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.00</td>\n","      <td>1.00</td>\n","      <td>neptune</td>\n","      <td>21</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>tcp</td>\n","      <td>private</td>\n","      <td>REJ</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>136</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.00</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.01</td>\n","      <td>0.06</td>\n","      <td>0.00</td>\n","      <td>255</td>\n","      <td>1</td>\n","      <td>0.00</td>\n","      <td>0.06</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.00</td>\n","      <td>1.00</td>\n","      <td>neptune</td>\n","      <td>21</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>tcp</td>\n","      <td>ftp_data</td>\n","      <td>SF</td>\n","      <td>12983</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.00</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>134</td>\n","      <td>86</td>\n","      <td>0.61</td>\n","      <td>0.04</td>\n","      <td>0.61</td>\n","      <td>0.02</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>normal</td>\n","      <td>21</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>icmp</td>\n","      <td>eco_i</td>\n","      <td>SF</td>\n","      <td>20</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>65</td>\n","      <td>0.0</td>\n","      <td>0.00</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.00</td>\n","      <td>0.00</td>\n","      <td>1.00</td>\n","      <td>3</td>\n","      <td>57</td>\n","      <td>1.00</td>\n","      <td>0.00</td>\n","      <td>1.00</td>\n","      <td>0.28</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>saint</td>\n","      <td>15</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>tcp</td>\n","      <td>telnet</td>\n","      <td>RSTO</td>\n","      <td>0</td>\n","      <td>15</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>8</td>\n","      <td>0.0</td>\n","      <td>0.12</td>\n","      <td>1.0</td>\n","      <td>0.5</td>\n","      <td>1.00</td>\n","      <td>0.00</td>\n","      <td>0.75</td>\n","      <td>29</td>\n","      <td>86</td>\n","      <td>0.31</td>\n","      <td>0.17</td>\n","      <td>0.03</td>\n","      <td>0.02</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.83</td>\n","      <td>0.71</td>\n","      <td>mscan</td>\n","      <td>11</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   duration protocol_type  ... attack_type difficulty_level\n","0         0           tcp  ...     neptune               21\n","1         0           tcp  ...     neptune               21\n","2         2           tcp  ...      normal               21\n","3         0          icmp  ...       saint               15\n","4         1           tcp  ...       mscan               11\n","\n","[5 rows x 43 columns]"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"id":"6W_BBPNoKI6h","colab":{"base_uri":"https://localhost:8080/","height":308},"executionInfo":{"status":"ok","timestamp":1608012609765,"user_tz":-330,"elapsed":1426,"user":{"displayName":"shiv ganesh","photoUrl":"","userId":"04377884227294419716"}},"outputId":"35bff9a7-2c19-4709-bd0d-0ace23a19436"},"source":["train.describe()"],"execution_count":null,"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>duration</th>\n","      <th>src_bytes</th>\n","      <th>dst_bytes</th>\n","      <th>land</th>\n","      <th>wrong_fragment</th>\n","      <th>urgent</th>\n","      <th>hot</th>\n","      <th>num_failed_logins</th>\n","      <th>logged_in</th>\n","      <th>num_compromised</th>\n","      <th>root_shell</th>\n","      <th>su_attempted</th>\n","      <th>num_root</th>\n","      <th>num_file_creations</th>\n","      <th>num_shells</th>\n","      <th>num_access_files</th>\n","      <th>num_outbound_cmds</th>\n","      <th>is_host_login</th>\n","      <th>is_guest_login</th>\n","      <th>count</th>\n","      <th>srv_count</th>\n","      <th>serror_rate</th>\n","      <th>srv_serror_rate</th>\n","      <th>rerror_rate</th>\n","      <th>srv_rerror_rate</th>\n","      <th>same_srv_rate</th>\n","      <th>diff_srv_rate</th>\n","      <th>srv_diff_host_rate</th>\n","      <th>dst_host_count</th>\n","      <th>dst_host_srv_count</th>\n","      <th>dst_host_same_srv_rate</th>\n","      <th>dst_host_diff_srv_rate</th>\n","      <th>dst_host_same_src_port_rate</th>\n","      <th>dst_host_srv_diff_host_rate</th>\n","      <th>dst_host_serror_rate</th>\n","      <th>dst_host_srv_serror_rate</th>\n","      <th>dst_host_rerror_rate</th>\n","      <th>dst_host_srv_rerror_rate</th>\n","      <th>difficulty_level</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>125973.00000</td>\n","      <td>1.259730e+05</td>\n","      <td>1.259730e+05</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.0</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","      <td>125973.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>287.14465</td>\n","      <td>4.556674e+04</td>\n","      <td>1.977911e+04</td>\n","      <td>0.000198</td>\n","      <td>0.022687</td>\n","      <td>0.000111</td>\n","      <td>0.204409</td>\n","      <td>0.001222</td>\n","      <td>0.395736</td>\n","      <td>0.279250</td>\n","      <td>0.001342</td>\n","      <td>0.001103</td>\n","      <td>0.302192</td>\n","      <td>0.012669</td>\n","      <td>0.000413</td>\n","      <td>0.004096</td>\n","      <td>0.0</td>\n","      <td>0.000008</td>\n","      <td>0.009423</td>\n","      <td>84.107555</td>\n","      <td>27.737888</td>\n","      <td>0.284485</td>\n","      <td>0.282485</td>\n","      <td>0.119958</td>\n","      <td>0.121183</td>\n","      <td>0.660928</td>\n","      <td>0.063053</td>\n","      <td>0.097322</td>\n","      <td>182.148945</td>\n","      <td>115.653005</td>\n","      <td>0.521242</td>\n","      <td>0.082951</td>\n","      <td>0.148379</td>\n","      <td>0.032542</td>\n","      <td>0.284452</td>\n","      <td>0.278485</td>\n","      <td>0.118832</td>\n","      <td>0.120240</td>\n","      <td>19.504060</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>2604.51531</td>\n","      <td>5.870331e+06</td>\n","      <td>4.021269e+06</td>\n","      <td>0.014086</td>\n","      <td>0.253530</td>\n","      <td>0.014366</td>\n","      <td>2.149968</td>\n","      <td>0.045239</td>\n","      <td>0.489010</td>\n","      <td>23.942042</td>\n","      <td>0.036603</td>\n","      <td>0.045154</td>\n","      <td>24.399618</td>\n","      <td>0.483935</td>\n","      <td>0.022181</td>\n","      <td>0.099370</td>\n","      <td>0.0</td>\n","      <td>0.002817</td>\n","      <td>0.096612</td>\n","      <td>114.508607</td>\n","      <td>72.635840</td>\n","      <td>0.446456</td>\n","      <td>0.447022</td>\n","      <td>0.320436</td>\n","      <td>0.323647</td>\n","      <td>0.439623</td>\n","      <td>0.180314</td>\n","      <td>0.259830</td>\n","      <td>99.206213</td>\n","      <td>110.702741</td>\n","      <td>0.448949</td>\n","      <td>0.188922</td>\n","      <td>0.308997</td>\n","      <td>0.112564</td>\n","      <td>0.444784</td>\n","      <td>0.445669</td>\n","      <td>0.306557</td>\n","      <td>0.319459</td>\n","      <td>2.291503</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.00000</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>0.00000</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>2.000000</td>\n","      <td>2.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.090000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>82.000000</td>\n","      <td>10.000000</td>\n","      <td>0.050000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>18.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>0.00000</td>\n","      <td>4.400000e+01</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>14.000000</td>\n","      <td>8.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>255.000000</td>\n","      <td>63.000000</td>\n","      <td>0.510000</td>\n","      <td>0.020000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>20.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>0.00000</td>\n","      <td>2.760000e+02</td>\n","      <td>5.160000e+02</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>143.000000</td>\n","      <td>18.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>0.060000</td>\n","      <td>0.000000</td>\n","      <td>255.000000</td>\n","      <td>255.000000</td>\n","      <td>1.000000</td>\n","      <td>0.070000</td>\n","      <td>0.060000</td>\n","      <td>0.020000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>21.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>42908.00000</td>\n","      <td>1.379964e+09</td>\n","      <td>1.309937e+09</td>\n","      <td>1.000000</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>77.000000</td>\n","      <td>5.000000</td>\n","      <td>1.000000</td>\n","      <td>7479.000000</td>\n","      <td>1.000000</td>\n","      <td>2.000000</td>\n","      <td>7468.000000</td>\n","      <td>43.000000</td>\n","      <td>2.000000</td>\n","      <td>9.000000</td>\n","      <td>0.0</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>511.000000</td>\n","      <td>511.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>255.000000</td>\n","      <td>255.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>21.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["           duration     src_bytes  ...  dst_host_srv_rerror_rate  difficulty_level\n","count  125973.00000  1.259730e+05  ...             125973.000000     125973.000000\n","mean      287.14465  4.556674e+04  ...                  0.120240         19.504060\n","std      2604.51531  5.870331e+06  ...                  0.319459          2.291503\n","min         0.00000  0.000000e+00  ...                  0.000000          0.000000\n","25%         0.00000  0.000000e+00  ...                  0.000000         18.000000\n","50%         0.00000  4.400000e+01  ...                  0.000000         20.000000\n","75%         0.00000  2.760000e+02  ...                  0.000000         21.000000\n","max     42908.00000  1.379964e+09  ...                  1.000000         21.000000\n","\n","[8 rows x 39 columns]"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"code","metadata":{"id":"rWvZPuXilk8i","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608013530492,"user_tz":-330,"elapsed":1191,"user":{"displayName":"shiv ganesh","photoUrl":"","userId":"04377884227294419716"}},"outputId":"9660b414-2543-4370-8e54-79c66b7042bb"},"source":["%%time\n","# Drop the diffficulty level column\n","train.drop('difficulty_level', axis=1, inplace=True)\n","test.drop('difficulty_level', axis=1, inplace=True)\n","gc.collect()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["CPU times: user 134 ms, sys: 10.8 ms, total: 145 ms\n","Wall time: 148 ms\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"DNU462pTP8ae"},"source":["le = LabelEncoder()\n","\n","# attack type\n","train[\"attack_type\"] = le.fit_transform(train[\"attack_type\"])\n","test[\"attack_type\"] = le.fit_transform(test[\"attack_type\"])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"jfwaBC1nmqGC"},"source":["# protocol type\n","train[\"protocol_type\"] = le.fit_transform(train[\"protocol_type\"])\n","test[\"protocol_type\"] = le.fit_transform(test[\"protocol_type\"])\n","\n","# service\n","train[\"service\"] = le.fit_transform(train[\"service\"])\n","test[\"service\"] = le.fit_transform(test[\"service\"])\n","\n","# flag\n","train[\"flag\"] = le.fit_transform(train[\"flag\"])\n","test[\"flag\"] = le.fit_transform(test[\"flag\"])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"YJL0c2GUJHVv","colab":{"base_uri":"https://localhost:8080/","height":218},"executionInfo":{"status":"ok","timestamp":1608013544001,"user_tz":-330,"elapsed":4092,"user":{"displayName":"shiv ganesh","photoUrl":"","userId":"04377884227294419716"}},"outputId":"9e6531e8-943f-407f-9873-805388a36924"},"source":["train.head()"],"execution_count":null,"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>duration</th>\n","      <th>protocol_type</th>\n","      <th>service</th>\n","      <th>flag</th>\n","      <th>src_bytes</th>\n","      <th>dst_bytes</th>\n","      <th>land</th>\n","      <th>wrong_fragment</th>\n","      <th>urgent</th>\n","      <th>hot</th>\n","      <th>num_failed_logins</th>\n","      <th>logged_in</th>\n","      <th>num_compromised</th>\n","      <th>root_shell</th>\n","      <th>su_attempted</th>\n","      <th>num_root</th>\n","      <th>num_file_creations</th>\n","      <th>num_shells</th>\n","      <th>num_access_files</th>\n","      <th>num_outbound_cmds</th>\n","      <th>is_host_login</th>\n","      <th>is_guest_login</th>\n","      <th>count</th>\n","      <th>srv_count</th>\n","      <th>serror_rate</th>\n","      <th>srv_serror_rate</th>\n","      <th>rerror_rate</th>\n","      <th>srv_rerror_rate</th>\n","      <th>same_srv_rate</th>\n","      <th>diff_srv_rate</th>\n","      <th>srv_diff_host_rate</th>\n","      <th>dst_host_count</th>\n","      <th>dst_host_srv_count</th>\n","      <th>dst_host_same_srv_rate</th>\n","      <th>dst_host_diff_srv_rate</th>\n","      <th>dst_host_same_src_port_rate</th>\n","      <th>dst_host_srv_diff_host_rate</th>\n","      <th>dst_host_serror_rate</th>\n","      <th>dst_host_srv_serror_rate</th>\n","      <th>dst_host_rerror_rate</th>\n","      <th>dst_host_srv_rerror_rate</th>\n","      <th>attack_type</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>20</td>\n","      <td>9</td>\n","      <td>491</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>150</td>\n","      <td>25</td>\n","      <td>0.17</td>\n","      <td>0.03</td>\n","      <td>0.17</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.05</td>\n","      <td>0.00</td>\n","      <td>11</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>44</td>\n","      <td>9</td>\n","      <td>146</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>13</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.08</td>\n","      <td>0.15</td>\n","      <td>0.00</td>\n","      <td>255</td>\n","      <td>1</td>\n","      <td>0.00</td>\n","      <td>0.60</td>\n","      <td>0.88</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>11</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>49</td>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>123</td>\n","      <td>6</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.05</td>\n","      <td>0.07</td>\n","      <td>0.00</td>\n","      <td>255</td>\n","      <td>26</td>\n","      <td>0.10</td>\n","      <td>0.05</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>1.00</td>\n","      <td>1.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>24</td>\n","      <td>9</td>\n","      <td>232</td>\n","      <td>8153</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>0.2</td>\n","      <td>0.2</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>30</td>\n","      <td>255</td>\n","      <td>1.00</td>\n","      <td>0.00</td>\n","      <td>0.03</td>\n","      <td>0.04</td>\n","      <td>0.03</td>\n","      <td>0.01</td>\n","      <td>0.00</td>\n","      <td>0.01</td>\n","      <td>11</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>24</td>\n","      <td>9</td>\n","      <td>199</td>\n","      <td>420</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>30</td>\n","      <td>32</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.00</td>\n","      <td>0.00</td>\n","      <td>0.09</td>\n","      <td>255</td>\n","      <td>255</td>\n","      <td>1.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>11</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   duration  protocol_type  ...  dst_host_srv_rerror_rate  attack_type\n","0         0              1  ...                      0.00           11\n","1         0              2  ...                      0.00           11\n","2         0              1  ...                      0.00            9\n","3         0              1  ...                      0.01           11\n","4         0              1  ...                      0.00           11\n","\n","[5 rows x 42 columns]"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"code","metadata":{"id":"z9Mxt7wqpSqy"},"source":["X = train.iloc[:, train.columns != 'attack_type'] # Features\n","y = train['attack_type'] # Target Variable"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZswZNqshL3-j","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608013561524,"user_tz":-330,"elapsed":1026,"user":{"displayName":"shiv ganesh","photoUrl":"","userId":"04377884227294419716"}},"outputId":"50cf752f-9a74-4cf3-bc7a-8d9126127e12"},"source":["print(sorted(train[\"attack_type\"].unique()))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"BhbQi2jfpo3Y"},"source":["# Train test split\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"jn7NB9HXrIX7","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608013576584,"user_tz":-330,"elapsed":1592,"user":{"displayName":"shiv ganesh","photoUrl":"","userId":"04377884227294419716"}},"outputId":"2f2f120a-88a8-4c0f-af87-abd9ae4aea75"},"source":["print(X_train.shape)\n","print(X_test.shape)\n","print(y_train.shape)\n","print(y_test.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["(100778, 41)\n","(25195, 41)\n","(100778,)\n","(25195,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"jFHNAuvBrmfp"},"source":["# scale the data to improve the performance   \n","scaler = StandardScaler()\n","\n","# then we call the transform method to scale both the training and testing data\n","X_train = scaler.fit_transform(X_train)\n","X_test = scaler.fit_transform(X_test)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"6AcUuRg7xfH4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608013593631,"user_tz":-330,"elapsed":1201,"user":{"displayName":"shiv ganesh","photoUrl":"","userId":"04377884227294419716"}},"outputId":"8dcf28a9-39c2-4bf3-8eed-2fd512c7d159"},"source":["print(X_train.shape)\n","print(X_test.shape)\n","print(y_train.shape)\n","print(y_test.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["(100778, 41)\n","(25195, 41)\n","(100778,)\n","(25195,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"gcMR1onSLC7v","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608013595745,"user_tz":-330,"elapsed":932,"user":{"displayName":"shiv ganesh","photoUrl":"","userId":"04377884227294419716"}},"outputId":"a313d44c-c826-4a26-ef8e-ce0a5dc40ef2"},"source":["print(\"Rows :\", X_train.shape[0], \"Columns :\" , X_train.shape[1])"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Rows : 100778 Columns : 41\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"SlV06GyEUW-V"},"source":["### Feature Importance Plot"]},{"cell_type":"code","metadata":{"id":"NP49P-RFUVAl"},"source":["# def plot_importance(importances):\n","#     indices = np.argsort(importances)\n","#     plt.title('Feature Importances')\n","#     plt.barh(range(len(indices)), importances[indices], color='b', align='center')\n","#     plt.yticks(range(len(indices)), [features[i] for i in indices])\n","#     plt.xlabel('Relative Importance')\n","#     plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"CFXXsUX0KbX_"},"source":["## Model Design"]},{"cell_type":"code","metadata":{"id":"Whh27jSg5wp9"},"source":["#used when model won't get improve then execution will stop( to Halt the Training of Neural Networks At the Right Time)\n","# patience: Number of epochs with no improvement after which training will be stopped, monitor: Quantity to be monitored, to discover the training epoch on which training was stopped, \n","# the “verbose” argument can be set to 1\n","EarlyStopper = EarlyStopping(patience=4, monitor='val_loss', mode='min')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"P0Oh5g9f1xv0","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608013627324,"user_tz":-330,"elapsed":7035,"user":{"displayName":"shiv ganesh","photoUrl":"","userId":"04377884227294419716"}},"outputId":"59dbef4f-d598-4378-be13-b77618c49025"},"source":["input_dim = X_train.shape[1]\n","model = Sequential()\n","# input layer=41, hidden layer=128, 64, output layer=23\n","# relu: to manage non-linear data, softmax=to get probablity\n","model.add(layers.Dense(128, input_dim=input_dim, activation='relu'))\n","model.add(layers.Dense(64, activation='relu'))\n","model.add(layers.Dense(23, activation='softmax'))\n","\n","# to improve performance. Adam optimization is a stochastic gradient descent method that is based on adaptive estimation of first-order and second-order moments.\n","opt = keras.optimizers.Adam(learning_rate=0.001)\n","\n","model.compile(loss='sparse_categorical_crossentropy', optimizer=opt, metrics=[\"accuracy\"])\n","print(model.summary())"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Model: \"sequential\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","dense (Dense)                (None, 128)               5376      \n","_________________________________________________________________\n","dense_1 (Dense)              (None, 64)                8256      \n","_________________________________________________________________\n","dense_2 (Dense)              (None, 23)                1495      \n","=================================================================\n","Total params: 15,127\n","Trainable params: 15,127\n","Non-trainable params: 0\n","_________________________________________________________________\n","None\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"49zthS0QNanB","colab":{"base_uri":"https://localhost:8080/","height":422},"executionInfo":{"status":"ok","timestamp":1608013632965,"user_tz":-330,"elapsed":1931,"user":{"displayName":"shiv ganesh","photoUrl":"","userId":"04377884227294419716"}},"outputId":"8d70a8a4-4132-40d0-a705-2ec1b9c9d305"},"source":["keras.utils.plot_model(model, show_shapes=True,  show_layer_names=True)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"image/png":"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\n","text/plain":["<IPython.core.display.Image object>"]},"metadata":{"tags":[]},"execution_count":28}]},{"cell_type":"code","metadata":{"id":"-JJYR6k_NC0L","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608013770957,"user_tz":-330,"elapsed":38130,"user":{"displayName":"shiv ganesh","photoUrl":"","userId":"04377884227294419716"}},"outputId":"32cbb17e-180d-4f53-8f63-c1848d7fb7a6"},"source":["history = model.fit(X_train, y_train, epochs=5, batch_size=32, verbose=1,validation_data=(X_test, y_test), \n","                    callbacks=[EarlyStopper], use_multiprocessing=True, workers=4)\n","# feature_importance = model.get_weights()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Epoch 1/5\n","3150/3150 [==============================] - 7s 2ms/step - loss: 0.0305 - accuracy: 0.9906 - val_loss: 0.0348 - val_accuracy: 0.9903\n","Epoch 2/5\n","3150/3150 [==============================] - 7s 2ms/step - loss: 0.0259 - accuracy: 0.9919 - val_loss: 0.0401 - val_accuracy: 0.9890\n","Epoch 3/5\n","3150/3150 [==============================] - 7s 2ms/step - loss: 0.0222 - accuracy: 0.9929 - val_loss: 0.0342 - val_accuracy: 0.9916\n","Epoch 4/5\n","3150/3150 [==============================] - 7s 2ms/step - loss: 0.0207 - accuracy: 0.9935 - val_loss: 0.0438 - val_accuracy: 0.9919\n","Epoch 5/5\n","3150/3150 [==============================] - 7s 2ms/step - loss: 0.0187 - accuracy: 0.9942 - val_loss: 0.0375 - val_accuracy: 0.9925\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"VsRkaS0_UpdR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608013824266,"user_tz":-330,"elapsed":5605,"user":{"displayName":"shiv ganesh","photoUrl":"","userId":"04377884227294419716"}},"outputId":"0f5dfc63-5dec-4ed6-dfc9-c00124fc2b2f"},"source":["train_loss, train_acc = model.evaluate(X_train, y_train, verbose=0)\n","print(f\"Train_Accuracy:{train_acc} Train Loss: {train_loss}\")\n","test_loss, test_acc = model.evaluate(X_test, y_test, verbose=0)\n","print(f\"Test Accuracy:{test_acc} Test Loss: {test_loss}\")\n","y_pred = np.argmax(model.predict(X_test, verbose=0), axis=1)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Train_Accuracy:0.9952767491340637 Train Loss: 0.015751464292407036\n","Test Accuracy:0.9925382137298584 Test Loss: 0.03745540231466293\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"yeLiOGg_UvZa","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608013826951,"user_tz":-330,"elapsed":1223,"user":{"displayName":"shiv ganesh","photoUrl":"","userId":"04377884227294419716"}},"outputId":"97ed24bd-7c6b-43fd-aaea-c71386e37cb6"},"source":["print(y_pred)\n","print(y_test.values)\n","# print('Sigmoid + Loss')\n","# plot_importance(feature_importance)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[11  9  9 ... 11 11  9]\n","[11  9  9 ... 11 11  9]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":198},"id":"E4wrV2ypFcey","executionInfo":{"status":"ok","timestamp":1608013830258,"user_tz":-330,"elapsed":1629,"user":{"displayName":"shiv ganesh","photoUrl":"","userId":"04377884227294419716"}},"outputId":"0bc5ff2c-754c-44e1-cf9a-fedea9f2e9e6"},"source":["pd.DataFrame(history.history)"],"execution_count":null,"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>loss</th>\n","      <th>accuracy</th>\n","      <th>val_loss</th>\n","      <th>val_accuracy</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.030479</td>\n","      <td>0.990603</td>\n","      <td>0.034798</td>\n","      <td>0.990316</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.025885</td>\n","      <td>0.991933</td>\n","      <td>0.040087</td>\n","      <td>0.988966</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.022166</td>\n","      <td>0.992856</td>\n","      <td>0.034226</td>\n","      <td>0.991586</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.020663</td>\n","      <td>0.993501</td>\n","      <td>0.043801</td>\n","      <td>0.991943</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.018743</td>\n","      <td>0.994155</td>\n","      <td>0.037455</td>\n","      <td>0.992538</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       loss  accuracy  val_loss  val_accuracy\n","0  0.030479  0.990603  0.034798      0.990316\n","1  0.025885  0.991933  0.040087      0.988966\n","2  0.022166  0.992856  0.034226      0.991586\n","3  0.020663  0.993501  0.043801      0.991943\n","4  0.018743  0.994155  0.037455      0.992538"]},"metadata":{"tags":[]},"execution_count":38}]},{"cell_type":"code","metadata":{"id":"Os7JBZ7gALcC","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1608013840394,"user_tz":-330,"elapsed":3506,"user":{"displayName":"shiv ganesh","photoUrl":"","userId":"04377884227294419716"}},"outputId":"abca0227-10b4-4d6f-d8a6-46de52b1e0a0"},"source":["# Plot the graph accuracy: a ratio of correctly predicted observation to the total observations, Precision is the ratio of \n","# correctly predicted positive observations to the total predicted positive observations. High precision relates to the low false positive rate. \n","# Recall is the ratio of correctly predicted positive observations to the all observations in actual class.\n","# F1 Score is the weighted average of Precision and Recall. Therefore, this score takes both false positives and false negatives into account. Intuitively it is not as easy to understand as accuracy, \n","# but F1 is usually more useful than accuracy, especially if you have an uneven class distribution. \n","# Accuracy works best if false positives and false negatives have similar cost. If the cost of false positives and false negatives are very different, it’s better to look at both Precision and Recall.\n","plt.figure(figsize = (15,6))\n","plt.subplot(121)\n","plt.plot(history.history['accuracy'])\n","plt.plot(history.history['val_accuracy'])\n","plt.title('model accuracy')\n","plt.ylabel('accuracy')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.subplot(122)\n","plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('model Loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()\n","\n","# predict probabilities for test set\n","\n","Target_names = ['back', 'buffer_overflow', 'ftp_write', 'guess_passwd', \n","                'imap', 'ipsweep', 'land', 'loadmodule', 'multihop',\n","                'neptune', 'nmap', 'normal', 'perl', 'phf', \n","                'pod', 'portsweep', 'rootkit', 'satan', 'smurf', \n","                'spy', 'teardrop', 'warezclient', 'warezmaster']\n","print(\"Classification Report:\\n\")\n","print()\n","print(classification_report(y_test, y_pred))\n","# confusion matrix\n","print(\"Confusion Matrix:\\n\")\n","print()\n","matrix = confusion_matrix(y_test, y_pred)\n","plt.figure(figsize = (15,7) )\n","sns.heatmap(matrix, annot=True)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1080x432 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Classification Report:\n","\n","\n","              precision    recall  f1-score   support\n","\n","           0       0.97      0.99      0.98       185\n","           1       0.50      0.11      0.18         9\n","           3       1.00      0.91      0.95        11\n","           4       1.00      1.00      1.00         1\n","           5       0.95      0.99      0.97       733\n","           6       0.00      0.00      0.00         3\n","           8       0.00      0.00      0.00         0\n","           9       1.00      1.00      1.00      8228\n","          10       0.98      0.93      0.95       313\n","          11       0.99      1.00      0.99     13422\n","          12       0.00      0.00      0.00         1\n","          13       1.00      1.00      1.00         1\n","          14       1.00      0.93      0.96        43\n","          15       0.98      0.99      0.99       573\n","          16       0.00      0.00      0.00         1\n","          17       0.98      0.96      0.97       738\n","          18       0.99      1.00      0.99       534\n","          19       0.00      0.00      0.00         1\n","          20       1.00      1.00      1.00       188\n","          21       0.94      0.86      0.90       202\n","          22       0.25      0.12      0.17         8\n","\n","    accuracy                           0.99     25195\n","   macro avg       0.69      0.66      0.67     25195\n","weighted avg       0.99      0.99      0.99     25195\n","\n","Confusion Matrix:\n","\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAyAAAAGbCAYAAAA84vT3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXhU1f3H8fd3krDLJkrIolARrTsKuKGCUEAqoNWiWFCplbZYRW21Vq1U21r7E63aWisoiwubVEUgKIsooCyJEJQEFFmEJERUFllUQnJ+f2RIA5JMCDP3ZnI/r+e5DzP3zsznnEvuzZycc+415xwiIiIiIiJeCPldABERERERCQ41QERERERExDNqgIiIiIiIiGfUABEREREREc+oASIiIiIiIp5JjHVA3Xrpvl5mq7ikxM94EREREfHIvr355ncZqqLoy3VR+36c1OIHcVHn8tQDIiIiIiIinol5D4iIiIiIiJRTUux3CXylHhAREREREfGMekBERERERLzkgj1HORY9IKOBLcDKQ21s3PgoXv3vaDKXvsXyZXO4/vr+RxzYrFlTMma8TM7K+WTMeJmmTZsAcO21V7Dsg9ksXzaHBe9O5YwzTjmsz+3Zows5K+ezOnchd991yxGXM16yla/8IOenpaUwZ9YrfLhiHiuy3+bW39zkaT7o3KN85Stfx36tV1ISvSUOmXNRv0jVxcAu4AXgtIOvgnX33b+hSeOjuO/+v9GiRXM++vBdjjv+bIqKiiJ/8MXnMWhQf26++c4D1j/813vZum07I0b8m9/9bijNmjbhvvv/xnnnnUNO7ids376DXj278sAf7+SCzn2qVIlQKMSqnAX06j2AvLzNLF6UwcBBQ1m1ak1V90O1+ZmtfOUHPT85+VhaJR/L8uyVNGrUkKVL3uSqq38eiPr7ve+Vr3zl69g/0vy4uQrW5lXRuwpWqx/GRZ3Li0UPyHxga0UbnXMcdVQjABo1asi2bdvZt28fAHfe8UveWzidrMxZ/PGPd1b0Ed/Tp08PXnppCgAvvTSFvn17ArB48Qds376j9PGSZaSmtqryZ3bq2J61azewfv1GioqKmDx5Kn379Kzy+4+En9nKV37Q8wsLt7A8u7QDd9eu3axevYbUlGTP8nXuUb7yla9j39t8PzhXErUlHkVsgJjZyWb2ezN7Krz83sx+WN3AZ54Zy0knt2XD+iw+yJrNb387HOcc3btfTNu2bbiw8+V07NSTs9ufTufO51bpM489tgWFhVuA0i8Pxx7b4nuv+fnga3nzrXlVLmdKajKb8grKnuflbybFoy8hfmYrX/lBzy/v+OPTOOvM01iydLlnmTr3KF/5yvc6P8h1903Ah2BVOgndzH4PDAAmAkvDq9OACWY20Tn3yKHe17179wHPPPPMCe3atctKSGxKQkKjsm0/+tElfLgil549r+GEH7QmI+NlFnZcSvduF9Ot+8UsXfImUNo70rZtaxYuXMKC+W9Qt24dGjVqSLNmTctec999f2P2nHe/l3/wsLIul1zA4MEDuKTLlVXbKyISeA0bNmDypFHc+bvh7Ny5y+/iiIiI1BqRroJ1E3Cqc+6ACRpm9jiQAxyyATJnzpwJwM+ccx0OngNyw/X9eXTEvwFYu24D6zds4qST2mJmPPro0zz33Mvf+7yLLu4LVDwHZMuWL0lOPpbCwi0kJx/LF198Vbbt9NN/yLP/eZTL+w5i69ZtEar7PwX5haSnpZQ9T0ttRUFBYZXffyT8zFa+8oOeD5CYmMgrk0YxYcJrvP76TE+zde5RvvKV73V+kOvumzgdOhUtkYZglQAph1jfKrztsG3aVEDXrhcCpUOn2p14AuvXf8bsOe9yw/XX0LBhAwBSUpI55pijq/SZ06fPZuDAqwEYOPBqpk2bBUB6egqvTBrFjYOHsWbNusMqZ2ZWNm3btqF163SSkpLo378f06bPOqzPqC4/s5Wv/KDnA4wa+RirVn/KE0+O9DQXdO5RvvKVr2Pfj/O+50qKo7fEoUg9ILcDc81sDbApvO44oC3wmwreMwHoArQA8m688RqSEpMAGPXcSzz8tyd5btTjfJA1GzPjvvsf5quvtjFnznxOPqkt89+dCpRO/hz882EH9GZU5NERTzP+5WcYfOO1bNyYx3U/GwrAvffeztFHN+Of/3wYgH379nHe+b0jfh5AcXExw26/n4wZ40kIhRg7bhK5uZ9U6b1Hys9s5Ss/6PkXXtCRQQOv5sOPcsnKLP0F+Mc/PsLMN9/2JF/nHuUrX/k69r3NF+9FvAyvmYWATkBqeFU+kOmcq1KT6+AhWF4rjtPJOSIiIiJyeOLlMrx7N2RF7ftxndYd4qLO5UW8E7orvb7XYg/KIiIiIiJS+wX8D+SxuA+IiIiIiIjIIUXsARERERERkeiJ1xsIRosaICIiIiIiXgr4EKyYN0A0CVxERERERPZTD4iIiIiIiJc0BEtERERERDwTpzcQjBZdBUtERERERDyjHhARERERES8FfAhWje0BGTXyMQryVpC9fG4g83v26ELOyvmszl3I3XfdonzlK98j7dqdQFbmrLJl65erue3WX3haBj/rH+T/e+UrP8j5Qa67L0pKorfEIXMuaneCP6TEOqnVCrio87ns2rWbMWOe5Kz23aJdrBqdHwqFWJWzgF69B5CXt5nFizIYOGgoq1atUb7yle+hUCjExg0fcEHny9m4Md+zTL/q7/e+V77yla9j/0jz9+3NtxgUMeq+y5kbtS/gdU/tFhd1Lq/G9oAsWLiErdu2BzK/U8f2rF27gfXrN1JUVMTkyVPp26en8pWvfI91u7Qz69Z95lnjA/ytv9/7XvnKV76Ofb/P+55xJdFb4lCNbYAEWUpqMpvyCsqe5+VvJiUlWfnKV77H+vfvx8RJr3ua6Wf9/d73yle+8nXs+5Hvi4APwap2A8TMBleybYiZZZlZVknJ7upGiIj4JikpiT6X92DKf6f7XRQREZFa5Uh6QB6saINzbqRzroNzrkMo1PAIIoKpIL+Q9LSUsudpqa0oKChUvvKV76FevbqyfPlHbNnypae5ftbf732vfOUrX8e+H/l+cK44aks8qrQBYmYfVrB8BLT0qIyBk5mVTdu2bWjdOp2kpCT69+/HtOmzlK985Xvo2muu8Hz4Ffhbf7/3vfKVr3wd+36e9z0V8Dkgke4D0hLoCWw7aL0B78ekRGEvvfg0l1x8Pi1aNGfDuiwefGgEY8ZOjGVkjckvLi5m2O33kzFjPAmhEGPHTSI39xNPspWv/KDnAzRoUJ/u3S7m10N/72ku+Ft/v/e98pWvfB37fp33xVuVXobXzJ4HxjjnFh5i23jn3HWRAqp7GV4RERERkcMRL5fh/XbZG1H7flzv7L5xUefyKu0Bcc7dVMm2iI0PERERERE5SJwOnYqWSEOwREREREQkmkric/J4tOg+ICIiIiIi4hn1gIiIiIiIeCngQ7DUAyIiIiIi4iUP74RuZqPNbIuZrSy37lEzWx2+vcZrZta03LY/mNmnZvaxmfUst75XeN2nZnZPufVtzGxJeP0kM6sTqUxqgIiIiIiI1F5jgV4HrZsNnOacOwP4BPgDgJmdAlwLnBp+z7/NLMHMEoCngcuAU4AB4dcC/B34h3OuLaW37qjwIlb7qQEiIiIiIuIlD29E6JybD2w9aN0s59y+8NPFQFr4cT9gonPuO+fceuBToFN4+dQ5t845txeYCPQzMwMuBaaE3z8OuCJSmdQAERERERHxUhSHYJnZEDPLKrcMOczS/ByYGX6cCmwqty0vvK6i9UcD28s1Zvavr5QmoYuIiIiIxCnn3EhgZHXea2b3AfuAl6NaqAjUABERERER8VIVJo/HmpndCFwOdHPO7b8zez6QXu5laeF1VLD+K6CpmSWGe0HKv75CNXoIVs8eXchZOZ/VuQu5+65ban3+qJGPUZC3guzlc8vWNWvWlDczJrAqZyFvZkygadMmMS8HBG/fK1/5+x3qOPSan/UP8v+98pUf5Pwg190PzhVHbakOM+sF3A30dc7tKbfpDeBaM6trZm2AE4GlQCZwYviKV3Uonaj+RrjhMg+4Ovz+G4CpEfP/1+CJjcQ6qdUKCIVCrMpZQK/eA8jL28ziRRkMHDSUVavWRLuINSb/os7nsmvXbsaMeZKz2ncD4JG/3cfWrdv5v0ef5u67bqFZsyb84d6HY1YGCOa+V77y9zvUceglP+vv975XvvKVr2P/SPP37c23GBQx6r6ZPzZqX8DrX3xjpXU2swlAF6AF8DkwnNKrXtWltAcDYLFz7lfh199H6byQfcDtzrmZ4fW9gSeABGC0c+6v4fU/oHRSenNgOTDQOfddZWWK2ANiZiebWTcza3TQ+oMv5xVVnTq2Z+3aDaxfv5GioiImT55K3z49I78xjvMXLFzC1m3bD1jXp09PXnjxFQBeePEV+vaN6W4Hgrnvla/8/Q51HHrJz/r7ve+Vr3zl69j3I98XHt4HxDk3wDnXyjmX5JxLc84975xr65xLd86dFV5+Ve71f3XOneCcO2l/4yO8PsM51y687a/l1q9zznUKf+ZPIzU+IEIDxMxuo7Qb5VZgpZn1K7c5pn+GT0lNZlNeQdnzvPzNpKQkxzKyRuXv1/LYFhQWbgGgsHALLY9tEfNMv+uufOXXhGPPL37W3+99r3zlK1/Hvh/5vvDwMrw1UaRJ6DcD5zjndplZa2CKmbV2zj0JVNjdE7781xAAS2hCKNQwSsWVWA+ZExERERGJpUgNkJBzbheAc26DmXWhtBFyPJU0QMpfDqy6c0AK8gtJT0spe56W2oqCgsLqfFS1+J2/3+dbviQ5+VgKC7eQnHwsW774KvKbjpDfdVe+8mvCsecXP+vv975XvvKVr2Pfj3xf1ICrYPkp0hyQz83srP1Pwo2RyymdxHJ6LAuWmZVN27ZtaN06naSkJPr378e06bNiGVmj8vebPm0W1w/6KQDXD/op06a9FfNMv+uufOXXhGPPL37W3+99r3zlK1/HfmDO+xqCVanrKZ0BXyZ8jd/rzezZmJUKKC4uZtjt95MxYzwJoRBjx00iN/eTWEb6nv/Si09zycXn06JFczasy+LBh0bw90efZuL4/zD4xgFs3JjHtdf9KvIHHaEg7nvlK3+/Qx2HY8ZO9Czfz/r7ve+Vr3zl69j3I1+8V2MvwysiIiIicjji5jK8s/4dvcvw9hgaF3UuT3dCFxERERHxUpwOnYqWGn0ndBERERERqV3UAyIiIiIi4qWAXwVLDRARERERES8FvAGiIVgiIiIiIuIZ9YCIiIiIiHgp4JPQ1QAREREREfGShmCJiIiIiIh4Qz0gIiIiIiJeCvgQrBrdA9KzRxdyVs5nde5C7r7rlkDl+133USMfoyBvBdnL53qeDf7XX/nKD2p+kOuufOUHOT/IdfdFSUn0ljhkzkXtTvCHlFgntVoBoVCIVTkL6NV7AHl5m1m8KIOBg4ayatWaaBexxuX7XXeAizqfy65duxkz5knOat/Ns1zwv/7KV35Q84Ncd+UrP8j5tanu+/bmWwyKGHXfvPZI1L6A17/ynrioc3kRe0DMrJOZdQw/PsXM7jSz3rEuWKeO7Vm7dgPr12+kqKiIyZOn0rdPz1jH1oh8v+sOsGDhErZu2+5p5n5+11/5yg9qfpDrrnzlBzk/yHX3jSuJ3hKHKm2AmNlw4CngGTP7G/AvoCFwj5ndF8uCpaQmsymvoOx5Xv5mUlKSYxlZY/L9rrvf/K6/8pUf1Pwg1135yg9yfpDr7puAD8GKNAn9auAsoC5QCKQ55742sxHAEuCvh3qTmQ0BhgBYQhNCoYbRK7GIiIiIiMStSA2Qfc65YmCPma11zn0N4Jz7xswqbHI550YCI6H6c0AK8gtJT0spe56W2oqCgsLqfFS1+Jnvd9395nf9la/8oOYHue7KV36Q84Ncd9/Eac9FtESaA7LXzBqEH5+zf6WZNQFiuucys7Jp27YNrVunk5SURP/+/Zg2fVYsI2tMvt9195vf9Ve+8oOaH+S6K1/5Qc4Pct1941z0ljgUqQfkYufcdwDOHTDLJQm4IWalAoqLixl2+/1kzBhPQijE2HGTyM39JJaRNSbf77oDvPTi01xy8fm0aNGcDeuyePChEYwZO9GTbL/rr3zlBzU/yHVXvvKDnB/kuos/auxleEVEREREDkfcXIZ3wvDoXYZ3wINxUefydCd0EREREREvaQ6IiIiIiIiIN9QDIiIiIiLipTi9gWC0qAEiMdOsfiNf87d9s8vXfBEREZFD0hAsERERERERb6gHRERERETES3F6/45oUQNERERERMRLGoIlIiIiIiLiDfWAiIiIiIh4ST0gNVfPHl3IWTmf1bkLufuuWwKVH491b9fuBLIyZ5Ut6zZ9wC9/fcMBr+nVuxvvvPcG8xa8zux3/su5551zxGVt2qwJr7w+miXL3uKV10fTpGljAAYMuJJlH8xm+bI5LHh3KmeccUqVPzMe939tya9bty6L3pvOB1mzWZH9NsMf+K2n+aNGPkZB3gqyl8/1NLc8nXuUr3zl69iv5VxJ9JY4ZC7Gk2AS66RWKyAUCrEqZwG9eg8gL28zixdlMHDQUFatWhPtIta4/NpQ91AoRN5ny+jZrT95mwrK1jds2IDdu/cAcMqpJ/Hc2Ce4oONlVfrMCzp3YsB1V3Lr0D8csP6Bh+5i+7btPPWPUdx2x800adqEPw8fwclnnsyq1WvYvn0HvXp25YE/3skFnft4Uv8jEfR8+N/PSWJiIvPfeY077hzOkqXLPMm+qPO57Nq1mzFjnuSs9t08ySxP5x7lK1/5Ovarn79vb77FoIhR981zd0btC3j9XzweF3Uu77B7QMzshVgU5GCdOrZn7doNrF+/kaKiIiZPnkrfPj29iPY9vzbUvdulndmwftMBjQ+grPEB0KBBfco3gG+57SZmzZvCO++9wd1/uLXKWZf17sak8a8DMGn86/T+cXcAFi3OYvv2HQAsXrKM1NRWVfq82rD/4zkf/vdzkpSUSGJSErH+Q0l5CxYuYeu27Z7lHUznHuUrX/k69r3/veM1V+KitsSjShsgZvbGQcs04Cf7n8eyYCmpyWzK+9+X17z8zaSkJMcyssbk14a69+/fj1enTD/ktt6Xd+f9zJmMf+VZht1yLwBdLr2QH5xwPD26Xk3Xzv0486xTOf+CDlXKOuaYo/n88y8A+PzzLzjmmKO/95qfD76WN9+aV6XPqw37P57zofSvYVmZs9ic/yFz585naeZyT/P9pHOP8pWvfK/zg1x335SURG+JQ5EmoacBucBzgAMM6AA8VtmbzGwIMATAEpoQCjU88pJK3EhKSqLP5T149C9PHXJ7xvQ5ZEyfw/kXdOCe+4dxdb/BdLn0Qrp0vZB5C0p7Mho2asAPTmjNovezeHPuZOrWqUPDRg1o2qxJ2Wse+tMI5s1d+L3Pdxz414Aul1zA4MEDuKTLlVGuqcRKSUkJHTr2oEmTxvz3lec59dSTyMn52O9iiYiISBREaoB0AIYB9wF3Oeeyzewb59y7lb3JOTcSGAnVnwNSkF9IelpK2fO01FYUFBRW56Oqxc/8eK97r15dWb78I7744qtKX7fo/SyOb51O8+bNMIwn/zGSF8ZM+v7ndesPVDwH5IsvvqJly2P4/PMvaNnyGL78YmvZttNP/yHP/udRLu87iK1bt1Wp/PG+/+M9v7wdO77mnXffK52cGJAGiM49yle+8r3OD3LdfROnk8ejpdIhWM65EufcP4DBwH1m9i88unRvZlY2bdu2oXXrdJKSkujfvx/Tps/yItr3/Hiv+7XXXMHESa8fclubHxxX9viMM0+hbp06bN26jXlvL+S6gVfRsGEDAJJbHUuLFs2rlPfmzLe55rorALjmuiuYmVF69aL09BRemTSKGwcPY82adVUuf7zv/3jPb9GiOU2alF7JrF69enTvdjEff7zWs3y/6dyjfOUrX8e+t/m+KHHRW+JQlRoTzrk84Kdm9mPg69gWqVRxcTHDbr+fjBnjSQiFGDtuErm5n3gR7Xt+PNe9QYP6dO92Mb8e+nsSwutu+Pm1AIwbPZHL+/ak/7X92Fe0j2++/ZabB98BwDtvv0e7dieQMXsiUDoJeeiQu/jyy62HijnAU4+P5LlxT/CzQVezaVMBv7jxdgDuv+8Ojj66Gf/858MA7Nu3j/PO7x3T+kdD0PNbtWrJ6OefICEhRCgUYsqUaczImONZ/ksvPs0lF59PixbN2bAuiwcfGsGYsRM9y9e5R/nKV76OfW/zxXs19jK8Ev+a1W/ka/62b3b5mi8iIiLeipfL8O7559CofT9ucOu/46LO5elO6CIiIiIiXorTq1dFixogIiIiIiJe8vD+VjXRYd+IUEREREREpLrUAyIxozkYIiIiIoegIVgiIiIiIuKZOL18brRoCJaIiIiIiHhGDRARERERES+5kugtEZjZaDPbYmYry61rbmazzWxN+N9m4fVmZk+Z2adm9qGZnV3uPTeEX7/GzG4ot/4cM/so/J6nzCziZYHVABERERER8ZK3d0IfC/Q6aN09wFzn3InA3PBzgMuAE8PLEOAZKG2wAMOBc4FOwPD9jZbwa24u976Ds75HDRARERERkVrKOTcf2HrQ6n7AuPDjccAV5da/4EotBpqaWSugJzDbObfVObcNmA30Cm9r7Jxb7Ervbv5Cuc+qUI1ugPTs0YWclfNZnbuQu++6JVD5Qa678pXvZ37dunVZ9N50PsiazYrstxn+wG89zQede5SvfOXr2K/tXElJ1BYzG2JmWeWWIVUoQkvn3Obw40KgZfhxKrCp3OvywusqW593iPWVqrENkFAoxFNP/pXL+wzk9DO7cs01V/DDH54YiPwg1135yvc7/7vvvqN7j/6c0+FHnNOhBz17dOHcTmdHfmOU6NyjfOUrX8e+t/m+iOIQLOfcSOdch3LLyMMpSrjnwtPLch1WA8TMOpvZnWbWI1YF2q9Tx/asXbuB9es3UlRUxOTJU+nbp2esY2tEfpDrrnzl+50PsHv3HgCSkhJJTErCeXjHWp17lK985evY9/68H0Cfh4dPEf53S3h9PpBe7nVp4XWVrU87xPpKVdoAMbOl5R7fDPwLOIrSiSf3VPjGKEhJTWZTXkHZ87z8zaSkJMcyssbkB7nuyle+3/lQ+te4rMxZbM7/kLlz57M0c7ln2Tr3KF/5yvc6P8h1942HV8GqwBvA/itZ3QBMLbf++vDVsM4DdoSHar0F9DCzZuHJ5z2At8Lbvjaz88JXv7q+3GdVKFIPSFK5x0OAHznnHgyH/qyiN5Ufi1ZSsjtSGUREapSSkhI6dOzB8W060LFDe0499SS/iyQiIrWJh1fBMrMJwCLgJDPLM7ObgEeAH5nZGqB7+DlABrAO+BQYBQwFcM5tBf4MZIaXh8LrCL/mufB71gIzI5Up0p3QQ+FWTggw59wX4ULsNrN9Fb0pPPZsJEBindRqjV0oyC8kPS2l7HlaaisKCgqr81HV4md+kOuufOX7nV/ejh1f886775VOjsz52JNMnXuUr3zle50f5LoHgXNuQAWbuh3itQ445FUAnHOjgdGHWJ8FnHY4ZYrUA9IE+ADIApqXGyvWCIh4k5EjkZmVTdu2bWjdOp2kpCT69+/HtOmzYhlZY/KDXHflK9/v/BYtmtOkSWMA6tWrR/duF/Pxx2s9y9e5R/nKV76OfW/zfVFSEr0lDlXaA+Kca13BphLgyqiXppzi4mKG3X4/GTPGkxAKMXbcJHJzP4llZI3JD3Ldla98v/NbtWrJ6OefICEhRCgUYsqUaczImONZvs49yle+8nXse5vvi6rdQLDWslhf3aW6Q7BERERERA7Hvr35MR2hEy27H7g2at+PGz40MS7qXF6kOSAiIiIiIhJN1b96Va2gBoiIiIiIiJcCPgSrxt4JXUREREREah/1gIiIiIiIeMjF6dWrokUNEBERERERL2kIloiIiIiIiDfUAyIiIiIi4qWA94CoASIiIiIi4qWAX4ZXQ7BERERERMQzNboB0rNHF3JWzmd17kLuvuuWQOUHue7KV77yde5RvvKVr2O/Vitx0VvikDkX24In1kmtVkAoFGJVzgJ69R5AXt5mFi/KYOCgoaxatSbaRaxx+UGuu/KVr3yde5SvfOXr2K9u/r69+RaDIkbdztv7RO0L+FFPTIuLOpdXaQ+ImZ1rZo3Dj+ub2YNmNs3M/m5mTWJZsE4d27N27QbWr99IUVERkydPpW+fnrGMrDH5Qa678pWvfJ17lK985evY9zJfvBdpCNZoYE/48ZNAE+Dv4XVjYlguUlKT2ZRXUPY8L38zKSnJsYysMflBrrvyla98nXuUr3zl69j3Mt8XAR+CFekqWCHn3L7w4w7OubPDjxeaWXZFbzKzIcAQAEtoQijU8MhLKiIiIiJSGwT8TuiRekBWmtng8OMVZtYBwMzaAUUVvck5N9I518E516G6jY+C/ELS01LKnqeltqKgoLBanxVv+UGuu/KVr3yde5SvfOXr2PcyX7wXqQHyC+ASM1sLnAIsMrN1wKjwtpjJzMqmbds2tG6dTlJSEv3792Pa9FmxjKwx+UGuu/KVr3yde5SvfOXr2Pcy3xcaglUx59wO4MbwRPQ24dfnOec+j3XBiouLGXb7/WTMGE9CKMTYcZPIzf0k1rE1Ij/IdVe+8pWvc4/yla98Hfte5vsiThsO0VJjL8MrIiIiInI44uYyvL/qFb3L8P7nzbioc3mRJqGLiIiIiEgUxboDoKZTA0RERERExEsBH4IVaRK6iIiIiIhI1KgHRERERETESwHvAVEDRKSWKuza1tf85Hmf+povIiJSU7mAN0A0BEtERERERDyjHhARERERES8FvAdEDRARERERES+V+F0Af2kIloiIiIiIeKZGN0B69uhCzsr5rM5dyN133RKo/CDXXfnVzx92282syH6b7OVzOeqeByCpzgHb6/2kP01HjqPpM6Np/MjjhI5tecRltaOOovHfHqPZ6Jdp/LfHsEaNAOjTpwfLPphNVuYsFi/K4MILOlb5M/3c/3Xr1mXRe9P5IGs2K7LfZvgDv/U0H3TuUT5zyVYAACAASURBVL7yla9jv7ZzJS5qSzyyWN+JMbFOarUCQqEQq3IW0Kv3APLyNrN4UQYDBw1l1ao10S5ijcsPct2VX/38lJRk3p33Gqef2ZVvv/2W3XNeYu/SxXw3+82y1ySd2Z6i1bnw3XfUu7wfSWecxc6HH6xSuZLOOIu6P+rFrsceOWB9g5t+hdv5Nd9MHk/9/tdhRx3Fnuef5YSlBezevQeA00//IRPG/4fTTr8kZvWPpoYNG7B79x4SExOZ/85r3HHncJYsXeZJts49yle+8nXsVz9/3958i0ERo277gK5R+wLedMK8uKhzeZX2gJjZbWaW7lVhyuvUsT1r125g/fqNFBUVMXnyVPr26RmI/CDXXflHlp+YmEj9+vVISEjA6tal5KsvD9hetGI5fPdd6eNVuYRaHFO2rf7V19LkqWdp+sxoGgwaXOXy1jn/Qr6dU9rI+XbOm9Q5vzNAWeMDoGGDBlT1jx1+73/4X9mTkhJJTEqqctmjQece5Stf+Tr2vT/vi7ciDcH6M7DEzBaY2VAzOybC66MmJTWZTXkFZc/z8jeTkpLsVbyv+UGuu/Krn19QUMjj//gP69cuJW/jckp276ZoWVaFr6/Xqzd7M5cAkHR2BxJS09hx2y/ZPvQmEk9sR+JpZ1SpvKFmzXBbtwLgtm4l1KxZ2bZ+/Xqx8qN3eWPqOG6+uWpDmfze/1D617iszFlszv+QuXPnszRzuWfZOvcoX/nK9zo/yHX3TUkUlzgUqQGyDkijtCFyDpBrZm+a2Q1mdlRFbzKzIWaWZWZZJSW7o1hcEalI06ZN6NunJ23bnUf68Wdj9epR99IfHfK1dS/9EYknnsQ3UyYCkHROR5LO7kDTfz9H06dHkZB+HAmpaQA0efIZmv77ORrdcRd1zr+w9DX/fo6kcyqY01Gus2Dq1Dc57fRLuOrqm3jwT3dFtb6xVFJSQoeOPTi+TQc6dmjPqaee5HeRRESkFgn6HJBIl+F1zrkSYBYwy8ySgMuAAcAI4JA9Is65kcBIqP4ckIL8QtLTUsqep6W2oqCgsDofVS1+5ge57sqvfn63bhexfsNGvvyytDdi73sLSDzlNL57e/YBr0tqfw71Bwxix+9ug6IiAMyMbya9zLcZ0773uTuG/br0fRXMASnZtg1r3hy3dSvWvDkl27d97zMWLFxCmzbHcfTRzfjqq+9vj0b9Y2HHjq955933SidH5nzsSabOPcpXvvK9zg9y3cUfkXpADpjU4pwrcs694ZwbABwfu2JBZlY2bdu2oXXrdJKSkujfvx/Tps+KZWSNyQ9y3ZVf/fxNG/M599yzqV+/HgBJZ51N8cbPDnhNwgkn0ui23/L18D/gdmwvW783ayl1e/aGevUBCB3dAmvStErl3bv4Pep17wVAve692LvoPQBOOKF12Wvan3UadevWidj4AP/3f4sWzWnSpDEA9erVo3u3i/n447We5evco3zlK1/Hvrf5vgj4EKxIPSDXVLTBObenom3RUFxczLDb7ydjxngSQiHGjptEbu4nsYysMflBrrvyq5+/NHM5r746g8ylb7Fv3z7YvJFvZ06jwfU/Z98nq9m7+H0a3vwrrH59Gt9feuWr4i1b2PmneylalsV3xx1P0yf+DYD75ht2/t9fDmikVOSbSeM56r4/Ua/XjyneUsjOv/4JgJ9c2ZuBA6+mqGgf337zLdf97NcxrX+0tGrVktHPP0FCQohQKMSUKdOYkTHHs3yde5SvfOXr2Pc23w/xOnQqWmrsZXhF5MgUdm3ra37yvE99zRcRkeCJl8vwbr3ykqh9P27+2rtxUefyIvWAiIiIiIhINMXp0KloUQNERERERMRDTg0QERERERHxTMAbIJGugiUiIiIiIhI16gERqaX8ngSelODv6aWoeJ+v+SIiIhXRECwREREREfFOwBsgGoIlIiIiIiKeUQ+IiIiIiIiHNARLREREREQ8E/QGSI0egtWzRxdyVs5nde5C7r7rlkDlB7nuALf+5iayl89lRfbb3HbrLzzP97v+8ZaflpbCnFmv8OGKeazIfptbf3PT917TtGljJk16lqVL32TBgqmcckq7Iy5nnTp1ePHFf7Fy5bvMn/86xx2XBsCll3ZmyeKZLF82hyWLZ9K1y4VV/sx27U4gK3NW2bL1y9We/wzq3KN8P/KD/rMf9Pwg1722M7M7zCzHzFaa2QQzq2dmbcxsiZl9amaTzKxO+LV1w88/DW9vXe5z/hBe/7GZ9TyiMjkXtTvBH1JindRqBYRCIVblLKBX7wHk5W1m8aIMBg4ayqpVa6JdxBqXH+S6A5x66km8/NK/Of+CH7N3bxEZ019m6G/uYe3aDZ7k+13/eMxPTj6WVsnHsjx7JY0aNWTpkje5pv8QVq/+33sefvhedu3azcMPP0m7difwxBN/pnfv66pUpuOOS2PUqBH07HntAeuHDBnEaaedzG233cdPf9qHvn17MmjQbzjzzFPJ31zI5s2fc+qpJ5Ex/WWOb9OhWvti44YPuKDz5WzcmH/Y768OnXuU71f+wWUJ0s9+0PNrU9337c23GBQx6j7veknUvoC3nPduhXU2s1RgIXCKc+4bM5sMZAC9gVedcxPN7D/ACufcM2Y2FDjDOfcrM7sWuNI5d42ZnQJMADoBKcAcoJ1zrrg6Za60B8TM6pjZ9WbWPfz8OjP7l5ndYmZJ1Qmsqk4d27N27QbWr99IUVERkydPpW+fI2psxU1+kOsOcPLJJ7J06XK++eZbiouLmb9gMVdecZln+X7XPx7zCwu3sDx7JQC7du1m9eo1pKS0POA1J598Iu+++z4An3yyluOPT+PYY1sAcO21V7JgwVQWL87gn/98mFCoap2zl1/+I15++b8AvPpqBl3CPR0rVuSwefPnAOTkfEz9+vWoU6dOFffA/3S7tDPr1n3m2Rcw0LlH+f7llxe0n/2g5we57r5xFr0lskSgvpklAg2AzcClwJTw9nHAFeHH/cLPCW/vZmYWXj/ROfedc2498CmljZFqifRbfgzwY2CYmb0I/BRYAnQEnqtuaFWkpCazKa+g7Hle/mZSUpJjGVlj8oNcd4CcnNV07nwuzZs3o379elzW61LS0lI8y/e7/vGef/zxaZx15mlkZmYfsP6jj3Lp168XAB06nMlxx6WSmprMSSe15eqrL6dr16s477zeFBeXcO21Vxzqo79f1pRk8sJlLS4u5uuvd3L00c0OeM1PfvJjli9fyd69e6tch/369+/HxEmvH/b7joTOPcr3K7+8oP3sBz0/yHWvDcxsiJlllVuG7N/mnMsHRgAbKW147AA+ALY75/bfMCsPSA0/TgU2hd+7L/z6o8uvP8R7DlukSeinO+fOCLeY8oEU51yxmb0ErKjoTeGKDwGwhCaEQg2rWz4JoNWrP+XRR59mZsZ49uzeQ/aKHIqLAz5bK040bNiAyZNGcefvhrNz564Dto0Y8QwjRgxn8eIMcnI+ZkX4/7Vr1ws5++zTWbjwDQDq16/HF198CcCkSc9y/PHp1KlTh/T0FBYvzgDg6afH8OKLr0QszymntONvf72Xy35ctaFe5SUlJdHn8h7cd//fDvu9IvFMP/sisRfNSejOuZHAyENtM7NmlPZetAG2A68AvaKXXj2RGiCh8KSUhpR22TQBtgJ1gQqHYJXfEdWdA1KQX0h6ub96p6W2oqCgsDofVS1+5ge57vuNGTuRMWMnAvCXP99DXt5mz7L9rn+85icmJvLKpFFMmPAar78+83t3Qt+5cxe//OVdZc9Xr17I+vUbufDCTrz00hQeeOD/vveZ11zzS6DiOSAFBYWkpaWQn19IQkICjRsfxVdfbQMgNbUVU155nsE/H8a6dZ9VfQeE9erVleXLP2LLli8P+71HQuce5fuVv18Qf/aDnh/kuvvFlXg2VaU7sN459wWAmb0KXAg0NbPEcC9HGqUdDYT/TQfywh0QTYCvyq3fr/x7DlukIVjPA6uBbOA+4BUzGwVkAhOrG1oVmVnZtG3bhtat00lKSqJ//35Mmz4rlpE1Jj/Idd/vmGOOBiA9PYUrrriMCRNf8yzb7/rHa/6okY+xavWnPPHkIf8IQ5MmjUlKKv27xeDB17Jw4VJ27tzFvHnvceWVvcv+z5s1a8Jxx1WtV3fGjDn87GdXAfCTn/Qum2PSpElj3pj6Avfe9zDvL8qq0mcd7NprrvB8CAro3KN8f8+9EMyf/aDnB7nuAbAROM/MGoTncnQDcoF5wNXh19wATA0/fiP8nPD2t13pFaveAK4NXyWrDXAisLS6haq0B8Q59w8zmxR+XGBmL1DakhrlnKt2aFUUFxcz7Pb7yZgxnoRQiLHjJpGb+0ksI2tMfpDrvt8rk0bR/OhmFBXt47bb7mPHjq89y/a7/vGYf+EFHRk08Go+/CiXrMzSXxp/Gv4o6emlf9F67rmXOfnktowa9RjOOVatWsOvflXaG7J69RoefHAE06a9SCgUoqhoH3fc8ccqTX4dO3YSo0f/g5Ur32Xbtu0MGvQbAH71qxtoe0Jr7r/vDu6/7w4ALus9gC+++KpK+6BBg/p073Yxvx76+yq9Ppp07lG+n+feoP7sBz0/yHX3i1f3AXHOLTGzKcAyYB+wnNJRSjOAiWb2l/C658NveR540cw+pXTU07Xhz8kJX0ErN/w5t1T3ClhQgy/DKyLx7eAhWF4rKt4X+UUiIlKrxMtlePPPvzRq349TF70dF3Uur0bfiFBERERERGoXf/9EKSIiIiISMF4Nwaqp1AAREREREfGQh1fBqpE0BEtERERERDyjHhARiQm/J4F/U7DA1/z6KRf5mi8iIjVXjK8BVeOpASIiIiIi4iENwRIREREREfGIekBERERERDwU9B4QNUBERERERDwU9DkgNXoI1qefLGb5sjlkZc5i8aIMT7NHjXyMgrwVZC+f62nufj17dCFn5XxW5y7k7rtuqfX5aWkpzJn1Ch+umMeK7Le59Tc3AfDAH+/ks/VZZGXOIitzFpf1ujTmZQF///8r2hfKr9wxLerS+rgGpKfWP+T2txcs4srrf81VN9xC/5/fxrIVK4+4rDu+3skvht1L72tu4hfD7mXH1zsP2F63TogftG5IwwYJh/W5fh7/QTv3lFe3bl0WvTedD7JmsyL7bYY/8FtP88H//Q8QCoXIXPoWU18b53n2sNtuZkX222Qvn8tLLz5N3bp1Pc33e/97nX+o33VXXXU5K7LfZu+3mzjn7DNiXgaoGceeeMtcjJtgiXVSqx3w6SeLOff8y/jqq23RLFKVXNT5XHbt2s2YMU9yVvtunmaHQiFW5SygV+8B5OVtZvGiDAYOGsqqVWtqbX5y8rG0Sj6W5dkradSoIUuXvMlVV/+cn17dh127dvP4P56NWfah+Pn/X9G+8Or/P17z69ULUVICLY+py6b8b753Faw9e76hfv16mBkff7qe3/3xYaZNGFWlMi1d9iFTM2bz1/sP/KX42NPP06TxUfxiUH+ee3EyX+/cyZ1DSxtM9VMuIiW5HiUOdu4sYvee4ipl+Xn8B/Hcc7CGDRuwe/ceEhMTmf/Oa9xx53CWLF3mSXZNqD/A7cOGcM45Z9D4qKPod+UNnuWmpCTz7rzXOP3Mrnz77bdMGP8fZs58mxdenOxJvt/734/8Q/2uO/nktpSUOJ55+hHu/v2f+WDZhzHLLy9ax96+vflxMbZp3ek9ovYF/AcfzYqLOpdXo3tA/LRg4RK2btvuS3anju1Zu3YD69dvpKioiMmTp9K3T89anV9YuIXl2aV/kd61azerV68hNSU5ppmV8fP/3+99Ea/5335bQklJxefzBg3qY1Z6jv7m22/B/ne+Hv3yFK656TauvP7X/Ou5F6tc1nkLFtHvsu4A9LusO2/PX1S2rUnjJHbtLqa4+PB+x/h5/Afx3HOw3bv3AJCUlEhiUhKx/iNdeTWh/qmpreh9WTdGj57gae5+iYmJ1K9fj4SEBBrUr8/mzYWeZfu9//3IP9TvutWrP+WTT9bGNPdQ/Dz2/OCcRW2JRxEbIGb2AzP7nZk9aWaPm9mvzKyxF4VzzjEzYwJLFs/kFzf9zIvIGiElNZlNeQVlz/PyN5Pi4RdAv/OPPz6Ns848jSVLlwMw9NeDWfbBbEaNfIymTZt4Vo6a4OB9ofwjM+fd9+gz4GaG/u4B/nzvHQC8t+QDNublM/G5J/nv2KfJ/fhTsrI/qtLnfbVtO8e0aA5Ai6Ob8VX4F/nnX3xJwwaJfL2z6LDL6Ofx5/ex73c+lP4VOitzFpvzP2Tu3PkszfTuZ78m1P/xxx7knj/8hZKSEk9zAQoKCnn8H/9h/dql5G1czo6vv2b2nPme5fu9//3O95ufx554r9IGiJndBvwHqAd0BOoC6cBiM+tSyfuGmFmWmWWVlOyuduEu6Xolnc7txeV9BvLrX9/IRZ3PrfZnSXxo2LABkyeN4s7fDWfnzl3859kXaHfyBZzToQeFhVt49P8e8LuInjl4Xyj/yHW/5EKmTRjFU488wL9GvQDA+5nLeH/pMq6+8Tf8dPCtrP9sE59tKv0SMODm27nqhlsY/sgTzFu4mKtuuIWrbriF95Z88L3PNrOyHpa/P/ksX237LiplFm+VlJTQoWMPjm/TgY4d2nPqqSf5XSTP/Lh3d7Zs+ZJly6vWAI+2pk2b0LdPT9q2O4/048+mYcMGXHfdT3wpi3gvaMeeK4neEo8iXQXrZuAs51yxmT0OZDjnupjZs8BUoP2h3uScGwmMhCObA1JQUNr1+sUXXzF16kw6djyLBQuXVPfj4kZBfiHpaSllz9NSW5Xti9qcn5iYyCuTRjFhwmu8/vpMALZs+bJs+3PPv8zU172fFOmHQ+0L5UdPh7NOJ6+gkG3bd4CDXwy6hv5X9P7e6yaMegKoeA7I0c2a8sWXWzmmRXO++HIrzcM9dDmr19DymHoAJCQYDRsk4L78jj1VmAfi5/Ef1HPPoezY8TXvvPte6aTgnI89yfS7/hdc0IE+l/fgsl6XUq9eXRo3PopxY5/ihhtv8yS/W7eLWL9hI19+uRWA116fyfnndWD8+Fc9yfd7//udX1P4cez5oSROh05FS1XmgOxvpNQFGgE45zYCSbEqFJSO127UqGHZ4x91v6RW/yCWl5mVTdu2bWjdOp2kpCT69+/HtOmzan3+qJGPsWr1pzzx5MiydcnJx5Y9vqLfZYH5GTjUvlD+kdmYV1A2pjj340/Zu7eIpk0ac0Gns3ltxiz27PkGKB0+9VUV5/906XweU2fOAWDqzDl0veh8AN6aMpaNeXvYmLeHXbv38UUVGx/g7/Ef1HPPfi1aNKdJk9IRxvXq1aN7t4v5+GPvxsL7Xf/77n+E1j/oQNt25/GzgUOZN+89zxofAJs25nPuuWdTv35p4/3Srp1Zvdq7Cfh+73+/8/3k97En3ovUA/IckGlmS4CLgL8DmNkxwNZYFqxly2OY8srzpYVMTGDixNd5a9Y7sYw8wEsvPs0lF59PixbN2bAuiwcfGsGYsRM9yS4uLmbY7feTMWM8CaEQY8dNIjf3E0+y/cq/8IKODBp4NR9+lEtWZukJ949/fIRrrrmCM888Beccn32Wx6+H/j6m5djPz///ivbFzDffVn4ljj2mLvXrJZCQYByf3oD/TnuLffv2AXDNlT9m9jsLeWPmXBITE6lXtw4jHroHM+PCc89h3Web+Nkv7wSgQf16/O2Buzi6WdOIZf3FoP789o8P8+r0t0hJPpbH/nzvEdbe3+M/iOee8lq1asno558gISFEKBRiypRpzMiY41m+3/X329LM5bz66gwyl5Yeu9nZOYx67mXP8v3e/37kH+p33dZt23nyH3/hmGOa88bUF1ixIofel8d2Hq7fx54f4nXyeLREvAyvmZ0K/BBY6ZxbfbgBRzIES0Skug6+DK/X6qdc5Gu+iEgQxctleFe36x2178cnf5IRF3UuL+Kd0J1zOUCOB2UREREREZFaLmIDREREREREoqeW3+YkIjVAREREREQ85EribtRUVOlO6CIiIiIi4hn1gIhIraRJ4CIiUlMF/T4gaoCIiIiIiHgo6Jfh1RAsERERERHxjHpAREREREQ8pKtgiYiIiIiIZ4I+B6RGD8Hq2aMLOSvnszp3IXffdUug8oNc96Dnjxr5GAV5K8hePtfT3PKCvP+Dnh/kuitf+UHOD3LdxXvmYtwHlFgntVoBoVCIVTkL6NV7AHl5m1m8KIOBg4ayatWaaBexxuUHue7Kh4s6n8uuXbsZM+ZJzmrfzZPM8vyuv/J17lG+8pWvY7+6+fv25sdF18Ly4/pF7Qt4+41T46LO5dXYHpBOHduzdu0G1q/fSFFREZMnT6Vvn56ByA9y3ZUPCxYuYeu27Z7lHczv+itf5x7lK1/5Ova9zPeDc9Fb4lGNbYCkpCazKa+g7Hle/mZSUpIDkR/kuivff37XX/k69yhf+crXsR+k37tBpEnoIiIiIiIe0iT0SphZEzN7xMxWm9lWM/vKzFaF1zWt5H1DzCzLzLJKSnZXq2AF+YWkp6WUPU9LbUVBQWG1Pive8oNcd+X7z+/6K1/nHuUrX/k69mv7713nLGpLPIo0BGsysA3o4pxr7pw7GugaXje5ojc550Y65zo45zqEQg2rVbDMrGzatm1D69bpJCUl0b9/P6ZNn1Wtz4q3/CDXXfn+87v+yte5R/nKV76O/SD93g2iSEOwWjvn/l5+hXOuEPi7mf08dsWC4uJiht1+PxkzxpMQCjF23CRycz+JZWSNyQ9y3ZUPL734NJdcfD4tWjRnw7osHnxoBGPGTvQs3+/6K1/nHuUrX/k69r3M90PQh2BVehleM5sFzAHGOec+D69rCdwI/Mg51z1SQHUvwysiIiIicjji5TK8i1N+ErXvx+cVvBoXdS4v0hCsa4CjgXfDc0C2Au8AzYGfxrhsIiIiIiK1TomzqC3xqNIhWM65bcDvw8sBzGwwMCZG5RIRERERkVroSO4D8mDUSiEiIiIiEhBBvwpWpT0gZvZhRZuAltEvjoiIiIhI7VbidwF8FukqWC2BnpRedrc8A96PSYlERERERKTWitQAmQ40cs5lH7zBzN6JSYlERERERGoxR3wOnYqWSJPQb6pk23XRL46IiIiISO1WEvCbVBzJJHQREREREZHDEmkIloiIiIiIRFGJhmCJiIiIiIhXgj4HpEYPwerZows5K+ezOnchd991S6Dyg1x35Stf+f7ljxr5GAV5K8hePtfT3P2CvO+Vr3x97/AvX7xlzsV2FkxindRqBYRCIVblLKBX7wHk5W1m8aIMBg4ayqpVa6JdxBqXH+S6K1/5yvc3/6LO57Jr127GjHmSs9p38yRzP7/rrnzlBzW/NtV93978uOhamN3ymqh9Af/R55Pios7l1dgekE4d27N27QbWr99IUVERkydPpW+fnoHID3Ldla985fubv2DhErZu2+5ZXnl+1135yg9qfpDr7heHRW2JxMyamtkUM1ttZqvM7Hwza25ms81sTfjfZuHXmpk9ZWafmtmHZnZ2uc+5Ifz6NWZ2w5HUv8Y2QFJSk9mUV1D2PC9/MykpyYHID3Ldla985fub7ye/66585Qc1P8h1D4gngTedcycDZwKrgHuAuc65E4G54ecAlwEnhpchwDMAZtYcGA6cC3QChu9vtFRHtRsgZjazkm1DzCzLzLJKSnZXN0JEREREpNYpieJSGTNrAlwMPA/gnNvrnNsO9APGhV82Drgi/Lgf8IIrtRhoamatgJ7AbOfcVufcNmA20Ku69a/0Kljlu10O3gScVdH7nHMjgZFQ/TkgBfmFpKellD1PS21FQUFhdT6qWvzMD3Ldla985fub7ye/66585Qc1P8h190ukhsPhMLMhlPZW7Dcy/F0coA3wBTDGzM4EPgCGAS2dc5vDrykEWoYfpwKbyn1WXnhdReurJVIPSCYwAnjsoGUE0LS6oVWRmZVN27ZtaN06naSkJPr378e06bNiGVlj8oNcd+UrX/n+5vvJ77orX/lBzQ9y3WsD59xI51yHcsvIcpsTgbOBZ5xz7YHd/G+41f73O8DTe7NHug/IKuCXzrnvXYbAzDYd4vVRU1xczLDb7ydjxngSQiHGjptEbu4nsYysMflBrrvyla98f/NfevFpLrn4fFq0aM6GdVk8+NAIxoyd6Em233VXvvKDmh/kuvvFw/uA5AF5zrkl4edTKG2AfG5mrZxzm8NDrLaEt+cD6eXenxZelw90OWj9O9UtVKWX4TWzq4GPnHMfH2LbFc651yMFVHcIloiIiIjI4YiXy/BOSx4Qte/HfQonVFpnM1sA/MI597GZ/QloGN70lXPuETO7B2junLvbzH4M/AboTemE86ecc53Ck9A/oLQ3BWAZcI5zbmt1ylxpD4hzbkolm6s9811ERERERDxxK/CymdUB1gGDKZ2GMdnMbgI+A/qHX5tBaePjU2BP+LU457aa2Z8pnZ4B8FB1Gx9wBDciNLONzrnjIr1OPSAiIiIi4oV46QGZmnxd1L4f9yscHxd1Li/SVbA+rGgT/5stLyIiIiIiVRT0v85HmoTektLr/m47aL0B78ekRCIiIiIiUmtFaoBMBxo557IP3mBm78SkRCIiIiIitVg07wMSjyJNQr+pkm3XRb84IiIiIiK1W4nF3bSNqIp0I0IREREREZGoiTQES0REREREokiT0EVERERExDNBnwNSo4dg9ezRhZyV81mdu5C777olUPlBrrvyle9nft26dVn03nQ+yJrNiuy3Gf7Abz3Nh2Cee0KhEJlL32Lqa+MAaN06nfcXTmN17kLGv/wMSUlJnpQjyD/7yg92fpDrLt6r9o0Iq6q6NyIMhUKsyllAr94DyMvbzOJFGQwcNJRVq9ZEu4g1Lj/IdVe+8v3OB2jYsAG7d+8hMTGR+e+8xh13DmfJ0mWeZAf13HP77L1gUAAAIABJREFUsCGcc84ZND7qKPpdeQMTxv+H117PYPLkN3j6X4/w4Ye5PDvyhZiWwe+fPeUrP4jHfrTz4+VGhBNSfha1L+ADCl6OizqXV2N7QDp1bM/atRtYv34jRUVFTJ48lb59egYiP8h1V77y/c4H2L17DwBJSYkkJiUR6z/UlBfEc09qait6X9aN0aMnlK3r2uVC/vvfGQC8+OIr9Osb+3L4/bOnfOUH7divKfl+KMGitsSjGtsASUlNZlNeQdnzvPzNpKQkByI/yHVXvvL9zofSv8ZlZc5ic/6HzJ07n6WZyz3LDuK55/HHHuSeP/yFkpLSUdFHH92M7dt3UFxc/L9ypMa+HH7/7Clf+UE79mtKvniv0gaImTU2s7+Z2Ytmdt1B2/5dyfuGmFmWmWWVlOyOVllFRDxRUlJCh449OL5NBzp2aM+pp57kd5FqrR/37s6WLV+ybPlHfhdFRMQzLopLPIp0FawxwBrgv8DPzewq4Drn3HfAeRW9yTk3EhgJ1Z8DUpBfSHpaStnztNRWFBQUVuejqsXP/CDXXfnK9zu/vB07vuadd98rnRyZ87EnmUE791xwQQf6XN6Dy3pdSr16dWnc+Cj+8fhDNG3ahISEBIqLi0vLkR/7feD3z57ylR+kY78m5fuhJD5HTkVNpCFYJzjn7nHOve6c6wssA942s6NjXbDMrGzatm1D69bpJCUl0b9/P6ZNnxXr2BqRH+S6K1/5fue3aNGcJk0aA1CvXj26d7uYjz9e61l+0M49993/CK1/0IG27c7jZwOHMm/ee1x/w6288+77XHXVjwEYNOinvDEt9vvA75895Ss/SMd+TcoX70XqAalrZiHnXAmAc+6vZpYPzAcaxbJgxcXFDLv9fjJmjCchFGLsuEnk5n4Sy8gakx/kuitf+X7nt2rVktHPP0FCQohQKMSUKdOYkTHHs/wgn3vK+8O9f2X8S//moT/dTfaKHEaPmRD5TUfI7/orX/lBPfb9zvdD0O8DUulleM3s/4BZzrk5B63vBfzTOXdipIDqDsESERERETkc8XIZ3jGpA6P2/Xhw/ktxUefyKu0Bcc7dXcH6N83s4dgUSUREREREaqsjuQzvg1ErhYiIiIhIQJRY9JZ4VGkPiJl9WNEmoGX0iyMiIiIiUrsFfQ5IpEnoLYGewLaD1hvwfkxKJCIicgSOqlPf1/yde7/xNV9EpKaL1ACZDjRyzmUfvMHM3olJiUREREREajH1gFTCOXdTJduuq2ibiIiIiIgcmovTuRvRciST0EVERERERA5LpCFYIiIiIiISRRqCJSIiIiIingl6A6RGD8Hq2aMLOSvn/397dx4fVXX/f/z1SUgqi2yiQBYJitpqa6FFrAoKoqwCQhWLioq2+HUraqttFetWq63i0la/CiKoIGtRIAIiIAICASRBSYIoi5IFRMAF7e9LCOf3xww0AsmEMHPvTO77yWMezNw7M+9zJsnJPbnnnMu6giXcfdctgcoPct2Vr3y/8z9Zv5zc1fNYtXIuy5fN8jxfbc+R51f8mi1Y9Poh+28b9msWLZ3BoqUzWLpiFl989RGNmzQ6qrKmpqYy+uVneH/NfN5+ZyqZJ6YDcFHXTuQsn03u6nnkLJ9Nl87nVfs9E/XzV37i5we57uI9cy5qV4I/rDqp6TUKSEpKojB/MT16DaKoqJTly2Zx9eCbKSz8ONpFjLv8INdd+cr3Ox9CB7Nnn9OTHTsOXoE89tT21Cy/4tcs0jK8PXpeyE23DqFf78HVKlfmiek898Lf6dPzqu9tv+E3V3HGj0/jzmF/ZsBlvendpxs3XDuMk08/iW3bvqC0dBtnnHEas7LH06p1+4g5ifz5Kz+x82tT3ffuKU6I6d3/zLw6agfgt20ZlxB1rihuz4B0OKsdGzZsZtOmzygrK2Py5On07dM9EPlBrrvyle93vt/U9sQ+/5eXX8K/p2QfeDzwin7MW/hvFi2dwVP/eJikpOr9auzZ+yImjA+dbZn++hwu6HwOAHl5+ZSWbgMgP/8j6tY9htTU1IjvF5TPX/nxlx/kuvsl6FdCj9sOSFp6C7YUlRx4XFRcSlpai0DkB7nuyle+3/kAzjlmz5pAzvLZ/PqGqyK/IIrU9tQsv+LX7NohV1T6vLp1j6HrReczY/ocAE497WT6/7I3PS66gvPP7Ut5+T4uv6Jv9cqb1pziolIAysvL+fqr3TQ9rsn3njNgQG9yc9eyZ8+eyO+XwJ+/8hM7P8h1F39UOQndzFoA9xOaK/Nn4Dbgl0AhMMw5V1rJ64YCQwEsuRFJSfWjWWYRkZi6oEt/Skq2cvzxxzFn9kQ++ugTFi/J8btYUoWKX7O350zi4/UbWfreykOe16PXheQsX82Xu74Kva7zufy03RksWDQNgGOOOYbt23cA8OqE52jVKoOU1FQyMlqyaOkMAJ5/7mVeG/fviGU6/fRTefSRe+jZW5fNEpHvC/ok9EirYI0F3gTqA+8A44FewKXA80C/w73IOTcSGAk1nwNSUryVzIy0A48z0ltSUrK1Jm9VI37mB7nuyle+3/nAgbzt23cwffpszjqrrWcdELU9Ncuv+DXLnvk2P/v5mYftgAy47BL+PWXmfzcYTBz/Og898MQhzx086Gag8jkgJSXbSM8IlTE5OZmGjRqwMzxvKD29JVOnjGbI9cPYuPHT6tUhgT9/5Sd2fpDr7pegd0AiDcFq7pz7p3PuMaCxc+5vzrktzrl/Aq1iWbCVq/Jo06Y1WVmZpKSkMHBgP2Zmz41lZNzkB7nuyle+3/n16tWlQYP6B+5ffNEF5Od/5Fm+2p4jzz/4a3bhhR0pLDh08mrDhg0477wOzHpz3oFtixYuo++lPWh2fFMAGjdpRGZm2iGvPZw5s+Yz6Kr+APTr34NF7y4HoFGjhsyY/gr33PtXli5bVa33gsT9/JWf+PlBrrv4I9IZkIodlFcO2pcc5bJ8T3l5OcNuH86sN18jOSmJsS9PoqBgfSwj4yY/yHVXvvL9zm/e/HimThkNQJ06yUyc+AZvzV3oWb7aniPPP/hr9vqUbObPW8SQGwYBMGb0BAB69+nGOwuW8N13/znw2o/WfcIjDz/JtOljSUpKoqxsL3fd+QBbtpQcGnSQV1+ezPMvjuD9NfPZtetLbrjudgBuuXkIbU7OYvi9dzD83jsA6Nlr0IGhXdGuf7QoP7j5Qa67X2K7Bm38q3IZXjN7CPi7c273QdvbAI855y6LFFDTIVgiIiI1EWkZ3lj7Zs9/Ij9JRGIiUZbh/Xur6C3De/enibcMb5VnQJxzf65k+ydm9mZsiiQiIiIiUntpDkjNPRi1UoiIiIiISCBEWob3g8p2Ac2jXxwRERERkdot6PMTIk1Cbw50B3YdtN2ApTEpkYiIyFHQHAwRiXf7At4FidQByQYaOOfyDt5hZgtjUiIREREREam1Ik1Cv6GKfbq0q4iIiIjIEQr6JPRIZ0BERERERCSKgj0A6+hWwRIRERERETkiOgMiIiIiIuKhoA/BiuszIN27dSZ/7SLWFSzh7rtuCVR+kOuufOX7mT9q5AhKitaQlzvf09yK1PYoX/nK189+7bbPondLROZcbEeh1UlNr1FAUlIShfmL6dFrEEVFpSxfNourB99MYeHH0S5i3OUHue7KV77f+Z06ns3u3d8yZswztG3X1ZPMitT2KF/5ytfPfs3z9+4pTohD8j9nXRW1A/CHNo9PiDpXFLdnQDqc1Y4NGzazadNnlJWVMXnydPr26R6I/CDXXfnK9zt/8ZIcdu760rO8g6ntUb7yla+ffW/z/bAPF7VbIorbDkhaegu2FJUceFxUXEpaWotA5Ae57spXvt/5flPbo3zlK9/r/CDX3S8uirdEdMQdEDM7oRrPGWpmq8xs1b5939asZCIiIiIiUutU2QExs6YH3Y4DVphZEzNrWtnrnHMjnXPtnXPtk5Lq16hgJcVbycxIO/A4I70lJSVba/ReiZYf5LorX/l+5/tNbY/yla98r/ODXHe/7IvirTrMLNnMcs0sO/y4tZnlmNknZjbJzFLD238QfvxJeH9Whff4U3j7R2Z2VGPkIp0B+QJ4v8JtFZAOrA7fj5mVq/Jo06Y1WVmZpKSkMHBgP2Zmz41lZNzkB7nuyle+3/l+U9ujfOUrXz/7tb/d92EOyDCgsMLjvwFPOefaALuAG8LbbwB2hbc/FX4eZnY68CvgDKAH8JyZJde0/pGuA3IXcDFwl3Puw3ABNjnnWtc0sLrKy8sZdvtwZr35GslJSYx9eRIFBetjHRsX+UGuu/KV73f+uFef5YLzz6FZs6Zs3riKBx96gjFjJ3qWr7ZH+cpXvn72vc2v7cwsA+gNPALcaWYGXAhcGX7Ky8ADwP8C/cL3AaYC/wo/vx8w0Tn3f8AmM/sE6AAsq1GZIi3DGy70U8AW4H5gjXPupOoG1HQZXhERERGRI5Eoy/DenTUoasfHj3868UZgaIVNI51zI/c/MLOpwKPAscDvgeuA5eGzHJhZJjDbOfdjM1sL9HDOFYX3bQDOJtQpWe6cGxfePjr8mqk1KXPEK6GHC3C5mfUF3gbq1SRIRERERESieyX0cGdj5OH2mdklwOfOuffNrHMUY49KtVfBcs7NALoAFwGY2ZBYFUpERERERI7aeUBfM9sMTCQ09OoZoLGZ7T8RkQEUh+8XA5kA4f2NgB0Vtx/mNUfsiJbhdc79xzm3NvzwwZqGioiIiIgElVeT0J1zf3LOZTjnsghNIl/gnLsKeAe4LPy0a4Hp4fszwo8J71/gQvM1ZgC/Cq+S1Ro4BVhR0/pXOQTLzD6obBfQvKahIiIiIiJBFQcTpP8ATDSzvwC5wOjw9tHAq+FJ5jsJdVpwzuWb2WSgANgL3OKcK69peKQ5IM2B7oSW56rIgKU1DRUREZHYaFK3ga/5u/6z29d8ETk859xCYGH4/kZCq1gd/Jz/B1xeyesfIbSS1lGL1AHJBho45/IO3mFmC6NRABERERGRIInmJPREVGUHxDl3QxX7rqxsn4iIiIiIHJ6Lh0FYPjqiSegiIiIiIiJHI+J1QEREREREJHqCPgQrrs+AdO/Wmfy1i1hXsIS777olUPlBrntGRhrz5k7hgzXvsCZvAbfdWulIwJgJ8ufvd/6okSMoKVpDXu58T/Kq+n675eYhrP3wXdbkLeCxR+/1pDygtkf5R5Z/6qkns2rl3AO3jVve58abrv3ec9qcchKz3p5I0ecfcvNt10elrKmpKYwa8xQrcucyZ/5kMk9MB+Cirp3IWT6b3NXzyFk+my6dz6v2eybi519b8oNcdz94tQxvvLLQ0r6xUyc1vUYBSUlJFOYvpkevQRQVlbJ82SyuHnwzhYUfR7uIcZcf5LoDtGhxAi1bnEBu3loaNKjPipw5/PKy6wNT/6Dnd+p4Nrt3f8uYMc/Qtl3XmOdV9v3W/ITj+dMff0ufftewZ88ejj/+OLZv3xHz8qjtUf7R5CclJVH06Wq6dx1I0ZaSA9ubNWtKxonp9OrdlS+//Jrn/vlStd8z88R0/vnco1x6yTXf2z7k11dy+hmncdcd93PpL3vR+5KL+c2QO2h1Wiu2bfuC0tJtnHHGaczKHk+r1u09qf/RCHJ+bar73j3FFoMiRt3NWQOjdgD+3ObJCVHniuL2DEiHs9qxYcNmNm36jLKyMiZPnk7fPt0DkR/kugNs3fo5uXmh613u3v0t69Z9THpaC8/y/a5/0PMXL8lh564vPcur7Pvtxhuv4e+PP8uePXsAPOl8gNoe5R9dftcLO7J505bvdT4AvvhiJ3mrP6SsbO8hr7lsYF/eWjCFdxa/wRNPP0hSUvUODXr2upBJr70OwMw33qLTBecAkJeXT2npNgDy8z+ibt1jSE1Njfh+teHzT9T8INfdLy6Kt0QUtx2QtPQWbCn6bwNaVFxKmocHoX7mB7nuB2vVKoO2P/0xOStyPcv0u/5Bz/dTxe+3U045iY4dO7B0yUwWzJtK+5//1JMyqO1R/tHkDxzYj2lTs6v9/FNOPYlLB/Skd7dBdOl0KeXl+7hsYJ9qvbZFy+YUF5cCUF5eztdff0PTpk2+95wBA3qTm7v2QEe+KrXh80/U/CDX3S9BH4IVk0noZjYUGApgyY1ISqofixip5erXr8fkSaO48/f38803urCVxNbB32916iTTpEljzu3Yh7Pat2XCa89zymnn+F1MkUqlpKTQ55JuPP6Xf1T7NedfcA4/bftj3n5nKgDH1D2GL8Jn+8aO+xetWmWQkppCRkZL3ln8BgAjn3+FCeOnRXzv008/lUcfuYeevbVqv4h8X5UdEDPr4ZybE77fCHgSOAtYC9zhnNt2uNc550YCI6Hmc0BKireSmZF24HFGektKSrbW5K1qxM/8INd9vzp16jBl0igmTHidN96Y7Wm23/UPer4fDvf9VlxUeuD+ylV57Nu3j2bNmvLFFztjWha1PcqvaX6PHl3Izf3wiIYLmhmTJrzOXx588pB91119K1D5HJCtpdtIT29Jack2kpOTadjwWHbu3AVAenpLpk4ZzZDrh7Fx46fVKkuif/6JnB/kuvtFq2BV7a8V7o8ASoE+wErghVgVCkK/8Nu0aU1WViYpKSkMHNiPmdlzYxkZN/lBrvt+o0aOoHDdJzz9zEhPc8H/+gc93w+H+36bPuMtOnc+F4BTTjmJ1NTUmHc+QG2P8mue/6srLmXipDeOKHPRu8vo0687zZo1BaBxk0ZkZKZFeFXInFkLuOLK/gD0ubQ7SxYtB6BRo4bMmP4K99z7V5YuW1XtsiT655/I+UGuu19cFP8loiMZgtXeOdc2fP8pM7u2ymcfpfLycobdPpxZb75GclISY1+eREHB+lhGxk1+kOsOcN65ZzH46sv44MMCVq0MNUD33fcYs+cs8CTf7/oHPX/cq89ywfnn0KxZUzZvXMWDDz3BmLETY5ZX2ffbmLETeXHUCPJy57NnTxnX33B7zMpQkdoe5dckv169ulzU9XxuuvkPJIe3XXv9rwB4+aWJnHBCM95e+G+OPbYB+/bt48abruW8s3ux/qMNPPqXp5ny+ktYUhJ795bxh989dMgk9sMZ/+pUnhv5OCty57Jr11cMvf4OILR8dZuTsxh+7x0Mvze0rWevQRHPzCTy55/o+UGuu/ijymV4zayI0LArA24BTnbhF5jZB865MyMF1HQIloiIiBy5JnUb+Jq/6z+asyf+SZRleK/Puixqx8cvbZ6aEHWuKNIZkFHAseH7LwPNgO1m1gLIi2XBRERERERqo0QdOhUtVXZAnHMPVrJ9q5m9E5siiYiIiIhIbXU01wE5bOdEREREREQqty+Kt0QUaRneDyrbBTSPfnFERERERGq3fVXMwQ6CSHNAmgPdgV0HbTdgaUxKJCIiIjXm9yTwY1Pr+pr/zZ7/+JovIpFF6oBkAw2cc4dMODezhTEpkYiIiIhILRbs8x+RJ6HfUMW+K6NfHBERERGR2m1fwLsgRzMJXURERERE5IgcyZXQRURERETkKAX9OiBxfQake7fO5K9dxLqCJdx91y2Byg9y3ZWvfD/zR40cQUnRGvJy53uaW5HaHuUnWv4n65eTu3oeq1bOZcGi1w/Z37P3RSxZns2ipTNYsOh1fnHOz4+6rI2bNGLajLGsypvHtBljadS4IQCDBvVn9ftvk7t6Hovfnc6ZZ55e7fdM1M8/0bPjId9rQV+G11yMlwGrk5peo4CkpCQK8xfTo9cgiopKWb5sFlcPvpnCwo+jXcS4yw9y3ZWvfL/zO3U8m927v2XMmGdo266rJ5kVqe1RfiLmf7J+OWef05MdO3YddhWs+vXr8e233wFwxhmn8dKr/+Dsn3WvVrnO63Q2V141gFv+5w/f2/7gw3eza9dXPP3kC9x+5400btyQB/78OD/+2RkUrvuYL7/8ih7du/Dn++7k3I59Ylr/aNDPfnTy9+4pthgUMequaHVp1A7AJ336RkLUuaK4PQPS4ax2bNiwmU2bPqOsrIzJk6fTt0/1GqtEzw9y3ZWvfL/zFy/JYeeuLz3LO5jaHuXXxvz9nQ+AevXrUfGPn7cN+zXz353GkuXZ/PHeYdV+z569L2LC+GkATBg/jV6XXAzAsuWr+PLLrwBYnrOa9PSW1Xq/2vz5x3N2POT7YR8uardEFLcdkLT0FmwpKjnwuKi4lLS0FoHID3Ldla98v/P9prZH+YmY75xj9qwJ5CyfzbVDrjjsc3r3uZic1W8xaeoobrvpTwB0ubAjJ7XJousFA+h0Th/atj2Dc887q1qZJ5zQjG3btgOwbdt2Tjih2SHPuX7Ir5jz1jvVer9E/vwTOTse8v3govgvER3xJHQzO845tyPCc4YCQwEsuRFJSfVrWDwRERGJdxd06U9JyVaOP/443p4ziY/Xb2Tpeyu/95w3Z77NmzPf5tzzzuKe+26nf59r6dK1Ixde2JFFS2cAUL9+fU46OYul763k7Xem8oMfpFK/fn2aNGl04DkP3Pc4C+YvPqQMBw8p73zBuQwZMogLOvePUa1FpKaq7ICY2WPAE865L8ysPTAZ2GdmKcA1zrl3D/c659xIYCTUfA5ISfFWMjPSDjzOSG9JScnWmrxVjfiZH+S6K1/5fuf7TW2P8hMxf//ztm/fQfbMt/nZz888pAOy39L3VpKVlUnT45pgZjw14nnGvjTxkOdd3OUyoPI5IJ9//gXNmx/Ptm3bad78eLZv/+/fRn/ykx/xwvOPc0nfwezcuat6dUjgzz+Rs+Mh3w+JOnk8WiINwertnPsifP9x4ArnXBvgYmBELAu2clUebdq0Jisrk5SUFAYO7MfM7LmxjIyb/CDXXfnK9zvfb2p7lJ9o+fXq1aVBg/oH7l94YUcKC74/ebj1Sa0O3D/zp2eQ+oNUdu7YxYJ5i7lq8GXUr18PgJYtm9Ps+KbVKu+cWfMZdNUAAAZdNYDZb84DIDMzjSmTRnHdkGF8/PHGar0XJO7nn+jZ8ZDvB+dc1G6JKNIQrDpmVsc5txeo65xbCeCcW29mP4hlwcrLyxl2+3BmvfkayUlJjH15EgUF62MZGTf5Qa678pXvd/64V5/lgvPPoVmzpmzeuIoHH3qCMWMP/etsrKjtUX6i5TdvfjxTp4wGoE6dZF6fks38eYsYcsMgAMaMnkDfft254sr+7C0r4z//+T9uuDY02fydBUs49YcnM3fBFAB27/6OG3/9O77YvjNi7lNPvsCYV/7B1ddczpYtxQy55rcADL/3Do47rgn//OdfAdi7dy+/OKdXzOofLfrZ9y9fvFflMrxmdhvQB3gMOB9oAkwDLgROcs4NjhRQ0yFYIiIikngOtwyvl77Z8x9f88VfibIMb78TL4na8fH0z7ITos4VVXkGxDn3TzP7ELgJODX8/FOAN4CHY188EREREZHaJehzQCKuguWcWwgsPHi7mQ0BxkS/SCIiIiIitVeiLp8bLUdzHZAHo1YKEREREREJhEjL8H5Q2S6gefSLIyIiIiJSuyXqFcyjJdIQrOZAd+DgRbQNWBqTEomIiEjC0iRwkcgSdfncaInUAckGGjjn8g7eYWYLY1IiERERERGptSKtgnVDFfuujH5xRERERERqN62CJSIiIiIintEqWCIiIiIiIh6J6w5I926dyV+7iHUFS7j7rlsClR/kuitf+X7mjxo5gpKiNeTlzvc0tyK1PcpXvvL1s1+77cNF7ZaILNaz8OukptcoICkpicL8xfToNYiiolKWL5vF1YNvprDw42gXMe7yg1x35Svf7/xOHc9m9+5vGTPmGdq26+pJZkVqe5SvfOXrZ7/m+Xv3FFsMihh1XTO6Re0AfH7R3ISoc0Vxewakw1nt2LBhM5s2fUZZWRmTJ0+nb5/ugcgPct2Vr3y/8xcvyWHnri89yzuY2h7lK1/5+tn3Nl+8F7cdkLT0FmwpKjnwuKi4lLS0FoHID3Ldla98v/P9prZH+cpXvtf5Qa67X4I+BCsmq2CZ2VBgKIAlNyIpqX4sYkREREREEo5WwaqCma02s+FmdvKRvKlzbqRzrr1zrn1NOx8lxVvJzEg78DgjvSUlJVtr9F6Jlh/kuitf+X7n+01tj/KVr3yv84Ncd/FHpCFYTYDGwDtmtsLM7jCztAiviYqVq/Jo06Y1WVmZpKSkMHBgP2Zmz/Ui2vf8INdd+cr3O99vanuUr3zl62e/9rf7+5yL2i0RRRqCtcs593vg92bWCRgErDazQmCCc25krApWXl7OsNuHM+vN10hOSmLsy5MoKFgfq7i4yg9y3ZWvfL/zx736LBecfw7NmjVl88ZVPPjQE4wZO9GzfLU9yle+8vWz722+HxKz2xA9VS7Da2arnXM/O2hbMnAxcIVzbkikgJouwysiIiIiciQSZRneTuldo3Z8vLh4fqV1NrNM4BWgOaF+z0jn3DNm1hSYBGQBm4GBzrldZmbAM0Av4DvgOufc6vB7XQsMD7/1X5xzL9e0zJGGYB3S/XTOlTvn5lSn8yEiIiIiIt/n4SpYe4HfOedOB34B3GJmpwN/BOY7504B5ocfA/QETgnfhgL/CxDusNwPnA10AO43syY1rX+VHRDn3K8q22dm6oCIiIiIiBwhrzogzrnS/WcwnHPfAIVAOtAP2H8G42Xg0vD9fsArLmQ50NjMWgLdgbedczudc7uAt4EeNa3/0VwH5MGjeK2IiIiIiBwlMxtqZqsq3IZW8rwsoB2QAzR3zpWGd20lNEQLQp2TLRVeVhTeVtn2GqlyErqZfVDZLv5bUBERERERqaaq5mDX4L1GAlUuDGVmDYB/A7c7574OTfU48HpnZp7O2Y60ClZzQqdcdh203YClMSmRiIiISIJKTjqawSVHr3zfPl/zpXq8vIK5maUQ6nyMd85NC2/eZmYtnXOl4SFWn4e3FwOZFV6eEd5WDHQ+aPvCmpYp0k9JNtDAOffpQbfNRxM/BAQtAAAZIklEQVQqIiIiIiKxFV7VajRQ6Jx7ssKuGcC14fvXAtMrbL/GQn4BfBUeqvUW0M3MmoQnn3cLb6uRKs+AOOduqGLflTUNFREREREJKufdGZDzgMHAh2aWF952D/AYMNnMbgA+BQaG980itATvJ4SW4R0C4JzbaWYPAyvDz3vIObezpoWq8jog0aDrgIiIiEhQaAiWvxLlOiDtW3aK2vHxqtLFCVHnivz9KRERERERkUCJ6w5I926dyV+7iHUFS7j7rlsClR/kuitf+cpX26N85SdK/qiRIygpWkNe7vzD7m/cuBGTJ41i1cq5LFk8k9NPP+2oy5mamsq4V5+jIH8xixfNoFWrDAC6du1EzvLZ5K6eR87y2XTpfF613zMRP/tE5uGFCONS3A7BSkpKojB/MT16DaKoqJTly2Zx9eCbKSz8ONpFjLv8INdd+cpXvtoe5Ss/kfI7dTyb3bu/ZcyYZ2jbrushQ7Ae/eu97P72Wx555GlOO/VknnnmL/ToOahaZWrVKoNRo56kW7eB39t+49Br+MlPfsitt93D5Zf3pV/fHlw9+GZ++tMzKN36OaWl2zjjjNOYlT2eVq3bx6zu0RLN/EQZgtWuxXlROwDP3fpeQtS5org9A9LhrHZs2LCZTZs+o6ysjMmTp9O3T/dA5Ae57spXvvLV9ihf+YmUv3hJDjt3fVnp/h/96BQWLgxdueCj9Rto1SqTE05oBsCgQf1ZsngmK3Lm8Oy/HiWpmvNH+vTpxqvjpgIwbdqbdOkSOtOxZk0+paXbAMjP/4i6dY8hNTU14vsl6mcviStuOyBp6S3YUlRy4HFRcSlpaS0CkR/kuitf+cpX26N85dem/A8+LOTSfj0BaN++LSeemE56ekt+eFobLr+sD5279KfD2T0oL9/HoEH9q1fWtBYUhctaXl7O119/w3HHNfnecwYM6E1u7lr27NkT+f1q6Wcfz4I+BCvShQhFREREpIYef/xZRox4kBU5c1ibv468vHzKy8vp0uU82rU7k6XvZQNQt+4xfL79CwAmTxpFVlYmqakpZGamsyJnDgD/evYlXnllcsTM008/lUcfuYeevXXFhHjl4TK8canKDoiZtQceJ3T1wz8BLwEdgPXAUOdcbiWvGwoMBbDkRiQl1T/igpUUbyUzI+3A44z0lpSUbD3i96kpP/ODXHflK1/5anuUr/zalP/NN7sZOvR3Bx5/9NFSNm36jI7ndWDc+Cncd9/fDnnNwCt+A1Q+B6SkZCsZGWkUF28lOTmZhg2PZceOXQCkp7dk6pTRDLl+GBs3flqtMtbWz17iV6QhWM8BfwfeBJYCLzjnGgF/DO87LOfcSOdce+dc+5p0PgBWrsqjTZvWZGVlkpKSwsCB/ZiZPbdG75Vo+UGuu/KVr3y1PcpXfm3Kb9SoISkpKQBcf/0glizJ4ZtvdrPgnfcY0L83xx9/HABNmjTmxBPTq/We2dlvM/jqy4DQUKuFC987kDVj+ivcc+9fWbpsVbXLWFs/+3i2z7mo3RJRpCFYKc652QBm9jfn3FQA59x8M3silgUrLy9n2O3DmfXmayQnJTH25UkUFKyPZWTc5Ae57spXvvLV9ihf+YmUP+7VZ7ng/HNo1qwpmzeu4uG/jCClTqjDMerFcfzwh20Y/eJTOOcoKFjPjf9zFwDr1n3M/Q88zpvZ40lKSqKsrIxhtw/ns8+KI2aOGTuRMS89TUH+Ynbu/JLB14SWrb3pputoc3IWw++9g+H33gFAz16D2L59R0zqHi1+5/sh6EOwqlyG18yWAfcDjYAngGHOuTfM7AJghHMu4tpuuhK6iIiIBIWuhO6vRFmG94zmZ0ft+Dh/W05C1LmiSGdA/ofQEKx9QHfgJjMbS2hOyG9iWzQRERERkdonUYdORUuV3XTn3BrnXHfnXE/n3Drn3DDnXGPn3BnA0V/KU0REREQkYFwU/yWiozlP+GDUSiEiIiIiIoEQaRneDyrbBTSPfnFERERERGq3oA/BijQHpDmhuR+7DtpuhJblFREREZEwvyeB+zkJ3u+6J5JEHToVLZE6INlAA+dc3sE7zGxhTEokIiIiIiK1VpUdEOfcDVXsuzL6xRERERERqd00BEtERERERDwT9CFY/l4tR0REREREAkVnQEREREREPORcsCfsx/UZkO7dOpO/dhHrCpZw9123BCo/yHVXvvK9zM/ISGPe3Cl8sOYd1uQt4LZbQ1PfzjzzdJYsmkHu6nm88fpYjj22QUzLUZHaHuUrX/nVzR81cgQlRWvIy51/2P133nEjK3LmsCJnDqvfn8d3326mSZPGR1XO1NRUxr36HAX5i1m8aAatWmUAcFHXTuQsn03u6nnkLJ9Nl87nVfs9/f7svbYPF7VbIjIX40kwdVLTaxSQlJREYf5ievQaRFFRKcuXzeLqwTdTWPhxtIsYd/lBrrvyle91fosWJ9CyxQnk5q2lQYP6rMiZwy8vu56XRj/NH/7wMIsWL+e6a6+gdesTuf+Bx2NShorU9ihf+co/kvxOHc9m9+5vGTPmGdq261rlMry9e13Ebb/9NT16/KpaZWrVKoNRo56kW7eB39t+49Br+MlPfsitt93D5Zf3pV/fHlw9+GZ+cuaP2LbtC0pLt3HGGacxK3s8rVq3j1ndD2fvnmI74hf5oNVxZ0btAPzTHR8kRJ0ritszIB3OaseGDZvZtOkzysrKmDx5On37dA9EfpDrrnzle52/devn5OatBWD37m9Zt+5j0tNacOopJ7Fo8XIA5s1fTP/+vWJWhorU9ihf+co/kvzFS3LYuevLamUMvKIfkydPP/B40KD+LFk8kxU5c3j2X4+SVM1riPTp041Xx00FYNq0N+nSJXSmIy8vn9LSbQDk539E3brHkJqaGvH9/P7s/eCci9otEcVtByQtvQVbikoOPC4qLiUtrUUg8oNcd+Ur38/8Vq0yaPvTH5OzIpeCgvX07Rv6BXjZLy8hMyPNkzKo7VG+8pUfi/y6dY+h28Wdef312QD88LQ2XH5ZHzp36U+Hs3tQXr6PQYP6V6+saS0oCpe1vLycr7/+huOOa/K95wwY0Jvc3LXs2bMn8vv5/Nn7IehDsKqchG5mDYC7gV8CGcAeYAPwvHNubBWvGwoMBbDkRiQl1Y9WeUVEYqJ+/XpMnjSKO39/P998s5tfD72Tp598mHvvuZ3s7Lns2VPmdxFFRGqsd++LWbZsJbvCZ0u6dDmPdu3OZOl72UCog/L59i8AmDxpFFlZmaSmppCZmc6KnDkA/OvZl3jllckRs04//VQefeQeevbWJePk8CKtgjUeeB3oDgwE6gMTgeFmdqpz7p7Dvcg5NxIYCTWfA1JSvPV7f3HMSG9JScnWmrxVjfiZH+S6K1/5fuTXqVOHKZNGMWHC67zxRuivgx99tOHAL89TTjmJXj27xrQM+6ntUb7ylR+L/IGX92XS5BkHHpsZ48ZP4b77/nboc6/4DVD5HJCSkq1kZKRRXLyV5ORkGjY8lh07dgGQnt6SqVNGM+T6YWzc+Gm1yub3Z++HRB06FS2RhmBlOefGOueKnHNPAn2dcx8DQ4ABsSzYylV5tGnTmqysTFJSUhg4sB8zs+fGMjJu8oNcd+Ur34/8USNHULjuE55+ZuSBbccffxwQ+iV9z5+G8cLIV2Nahv3U9ihf+cqPdn7DhsfSqdMvmDnzrQPbFrzzHgP69z7Q1jVp0pgTT0yv1vtlZ7/N4KsvA0JDrRYufA+ARo0aMmP6K9xz719ZumxVtcvn92fvh33ORe2WiCKdAfnWzDo655aYWV9gJ4Bzbp+ZxXTGfXl5OcNuH86sN18jOSmJsS9PoqBgfSwj4yY/yHVXvvK9zj/v3LMYfPVlfPBhAatWhn7h3XffY7Rp05qbbroOgDfemMXYlyfFrAwVqe1RvvKVfyT54159lgvOP4dmzZqyeeMqHv7LCFLqpAAw6sVxAPTr14N58xbx3Xf/OfC6des+5v4HHufN7PEkJSVRVlbGsNuH89lnxREzx4ydyJiXnqYgfzE7d37J4GtCy+becvMQ2pycxfB772D4vXcA0LPXILZv3xGTukviqnIZXjM7E3gROAXIB653zq03s+OBQc65f0QKqOkQLBERERE5MlUtwxtr5fv8v7heoizD26Lxj6J2fLz1y8KEqHNFVZ4Bcc59AHQ4zPbtZvZNzEolIiIiIlJLaQ5IzT0YtVKIiIiIiASEluGtgpl9UNkuoHn0iyMiIiIiIrVZpEnozQktwbvroO0GLI1JiURERESkRuJhHoZEFvQhWJE6INlAA+dc3sE7zGxhTEokIiIiIlKLJeryudESaRL6DVXs0+UtRURERETkiEQ6AyIiIiIiIlGkIVgiIiIiIuKZRF29Klr8u1qNiIiIiIgETlx3QLp360z+2kWsK1jC3XfdEqj8INdd+cr3M3/UyBGUFK0hL3e+p7kVqe3xL79Ro4ZMmjiStR++y4cfLOQXZ//c03y/66/84Ob7Xfd4aHu95JyL2i0RWawLXic1vUYBSUlJFOYvpkevQRQVlbJ82SyuHnwzhYUfR7uIcZcf5LorX/l+53fqeDa7d3/LmDHP0LZdV08yK1Lb418+wEujn2bJkhxeGjOBlJQU6tWry1dffe1Jtt/1V35w8/2uO0Sv7d27p9iiWKyYaVCvddQOwHd/tykh6lxR3J4B6XBWOzZs2MymTZ9RVlbG5MnT6duneyDyg1x35Svf7/zFS3LYuetLz/IOprbHv/yGDY+lU8ezeWnMBADKyso863yA//VXfnDz/a47+N/2ireq7ICYWSMze8zM1pnZTjPbYWaF4W2NY1mwtPQWbCkqOfC4qLiUtLQWsYyMm/wg1135yvc7329qe/zLb936RL74YgejX3yKlSve4oXnH6devbqe5ftdf+UHN9/vugeRi+K/RBTpDMhkQldB7+yca+qcOw7oEt42ubIXmdlQM1tlZqv27fs2eqUVERGJkTrJybRr9xNeeOEVzurQnW+//Y4/3H2r38USkVpon3NRuyWiSB2QLOfc35xzW/dvcM5tdc79DWhV2YuccyOdc+2dc+2TkurXqGAlxVvJzEg78DgjvSUlJVureEV0+Zkf5LorX/l+5/tNbY9/+UXFpRQVlbJiZS4A06a9Sbu2P/Es3+/6Kz+4+X7XXYInUgfkUzO728ya799gZs3N7A/AllgWbOWqPNq0aU1WViYpKSkMHNiPmdlzYxkZN/lBrrvyle93vt/U9viXv23bdoqKSjj11JMBuPDCjhQWrvcs3+/6Kz+4+X7XPYiCvgpWpAsRXgH8EXg33AlxwDZgBjAwlgUrLy9n2O3DmfXmayQnJTH25UkUFHj3i8DP/CDXXfnK9zt/3KvPcsH559CsWVM2b1zFgw89wZixEz3LV9vjXz7AsDvu45WX/0lqagqbNn3GDb++07Nsv+uv/ODm+1138L/t9Vqizt2IlojL8JrZD4EMYLlzbneF7T2cc3MiBdR0GV4RERERkSORKMvw/uCYzKgdH//f/9uSEHWuKNIqWL8FpgO3AmvNrF+F3X+NZcFERERERGojDcGq2m+AnzvndptZFjDVzLKcc88ACdfbEhERERHxm5cdBzPrATwDJAMvOuce8yy8EpE6IEn7h1055zabWWdCnZBWqAMiIiIiIhK3zCwZeBa4GCgCVprZDOdcgZ/lirQK1jYza7v/QbgzcgnQDPBubUIRERERkVrCRfEWQQfgE+fcRufcHmAi0C/Ca2IvwpiyDKBFJfvOi+b4tSrKMNSLHOUrP97yg1x35Stf+Wp7lK983ar/mQGrKtyGVth3GaFhV/sfDwb+5XeZqzwD4pwrchUuQnjQvveqem0UDfUoR/nKj7f8INdd+cpXfjCzla98v/MTjqtwAfDwbaTfZYok0hAsERERERFJTMVAZoXHGeFtvlIHRERERESkdloJnGJmrc0sFfgVoQuK+yrSKljxwO/TSMpXfhCzla985Qc3P8h1V77yaxXn3F4zuxV4i9AyvC855/J9LlbkK6GLiIiIiIhEi4ZgiYiIiIiIZ9QBERERERERz8R1B8TMepjZR2b2iZn90ePsl8zsczNb62VuODvTzN4xswIzyzezYR7nH2NmK8xsTTj/QS/zK5Qj2cxyzSzbh+zNZvahmeWZ2Sof8hub2VQzW2dmhWZ2jofZp4Xrvf/2tZnd7lV+uAx3hL/31prZBDM7xuP8YeHsfC/qfrj2xsyamtnbZvZx+P8mHudfHq7/PjNrH6vsKvIfD3//f2Bmr5tZYw+zHw7n5pnZXDNLi0V2ZfkV9v3OzJyZNfMy38weMLPiCm1ALy/zw9tvC3/9883s717mm9mkCnXfbGZ5Hue3NbPl+3//mFkHj/N/ambLwr8DZ5pZwxhlH/ZYx8u2T3zk94VIqrioSjKwATgJSAXWAKd7mH8+8DNgrQ91bwn8LHz/WGC9x3U3oEH4fgqQA/zCh8/hTuA1INuH7M1AM69zK+S/DPw6fD8VaOxTOZKBrUArDzPTgU1A3fDjycB1Hub/GFgL1CO0UMc8oE2MMw9pb4C/A38M3/8j8DeP838EnAYsBNr7UP9uQJ3w/b/Fqv6VZDescP+3wPNe1j28PZPQpNFPY9kWVVL/B4Dfx/JrHiG/S/jn7gfhxyd4/flX2D8C+LPH9Z8L9Azf7wUs9Dh/JXBB+P71wMMxyj7ssY6XbZ9u/t3i+QyIr5eOd84tAnZ6lXdQdqlzbnX4/jdAIaGDMq/ynXNud/hhSvjm6WoFZpYB9AZe9DI3HphZI0K/FEYDOOf2OOe+9Kk4XYENzrlPPc6tA9Q1szqEOgIlHmb/CMhxzn3nnNsLvAsMiGVgJe1NP0IdUcL/X+plvnOu0Dn3Uawyq5E/N/z5AywntHa9V9lfV3hYnxi2f1X8rnkKuDuW2RHyPVFJ/k3AY865/ws/53OP8wEwMwMGAhM8znfA/rMOjYhh+1dJ/qnAovD9t4Ffxii7smMdz9o+8U88d0DSgS0VHhfh4UF4vDCzLKAdobMQXuYmh087fw687ZzzNB94mtAv330e5+7ngLlm9r6ZeX1V1tbAdmBMeAjai2ZW3+My7PcrYvjL93Ccc8XAE8BnQCnwlXNurodFWAt0MrPjzKweob9AZkZ4TSw0d86Vhu9vBZr7UIZ4cT0w28tAM3vEzLYAVwF/9ji7H1DsnFvjZe5Bbg0PQ3vJhyEwpxL6Gcwxs3fN7CyP8/frBGxzzn3sce7twOPh778ngD95nJ/Pf//gezketH8HHeuo7QuAeO6ABJ6ZNQD+Ddx+0F/kYs45V+6ca0vor44dzOzHXmWb2SXA5865973KPIyOzrmfAT2BW8zsfA+z6xA6Jf6/zrl2wLeETkN7ykIXLOoLTPE4twmhX36tgTSgvpld7VW+c66Q0JCfucAcIA8o9yq/kjI5PD4LGS/M7F5gLzDey1zn3L3Oucxw7q1e5YY7vffgcafnIP8LnAy0JfRHgBEe59cBmgK/AO4CJofPRnhtEB7/ASbsJuCO8PffHYTPhnvoeuBmM3uf0NCoPbEMq+pYJ8htX20Xzx2QuLx0vFfMLIXQD+R459w0v8oRHvrzDtDDw9jzgL5mtpnQ0LsLzWych/n7/wq//9T/64SGBHqlCCiqcNZpKqEOidd6Aqudc9s8zr0I2OSc2+6cKwOmAed6WQDn3Gjn3M+dc+cDuwiNTfbaNjNrCRD+P2bDUOKVmV0HXAJcFT4Q8cN4YjQEpRInE+p8rwm3gRnAajNr4VUBnHPbwn+E2geMwtv2D0Jt4LTwcOAVhM6Ex2wi/uGEh38OACZ5mRt2LaF2D0J/APL083fOrXPOdXPO/ZxQB2xDrLIqOdYJfNsXBPHcAYnLS8d7IfyXntFAoXPuSR/yj9+/4oyZ1QUuBtZ5le+c+5NzLsM5l0Xo677AOefZX8DNrL6ZHbv/PqHJsJ6thuac2wpsMbPTwpu6AgVe5Vfg11//PgN+YWb1wj8LXQmNDfaMmZ0Q/v9EQgchr3mZHzaD0IEI4f+n+1AG35hZD0LDMPs6577zOPuUCg/74W3796Fz7gTnXFa4DSwiNFF3q1dl2H/wF9YfD9u/sDcITUTHzE4ltBDHFx6X4SJgnXOuyONcCM35uCB8/0LA0yFgFdq/JGA48HyMcio71gl02xcYfs1+r86N0Njr9YR63/d6nD2B0KnnMkK/AG7wMLsjoVOOHxAa/pEH9PIw/0wgN5y/lhiuAFKNsnTG41WwCK28tiZ8y/f6ey9chrbAqvDX4A2gicf59YEdQCOfvu4PEjroWwu8Sng1HA/zFxPq9K0BunqQd0h7AxwHzCd08DEPaOpxfv/w/f8DtgFveZz/CaF5gPvbwJisRFVJ9r/D33sfADOBdC/rftD+zcR2FazD1f9V4MNw/WcALT3OTwXGhb8Gq4ELvf78gbHA/8QqN0L9OwLvh9ufHODnHucPI3TstR54DLAYZR/2WMfLtk83/24W/iYQERERERGJuXgegiUiIiIiIrWMOiAiIiIiIuIZdUBERERERMQz6oCIiIiIiIhn1AERERERERHPqAMiIiIiIiKeUQdEREREREQ88/8BSfj5B+jvPjkAAAAASUVORK5CYII=\n","text/plain":["<Figure size 1080x504 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"Wb55c8FVX0In"},"source":["### PSO with NN"]},{"cell_type":"code","metadata":{"id":"B21R_k94shgV"},"source":["#fine_tuning,nnunits,dropout,learning_rate\n","lb=[8,0.001]\n","ub=[16,0.005]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"WbrmvjgQsF26"},"source":["from tensorflow.keras import backend as K\n","\n","def model_design(x): \n","  \n","  print(f\"Units : {x[0]}, Learning Rate : {x[1]}\")\n","  K.clear_session()\n","  input_dim = X_train.shape[1]\n","\n","  model = Sequential()\n","  model.add(layers.Dense(x[0], input_dim=input_dim, activation='relu'))\n","  model.add(layers.Dense(x[0], activation='relu'))\n","  model.add(layers.Dense(23, activation='softmax'))\n","\n","  if x[1]< 0.003:\n","    learning_rate = 0.001\n","  elif x[1]< 0.0075:\n","    learning_rate = 0.005\n","  elif x[1]< 0.015:\n","    learning_rate = 0.01\n","  elif x[1]< 0.035:\n","    learning_rate = 0.02\n","  elif x[1]< 0.075:\n","    learning_rate = 0.05\n","  elif x[1]< 0.125:\n","    learning_rate = 0.1\n","  elif x[1]< 0.175:\n","    learning_rate = 0.15\n","  else:\n","    learning_rate = 0.2\n","\n","  opt = keras.optimizers.Adam(learning_rate=x[1])\n","  model.compile(loss='sparse_categorical_crossentropy', optimizer=opt, metrics=[\"accuracy\"])\n","  return model"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ALdS2EA1txbW"},"source":["EarlyStopper = EarlyStopping(patience=4, monitor='val_loss', mode='min')\n","count = 0"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"KlT3Vh95t5Fr"},"source":["def best_model(x, count = 3):\n","  model = model_design(x)\n","  \n","  history = model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=1, validation_data=(X_test, y_test),callbacks=[EarlyStopper], workers=8, use_multiprocessing=True)\n","  \n","  train_loss, train_acc = model.evaluate(X_train, y_train, verbose=0)\n","  print(f\"Train Accuracy:{train_acc} Train Loss: {train_loss}\")\n","  \n","  test_loss, test_acc = model.evaluate(X_test, y_test, verbose=0)\n","  print(f\"Test Accuracy:{test_acc} Test Loss: {test_loss}\")\n","\n","  count = count\n","  test_acc_list = []\n","  test_loss_list = []\n","  count_no = []\n","  test_units = []\n","  test_learning_rate = []\n","\n","  if test_acc > 0.99 and count < 0:\n","    # Plot the graph\n","    count = count - 1\n","    count_no.append(count)\n","    test_acc_list.append(test_acc)\n","    test_loss_list.append(test_loss)\n","    test_units.append(x[0])\n","    test_learning_rate.append(x[1])\n","    # plt.figure(figsize = (15,6))\n","    # plt.subplot(121)\n","    # plt.plot(history.history['accuracy'])\n","    # plt.plot(history.history['val_accuracy'])\n","    # plt.plot(np.argmax(history.history[\"val_accuracy\"]), np.max(history.history[\"val_accuracy\"]), marker=\"x\", color=\"r\", label=\"best model\")\n","    # plt.title('model accuracy')\n","    # plt.ylabel('accuracy')\n","    # plt.xlabel('epoch')\n","    # plt.legend(['train', 'val'], loc='upper left')\n","    # plt.subplot(122)\n","    # plt.plot(history.history['loss'])\n","    # plt.plot(history.history['val_loss'])\n","    # plt.plot(np.argmin(history.history[\"val_loss\"]), np.min(history.history[\"val_loss\"]), marker=\"x\", color=\"r\", label=\"best model\")\n","    # plt.title('model Loss')\n","    # plt.ylabel('loss')\n","    # plt.xlabel('epoch')\n","    # plt.legend(['train', 'val'], loc='upper left')\n","    # plt.show()\n","\n","    # predict probabilities for test set\n","    y_pred = np.argmax(model.predict(X_test, verbose=0), axis=1)\n","    print(classification_report(y_test, y_pred))\n","    # confusion matrix\n","    matrix = confusion_matrix(y_test, y_pred)\n","    plt.figure(figsize = (15,7) )\n","    sns.heatmap(matrix, annot=True)\n","    plt.show()\n","\n","    model.save(f\"model-{count}-{round(test_acc, 3)}-{round(test_loss, 3)}--Units-{x[0]}--Learning_rate-{x[1]}\")\n","    np.savetxt(f\"data-{count}.csv\", x, delimiter=',')\n","\n","  global result\n","  result = pd.DataFrame()\n","  result[\"count_no\"] =  count_no \n","  result[\"Test_Acc\"] = test_acc_list\n","  result[\"Test_Loss\"] = test_loss_list \n","  result[\"Units\"] = test_units\n","  result[\"Learning_rate\"] = test_learning_rate \n","  \n","  return test_loss"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"1wlGbeZ0uWfn","colab":{"base_uri":"https://localhost:8080/"},"outputId":"393f60b5-0245-4d97-d8ba-eb9ec39071f2"},"source":["xopt, fopt = pso(best_model, lb, ub)\n","print (\"Best position\"+str(xopt))\n","print (\"Loss:\" + str(fopt))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Units : 15.494689678237926, Learning Rate : 0.0024416533209140195\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1308 - accuracy: 0.9676 - val_loss: 0.0576 - val_accuracy: 0.9869\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0410 - accuracy: 0.9884 - val_loss: 0.0456 - val_accuracy: 0.9866\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0337 - accuracy: 0.9901 - val_loss: 0.0411 - val_accuracy: 0.9894\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0305 - accuracy: 0.9912 - val_loss: 0.0435 - val_accuracy: 0.9898\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0281 - accuracy: 0.9919 - val_loss: 0.0479 - val_accuracy: 0.9908\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0274 - accuracy: 0.9918 - val_loss: 0.0570 - val_accuracy: 0.9877\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0261 - accuracy: 0.9922 - val_loss: 0.0482 - val_accuracy: 0.9911\n","Train Accuracy:0.9937387108802795 Train Loss: 0.022071242332458496\n","Test Accuracy:0.9911490082740784 Test Loss: 0.0481724813580513\n","Units : 11.206292508761553, Learning Rate : 0.001954262630581401\n","Epoch 1/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.1931 - accuracy: 0.9534 - val_loss: 0.0714 - val_accuracy: 0.9825\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0473 - accuracy: 0.9870 - val_loss: 0.0564 - val_accuracy: 0.9859\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0388 - accuracy: 0.9889 - val_loss: 0.0514 - val_accuracy: 0.9873\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0354 - accuracy: 0.9894 - val_loss: 0.0468 - val_accuracy: 0.9871\n","Epoch 5/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0332 - accuracy: 0.9903 - val_loss: 0.0490 - val_accuracy: 0.9880\n","Epoch 6/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0317 - accuracy: 0.9904 - val_loss: 0.0498 - val_accuracy: 0.9870\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0307 - accuracy: 0.9910 - val_loss: 0.0485 - val_accuracy: 0.9895\n","Epoch 8/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0297 - accuracy: 0.9911 - val_loss: 0.0497 - val_accuracy: 0.9867\n","Train Accuracy:0.9911686778068542 Train Loss: 0.03084748238325119\n","Test Accuracy:0.9866639971733093 Test Loss: 0.049746058881282806\n","Units : 10.175833306911935, Learning Rate : 0.002773673207555772\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1524 - accuracy: 0.9638 - val_loss: 0.0648 - val_accuracy: 0.9851\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0486 - accuracy: 0.9871 - val_loss: 0.0526 - val_accuracy: 0.9883\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0402 - accuracy: 0.9887 - val_loss: 0.0473 - val_accuracy: 0.9886\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0386 - accuracy: 0.9893 - val_loss: 0.0453 - val_accuracy: 0.9881\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0339 - accuracy: 0.9900 - val_loss: 0.0464 - val_accuracy: 0.9898\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0329 - accuracy: 0.9904 - val_loss: 0.0481 - val_accuracy: 0.9884\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0318 - accuracy: 0.9908 - val_loss: 0.0565 - val_accuracy: 0.9896\n","Epoch 8/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0307 - accuracy: 0.9910 - val_loss: 0.0593 - val_accuracy: 0.9882\n","Train Accuracy:0.9908313155174255 Train Loss: 0.029174834489822388\n","Test Accuracy:0.9882119297981262 Test Loss: 0.059307459741830826\n","Units : 15.784479482278075, Learning Rate : 0.003760051638243958\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1102 - accuracy: 0.9722 - val_loss: 0.0614 - val_accuracy: 0.9855\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0407 - accuracy: 0.9881 - val_loss: 0.0649 - val_accuracy: 0.9858\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0348 - accuracy: 0.9896 - val_loss: 0.0625 - val_accuracy: 0.9890\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0327 - accuracy: 0.9903 - val_loss: 0.0943 - val_accuracy: 0.9880\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0300 - accuracy: 0.9911 - val_loss: 0.0679 - val_accuracy: 0.9904\n","Train Accuracy:0.9922205209732056 Train Loss: 0.025571612641215324\n","Test Accuracy:0.9904345870018005 Test Loss: 0.06791795790195465\n","Units : 14.5485975100672, Learning Rate : 0.004706578025729659\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1098 - accuracy: 0.9711 - val_loss: 0.0620 - val_accuracy: 0.9816\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0398 - accuracy: 0.9886 - val_loss: 0.0576 - val_accuracy: 0.9865\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0344 - accuracy: 0.9896 - val_loss: 0.0519 - val_accuracy: 0.9889\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0325 - accuracy: 0.9901 - val_loss: 0.0576 - val_accuracy: 0.9849\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0305 - accuracy: 0.9906 - val_loss: 0.0533 - val_accuracy: 0.9892\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0309 - accuracy: 0.9908 - val_loss: 0.0719 - val_accuracy: 0.9857\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0289 - accuracy: 0.9912 - val_loss: 0.0643 - val_accuracy: 0.9906\n","Train Accuracy:0.9931830167770386 Train Loss: 0.022796660661697388\n","Test Accuracy:0.9905536770820618 Test Loss: 0.06427370011806488\n","Units : 15.301747275789772, Learning Rate : 0.004145527620972403\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0997 - accuracy: 0.9742 - val_loss: 0.0648 - val_accuracy: 0.9834\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0386 - accuracy: 0.9889 - val_loss: 0.0536 - val_accuracy: 0.9890\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0319 - accuracy: 0.9905 - val_loss: 0.0489 - val_accuracy: 0.9897\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0307 - accuracy: 0.9908 - val_loss: 0.0562 - val_accuracy: 0.9899\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0280 - accuracy: 0.9916 - val_loss: 0.0599 - val_accuracy: 0.9891\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0267 - accuracy: 0.9920 - val_loss: 0.0745 - val_accuracy: 0.9882\n","Epoch 7/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0255 - accuracy: 0.9924 - val_loss: 0.0836 - val_accuracy: 0.9921\n","Train Accuracy:0.9944829344749451 Train Loss: 0.019619708880782127\n","Test Accuracy:0.9920619130134583 Test Loss: 0.0835966244339943\n","Units : 12.037032976967442, Learning Rate : 0.0037355281905140656\n","Epoch 1/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.1242 - accuracy: 0.9690 - val_loss: 0.0496 - val_accuracy: 0.9863\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0400 - accuracy: 0.9887 - val_loss: 0.0606 - val_accuracy: 0.9869\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0348 - accuracy: 0.9898 - val_loss: 0.0543 - val_accuracy: 0.9893\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0328 - accuracy: 0.9903 - val_loss: 0.0551 - val_accuracy: 0.9890\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0303 - accuracy: 0.9913 - val_loss: 0.0520 - val_accuracy: 0.9891\n","Train Accuracy:0.9915556907653809 Train Loss: 0.02677151933312416\n","Test Accuracy:0.9891248345375061 Test Loss: 0.05202070251107216\n","Units : 15.41382516966404, Learning Rate : 0.004574182866352397\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0956 - accuracy: 0.9759 - val_loss: 0.0543 - val_accuracy: 0.9859\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0401 - accuracy: 0.9883 - val_loss: 0.0566 - val_accuracy: 0.9874\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0339 - accuracy: 0.9899 - val_loss: 0.0539 - val_accuracy: 0.9898\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0336 - accuracy: 0.9897 - val_loss: 0.0860 - val_accuracy: 0.9886\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0298 - accuracy: 0.9907 - val_loss: 0.0995 - val_accuracy: 0.9898\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0296 - accuracy: 0.9910 - val_loss: 0.0975 - val_accuracy: 0.9869\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0291 - accuracy: 0.9916 - val_loss: 0.0783 - val_accuracy: 0.9903\n","Train Accuracy:0.9925479888916016 Train Loss: 0.02465100586414337\n","Test Accuracy:0.9903155565261841 Test Loss: 0.07828667759895325\n","Units : 13.103088285452909, Learning Rate : 0.0022444591665074305\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1523 - accuracy: 0.9644 - val_loss: 0.0599 - val_accuracy: 0.9846\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0419 - accuracy: 0.9888 - val_loss: 0.0483 - val_accuracy: 0.9886\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0353 - accuracy: 0.9901 - val_loss: 0.0537 - val_accuracy: 0.9900\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0318 - accuracy: 0.9908 - val_loss: 0.0583 - val_accuracy: 0.9896\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0284 - accuracy: 0.9916 - val_loss: 0.0557 - val_accuracy: 0.9909\n","Epoch 6/10\n","3150/3150 [==============================] - 10s 3ms/step - loss: 0.0268 - accuracy: 0.9921 - val_loss: 0.0729 - val_accuracy: 0.9862\n","Train Accuracy:0.9891444444656372 Train Loss: 0.031123623251914978\n","Test Accuracy:0.9862274527549744 Test Loss: 0.07294277846813202\n","Units : 14.897700002545648, Learning Rate : 0.0022327268574981983\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1561 - accuracy: 0.9629 - val_loss: 0.0522 - val_accuracy: 0.9861\n","Epoch 2/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0387 - accuracy: 0.9890 - val_loss: 0.0449 - val_accuracy: 0.9880\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0332 - accuracy: 0.9902 - val_loss: 0.0404 - val_accuracy: 0.9901\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0310 - accuracy: 0.9908 - val_loss: 0.0402 - val_accuracy: 0.9879\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0280 - accuracy: 0.9914 - val_loss: 0.0376 - val_accuracy: 0.9895\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0272 - accuracy: 0.9916 - val_loss: 0.0452 - val_accuracy: 0.9882\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0257 - accuracy: 0.9920 - val_loss: 0.0398 - val_accuracy: 0.9909\n","Epoch 8/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0242 - accuracy: 0.9925 - val_loss: 0.0410 - val_accuracy: 0.9897\n","Epoch 9/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0239 - accuracy: 0.9924 - val_loss: 0.0381 - val_accuracy: 0.9910\n","Train Accuracy:0.9938875436782837 Train Loss: 0.019859815016388893\n","Test Accuracy:0.9910299777984619 Test Loss: 0.03806907311081886\n","Units : 14.474048687651742, Learning Rate : 0.004241848277450375\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1038 - accuracy: 0.9746 - val_loss: 0.0535 - val_accuracy: 0.9870\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0403 - accuracy: 0.9883 - val_loss: 0.0480 - val_accuracy: 0.9866\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0347 - accuracy: 0.9899 - val_loss: 0.0433 - val_accuracy: 0.9896\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0354 - accuracy: 0.9904 - val_loss: 0.0482 - val_accuracy: 0.9877\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0314 - accuracy: 0.9909 - val_loss: 0.0539 - val_accuracy: 0.9899\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0304 - accuracy: 0.9909 - val_loss: 0.0590 - val_accuracy: 0.9892\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0293 - accuracy: 0.9912 - val_loss: 0.0467 - val_accuracy: 0.9894\n","Train Accuracy:0.9924784898757935 Train Loss: 0.023623237386345863\n","Test Accuracy:0.9893629550933838 Test Loss: 0.04670565202832222\n","Units : 11.201681677876618, Learning Rate : 0.0033187231392308183\n","Epoch 1/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.1316 - accuracy: 0.9670 - val_loss: 0.0605 - val_accuracy: 0.9861\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0429 - accuracy: 0.9880 - val_loss: 0.0621 - val_accuracy: 0.9877\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0360 - accuracy: 0.9894 - val_loss: 0.0591 - val_accuracy: 0.9891\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0351 - accuracy: 0.9895 - val_loss: 0.0612 - val_accuracy: 0.9854\n","Epoch 5/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0317 - accuracy: 0.9904 - val_loss: 0.0539 - val_accuracy: 0.9897\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0311 - accuracy: 0.9906 - val_loss: 0.0618 - val_accuracy: 0.9887\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0291 - accuracy: 0.9909 - val_loss: 0.0541 - val_accuracy: 0.9908\n","Epoch 8/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0276 - accuracy: 0.9915 - val_loss: 0.0743 - val_accuracy: 0.9869\n","Epoch 9/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0317 - accuracy: 0.9915 - val_loss: 0.0677 - val_accuracy: 0.9905\n","Train Accuracy:0.9928059577941895 Train Loss: 0.0232949648052454\n","Test Accuracy:0.9905139803886414 Test Loss: 0.06771848350763321\n","Units : 15.93320654827631, Learning Rate : 0.0016895744488592845\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1555 - accuracy: 0.9627 - val_loss: 0.0561 - val_accuracy: 0.9855\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0411 - accuracy: 0.9885 - val_loss: 0.0483 - val_accuracy: 0.9875\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0329 - accuracy: 0.9903 - val_loss: 0.0469 - val_accuracy: 0.9896\n","Epoch 4/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0294 - accuracy: 0.9911 - val_loss: 0.0565 - val_accuracy: 0.9902\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0263 - accuracy: 0.9919 - val_loss: 0.0608 - val_accuracy: 0.9894\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0251 - accuracy: 0.9925 - val_loss: 0.0559 - val_accuracy: 0.9902\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0242 - accuracy: 0.9926 - val_loss: 0.0536 - val_accuracy: 0.9913\n","Train Accuracy:0.9935005903244019 Train Loss: 0.0208884347230196\n","Test Accuracy:0.9913474917411804 Test Loss: 0.053552184253931046\n","Units : 10.371492455657862, Learning Rate : 0.0032879757174964414\n","Epoch 1/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.1442 - accuracy: 0.9651 - val_loss: 0.0684 - val_accuracy: 0.9842\n","Epoch 2/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0472 - accuracy: 0.9865 - val_loss: 0.0640 - val_accuracy: 0.9850\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0411 - accuracy: 0.9878 - val_loss: 0.0808 - val_accuracy: 0.9854\n","Epoch 4/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0394 - accuracy: 0.9886 - val_loss: 0.0751 - val_accuracy: 0.9881\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0361 - accuracy: 0.9891 - val_loss: 0.0736 - val_accuracy: 0.9877\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0342 - accuracy: 0.9897 - val_loss: 0.0736 - val_accuracy: 0.9867\n","Train Accuracy:0.9894223213195801 Train Loss: 0.033915549516677856\n","Test Accuracy:0.9867036938667297 Test Loss: 0.07360858470201492\n","Units : 13.37791785237355, Learning Rate : 0.0030806946539171536\n","Epoch 1/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.1271 - accuracy: 0.9684 - val_loss: 0.0605 - val_accuracy: 0.9845\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0433 - accuracy: 0.9881 - val_loss: 0.0492 - val_accuracy: 0.9885\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0355 - accuracy: 0.9894 - val_loss: 0.0479 - val_accuracy: 0.9889\n","Epoch 4/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0330 - accuracy: 0.9902 - val_loss: 0.0495 - val_accuracy: 0.9862\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0292 - accuracy: 0.9910 - val_loss: 0.0462 - val_accuracy: 0.9909\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0287 - accuracy: 0.9911 - val_loss: 0.0506 - val_accuracy: 0.9878\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0281 - accuracy: 0.9917 - val_loss: 0.0474 - val_accuracy: 0.9902\n","Epoch 8/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0273 - accuracy: 0.9918 - val_loss: 0.0610 - val_accuracy: 0.9876\n","Epoch 9/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0261 - accuracy: 0.9921 - val_loss: 0.0499 - val_accuracy: 0.9900\n","Train Accuracy:0.9926273822784424 Train Loss: 0.02291148528456688\n","Test Accuracy:0.9899980425834656 Test Loss: 0.04986082762479782\n","Units : 10.08372611170571, Learning Rate : 0.004865973903957969\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1215 - accuracy: 0.9715 - val_loss: 0.0614 - val_accuracy: 0.9825\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0436 - accuracy: 0.9869 - val_loss: 0.0537 - val_accuracy: 0.9867\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0390 - accuracy: 0.9885 - val_loss: 0.0628 - val_accuracy: 0.9882\n","Epoch 4/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0363 - accuracy: 0.9887 - val_loss: 0.0770 - val_accuracy: 0.9858\n","Epoch 5/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0339 - accuracy: 0.9894 - val_loss: 0.0779 - val_accuracy: 0.9875\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0328 - accuracy: 0.9897 - val_loss: 0.1051 - val_accuracy: 0.9874\n","Train Accuracy:0.9898886680603027 Train Loss: 0.03318105638027191\n","Test Accuracy:0.9873784184455872 Test Loss: 0.10511945188045502\n","Units : 13.424610454263533, Learning Rate : 0.001323778982796787\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.2066 - accuracy: 0.9486 - val_loss: 0.0720 - val_accuracy: 0.9809\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0504 - accuracy: 0.9863 - val_loss: 0.0513 - val_accuracy: 0.9857\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0384 - accuracy: 0.9890 - val_loss: 0.0456 - val_accuracy: 0.9881\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0336 - accuracy: 0.9898 - val_loss: 0.0432 - val_accuracy: 0.9888\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0306 - accuracy: 0.9907 - val_loss: 0.0420 - val_accuracy: 0.9898\n","Epoch 6/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0289 - accuracy: 0.9913 - val_loss: 0.0393 - val_accuracy: 0.9898\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0274 - accuracy: 0.9917 - val_loss: 0.0393 - val_accuracy: 0.9901\n","Epoch 8/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0263 - accuracy: 0.9919 - val_loss: 0.0360 - val_accuracy: 0.9907\n","Epoch 9/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0255 - accuracy: 0.9921 - val_loss: 0.0400 - val_accuracy: 0.9908\n","Epoch 10/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0250 - accuracy: 0.9926 - val_loss: 0.0398 - val_accuracy: 0.9911\n","Train Accuracy:0.9932128190994263 Train Loss: 0.022620733827352524\n","Test Accuracy:0.9911093711853027 Test Loss: 0.03977503627538681\n","Units : 12.632980031900441, Learning Rate : 0.0022995351478362494\n","Epoch 1/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.1586 - accuracy: 0.9574 - val_loss: 0.0587 - val_accuracy: 0.9869\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0421 - accuracy: 0.9881 - val_loss: 0.0515 - val_accuracy: 0.9887\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0357 - accuracy: 0.9899 - val_loss: 0.0495 - val_accuracy: 0.9888\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0328 - accuracy: 0.9906 - val_loss: 0.0562 - val_accuracy: 0.9885\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0305 - accuracy: 0.9911 - val_loss: 0.0581 - val_accuracy: 0.9891\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0295 - accuracy: 0.9912 - val_loss: 0.0596 - val_accuracy: 0.9870\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0274 - accuracy: 0.9914 - val_loss: 0.0605 - val_accuracy: 0.9869\n","Train Accuracy:0.9896207451820374 Train Loss: 0.029075568541884422\n","Test Accuracy:0.9868624806404114 Test Loss: 0.06050402671098709\n","Units : 11.754664616962753, Learning Rate : 0.004309992450051043\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1288 - accuracy: 0.9681 - val_loss: 0.0662 - val_accuracy: 0.9839\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0451 - accuracy: 0.9873 - val_loss: 0.0585 - val_accuracy: 0.9854\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0398 - accuracy: 0.9885 - val_loss: 0.0556 - val_accuracy: 0.9886\n","Epoch 4/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0379 - accuracy: 0.9889 - val_loss: 0.0707 - val_accuracy: 0.9862\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0354 - accuracy: 0.9897 - val_loss: 0.0666 - val_accuracy: 0.9889\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0347 - accuracy: 0.9895 - val_loss: 0.0610 - val_accuracy: 0.9855\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0340 - accuracy: 0.9902 - val_loss: 0.0732 - val_accuracy: 0.9875\n","Train Accuracy:0.9901367425918579 Train Loss: 0.035179171711206436\n","Test Accuracy:0.9875372052192688 Test Loss: 0.07319118082523346\n","Units : 8.318805767439962, Learning Rate : 0.002802553832836459\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1672 - accuracy: 0.9563 - val_loss: 0.0722 - val_accuracy: 0.9802\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0560 - accuracy: 0.9847 - val_loss: 0.0545 - val_accuracy: 0.9848\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0463 - accuracy: 0.9868 - val_loss: 0.0541 - val_accuracy: 0.9861\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0439 - accuracy: 0.9874 - val_loss: 0.0492 - val_accuracy: 0.9867\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0395 - accuracy: 0.9884 - val_loss: 0.0479 - val_accuracy: 0.9868\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0372 - accuracy: 0.9889 - val_loss: 0.0464 - val_accuracy: 0.9854\n","Epoch 7/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0356 - accuracy: 0.9892 - val_loss: 0.0432 - val_accuracy: 0.9885\n","Epoch 8/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0355 - accuracy: 0.9893 - val_loss: 0.0456 - val_accuracy: 0.9870\n","Epoch 9/10\n","3150/3150 [==============================] - 10s 3ms/step - loss: 0.0346 - accuracy: 0.9896 - val_loss: 0.0406 - val_accuracy: 0.9877\n","Epoch 10/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0335 - accuracy: 0.9896 - val_loss: 0.0416 - val_accuracy: 0.9889\n","Train Accuracy:0.990642786026001 Train Loss: 0.030540866777300835\n","Test Accuracy:0.988926351070404 Test Loss: 0.041570767760276794\n","Units : 15.213781577907294, Learning Rate : 0.004834590845268003\n","Epoch 1/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0920 - accuracy: 0.9760 - val_loss: 0.0604 - val_accuracy: 0.9847\n","Epoch 2/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0409 - accuracy: 0.9883 - val_loss: 0.0540 - val_accuracy: 0.9890\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0351 - accuracy: 0.9900 - val_loss: 0.0566 - val_accuracy: 0.9881\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0320 - accuracy: 0.9906 - val_loss: 0.0613 - val_accuracy: 0.9873\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0298 - accuracy: 0.9908 - val_loss: 0.0932 - val_accuracy: 0.9897\n","Epoch 6/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0291 - accuracy: 0.9912 - val_loss: 0.0793 - val_accuracy: 0.9888\n","Train Accuracy:0.9911488890647888 Train Loss: 0.02527119591832161\n","Test Accuracy:0.9888073205947876 Test Loss: 0.07928065210580826\n","Units : 9.9304584149489, Learning Rate : 0.0013762740169721095\n","Epoch 1/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.2459 - accuracy: 0.9361 - val_loss: 0.0812 - val_accuracy: 0.9810\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0611 - accuracy: 0.9833 - val_loss: 0.0626 - val_accuracy: 0.9844\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0493 - accuracy: 0.9858 - val_loss: 0.0571 - val_accuracy: 0.9857\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0439 - accuracy: 0.9876 - val_loss: 0.0535 - val_accuracy: 0.9850\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0409 - accuracy: 0.9881 - val_loss: 0.0553 - val_accuracy: 0.9863\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0388 - accuracy: 0.9887 - val_loss: 0.0505 - val_accuracy: 0.9863\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0373 - accuracy: 0.9887 - val_loss: 0.0481 - val_accuracy: 0.9866\n","Epoch 8/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0358 - accuracy: 0.9892 - val_loss: 0.0497 - val_accuracy: 0.9869\n","Epoch 9/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0348 - accuracy: 0.9894 - val_loss: 0.0491 - val_accuracy: 0.9860\n","Epoch 10/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0335 - accuracy: 0.9900 - val_loss: 0.0488 - val_accuracy: 0.9872\n","Train Accuracy:0.9901367425918579 Train Loss: 0.031895946711301804\n","Test Accuracy:0.9872196912765503 Test Loss: 0.04877738282084465\n","Units : 9.058494757425358, Learning Rate : 0.0011969198175632077\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.2342 - accuracy: 0.9458 - val_loss: 0.0850 - val_accuracy: 0.9769\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0632 - accuracy: 0.9818 - val_loss: 0.0651 - val_accuracy: 0.9829\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0501 - accuracy: 0.9854 - val_loss: 0.0581 - val_accuracy: 0.9859\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0441 - accuracy: 0.9875 - val_loss: 0.0547 - val_accuracy: 0.9868\n","Epoch 5/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0406 - accuracy: 0.9884 - val_loss: 0.0553 - val_accuracy: 0.9876\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0384 - accuracy: 0.9889 - val_loss: 0.0536 - val_accuracy: 0.9875\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0364 - accuracy: 0.9892 - val_loss: 0.0550 - val_accuracy: 0.9880\n","Epoch 8/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0346 - accuracy: 0.9898 - val_loss: 0.0537 - val_accuracy: 0.9873\n","Epoch 9/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0337 - accuracy: 0.9898 - val_loss: 0.0544 - val_accuracy: 0.9881\n","Epoch 10/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0327 - accuracy: 0.9902 - val_loss: 0.0552 - val_accuracy: 0.9883\n","Train Accuracy:0.9903054237365723 Train Loss: 0.03137188032269478\n","Test Accuracy:0.9882516264915466 Test Loss: 0.05519134923815727\n","Units : 11.910619358409779, Learning Rate : 0.0042714555193677835\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1142 - accuracy: 0.9696 - val_loss: 0.0597 - val_accuracy: 0.9828\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0461 - accuracy: 0.9867 - val_loss: 0.0570 - val_accuracy: 0.9871\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0408 - accuracy: 0.9879 - val_loss: 0.0740 - val_accuracy: 0.9881\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0380 - accuracy: 0.9890 - val_loss: 0.0771 - val_accuracy: 0.9848\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0354 - accuracy: 0.9893 - val_loss: 0.0716 - val_accuracy: 0.9881\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0343 - accuracy: 0.9899 - val_loss: 0.0762 - val_accuracy: 0.9885\n","Train Accuracy:0.9907221794128418 Train Loss: 0.031912900507450104\n","Test Accuracy:0.9884898066520691 Test Loss: 0.07620652765035629\n","Units : 11.56126531038981, Learning Rate : 0.004314793871199502\n","Epoch 1/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.1263 - accuracy: 0.9680 - val_loss: 0.0615 - val_accuracy: 0.9840\n","Epoch 2/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0449 - accuracy: 0.9869 - val_loss: 0.0615 - val_accuracy: 0.9841\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0395 - accuracy: 0.9885 - val_loss: 0.0567 - val_accuracy: 0.9880\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0363 - accuracy: 0.9888 - val_loss: 0.0548 - val_accuracy: 0.9873\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0342 - accuracy: 0.9895 - val_loss: 0.0550 - val_accuracy: 0.9871\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0330 - accuracy: 0.9900 - val_loss: 0.0692 - val_accuracy: 0.9865\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0318 - accuracy: 0.9905 - val_loss: 0.0733 - val_accuracy: 0.9893\n","Epoch 8/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0304 - accuracy: 0.9907 - val_loss: 0.0832 - val_accuracy: 0.9863\n","Train Accuracy:0.9902756810188293 Train Loss: 0.0322311595082283\n","Test Accuracy:0.9863067865371704 Test Loss: 0.08323803544044495\n","Units : 8.52703094496201, Learning Rate : 0.0024240936608992776\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.2463 - accuracy: 0.9330 - val_loss: 0.0987 - val_accuracy: 0.9749\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0673 - accuracy: 0.9825 - val_loss: 0.0734 - val_accuracy: 0.9809\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0537 - accuracy: 0.9858 - val_loss: 0.0680 - val_accuracy: 0.9860\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0477 - accuracy: 0.9872 - val_loss: 0.0672 - val_accuracy: 0.9859\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0443 - accuracy: 0.9883 - val_loss: 0.0670 - val_accuracy: 0.9873\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0421 - accuracy: 0.9889 - val_loss: 0.0682 - val_accuracy: 0.9873\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0396 - accuracy: 0.9895 - val_loss: 0.0683 - val_accuracy: 0.9887\n","Epoch 8/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0403 - accuracy: 0.9896 - val_loss: 0.0792 - val_accuracy: 0.9861\n","Epoch 9/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0386 - accuracy: 0.9898 - val_loss: 0.0587 - val_accuracy: 0.9889\n","Epoch 10/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0365 - accuracy: 0.9901 - val_loss: 0.0608 - val_accuracy: 0.9880\n","Train Accuracy:0.9908015727996826 Train Loss: 0.03530639410018921\n","Test Accuracy:0.988013505935669 Test Loss: 0.06084056198596954\n","Units : 11.795822278618527, Learning Rate : 0.004983206330347271\n","Epoch 1/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.1126 - accuracy: 0.9715 - val_loss: 0.0555 - val_accuracy: 0.9840\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0459 - accuracy: 0.9871 - val_loss: 0.0481 - val_accuracy: 0.9866\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0392 - accuracy: 0.9881 - val_loss: 0.0529 - val_accuracy: 0.9871\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0387 - accuracy: 0.9889 - val_loss: 0.0444 - val_accuracy: 0.9892\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0344 - accuracy: 0.9898 - val_loss: 0.0481 - val_accuracy: 0.9891\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0331 - accuracy: 0.9897 - val_loss: 0.0599 - val_accuracy: 0.9854\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0338 - accuracy: 0.9905 - val_loss: 0.0801 - val_accuracy: 0.9898\n","Epoch 8/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0304 - accuracy: 0.9910 - val_loss: 0.0679 - val_accuracy: 0.9876\n","Train Accuracy:0.9907221794128418 Train Loss: 0.02769593521952629\n","Test Accuracy:0.9875769019126892 Test Loss: 0.06789514422416687\n","Units : 13.434844653202024, Learning Rate : 0.0027698943319398326\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1303 - accuracy: 0.9671 - val_loss: 0.0614 - val_accuracy: 0.9855\n","Epoch 2/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0444 - accuracy: 0.9879 - val_loss: 0.0502 - val_accuracy: 0.9866\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0367 - accuracy: 0.9896 - val_loss: 0.0529 - val_accuracy: 0.9886\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0339 - accuracy: 0.9905 - val_loss: 0.0489 - val_accuracy: 0.9880\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0313 - accuracy: 0.9909 - val_loss: 0.0528 - val_accuracy: 0.9896\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0303 - accuracy: 0.9910 - val_loss: 0.0574 - val_accuracy: 0.9898\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0284 - accuracy: 0.9915 - val_loss: 0.0602 - val_accuracy: 0.9899\n","Epoch 8/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0269 - accuracy: 0.9916 - val_loss: 0.0709 - val_accuracy: 0.9859\n","Train Accuracy:0.9888169765472412 Train Loss: 0.033019520342350006\n","Test Accuracy:0.9858702421188354 Test Loss: 0.07085475325584412\n","Units : 12.079623109313973, Learning Rate : 0.0018035936086655885\n","Epoch 1/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.1815 - accuracy: 0.9560 - val_loss: 0.0706 - val_accuracy: 0.9829\n","Epoch 2/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0478 - accuracy: 0.9873 - val_loss: 0.0572 - val_accuracy: 0.9860\n","Epoch 3/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0389 - accuracy: 0.9892 - val_loss: 0.0521 - val_accuracy: 0.9881\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0357 - accuracy: 0.9896 - val_loss: 0.0506 - val_accuracy: 0.9889\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0328 - accuracy: 0.9903 - val_loss: 0.0550 - val_accuracy: 0.9878\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0320 - accuracy: 0.9905 - val_loss: 0.0525 - val_accuracy: 0.9895\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0300 - accuracy: 0.9908 - val_loss: 0.0530 - val_accuracy: 0.9897\n","Epoch 8/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0284 - accuracy: 0.9912 - val_loss: 0.0502 - val_accuracy: 0.9894\n","Epoch 9/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0286 - accuracy: 0.9911 - val_loss: 0.0592 - val_accuracy: 0.9911\n","Epoch 10/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0272 - accuracy: 0.9916 - val_loss: 0.0590 - val_accuracy: 0.9894\n","Train Accuracy:0.9917343258857727 Train Loss: 0.025609292089939117\n","Test Accuracy:0.9894026517868042 Test Loss: 0.05900159478187561\n","Units : 12.652537783212123, Learning Rate : 0.0010817918401125443\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.2220 - accuracy: 0.9457 - val_loss: 0.0751 - val_accuracy: 0.9801\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0532 - accuracy: 0.9864 - val_loss: 0.0543 - val_accuracy: 0.9874\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0403 - accuracy: 0.9890 - val_loss: 0.0475 - val_accuracy: 0.9878\n","Epoch 4/10\n","3150/3150 [==============================] - 7s 2ms/step - loss: 0.0363 - accuracy: 0.9900 - val_loss: 0.0439 - val_accuracy: 0.9883\n","Epoch 5/10\n","3150/3150 [==============================] - 7s 2ms/step - loss: 0.0325 - accuracy: 0.9908 - val_loss: 0.0428 - val_accuracy: 0.9889\n","Epoch 6/10\n","3150/3150 [==============================] - 7s 2ms/step - loss: 0.0308 - accuracy: 0.9911 - val_loss: 0.0424 - val_accuracy: 0.9879\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0292 - accuracy: 0.9915 - val_loss: 0.0418 - val_accuracy: 0.9900\n","Epoch 8/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0278 - accuracy: 0.9921 - val_loss: 0.0411 - val_accuracy: 0.9894\n","Epoch 9/10\n","3150/3150 [==============================] - 12s 4ms/step - loss: 0.0272 - accuracy: 0.9925 - val_loss: 0.0406 - val_accuracy: 0.9906\n","Epoch 10/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0261 - accuracy: 0.9926 - val_loss: 0.0439 - val_accuracy: 0.9904\n","Train Accuracy:0.9933516979217529 Train Loss: 0.024073723703622818\n","Test Accuracy:0.9904345870018005 Test Loss: 0.04391198232769966\n","Units : 11.096011665786397, Learning Rate : 0.004118424960056412\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1232 - accuracy: 0.9679 - val_loss: 0.0606 - val_accuracy: 0.9803\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0440 - accuracy: 0.9868 - val_loss: 0.0467 - val_accuracy: 0.9856\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0375 - accuracy: 0.9884 - val_loss: 0.0509 - val_accuracy: 0.9872\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0354 - accuracy: 0.9890 - val_loss: 0.0485 - val_accuracy: 0.9863\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0340 - accuracy: 0.9897 - val_loss: 0.0432 - val_accuracy: 0.9882\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0341 - accuracy: 0.9894 - val_loss: 0.0517 - val_accuracy: 0.9863\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0324 - accuracy: 0.9901 - val_loss: 0.0426 - val_accuracy: 0.9869\n","Epoch 8/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0372 - accuracy: 0.9899 - val_loss: 0.0534 - val_accuracy: 0.9840\n","Epoch 9/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0315 - accuracy: 0.9905 - val_loss: 0.0447 - val_accuracy: 0.9887\n","Epoch 10/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0299 - accuracy: 0.9907 - val_loss: 0.0448 - val_accuracy: 0.9898\n","Train Accuracy:0.9921808242797852 Train Loss: 0.027870837599039078\n","Test Accuracy:0.9898392558097839 Test Loss: 0.04477967321872711\n","Units : 13.342084242449602, Learning Rate : 0.001280697302173704\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.1901 - accuracy: 0.9556 - val_loss: 0.0668 - val_accuracy: 0.9820\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0462 - accuracy: 0.9877 - val_loss: 0.0493 - val_accuracy: 0.9882\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0372 - accuracy: 0.9899 - val_loss: 0.0447 - val_accuracy: 0.9881\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0334 - accuracy: 0.9908 - val_loss: 0.0427 - val_accuracy: 0.9888\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0305 - accuracy: 0.9912 - val_loss: 0.0389 - val_accuracy: 0.9900\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0293 - accuracy: 0.9915 - val_loss: 0.0432 - val_accuracy: 0.9898\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0275 - accuracy: 0.9919 - val_loss: 0.0397 - val_accuracy: 0.9902\n","Epoch 8/10\n","3150/3150 [==============================] - 13s 4ms/step - loss: 0.0262 - accuracy: 0.9921 - val_loss: 0.0364 - val_accuracy: 0.9909\n","Epoch 9/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0252 - accuracy: 0.9925 - val_loss: 0.0345 - val_accuracy: 0.9921\n","Epoch 10/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0240 - accuracy: 0.9927 - val_loss: 0.0367 - val_accuracy: 0.9910\n","Train Accuracy:0.993421196937561 Train Loss: 0.02422986552119255\n","Test Accuracy:0.9909505844116211 Test Loss: 0.03666179999709129\n","Units : 10.778171161926492, Learning Rate : 0.004200977724192945\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1364 - accuracy: 0.9656 - val_loss: 0.0732 - val_accuracy: 0.9828\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0450 - accuracy: 0.9871 - val_loss: 0.0755 - val_accuracy: 0.9844\n","Epoch 3/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0391 - accuracy: 0.9887 - val_loss: 0.0602 - val_accuracy: 0.9886\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0370 - accuracy: 0.9891 - val_loss: 0.0727 - val_accuracy: 0.9858\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0353 - accuracy: 0.9897 - val_loss: 0.0721 - val_accuracy: 0.9880\n","Epoch 6/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0349 - accuracy: 0.9897 - val_loss: 0.0828 - val_accuracy: 0.9863\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0330 - accuracy: 0.9901 - val_loss: 0.0770 - val_accuracy: 0.9897\n","Train Accuracy:0.9917144775390625 Train Loss: 0.029579399153590202\n","Test Accuracy:0.9897201657295227 Test Loss: 0.0770268440246582\n","Units : 12.576056196572505, Learning Rate : 0.004250799938151882\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1118 - accuracy: 0.9728 - val_loss: 0.0630 - val_accuracy: 0.9834\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0422 - accuracy: 0.9878 - val_loss: 0.0500 - val_accuracy: 0.9859\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0371 - accuracy: 0.9890 - val_loss: 0.0458 - val_accuracy: 0.9881\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0343 - accuracy: 0.9899 - val_loss: 0.0481 - val_accuracy: 0.9868\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0311 - accuracy: 0.9906 - val_loss: 0.0504 - val_accuracy: 0.9882\n","Epoch 6/10\n","3150/3150 [==============================] - 13s 4ms/step - loss: 0.0319 - accuracy: 0.9903 - val_loss: 0.0560 - val_accuracy: 0.9873\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0325 - accuracy: 0.9911 - val_loss: 0.0561 - val_accuracy: 0.9892\n","Train Accuracy:0.9912877678871155 Train Loss: 0.027926329523324966\n","Test Accuracy:0.9891645312309265 Test Loss: 0.05608854815363884\n","Units : 11.314675969936925, Learning Rate : 0.002214999046000676\n","Epoch 1/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.1636 - accuracy: 0.9578 - val_loss: 0.0718 - val_accuracy: 0.9811\n","Epoch 2/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0480 - accuracy: 0.9869 - val_loss: 0.0532 - val_accuracy: 0.9862\n","Epoch 3/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0396 - accuracy: 0.9886 - val_loss: 0.0520 - val_accuracy: 0.9879\n","Epoch 4/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0346 - accuracy: 0.9900 - val_loss: 0.0564 - val_accuracy: 0.9881\n","Epoch 5/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0323 - accuracy: 0.9904 - val_loss: 0.0540 - val_accuracy: 0.9877\n","Epoch 6/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0309 - accuracy: 0.9908 - val_loss: 0.0558 - val_accuracy: 0.9873\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0297 - accuracy: 0.9914 - val_loss: 0.0490 - val_accuracy: 0.9901\n","Epoch 8/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0281 - accuracy: 0.9917 - val_loss: 0.0512 - val_accuracy: 0.9892\n","Epoch 9/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0278 - accuracy: 0.9919 - val_loss: 0.0531 - val_accuracy: 0.9906\n","Epoch 10/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0283 - accuracy: 0.9924 - val_loss: 0.0490 - val_accuracy: 0.9903\n","Train Accuracy:0.992994487285614 Train Loss: 0.02374192327260971\n","Test Accuracy:0.9902758598327637 Test Loss: 0.049030035734176636\n","Units : 8.109174866275291, Learning Rate : 0.00226277758539688\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1978 - accuracy: 0.9464 - val_loss: 0.0812 - val_accuracy: 0.9770\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0559 - accuracy: 0.9853 - val_loss: 0.0672 - val_accuracy: 0.9834\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0454 - accuracy: 0.9875 - val_loss: 0.0600 - val_accuracy: 0.9869\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0412 - accuracy: 0.9884 - val_loss: 0.0618 - val_accuracy: 0.9846\n","Epoch 5/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0389 - accuracy: 0.9889 - val_loss: 0.0651 - val_accuracy: 0.9859\n","Epoch 6/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0372 - accuracy: 0.9894 - val_loss: 0.0751 - val_accuracy: 0.9866\n","Epoch 7/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0371 - accuracy: 0.9895 - val_loss: 0.0860 - val_accuracy: 0.9884\n","Train Accuracy:0.9902359843254089 Train Loss: 0.03288153558969498\n","Test Accuracy:0.9884104132652283 Test Loss: 0.08600655198097229\n","Units : 8.109583068692912, Learning Rate : 0.0037438120336001353\n","Epoch 1/10\n","3150/3150 [==============================] - 10s 3ms/step - loss: 0.1554 - accuracy: 0.9594 - val_loss: 0.0730 - val_accuracy: 0.9814\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0567 - accuracy: 0.9851 - val_loss: 0.0636 - val_accuracy: 0.9856\n","Epoch 3/10\n","3150/3150 [==============================] - 12s 4ms/step - loss: 0.0470 - accuracy: 0.9871 - val_loss: 0.0587 - val_accuracy: 0.9864\n","Epoch 4/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0434 - accuracy: 0.9879 - val_loss: 0.0526 - val_accuracy: 0.9860\n","Epoch 5/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0387 - accuracy: 0.9889 - val_loss: 0.0493 - val_accuracy: 0.9865\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0374 - accuracy: 0.9891 - val_loss: 0.0498 - val_accuracy: 0.9863\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0353 - accuracy: 0.9898 - val_loss: 0.0514 - val_accuracy: 0.9881\n","Epoch 8/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0338 - accuracy: 0.9901 - val_loss: 0.0588 - val_accuracy: 0.9862\n","Epoch 9/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0332 - accuracy: 0.9904 - val_loss: 0.0493 - val_accuracy: 0.9883\n","Epoch 10/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0322 - accuracy: 0.9903 - val_loss: 0.0436 - val_accuracy: 0.9905\n","Train Accuracy:0.9920418858528137 Train Loss: 0.02883930504322052\n","Test Accuracy:0.990474283695221 Test Loss: 0.043565355241298676\n","Units : 10.546424431226779, Learning Rate : 0.003665389963809064\n","Epoch 1/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.1279 - accuracy: 0.9664 - val_loss: 0.0601 - val_accuracy: 0.9839\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0465 - accuracy: 0.9873 - val_loss: 0.0485 - val_accuracy: 0.9878\n","Epoch 3/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0382 - accuracy: 0.9894 - val_loss: 0.0434 - val_accuracy: 0.9892\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0346 - accuracy: 0.9899 - val_loss: 0.0436 - val_accuracy: 0.9891\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0329 - accuracy: 0.9901 - val_loss: 0.0469 - val_accuracy: 0.9879\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0321 - accuracy: 0.9901 - val_loss: 0.0510 - val_accuracy: 0.9869\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0313 - accuracy: 0.9904 - val_loss: 0.0440 - val_accuracy: 0.9905\n","Train Accuracy:0.9926472306251526 Train Loss: 0.026261188089847565\n","Test Accuracy:0.9905139803886414 Test Loss: 0.04397489130496979\n","Units : 13.024718424830827, Learning Rate : 0.0013543055777564005\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1862 - accuracy: 0.9541 - val_loss: 0.0730 - val_accuracy: 0.9839\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0534 - accuracy: 0.9860 - val_loss: 0.0538 - val_accuracy: 0.9877\n","Epoch 3/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0411 - accuracy: 0.9888 - val_loss: 0.0477 - val_accuracy: 0.9888\n","Epoch 4/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0356 - accuracy: 0.9905 - val_loss: 0.0440 - val_accuracy: 0.9895\n","Epoch 5/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0318 - accuracy: 0.9913 - val_loss: 0.0458 - val_accuracy: 0.9894\n","Epoch 6/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0299 - accuracy: 0.9918 - val_loss: 0.0478 - val_accuracy: 0.9872\n","Epoch 7/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0279 - accuracy: 0.9922 - val_loss: 0.0433 - val_accuracy: 0.9894\n","Epoch 8/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0275 - accuracy: 0.9923 - val_loss: 0.0512 - val_accuracy: 0.9866\n","Epoch 9/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0261 - accuracy: 0.9925 - val_loss: 0.0496 - val_accuracy: 0.9908\n","Epoch 10/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0258 - accuracy: 0.9926 - val_loss: 0.0542 - val_accuracy: 0.9905\n","Train Accuracy:0.9929647445678711 Train Loss: 0.02401958778500557\n","Test Accuracy:0.990474283695221 Test Loss: 0.054206229746341705\n","Units : 10.995005975415133, Learning Rate : 0.00255174320205479\n","Epoch 1/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.1793 - accuracy: 0.9569 - val_loss: 0.0712 - val_accuracy: 0.9821\n","Epoch 2/10\n","3150/3150 [==============================] - 12s 4ms/step - loss: 0.0507 - accuracy: 0.9861 - val_loss: 0.0580 - val_accuracy: 0.9850\n","Epoch 3/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0413 - accuracy: 0.9887 - val_loss: 0.0486 - val_accuracy: 0.9887\n","Epoch 4/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0372 - accuracy: 0.9891 - val_loss: 0.0554 - val_accuracy: 0.9862\n","Epoch 5/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0353 - accuracy: 0.9897 - val_loss: 0.0500 - val_accuracy: 0.9886\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0335 - accuracy: 0.9901 - val_loss: 0.0680 - val_accuracy: 0.9866\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0337 - accuracy: 0.9906 - val_loss: 0.0585 - val_accuracy: 0.9883\n","Train Accuracy:0.9908908605575562 Train Loss: 0.030457334592938423\n","Test Accuracy:0.988291323184967 Test Loss: 0.058525100350379944\n","Units : 14.757501525468129, Learning Rate : 0.0043065513275633\n","Epoch 1/10\n","   1/3150 [..............................] - ETA: 0s - loss: 3.4366 - accuracy: 0.0000e+00WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0031s vs `on_train_batch_end` time: 0.0071s). Check your callbacks.\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.1044 - accuracy: 0.9728 - val_loss: 0.0508 - val_accuracy: 0.9849\n","Epoch 2/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0410 - accuracy: 0.9870 - val_loss: 0.0479 - val_accuracy: 0.9861\n","Epoch 3/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0342 - accuracy: 0.9893 - val_loss: 0.0422 - val_accuracy: 0.9892\n","Epoch 4/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0321 - accuracy: 0.9897 - val_loss: 0.0517 - val_accuracy: 0.9861\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0291 - accuracy: 0.9913 - val_loss: 0.0538 - val_accuracy: 0.9896\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0285 - accuracy: 0.9915 - val_loss: 0.0503 - val_accuracy: 0.9886\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0283 - accuracy: 0.9915 - val_loss: 0.0615 - val_accuracy: 0.9910\n","Train Accuracy:0.9932028651237488 Train Loss: 0.02267441339790821\n","Test Accuracy:0.9910299777984619 Test Loss: 0.06154441460967064\n","Units : 8.001665091274063, Learning Rate : 0.002201890718479944\n","Epoch 1/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.2347 - accuracy: 0.9411 - val_loss: 0.0756 - val_accuracy: 0.9806\n","Epoch 2/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0624 - accuracy: 0.9828 - val_loss: 0.0641 - val_accuracy: 0.9820\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0512 - accuracy: 0.9858 - val_loss: 0.0587 - val_accuracy: 0.9848\n","Epoch 4/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0457 - accuracy: 0.9872 - val_loss: 0.0570 - val_accuracy: 0.9848\n","Epoch 5/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0423 - accuracy: 0.9884 - val_loss: 0.0522 - val_accuracy: 0.9865\n","Epoch 6/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0401 - accuracy: 0.9888 - val_loss: 0.0499 - val_accuracy: 0.9870\n","Epoch 7/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0387 - accuracy: 0.9896 - val_loss: 0.0501 - val_accuracy: 0.9881\n","Epoch 8/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0367 - accuracy: 0.9899 - val_loss: 0.0518 - val_accuracy: 0.9881\n","Epoch 9/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0371 - accuracy: 0.9896 - val_loss: 0.0527 - val_accuracy: 0.9896\n","Epoch 10/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0364 - accuracy: 0.9904 - val_loss: 0.0595 - val_accuracy: 0.9882\n","Train Accuracy:0.9906626343727112 Train Loss: 0.03323974832892418\n","Test Accuracy:0.9882119297981262 Test Loss: 0.05946746841073036\n","Units : 12.34305727175003, Learning Rate : 0.0013961614344593313\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.2310 - accuracy: 0.9464 - val_loss: 0.0735 - val_accuracy: 0.9829\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0502 - accuracy: 0.9870 - val_loss: 0.0545 - val_accuracy: 0.9881\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0394 - accuracy: 0.9892 - val_loss: 0.0497 - val_accuracy: 0.9884\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0348 - accuracy: 0.9902 - val_loss: 0.0461 - val_accuracy: 0.9885\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0325 - accuracy: 0.9907 - val_loss: 0.0438 - val_accuracy: 0.9906\n","Epoch 6/10\n","3150/3150 [==============================] - 13s 4ms/step - loss: 0.0304 - accuracy: 0.9912 - val_loss: 0.0411 - val_accuracy: 0.9895\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0293 - accuracy: 0.9917 - val_loss: 0.0393 - val_accuracy: 0.9911\n","Epoch 8/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0281 - accuracy: 0.9917 - val_loss: 0.0390 - val_accuracy: 0.9902\n","Epoch 9/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0270 - accuracy: 0.9921 - val_loss: 0.0393 - val_accuracy: 0.9902\n","Epoch 10/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0262 - accuracy: 0.9920 - val_loss: 0.0370 - val_accuracy: 0.9912\n","Train Accuracy:0.9934112429618835 Train Loss: 0.023711426183581352\n","Test Accuracy:0.9911887049674988 Test Loss: 0.03704926744103432\n","Units : 9.603720319057336, Learning Rate : 0.004695635046997495\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1487 - accuracy: 0.9618 - val_loss: 0.0796 - val_accuracy: 0.9800\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0498 - accuracy: 0.9862 - val_loss: 0.0620 - val_accuracy: 0.9855\n","Epoch 3/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0429 - accuracy: 0.9879 - val_loss: 0.0574 - val_accuracy: 0.9867\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0476 - accuracy: 0.9885 - val_loss: 0.0539 - val_accuracy: 0.9851\n","Epoch 5/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0377 - accuracy: 0.9893 - val_loss: 0.0610 - val_accuracy: 0.9867\n","Epoch 6/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0368 - accuracy: 0.9897 - val_loss: 0.0701 - val_accuracy: 0.9860\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0354 - accuracy: 0.9898 - val_loss: 0.0652 - val_accuracy: 0.9893\n","Epoch 8/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0333 - accuracy: 0.9905 - val_loss: 0.0745 - val_accuracy: 0.9880\n","Train Accuracy:0.9904840588569641 Train Loss: 0.03124196268618107\n","Test Accuracy:0.988013505935669 Test Loss: 0.0745353251695633\n","Units : 10.329606461632777, Learning Rate : 0.003769576518215875\n","Epoch 1/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.1332 - accuracy: 0.9677 - val_loss: 0.0584 - val_accuracy: 0.9832\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0444 - accuracy: 0.9877 - val_loss: 0.0498 - val_accuracy: 0.9868\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0406 - accuracy: 0.9886 - val_loss: 0.0482 - val_accuracy: 0.9882\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0368 - accuracy: 0.9891 - val_loss: 0.0466 - val_accuracy: 0.9865\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0351 - accuracy: 0.9898 - val_loss: 0.0470 - val_accuracy: 0.9886\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0340 - accuracy: 0.9898 - val_loss: 0.0482 - val_accuracy: 0.9881\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0322 - accuracy: 0.9905 - val_loss: 0.0485 - val_accuracy: 0.9889\n","Epoch 8/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0329 - accuracy: 0.9907 - val_loss: 0.0640 - val_accuracy: 0.9805\n","Train Accuracy:0.9850066304206848 Train Loss: 0.04609058052301407\n","Test Accuracy:0.9804723262786865 Test Loss: 0.06397169828414917\n","Units : 11.489653550174705, Learning Rate : 0.002175660808238169\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1696 - accuracy: 0.9580 - val_loss: 0.0618 - val_accuracy: 0.9832\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0465 - accuracy: 0.9869 - val_loss: 0.0529 - val_accuracy: 0.9859\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0388 - accuracy: 0.9889 - val_loss: 0.0501 - val_accuracy: 0.9885\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0349 - accuracy: 0.9896 - val_loss: 0.0507 - val_accuracy: 0.9889\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0323 - accuracy: 0.9905 - val_loss: 0.0588 - val_accuracy: 0.9881\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0306 - accuracy: 0.9908 - val_loss: 0.0730 - val_accuracy: 0.9875\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0296 - accuracy: 0.9912 - val_loss: 0.0813 - val_accuracy: 0.9901\n","Train Accuracy:0.9926868677139282 Train Loss: 0.025278355926275253\n","Test Accuracy:0.990117073059082 Test Loss: 0.08133663237094879\n","Units : 15.317173214385301, Learning Rate : 0.0044030080733945196\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1006 - accuracy: 0.9741 - val_loss: 0.0550 - val_accuracy: 0.9859\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0394 - accuracy: 0.9881 - val_loss: 0.0534 - val_accuracy: 0.9867\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0347 - accuracy: 0.9893 - val_loss: 0.0556 - val_accuracy: 0.9891\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0330 - accuracy: 0.9895 - val_loss: 0.0633 - val_accuracy: 0.9859\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0310 - accuracy: 0.9908 - val_loss: 0.0584 - val_accuracy: 0.9901\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0301 - accuracy: 0.9909 - val_loss: 0.0686 - val_accuracy: 0.9865\n","Train Accuracy:0.9893726706504822 Train Loss: 0.03156186267733574\n","Test Accuracy:0.986465573310852 Test Loss: 0.06855595856904984\n","Units : 12.367992162858709, Learning Rate : 0.0034279410560638926\n","Epoch 1/10\n","3150/3150 [==============================] - 12s 4ms/step - loss: 0.1219 - accuracy: 0.9676 - val_loss: 0.0580 - val_accuracy: 0.9842\n","Epoch 2/10\n","3150/3150 [==============================] - 10s 3ms/step - loss: 0.0411 - accuracy: 0.9878 - val_loss: 0.0559 - val_accuracy: 0.9854\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0364 - accuracy: 0.9892 - val_loss: 0.0544 - val_accuracy: 0.9892\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0351 - accuracy: 0.9897 - val_loss: 0.0583 - val_accuracy: 0.9857\n","Epoch 5/10\n","3150/3150 [==============================] - 9s 3ms/step - loss: 0.0320 - accuracy: 0.9905 - val_loss: 0.0575 - val_accuracy: 0.9892\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0308 - accuracy: 0.9905 - val_loss: 0.0622 - val_accuracy: 0.9882\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0297 - accuracy: 0.9910 - val_loss: 0.0539 - val_accuracy: 0.9898\n","Epoch 8/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0286 - accuracy: 0.9911 - val_loss: 0.0638 - val_accuracy: 0.9864\n","Epoch 9/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0283 - accuracy: 0.9913 - val_loss: 0.0575 - val_accuracy: 0.9908\n","Epoch 10/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0276 - accuracy: 0.9916 - val_loss: 0.0815 - val_accuracy: 0.9915\n","Train Accuracy:0.9926273822784424 Train Loss: 0.025112351402640343\n","Test Accuracy:0.9915459156036377 Test Loss: 0.08146830648183823\n","Units : 13.305525408245483, Learning Rate : 0.004980772927198069\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1011 - accuracy: 0.9747 - val_loss: 0.0561 - val_accuracy: 0.9857\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0421 - accuracy: 0.9873 - val_loss: 0.0491 - val_accuracy: 0.9877\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0374 - accuracy: 0.9889 - val_loss: 0.0477 - val_accuracy: 0.9893\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0335 - accuracy: 0.9898 - val_loss: 0.0609 - val_accuracy: 0.9862\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0314 - accuracy: 0.9902 - val_loss: 0.0634 - val_accuracy: 0.9915\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0304 - accuracy: 0.9904 - val_loss: 0.0790 - val_accuracy: 0.9858\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0291 - accuracy: 0.9910 - val_loss: 0.0699 - val_accuracy: 0.9897\n","Train Accuracy:0.992031991481781 Train Loss: 0.025030232965946198\n","Test Accuracy:0.9897201657295227 Test Loss: 0.06987553834915161\n","Units : 15.31029295103011, Learning Rate : 0.003352123902135358\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1098 - accuracy: 0.9729 - val_loss: 0.0546 - val_accuracy: 0.9862\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0403 - accuracy: 0.9884 - val_loss: 0.0495 - val_accuracy: 0.9865\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0353 - accuracy: 0.9900 - val_loss: 0.0459 - val_accuracy: 0.9904\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0341 - accuracy: 0.9908 - val_loss: 0.0414 - val_accuracy: 0.9894\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0295 - accuracy: 0.9912 - val_loss: 0.0397 - val_accuracy: 0.9909\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0285 - accuracy: 0.9913 - val_loss: 0.0512 - val_accuracy: 0.9867\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0278 - accuracy: 0.9918 - val_loss: 0.0434 - val_accuracy: 0.9909\n","Epoch 8/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0272 - accuracy: 0.9922 - val_loss: 0.0463 - val_accuracy: 0.9893\n","Epoch 9/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0255 - accuracy: 0.9924 - val_loss: 0.0475 - val_accuracy: 0.9919\n","Train Accuracy:0.9940760731697083 Train Loss: 0.021469641476869583\n","Test Accuracy:0.991863489151001 Test Loss: 0.04749830812215805\n","Units : 9.740984027589938, Learning Rate : 0.003013437038513154\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1523 - accuracy: 0.9600 - val_loss: 0.0687 - val_accuracy: 0.9804\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0502 - accuracy: 0.9856 - val_loss: 0.0580 - val_accuracy: 0.9854\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0410 - accuracy: 0.9881 - val_loss: 0.0509 - val_accuracy: 0.9872\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0372 - accuracy: 0.9892 - val_loss: 0.0603 - val_accuracy: 0.9867\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0344 - accuracy: 0.9897 - val_loss: 0.0565 - val_accuracy: 0.9894\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 2ms/step - loss: 0.0338 - accuracy: 0.9901 - val_loss: 0.0616 - val_accuracy: 0.9875\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0320 - accuracy: 0.9903 - val_loss: 0.0592 - val_accuracy: 0.9895\n","Train Accuracy:0.9918434619903564 Train Loss: 0.028205614537000656\n","Test Accuracy:0.9895217418670654 Test Loss: 0.05924594774842262\n","Units : 14.52701325999672, Learning Rate : 0.0031304296300124334\n","Epoch 1/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.1154 - accuracy: 0.9689 - val_loss: 0.0538 - val_accuracy: 0.9847\n","Epoch 2/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0407 - accuracy: 0.9886 - val_loss: 0.0483 - val_accuracy: 0.9864\n","Epoch 3/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0345 - accuracy: 0.9897 - val_loss: 0.0459 - val_accuracy: 0.9888\n","Epoch 4/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0332 - accuracy: 0.9906 - val_loss: 0.0511 - val_accuracy: 0.9870\n","Epoch 5/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0298 - accuracy: 0.9914 - val_loss: 0.0437 - val_accuracy: 0.9890\n","Epoch 6/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0286 - accuracy: 0.9913 - val_loss: 0.0635 - val_accuracy: 0.9871\n","Epoch 7/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0280 - accuracy: 0.9917 - val_loss: 0.0578 - val_accuracy: 0.9897\n","Epoch 8/10\n","3150/3150 [==============================] - 8s 3ms/step - loss: 0.0258 - accuracy: 0.9920 - val_loss: 0.0618 - val_accuracy: 0.9887\n","Epoch 9/10\n"," 481/3150 [===>..........................] - ETA: 6s - loss: 0.0260 - accuracy: 0.9915Buffered data was truncated after reaching the output size limit."],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Fezr9QeIBDc8"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"SiR6SjTHW-yl"},"source":[""],"execution_count":null,"outputs":[]}]}