{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "flexible-doctrine"
      },
      "outputs": [],
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import glob\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import itertools\n",
        "\n",
        "from sklearn.metrics import make_scorer, accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Flatten, Conv1D, MaxPool1D\n",
        "from keras.initializers import random_uniform\n",
        "# from keras.layers.advanced_activations import LeakyReLU\n",
        "from keras.layers import LeakyReLU\n",
        "from keras.callbacks import EarlyStopping\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler as SS\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.models import Sequential\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from matplotlib import pyplot as plt\n",
        "# from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.utils import pad_sequences\n",
        "from keras.layers import Embedding,Dense,LSTM,Dropout,Flatten,BatchNormalization,Conv1D,GlobalMaxPooling1D,MaxPooling1D\n",
        "from keras.optimizers import  SGD\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.regularizers import l2\n",
        "from keras.optimizers import Adam\n",
        "from keras import regularizers\n",
        "from keras.callbacks import EarlyStopping\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from keras.preprocessing import sequence\n",
        "from keras.layers import SimpleRNN\n",
        "#from hyperas.distributions import uniform\n",
        "\n",
        "from keras.utils.np_utils import to_categorical\n",
        "from keras import regularizers\n",
        "import pandas as pd\n",
        "import string\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# import warnings\n",
        "import warnings\n",
        "# filter warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "id": "flexible-doctrine"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BxiyZY43El_n",
        "outputId": "48d30922-5465-45dd-fe1d-f73f17086bdd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "id": "BxiyZY43El_n"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pointed-reality"
      },
      "outputs": [],
      "source": [
        "dataset = pd.read_csv(\"/content/drive/MyDrive/Datasets/new_data/part-00000-363d1ba3-8ab5-4f96-bc25-4d5862db7cb9-c000.csv\")  # use your path"
      ],
      "id": "pointed-reality"
    },
    {
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      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 404
        },
        "id": "adverse-spread",
        "outputId": "c8a06563-caed-4cbb-f0b4-0fe75ed1fe98"
      },
      "outputs": [
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              "      <th></th>\n",
              "      <th>flow_duration</th>\n",
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              "      <th>syn_flag_number</th>\n",
              "      <th>rst_flag_number</th>\n",
              "      <th>...</th>\n",
              "      <th>Std</th>\n",
              "      <th>Tot size</th>\n",
              "      <th>IAT</th>\n",
              "      <th>Number</th>\n",
              "      <th>Magnitue</th>\n",
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              "      <td>141.55</td>\n",
              "      <td>DDoS-RSTFINFlood</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000000</td>\n",
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              "      <td>6.33</td>\n",
              "      <td>64.00</td>\n",
              "      <td>4.290556</td>\n",
              "      <td>4.290556</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>2.822973</td>\n",
              "      <td>57.04</td>\n",
              "      <td>8.292607e+07</td>\n",
              "      <td>9.5</td>\n",
              "      <td>10.464666</td>\n",
              "      <td>4.010353</td>\n",
              "      <td>160.987842</td>\n",
              "      <td>0.05</td>\n",
              "      <td>141.55</td>\n",
              "      <td>DoS-TCP_Flood</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>64.00</td>\n",
              "      <td>33.396799</td>\n",
              "      <td>33.396799</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>42.00</td>\n",
              "      <td>8.312799e+07</td>\n",
              "      <td>9.5</td>\n",
              "      <td>9.165151</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>141.55</td>\n",
              "      <td>DDoS-ICMP_Flood</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.328175</td>\n",
              "      <td>76175.00</td>\n",
              "      <td>17.00</td>\n",
              "      <td>64.00</td>\n",
              "      <td>4642.133010</td>\n",
              "      <td>4642.133010</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>DoS-UDP_Flood</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.117320</td>\n",
              "      <td>101.73</td>\n",
              "      <td>6.11</td>\n",
              "      <td>65.91</td>\n",
              "      <td>6.202211</td>\n",
              "      <td>6.202211</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>DoS-SYN_Flood</td>\n",
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              "                                                     [key], {});\n",
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            ],
            "text/plain": [
              "   flow_duration  Header_Length  Protocol Type  Duration         Rate  \\\n",
              "0       0.000000          54.00           6.00     64.00     0.329807   \n",
              "1       0.000000          57.04           6.33     64.00     4.290556   \n",
              "2       0.000000           0.00           1.00     64.00    33.396799   \n",
              "3       0.328175       76175.00          17.00     64.00  4642.133010   \n",
              "4       0.117320         101.73           6.11     65.91     6.202211   \n",
              "\n",
              "         Srate  Drate  fin_flag_number  syn_flag_number  rst_flag_number  ...  \\\n",
              "0     0.329807    0.0              1.0              0.0              1.0  ...   \n",
              "1     4.290556    0.0              0.0              0.0              0.0  ...   \n",
              "2    33.396799    0.0              0.0              0.0              0.0  ...   \n",
              "3  4642.133010    0.0              0.0              0.0              0.0  ...   \n",
              "4     6.202211    0.0              0.0              1.0              0.0  ...   \n",
              "\n",
              "         Std  Tot size           IAT  Number   Magnitue     Radius  \\\n",
              "0   0.000000     54.00  8.334383e+07     9.5  10.392305   0.000000   \n",
              "1   2.822973     57.04  8.292607e+07     9.5  10.464666   4.010353   \n",
              "2   0.000000     42.00  8.312799e+07     9.5   9.165151   0.000000   \n",
              "3   0.000000     50.00  8.301570e+07     9.5  10.000000   0.000000   \n",
              "4  23.113111     57.88  8.297300e+07     9.5  11.346876  32.716243   \n",
              "\n",
              "    Covariance  Variance  Weight             label  \n",
              "0     0.000000      0.00  141.55  DDoS-RSTFINFlood  \n",
              "1   160.987842      0.05  141.55     DoS-TCP_Flood  \n",
              "2     0.000000      0.00  141.55   DDoS-ICMP_Flood  \n",
              "3     0.000000      0.00  141.55     DoS-UDP_Flood  \n",
              "4  3016.808286      0.19  141.55     DoS-SYN_Flood  \n",
              "\n",
              "[5 rows x 47 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset.head()"
      ],
      "id": "adverse-spread"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "informational-float"
      },
      "outputs": [],
      "source": [
        "# Splitting dataset into features and labels.\n",
        "labels = dataset['label']\n",
        "features = dataset.loc[:, dataset.columns != 'label']"
      ],
      "id": "informational-float"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
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        "id": "Sv6adNvcE78u",
        "outputId": "cd0b6ffe-b094-4c5e-8d35-3c7c456bc595"
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              "      <th></th>\n",
              "      <th>flow_duration</th>\n",
              "      <th>Header_Length</th>\n",
              "      <th>Protocol Type</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Rate</th>\n",
              "      <th>Srate</th>\n",
              "      <th>Drate</th>\n",
              "      <th>fin_flag_number</th>\n",
              "      <th>syn_flag_number</th>\n",
              "      <th>rst_flag_number</th>\n",
              "      <th>...</th>\n",
              "      <th>AVG</th>\n",
              "      <th>Std</th>\n",
              "      <th>Tot size</th>\n",
              "      <th>IAT</th>\n",
              "      <th>Number</th>\n",
              "      <th>Magnitue</th>\n",
              "      <th>Radius</th>\n",
              "      <th>Covariance</th>\n",
              "      <th>Variance</th>\n",
              "      <th>Weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>54.00</td>\n",
              "      <td>6.00</td>\n",
              "      <td>64.00</td>\n",
              "      <td>0.329807</td>\n",
              "      <td>0.329807</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>54.00</td>\n",
              "      <td>8.334383e+07</td>\n",
              "      <td>9.5</td>\n",
              "      <td>10.392305</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>141.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>57.04</td>\n",
              "      <td>6.33</td>\n",
              "      <td>64.00</td>\n",
              "      <td>4.290556</td>\n",
              "      <td>4.290556</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>54.796404</td>\n",
              "      <td>2.822973</td>\n",
              "      <td>57.04</td>\n",
              "      <td>8.292607e+07</td>\n",
              "      <td>9.5</td>\n",
              "      <td>10.464666</td>\n",
              "      <td>4.010353</td>\n",
              "      <td>160.987842</td>\n",
              "      <td>0.05</td>\n",
              "      <td>141.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>64.00</td>\n",
              "      <td>33.396799</td>\n",
              "      <td>33.396799</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>42.00</td>\n",
              "      <td>8.312799e+07</td>\n",
              "      <td>9.5</td>\n",
              "      <td>9.165151</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>141.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.328175</td>\n",
              "      <td>76175.00</td>\n",
              "      <td>17.00</td>\n",
              "      <td>64.00</td>\n",
              "      <td>4642.133010</td>\n",
              "      <td>4642.133010</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>50.00</td>\n",
              "      <td>8.301570e+07</td>\n",
              "      <td>9.5</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>141.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.117320</td>\n",
              "      <td>101.73</td>\n",
              "      <td>6.11</td>\n",
              "      <td>65.91</td>\n",
              "      <td>6.202211</td>\n",
              "      <td>6.202211</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>67.959230</td>\n",
              "      <td>23.113111</td>\n",
              "      <td>57.88</td>\n",
              "      <td>8.297300e+07</td>\n",
              "      <td>9.5</td>\n",
              "      <td>11.346876</td>\n",
              "      <td>32.716243</td>\n",
              "      <td>3016.808286</td>\n",
              "      <td>0.19</td>\n",
              "      <td>141.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>238682</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>54.00</td>\n",
              "      <td>6.00</td>\n",
              "      <td>64.00</td>\n",
              "      <td>3.049186</td>\n",
              "      <td>3.049186</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>54.00</td>\n",
              "      <td>8.334449e+07</td>\n",
              "      <td>9.5</td>\n",
              "      <td>10.392305</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>141.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>238683</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>54.00</td>\n",
              "      <td>6.00</td>\n",
              "      <td>64.00</td>\n",
              "      <td>183.433732</td>\n",
              "      <td>183.433732</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>54.00</td>\n",
              "      <td>8.331392e+07</td>\n",
              "      <td>9.5</td>\n",
              "      <td>10.392305</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>141.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>238684</th>\n",
              "      <td>0.000785</td>\n",
              "      <td>56.29</td>\n",
              "      <td>6.11</td>\n",
              "      <td>64.00</td>\n",
              "      <td>306.952216</td>\n",
              "      <td>306.952216</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>54.033219</td>\n",
              "      <td>0.140764</td>\n",
              "      <td>54.21</td>\n",
              "      <td>8.308883e+07</td>\n",
              "      <td>9.5</td>\n",
              "      <td>10.395538</td>\n",
              "      <td>0.200659</td>\n",
              "      <td>0.671167</td>\n",
              "      <td>0.03</td>\n",
              "      <td>141.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>238685</th>\n",
              "      <td>0.000901</td>\n",
              "      <td>72.09</td>\n",
              "      <td>6.11</td>\n",
              "      <td>64.64</td>\n",
              "      <td>158.475986</td>\n",
              "      <td>158.475986</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>54.680248</td>\n",
              "      <td>2.450404</td>\n",
              "      <td>55.48</td>\n",
              "      <td>8.333177e+07</td>\n",
              "      <td>9.5</td>\n",
              "      <td>10.456522</td>\n",
              "      <td>3.475801</td>\n",
              "      <td>55.994224</td>\n",
              "      <td>0.17</td>\n",
              "      <td>141.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>238686</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>64.00</td>\n",
              "      <td>1.291274</td>\n",
              "      <td>1.291274</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>42.00</td>\n",
              "      <td>8.312453e+07</td>\n",
              "      <td>9.5</td>\n",
              "      <td>9.165151</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>141.55</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>238687 rows × 46 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-92c3e116-8d90-4ffd-9069-d9abf3441da1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-761ff54c-50dc-4b0a-8c66-d2f93e7c9de1')\"\n",
              "              title=\"Suggest charts.\"\n",
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              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
              "      <script>\n",
              "\n",
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              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-761ff54c-50dc-4b0a-8c66-d2f93e7c9de1 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-92c3e116-8d90-4ffd-9069-d9abf3441da1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "        flow_duration  Header_Length  Protocol Type  Duration         Rate  \\\n",
              "0            0.000000          54.00           6.00     64.00     0.329807   \n",
              "1            0.000000          57.04           6.33     64.00     4.290556   \n",
              "2            0.000000           0.00           1.00     64.00    33.396799   \n",
              "3            0.328175       76175.00          17.00     64.00  4642.133010   \n",
              "4            0.117320         101.73           6.11     65.91     6.202211   \n",
              "...               ...            ...            ...       ...          ...   \n",
              "238682       0.000000          54.00           6.00     64.00     3.049186   \n",
              "238683       0.000000          54.00           6.00     64.00   183.433732   \n",
              "238684       0.000785          56.29           6.11     64.00   306.952216   \n",
              "238685       0.000901          72.09           6.11     64.64   158.475986   \n",
              "238686       0.000000           0.00           1.00     64.00     1.291274   \n",
              "\n",
              "              Srate  Drate  fin_flag_number  syn_flag_number  rst_flag_number  \\\n",
              "0          0.329807    0.0              1.0              0.0              1.0   \n",
              "1          4.290556    0.0              0.0              0.0              0.0   \n",
              "2         33.396799    0.0              0.0              0.0              0.0   \n",
              "3       4642.133010    0.0              0.0              0.0              0.0   \n",
              "4          6.202211    0.0              0.0              1.0              0.0   \n",
              "...             ...    ...              ...              ...              ...   \n",
              "238682     3.049186    0.0              1.0              0.0              1.0   \n",
              "238683   183.433732    0.0              0.0              0.0              0.0   \n",
              "238684   306.952216    0.0              0.0              1.0              0.0   \n",
              "238685   158.475986    0.0              0.0              0.0              0.0   \n",
              "238686     1.291274    0.0              0.0              0.0              0.0   \n",
              "\n",
              "        ...        AVG        Std  Tot size           IAT  Number   Magnitue  \\\n",
              "0       ...  54.000000   0.000000     54.00  8.334383e+07     9.5  10.392305   \n",
              "1       ...  54.796404   2.822973     57.04  8.292607e+07     9.5  10.464666   \n",
              "2       ...  42.000000   0.000000     42.00  8.312799e+07     9.5   9.165151   \n",
              "3       ...  50.000000   0.000000     50.00  8.301570e+07     9.5  10.000000   \n",
              "4       ...  67.959230  23.113111     57.88  8.297300e+07     9.5  11.346876   \n",
              "...     ...        ...        ...       ...           ...     ...        ...   \n",
              "238682  ...  54.000000   0.000000     54.00  8.334449e+07     9.5  10.392305   \n",
              "238683  ...  54.000000   0.000000     54.00  8.331392e+07     9.5  10.392305   \n",
              "238684  ...  54.033219   0.140764     54.21  8.308883e+07     9.5  10.395538   \n",
              "238685  ...  54.680248   2.450404     55.48  8.333177e+07     9.5  10.456522   \n",
              "238686  ...  42.000000   0.000000     42.00  8.312453e+07     9.5   9.165151   \n",
              "\n",
              "           Radius   Covariance  Variance  Weight  \n",
              "0        0.000000     0.000000      0.00  141.55  \n",
              "1        4.010353   160.987842      0.05  141.55  \n",
              "2        0.000000     0.000000      0.00  141.55  \n",
              "3        0.000000     0.000000      0.00  141.55  \n",
              "4       32.716243  3016.808286      0.19  141.55  \n",
              "...           ...          ...       ...     ...  \n",
              "238682   0.000000     0.000000      0.00  141.55  \n",
              "238683   0.000000     0.000000      0.00  141.55  \n",
              "238684   0.200659     0.671167      0.03  141.55  \n",
              "238685   3.475801    55.994224      0.17  141.55  \n",
              "238686   0.000000     0.000000      0.00  141.55  \n",
              "\n",
              "[238687 rows x 46 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "features"
      ],
      "id": "Sv6adNvcE78u"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "unavailable-processing"
      },
      "outputs": [],
      "source": [
        "# For scaling the data, we use RobustScaler class from sklearn.\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler"
      ],
      "id": "unavailable-processing"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "czech-irrigation"
      },
      "outputs": [],
      "source": [
        "scaler = RobustScaler()\n",
        "scaler.fit(features)\n",
        "\n",
        "features = scaler.transform(features)"
      ],
      "id": "czech-irrigation"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "victorian-enlargement"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "id": "victorian-enlargement"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "worthy-domestic"
      },
      "outputs": [],
      "source": [
        "LE = LabelEncoder()\n",
        "\n",
        "LE.fit(labels)\n",
        "labels = LE.transform(labels)"
      ],
      "id": "worthy-domestic"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "respected-effectiveness",
        "outputId": "66a91f96-61db-443d-f362-b1971011c9ce"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
              "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33])"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Labels have been replaced with integers.\n",
        "\n",
        "np.unique(labels)"
      ],
      "id": "respected-effectiveness"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "restricted-couple",
        "outputId": "d3a61b23-6a17-401e-efd4-4f2bdff6f133"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['DDoS-RSTFINFlood', 'DoS-TCP_Flood', 'DDoS-ICMP_Flood',\n",
              "       'DoS-UDP_Flood', 'DoS-SYN_Flood', 'Mirai-greeth_flood',\n",
              "       'DDoS-SynonymousIP_Flood', 'Mirai-udpplain', 'DDoS-SYN_Flood',\n",
              "       'DDoS-PSHACK_Flood', 'DDoS-TCP_Flood', 'DDoS-UDP_Flood',\n",
              "       'BenignTraffic', 'MITM-ArpSpoofing', 'DDoS-ACK_Fragmentation',\n",
              "       'Mirai-greip_flood', 'DoS-HTTP_Flood', 'DDoS-ICMP_Fragmentation',\n",
              "       'Recon-PortScan', 'DNS_Spoofing', 'DDoS-UDP_Fragmentation',\n",
              "       'Recon-OSScan', 'XSS', 'DDoS-HTTP_Flood', 'Recon-HostDiscovery',\n",
              "       'CommandInjection', 'VulnerabilityScan', 'DDoS-SlowLoris',\n",
              "       'Backdoor_Malware', 'BrowserHijacking', 'DictionaryBruteForce',\n",
              "       'SqlInjection', 'Recon-PingSweep', 'Uploading_Attack'],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Checking that encoding reversal works.\n",
        "\n",
        "d = LE.inverse_transform(labels)\n",
        "d = pd.Series(d)\n",
        "d.unique()\n"
      ],
      "id": "restricted-couple"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "miniature-habitat"
      },
      "source": [
        "### Splitting the data"
      ],
      "id": "miniature-habitat"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "defensive-subscriber"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "id": "defensive-subscriber"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "exact-hawaii"
      },
      "outputs": [],
      "source": [
        "# The next step is to split training and testing data. For this we will use sklearn function train_test_split().\n",
        "\n",
        "features_train, features_test, labels_train, labels_test = train_test_split(features, labels, test_size= 0.2,random_state=1)"
      ],
      "id": "exact-hawaii"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "regional-secretary",
        "outputId": "961ffd35-d625-48f7-d108-d4c11d500fd2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((190949, 46), (190949,), (47738, 46), (47738,))"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "features_train.shape, labels_train.shape ,features_test.shape , labels_test.shape"
      ],
      "id": "regional-secretary"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "objective-offer",
        "outputId": "4d577edd-5b04-45a4-a521-3ee082c3a2a0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((143211, 46), (143211,), (47738, 46), (47738,), (47738, 46), (47738,))"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "features_train,  features_val, labels_train, labels_val = train_test_split(features_train, labels_train, test_size=0.25, random_state=1)\n",
        "features_train.shape, labels_train.shape ,features_test.shape , labels_test.shape, features_val.shape ,labels_val.shape"
      ],
      "id": "objective-offer"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "damaged-massachusetts"
      },
      "outputs": [],
      "source": [
        "import datetime\n",
        "import os\n",
        "log_dir = os.path.join(\"train_logs\",datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"),)\n",
        "\n",
        "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
        "\n",
        "# TF callback that stops training when best value of validationi loss function is reached\n",
        "eary_stop_callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=10, restore_best_weights=True)"
      ],
      "id": "damaged-massachusetts"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "silent-detective"
      },
      "outputs": [],
      "source": [
        "# this function has evaluation metrics\n",
        "def report(prediction, y_true):\n",
        "    print('        ----------Classification Report Of Classes-------------')\n",
        "    print(classification_report(y_true,prediction))\n",
        "    print('\\n        ----------Validation Data------------------')\n",
        "    print('Accuarcy:',accuracy_score(y_true, prediction)* 100)\n",
        "    print('Precision: {:,.4f} %'.format(precision_score(y_true, prediction, average='weighted') * 100))\n",
        "    print('Recall-score: {:,.4f}'.format(recall_score(y_true, prediction, average='weighted') * 100))\n",
        "    print('F1-score: {:,.4f}'.format(f1_score(y_true, prediction, average='weighted') * 100))"
      ],
      "id": "silent-detective"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "active-advice"
      },
      "outputs": [],
      "source": [
        "# plotting loss graphs w.r.t each epoch\n",
        "def plotgraphs(model):\n",
        "    f, ax = plt.subplots()\n",
        "    ax.plot([None] + model.history.history[\"accuracy\"], 'o-')\n",
        "    ax.plot([None] + model.history.history[\"val_accuracy\"], 'x-')\n",
        "\n",
        "    ax.legend(['Train_Accuracy', 'Val_Accuracy'], loc = 0)\n",
        "    ax.set_title('Training/Val Accuracy Per Epoch')\n",
        "    ax.set_xlabel('epoch')\n",
        "    ax.set_ylabel('acc')\n",
        "    plt.show()\n",
        "\n",
        "    f, ax = plt.subplots()\n",
        "    ax.plot([None] + model.history.history[\"loss\"], 'o-')\n",
        "    ax.plot([None] + model.history.history[\"val_loss\"], 'x-')\n",
        "\n",
        "    ax.legend(['Train_Loss', 'Val_Loss'], loc = 0)\n",
        "    ax.set_title('Training/Val Losses Per Epoch')\n",
        "    ax.set_xlabel('epoch')\n",
        "    ax.set_ylabel('loss')\n",
        "    plt.show()"
      ],
      "id": "active-advice"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "atomic-envelope"
      },
      "source": [
        "### DNN Models"
      ],
      "id": "atomic-envelope"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "helpful-franklin",
        "outputId": "2177ef3b-eed6-46bc-b2fe-453f6737af4e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 256)               12032     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 256)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 34)                8738      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 20,770\n",
            "Trainable params: 20,770\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "dnn1= Sequential()\n",
        "\n",
        "dnn1.add(Dense(256, activation='relu', input_shape=(features_train.shape[1],)))\n",
        "dnn1.add(Dropout(0.1))\n",
        "dnn1.add(Dense(34, activation='softmax'))\n",
        "\n",
        "dnn1.summary()"
      ],
      "id": "helpful-franklin"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "friendly-devices",
        "outputId": "f2ff920a-ed20-452e-a9bd-6355361e0acb",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "2238/2238 [==============================] - 12s 5ms/step - loss: 780.1960 - accuracy: 0.6841 - val_loss: 278.7737 - val_accuracy: 0.7780\n",
            "Epoch 2/30\n",
            "2238/2238 [==============================] - 11s 5ms/step - loss: 175.3023 - accuracy: 0.7596 - val_loss: 14.4664 - val_accuracy: 0.7790\n",
            "Epoch 3/30\n",
            "2238/2238 [==============================] - 10s 5ms/step - loss: 7.6686 - accuracy: 0.7787 - val_loss: 3.1060 - val_accuracy: 0.7998\n",
            "Epoch 4/30\n",
            "2238/2238 [==============================] - 12s 5ms/step - loss: 2.1167 - accuracy: 0.7856 - val_loss: 1.2228 - val_accuracy: 0.8008\n",
            "Epoch 5/30\n",
            "2238/2238 [==============================] - 10s 4ms/step - loss: 1.0548 - accuracy: 0.7975 - val_loss: 3.6502 - val_accuracy: 0.8027\n",
            "Epoch 6/30\n",
            "2238/2238 [==============================] - 11s 5ms/step - loss: 1.5187 - accuracy: 0.8021 - val_loss: 2.4080 - val_accuracy: 0.8062\n",
            "Epoch 7/30\n",
            "2238/2238 [==============================] - 11s 5ms/step - loss: 0.8912 - accuracy: 0.8109 - val_loss: 2.4644 - val_accuracy: 0.8173\n",
            "Epoch 8/30\n",
            "2238/2238 [==============================] - 11s 5ms/step - loss: 0.9065 - accuracy: 0.8190 - val_loss: 1.7579 - val_accuracy: 0.8271\n",
            "Epoch 9/30\n",
            "2238/2238 [==============================] - 10s 5ms/step - loss: 1.0687 - accuracy: 0.8283 - val_loss: 1.2291 - val_accuracy: 0.8451\n",
            "Epoch 10/30\n",
            "2238/2238 [==============================] - 11s 5ms/step - loss: 0.8216 - accuracy: 0.8351 - val_loss: 1.0149 - val_accuracy: 0.8432\n",
            "Epoch 11/30\n",
            "2238/2238 [==============================] - 11s 5ms/step - loss: 0.7799 - accuracy: 0.8438 - val_loss: 0.8891 - val_accuracy: 0.8614\n",
            "Epoch 12/30\n",
            "2238/2238 [==============================] - 12s 5ms/step - loss: 0.8901 - accuracy: 0.8472 - val_loss: 0.6231 - val_accuracy: 0.8651\n",
            "Epoch 13/30\n",
            "2238/2238 [==============================] - 10s 4ms/step - loss: 0.8401 - accuracy: 0.8512 - val_loss: 0.5965 - val_accuracy: 0.8596\n",
            "Epoch 14/30\n",
            "2238/2238 [==============================] - 12s 5ms/step - loss: 0.7255 - accuracy: 0.8522 - val_loss: 0.6556 - val_accuracy: 0.8632\n",
            "Epoch 15/30\n",
            "2238/2238 [==============================] - 12s 5ms/step - loss: 0.6297 - accuracy: 0.8565 - val_loss: 0.6942 - val_accuracy: 0.8667\n",
            "Epoch 16/30\n",
            "2238/2238 [==============================] - 13s 6ms/step - loss: 0.8124 - accuracy: 0.8592 - val_loss: 1.2730 - val_accuracy: 0.8734\n",
            "Epoch 17/30\n",
            "2238/2238 [==============================] - 11s 5ms/step - loss: 0.9388 - accuracy: 0.8580 - val_loss: 1.9776 - val_accuracy: 0.8753\n",
            "Epoch 18/30\n",
            "2238/2238 [==============================] - 10s 4ms/step - loss: 0.7105 - accuracy: 0.8638 - val_loss: 1.1773 - val_accuracy: 0.8755\n",
            "Epoch 19/30\n",
            "2238/2238 [==============================] - 11s 5ms/step - loss: 0.8672 - accuracy: 0.8620 - val_loss: 1.3586 - val_accuracy: 0.8697\n",
            "Epoch 20/30\n",
            "2238/2238 [==============================] - 11s 5ms/step - loss: 0.6281 - accuracy: 0.8646 - val_loss: 1.0031 - val_accuracy: 0.8774\n",
            "Epoch 21/30\n",
            "2238/2238 [==============================] - 10s 4ms/step - loss: 0.6893 - accuracy: 0.8649 - val_loss: 3.2810 - val_accuracy: 0.8739\n",
            "Epoch 22/30\n",
            "2238/2238 [==============================] - 11s 5ms/step - loss: 1.5020 - accuracy: 0.8640 - val_loss: 1.0261 - val_accuracy: 0.8687\n",
            "Epoch 23/30\n",
            "2238/2238 [==============================] - 11s 5ms/step - loss: 0.6220 - accuracy: 0.8636 - val_loss: 1.1297 - val_accuracy: 0.8716\n",
            "Epoch 24/30\n",
            "2238/2238 [==============================] - 12s 5ms/step - loss: 0.8585 - accuracy: 0.8645 - val_loss: 1.5419 - val_accuracy: 0.8753\n",
            "Epoch 25/30\n",
            "2238/2238 [==============================] - 11s 5ms/step - loss: 1.1021 - accuracy: 0.8633 - val_loss: 1.6382 - val_accuracy: 0.8720\n",
            "Epoch 26/30\n",
            "2238/2238 [==============================] - 10s 4ms/step - loss: 1.2977 - accuracy: 0.8618 - val_loss: 1.1520 - val_accuracy: 0.8545\n",
            "Epoch 27/30\n",
            "2238/2238 [==============================] - 11s 5ms/step - loss: 1.1667 - accuracy: 0.8648 - val_loss: 1.0607 - val_accuracy: 0.8723\n",
            "Epoch 28/30\n",
            "2238/2238 [==============================] - 11s 5ms/step - loss: 0.8102 - accuracy: 0.8657 - val_loss: 4.9110 - val_accuracy: 0.8672\n",
            "Epoch 29/30\n",
            "2238/2238 [==============================] - 11s 5ms/step - loss: 0.9766 - accuracy: 0.8653 - val_loss: 1.9981 - val_accuracy: 0.8706\n",
            "Epoch 30/30\n",
            "2238/2238 [==============================] - 10s 4ms/step - loss: 0.9712 - accuracy: 0.8658 - val_loss: 3.3461 - val_accuracy: 0.8740\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7e271c117100>"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dnn1.compile(loss = 'sparse_categorical_crossentropy', optimizer= 'adam', metrics = ['accuracy'])\n",
        "dnn1.fit(features_train,labels_train,epochs=30,batch_size=64,\n",
        "         validation_data=(features_val,labels_val),callbacks=[tensorboard_callback, eary_stop_callback])"
      ],
      "id": "friendly-devices"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "ongoing-helena",
        "outputId": "4cd3ce83-0dba-429a-dc7d-4a40ad0aa0e2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plotgraphs(dnn1)"
      ],
      "id": "ongoing-helena"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "secure-seeking",
        "outputId": "0f341c56-b496-43f3-cd8d-7b830db617c1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1492/1492 [==============================] - 3s 2ms/step\n"
          ]
        }
      ],
      "source": [
        "predict = np.argmax(dnn1.predict(features_test),axis=1)\n",
        "\n",
        "a = np.unique(predict)\n",
        "b = np.unique(labels_test)\n",
        "c = list(set(a) | set(b))"
      ],
      "id": "secure-seeking"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mental-electricity",
        "outputId": "aafd06cd-ef6b-45f0-d274-620f7d6ef874"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "        ----------Classification Report Of Classes-------------\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00         5\n",
            "           1       0.29      0.98      0.45      1117\n",
            "           2       0.00      0.00      0.00         6\n",
            "           3       0.00      0.00      0.00         5\n",
            "           4       0.00      0.00      0.00       290\n",
            "           5       0.60      0.10      0.18        29\n",
            "           6       1.00      0.99      0.99      7110\n",
            "           7       0.00      0.00      0.00       463\n",
            "           8       1.00      0.98      0.99      4225\n",
            "           9       0.98      0.98      0.98      4180\n",
            "          10       0.94      0.92      0.93      4249\n",
            "          11       0.00      0.00      0.00        25\n",
            "          12       0.96      0.95      0.96      3602\n",
            "          13       0.97      0.96      0.97      4615\n",
            "          14       0.83      0.95      0.89      5591\n",
            "          15       0.00      0.00      0.00       295\n",
            "          16       0.33      0.04      0.08       179\n",
            "          17       0.00      0.00      0.00        13\n",
            "          18       0.67      0.12      0.20        86\n",
            "          19       0.91      0.90      0.90      2114\n",
            "          20       0.97      0.90      0.93      2756\n",
            "          21       0.89      0.65      0.75      3380\n",
            "          22       0.04      0.00      0.01       315\n",
            "          23       0.00      0.00      0.00      1007\n",
            "          24       0.42      0.94      0.58       754\n",
            "          25       0.99      0.95      0.97       965\n",
            "          26       0.60      0.40      0.48       134\n",
            "          27       0.11      0.07      0.08        88\n",
            "          29       0.64      0.11      0.19        81\n",
            "          30       0.00      0.00      0.00         8\n",
            "          31       0.00      0.00      0.00         1\n",
            "          32       0.00      0.00      0.00        49\n",
            "          33       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.87     47738\n",
            "   macro avg       0.43      0.39      0.38     47738\n",
            "weighted avg       0.87      0.87      0.86     47738\n",
            "\n",
            "\n",
            "        ----------Validation Data------------------\n",
            "Accuarcy: 87.4209225355063\n",
            "Precision: 86.9454 %\n",
            "Recall-score: 87.4209\n",
            "F1-score: 86.2621\n"
          ]
        }
      ],
      "source": [
        "report(predict,labels_test)"
      ],
      "id": "mental-electricity"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "prompt-maintenance",
        "outputId": "34297fca-2754-40e6-bb9e-bd4f2d65c440"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_2 (Dense)             (None, 256)               12032     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 128)               32896     \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 34)                4386      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 49,314\n",
            "Trainable params: 49,314\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "dnn2= Sequential()\n",
        "\n",
        "dnn2.add(Dense(256, activation='relu', input_shape=(features_train.shape[1],)))\n",
        "dnn2.add(Dropout(0.1))\n",
        "dnn2.add(Dense(128, activation='relu'))\n",
        "dnn2.add(Dropout(0.1))\n",
        "dnn2.add(Dense(34, activation = 'softmax'))\n",
        "\n",
        "dnn2.summary()"
      ],
      "id": "prompt-maintenance"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "maritime-packet",
        "outputId": "9816c4ed-79ba-4fda-8898-94f18465a15b",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "2238/2238 [==============================] - 17s 7ms/step - loss: 86.2707 - accuracy: 0.6594 - val_loss: 1.6653 - val_accuracy: 0.7158\n",
            "Epoch 2/30\n",
            "2238/2238 [==============================] - 15s 7ms/step - loss: 1.0912 - accuracy: 0.7454 - val_loss: 0.6995 - val_accuracy: 0.7907\n",
            "Epoch 3/30\n",
            "2238/2238 [==============================] - 15s 6ms/step - loss: 0.6605 - accuracy: 0.8022 - val_loss: 0.5075 - val_accuracy: 0.8370\n",
            "Epoch 4/30\n",
            "2238/2238 [==============================] - 18s 8ms/step - loss: 0.6734 - accuracy: 0.8169 - val_loss: 0.5269 - val_accuracy: 0.8289\n",
            "Epoch 5/30\n",
            "2238/2238 [==============================] - 16s 7ms/step - loss: 0.6140 - accuracy: 0.8252 - val_loss: 0.4828 - val_accuracy: 0.8355\n",
            "Epoch 6/30\n",
            "2238/2238 [==============================] - 15s 7ms/step - loss: 0.6298 - accuracy: 0.8260 - val_loss: 0.5391 - val_accuracy: 0.8339\n",
            "Epoch 7/30\n",
            "2238/2238 [==============================] - 16s 7ms/step - loss: 0.5891 - accuracy: 0.8313 - val_loss: 0.4817 - val_accuracy: 0.8422\n",
            "Epoch 8/30\n",
            "2238/2238 [==============================] - 16s 7ms/step - loss: 0.6242 - accuracy: 0.8356 - val_loss: 0.5604 - val_accuracy: 0.8131\n",
            "Epoch 9/30\n",
            "2238/2238 [==============================] - 15s 7ms/step - loss: 0.5787 - accuracy: 0.8352 - val_loss: 0.4847 - val_accuracy: 0.8458\n",
            "Epoch 10/30\n",
            "2238/2238 [==============================] - 15s 7ms/step - loss: 0.6409 - accuracy: 0.8393 - val_loss: 0.4594 - val_accuracy: 0.8468\n",
            "Epoch 11/30\n",
            "2238/2238 [==============================] - 14s 6ms/step - loss: 0.5637 - accuracy: 0.8394 - val_loss: 0.4805 - val_accuracy: 0.8354\n",
            "Epoch 12/30\n",
            "2238/2238 [==============================] - 15s 7ms/step - loss: 0.7136 - accuracy: 0.8354 - val_loss: 0.4792 - val_accuracy: 0.8447\n",
            "Epoch 13/30\n",
            "2238/2238 [==============================] - 15s 7ms/step - loss: 0.6798 - accuracy: 0.8375 - val_loss: 0.4551 - val_accuracy: 0.8410\n",
            "Epoch 14/30\n",
            "2238/2238 [==============================] - 16s 7ms/step - loss: 0.5187 - accuracy: 0.8408 - val_loss: 0.4862 - val_accuracy: 0.8486\n",
            "Epoch 15/30\n",
            "2238/2238 [==============================] - 16s 7ms/step - loss: 0.7428 - accuracy: 0.8386 - val_loss: 0.4802 - val_accuracy: 0.8393\n",
            "Epoch 16/30\n",
            "2238/2238 [==============================] - 16s 7ms/step - loss: 0.7943 - accuracy: 0.8372 - val_loss: 0.4604 - val_accuracy: 0.8474\n",
            "Epoch 17/30\n",
            "2238/2238 [==============================] - 15s 6ms/step - loss: 0.6635 - accuracy: 0.8451 - val_loss: 0.4326 - val_accuracy: 0.8563\n",
            "Epoch 18/30\n",
            "2238/2238 [==============================] - 14s 6ms/step - loss: 0.9298 - accuracy: 0.8424 - val_loss: 0.4679 - val_accuracy: 0.8518\n",
            "Epoch 19/30\n",
            "2238/2238 [==============================] - 15s 7ms/step - loss: 0.5831 - accuracy: 0.8445 - val_loss: 0.4452 - val_accuracy: 0.8499\n",
            "Epoch 20/30\n",
            "2238/2238 [==============================] - 14s 6ms/step - loss: 0.6146 - accuracy: 0.8433 - val_loss: 0.4619 - val_accuracy: 0.8561\n",
            "Epoch 21/30\n",
            "2238/2238 [==============================] - 15s 7ms/step - loss: 0.5880 - accuracy: 0.8425 - val_loss: 0.4605 - val_accuracy: 0.8495\n",
            "Epoch 22/30\n",
            "2238/2238 [==============================] - 15s 7ms/step - loss: 0.8576 - accuracy: 0.8457 - val_loss: 0.4696 - val_accuracy: 0.8484\n",
            "Epoch 23/30\n",
            "2238/2238 [==============================] - 16s 7ms/step - loss: 0.9735 - accuracy: 0.8428 - val_loss: 0.4606 - val_accuracy: 0.8518\n",
            "Epoch 24/30\n",
            "2238/2238 [==============================] - 16s 7ms/step - loss: 9.7639 - accuracy: 0.8445 - val_loss: 0.4606 - val_accuracy: 0.8453\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7e27048c0940>"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dnn2.compile(loss = 'sparse_categorical_crossentropy', optimizer= 'adam', metrics = ['accuracy'])\n",
        "dnn2.fit(features_train,labels_train,epochs=30,batch_size=64,\n",
        "         validation_data=(features_val,labels_val),callbacks=[tensorboard_callback, eary_stop_callback])"
      ],
      "id": "maritime-packet"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "guided-orbit",
        "outputId": "efcac7f1-9bc5-48c7-86fc-767ba14e914c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plotgraphs(dnn2)"
      ],
      "id": "guided-orbit"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fitting-greek",
        "outputId": "180a5d5c-35f3-49b9-dcb8-61af6b5b963d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1492/1492 [==============================] - 3s 2ms/step\n"
          ]
        }
      ],
      "source": [
        "predict = np.argmax(dnn2.predict(features_test),axis=1)\n",
        "\n",
        "a = np.unique(predict)\n",
        "b = np.unique(labels_test)\n",
        "c = list(set(a) | set(b))"
      ],
      "id": "fitting-greek"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "naughty-magazine",
        "outputId": "6327c2cf-bae7-4d8d-f496-ab0a819ed059"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "        ----------Classification Report Of Classes-------------\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00         5\n",
            "           1       0.23      1.00      0.37      1117\n",
            "           2       0.00      0.00      0.00         6\n",
            "           3       0.00      0.00      0.00         5\n",
            "           4       0.00      0.00      0.00       290\n",
            "           5       0.00      0.00      0.00        29\n",
            "           6       1.00      0.98      0.99      7110\n",
            "           7       0.00      0.00      0.00       463\n",
            "           8       0.99      0.97      0.98      4225\n",
            "           9       1.00      0.97      0.98      4180\n",
            "          10       0.97      0.93      0.95      4249\n",
            "          11       0.00      0.00      0.00        25\n",
            "          12       1.00      0.96      0.98      3602\n",
            "          13       0.99      0.93      0.96      4615\n",
            "          14       0.85      0.74      0.79      5591\n",
            "          15       0.00      0.00      0.00       295\n",
            "          16       0.00      0.00      0.00       179\n",
            "          17       0.00      0.00      0.00        13\n",
            "          18       0.64      0.10      0.18        86\n",
            "          19       0.93      0.91      0.92      2114\n",
            "          20       0.94      0.90      0.92      2756\n",
            "          21       0.66      0.68      0.67      3380\n",
            "          22       0.00      0.00      0.00       315\n",
            "          23       0.00      0.00      0.00      1007\n",
            "          24       0.41      0.91      0.57       754\n",
            "          25       0.99      0.96      0.98       965\n",
            "          26       0.59      0.37      0.45       134\n",
            "          27       0.00      0.00      0.00        88\n",
            "          29       0.00      0.00      0.00        81\n",
            "          30       0.00      0.00      0.00         8\n",
            "          31       0.00      0.00      0.00         1\n",
            "          32       0.00      0.00      0.00        49\n",
            "          33       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.85     47738\n",
            "   macro avg       0.37      0.37      0.35     47738\n",
            "weighted avg       0.86      0.85      0.84     47738\n",
            "\n",
            "\n",
            "        ----------Validation Data------------------\n",
            "Accuarcy: 84.731241359085\n",
            "Precision: 85.6991 %\n",
            "Recall-score: 84.7312\n",
            "F1-score: 84.3913\n"
          ]
        }
      ],
      "source": [
        "report(predict,labels_test)"
      ],
      "id": "naughty-magazine"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "incomplete-strength",
        "outputId": "3fa2e456-7e22-462c-bf2a-fd671de54c88"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_5 (Dense)             (None, 256)               12032     \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 128)               32896     \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 34)                2210      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 55,394\n",
            "Trainable params: 55,394\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "dnn3= Sequential()\n",
        "\n",
        "dnn3.add(Dense(256, activation='relu', input_shape=(features_train.shape[1],)))\n",
        "dnn3.add(Dropout(0.1))\n",
        "dnn3.add(Dense(128, activation='relu'))\n",
        "dnn3.add(Dropout(0.1))\n",
        "dnn3.add(Dense(64, activation='relu'))\n",
        "dnn3.add(Dropout(0.1))\n",
        "dnn3.add(Dense(34, activation = 'softmax'))\n",
        "\n",
        "dnn3.summary()"
      ],
      "id": "incomplete-strength"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "alleged-process",
        "outputId": "ac0d686d-a52b-4fb4-8947-5670f90d0211",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "1119/1119 [==============================] - 11s 9ms/step - loss: 73.8832 - accuracy: 0.6138 - val_loss: 1.1208 - val_accuracy: 0.6864\n",
            "Epoch 2/30\n",
            "1119/1119 [==============================] - 11s 10ms/step - loss: 1.3045 - accuracy: 0.7055 - val_loss: 0.8769 - val_accuracy: 0.7466\n",
            "Epoch 3/30\n",
            "1119/1119 [==============================] - 10s 9ms/step - loss: 1.0826 - accuracy: 0.7475 - val_loss: 0.7908 - val_accuracy: 0.7711\n",
            "Epoch 4/30\n",
            "1119/1119 [==============================] - 11s 9ms/step - loss: 0.8062 - accuracy: 0.7696 - val_loss: 0.7171 - val_accuracy: 0.7929\n",
            "Epoch 5/30\n",
            "1119/1119 [==============================] - 10s 9ms/step - loss: 0.7778 - accuracy: 0.7870 - val_loss: 0.7638 - val_accuracy: 0.7699\n",
            "Epoch 6/30\n",
            "1119/1119 [==============================] - 10s 9ms/step - loss: 0.9063 - accuracy: 0.7930 - val_loss: 0.6264 - val_accuracy: 0.8190\n",
            "Epoch 7/30\n",
            "1119/1119 [==============================] - 11s 10ms/step - loss: 0.8467 - accuracy: 0.7932 - val_loss: 0.6814 - val_accuracy: 0.7975\n",
            "Epoch 8/30\n",
            "1119/1119 [==============================] - 9s 8ms/step - loss: 0.7790 - accuracy: 0.7925 - val_loss: 0.6868 - val_accuracy: 0.7907\n",
            "Epoch 9/30\n",
            "1119/1119 [==============================] - 11s 10ms/step - loss: 0.7671 - accuracy: 0.8010 - val_loss: 0.6044 - val_accuracy: 0.8228\n",
            "Epoch 10/30\n",
            "1119/1119 [==============================] - 10s 9ms/step - loss: 0.6619 - accuracy: 0.8137 - val_loss: 0.6072 - val_accuracy: 0.8262\n",
            "Epoch 11/30\n",
            "1119/1119 [==============================] - 9s 8ms/step - loss: 0.6670 - accuracy: 0.8217 - val_loss: 0.5471 - val_accuracy: 0.8506\n",
            "Epoch 12/30\n",
            "1119/1119 [==============================] - 10s 9ms/step - loss: 0.7096 - accuracy: 0.8355 - val_loss: 0.5308 - val_accuracy: 0.8538\n",
            "Epoch 13/30\n",
            "1119/1119 [==============================] - 10s 9ms/step - loss: 0.5835 - accuracy: 0.8478 - val_loss: 0.4858 - val_accuracy: 0.8751\n",
            "Epoch 14/30\n",
            "1119/1119 [==============================] - 10s 9ms/step - loss: 1.9019 - accuracy: 0.8515 - val_loss: 0.5251 - val_accuracy: 0.8620\n",
            "Epoch 15/30\n",
            "1119/1119 [==============================] - 9s 8ms/step - loss: 0.7435 - accuracy: 0.8566 - val_loss: 0.5169 - val_accuracy: 0.8609\n",
            "Epoch 16/30\n",
            "1119/1119 [==============================] - 10s 9ms/step - loss: 0.5195 - accuracy: 0.8637 - val_loss: 0.4868 - val_accuracy: 0.8721\n",
            "Epoch 17/30\n",
            "1119/1119 [==============================] - 10s 9ms/step - loss: 0.4949 - accuracy: 0.8682 - val_loss: 0.5304 - val_accuracy: 0.8428\n",
            "Epoch 18/30\n",
            "1119/1119 [==============================] - 9s 8ms/step - loss: 0.5203 - accuracy: 0.8664 - val_loss: 0.4697 - val_accuracy: 0.8764\n",
            "Epoch 19/30\n",
            "1119/1119 [==============================] - 11s 10ms/step - loss: 0.8936 - accuracy: 0.8668 - val_loss: 0.4781 - val_accuracy: 0.8683\n",
            "Epoch 20/30\n",
            "1119/1119 [==============================] - 11s 10ms/step - loss: 0.8461 - accuracy: 0.8650 - val_loss: 0.4784 - val_accuracy: 0.8748\n",
            "Epoch 21/30\n",
            "1119/1119 [==============================] - 9s 8ms/step - loss: 0.5035 - accuracy: 0.8687 - val_loss: 0.4481 - val_accuracy: 0.8765\n",
            "Epoch 22/30\n",
            "1119/1119 [==============================] - 11s 10ms/step - loss: 0.6713 - accuracy: 0.8708 - val_loss: 0.4815 - val_accuracy: 0.8704\n",
            "Epoch 23/30\n",
            "1119/1119 [==============================] - 10s 9ms/step - loss: 0.5570 - accuracy: 0.8654 - val_loss: 0.3923 - val_accuracy: 0.8773\n",
            "Epoch 24/30\n",
            "1119/1119 [==============================] - 9s 8ms/step - loss: 0.7562 - accuracy: 0.8644 - val_loss: 0.3675 - val_accuracy: 0.8931\n",
            "Epoch 25/30\n",
            "1119/1119 [==============================] - 10s 9ms/step - loss: 0.4036 - accuracy: 0.8830 - val_loss: 0.3477 - val_accuracy: 0.8958\n",
            "Epoch 26/30\n",
            "1119/1119 [==============================] - 11s 10ms/step - loss: 0.4016 - accuracy: 0.8904 - val_loss: 0.3420 - val_accuracy: 0.8980\n",
            "Epoch 27/30\n",
            "1119/1119 [==============================] - 8s 8ms/step - loss: 0.4464 - accuracy: 0.8891 - val_loss: 0.3529 - val_accuracy: 0.8938\n",
            "Epoch 28/30\n",
            "1119/1119 [==============================] - 11s 10ms/step - loss: 0.4011 - accuracy: 0.8896 - val_loss: 0.3399 - val_accuracy: 0.8949\n",
            "Epoch 29/30\n",
            "1119/1119 [==============================] - 11s 10ms/step - loss: 1.1498 - accuracy: 0.8900 - val_loss: 0.3829 - val_accuracy: 0.8879\n",
            "Epoch 30/30\n",
            "1119/1119 [==============================] - 9s 8ms/step - loss: 0.9556 - accuracy: 0.8804 - val_loss: 0.3738 - val_accuracy: 0.8877\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7e271e9666e0>"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dnn3.compile(loss = 'sparse_categorical_crossentropy', optimizer= 'adam', metrics = ['accuracy'])\n",
        "dnn3.fit(features_train,labels_train,epochs=30,batch_size=128,\n",
        "         validation_data=(features_val,labels_val),callbacks=[tensorboard_callback, eary_stop_callback])"
      ],
      "id": "alleged-process"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "natural-assistant",
        "outputId": "15ad26fd-c9b2-4206-a4c2-d7ad923550f6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plotgraphs(dnn3)"
      ],
      "id": "natural-assistant"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "residential-thing",
        "outputId": "7e3ba103-4b43-49e2-9556-ea9a6565372d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1492/1492 [==============================] - 2s 2ms/step\n",
            "        ----------Classification Report Of Classes-------------\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00         5\n",
            "           1       0.22      1.00      0.36      1117\n",
            "           2       0.00      0.00      0.00         6\n",
            "           3       0.00      0.00      0.00         5\n",
            "           4       0.00      0.00      0.00       290\n",
            "           5       1.00      0.07      0.13        29\n",
            "           6       1.00      0.97      0.99      7110\n",
            "           7       0.00      0.00      0.00       463\n",
            "           8       1.00      0.95      0.98      4225\n",
            "           9       1.00      0.96      0.98      4180\n",
            "          10       0.98      0.96      0.97      4249\n",
            "          11       0.00      0.00      0.00        25\n",
            "          12       1.00      0.97      0.98      3602\n",
            "          13       0.99      0.95      0.97      4615\n",
            "          14       0.99      0.95      0.97      5591\n",
            "          15       0.00      0.00      0.00       295\n",
            "          16       0.00      0.00      0.00       179\n",
            "          17       0.00      0.00      0.00        13\n",
            "          18       0.75      0.10      0.18        86\n",
            "          19       0.95      0.94      0.94      2114\n",
            "          20       0.99      0.88      0.93      2756\n",
            "          21       0.95      0.90      0.93      3380\n",
            "          22       0.00      0.00      0.00       315\n",
            "          23       0.62      0.01      0.02      1007\n",
            "          24       0.40      0.70      0.51       754\n",
            "          25       1.00      0.96      0.98       965\n",
            "          26       0.50      0.19      0.27       134\n",
            "          27       0.00      0.00      0.00        88\n",
            "          29       0.00      0.00      0.00        81\n",
            "          30       0.00      0.00      0.00         8\n",
            "          31       0.00      0.00      0.00         1\n",
            "          32       0.00      0.00      0.00        49\n",
            "          33       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.89     47738\n",
            "   macro avg       0.43      0.38      0.37     47738\n",
            "weighted avg       0.91      0.89      0.89     47738\n",
            "\n",
            "\n",
            "        ----------Validation Data------------------\n",
            "Accuarcy: 88.64426662197829\n",
            "Precision: 91.2016 %\n",
            "Recall-score: 88.6443\n",
            "F1-score: 88.5149\n"
          ]
        }
      ],
      "source": [
        "predict = np.argmax(dnn3.predict(features_test),axis=1)\n",
        "\n",
        "a = np.unique(predict)\n",
        "b = np.unique(labels_test)\n",
        "c = list(set(a) | set(b))\n",
        "report(predict,labels_test)"
      ],
      "id": "residential-thing"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "understood-invention"
      },
      "source": [
        "### CNN Models"
      ],
      "id": "understood-invention"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mental-amendment",
        "outputId": "87a4b365-9b7c-4dee-bccd-63f8344d30fe"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((143211, 46, 1), (47738, 46, 1), (47738, 46, 1))"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# reshape input to be [samples, time steps, features]\n",
        "y_train= to_categorical(labels_train)\n",
        "y_test= to_categorical(labels_test)\n",
        "y_val= to_categorical(labels_val)\n",
        "\n",
        "X_train = np.array(features_train).reshape(features_train.shape[0], features_train.shape[1], 1)\n",
        "X_test = np.array(features_test).reshape(features_test.shape[0], features_test.shape[1], 1)\n",
        "X_val = np.array(features_val).reshape(features_val.shape[0], features_val.shape[1], 1)\n",
        "X_train.shape , X_test.shape , X_val.shape"
      ],
      "id": "mental-amendment"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "freelance-conversion",
        "outputId": "2c7c6462-fea4-4595-b473-d6cddcd4fc9e",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d (Conv1D)             (None, 44, 64)            256       \n",
            "                                                                 \n",
            " leaky_re_lu (LeakyReLU)     (None, 44, 64)            0         \n",
            "                                                                 \n",
            " max_pooling1d (MaxPooling1D  (None, 22, 64)           0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 22, 64)            0         \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, 22, 64)            4160      \n",
            "                                                                 \n",
            " leaky_re_lu_1 (LeakyReLU)   (None, 22, 64)            0         \n",
            "                                                                 \n",
            " max_pooling1d_1 (MaxPooling  (None, 11, 64)           0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 11, 64)            0         \n",
            "                                                                 \n",
            " conv1d_2 (Conv1D)           (None, 11, 64)            4160      \n",
            "                                                                 \n",
            " leaky_re_lu_2 (LeakyReLU)   (None, 11, 64)            0         \n",
            "                                                                 \n",
            " max_pooling1d_2 (MaxPooling  (None, 5, 64)            0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 5, 64)             0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 320)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                20544     \n",
            "                                                                 \n",
            " leaky_re_lu_3 (LeakyReLU)   (None, 64)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 32)                2080      \n",
            "                                                                 \n",
            " leaky_re_lu_4 (LeakyReLU)   (None, 32)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 34)                1122      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 32,322\n",
            "Trainable params: 32,322\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "#hyperparameters\n",
        "keras.backend.clear_session()\n",
        "SEED = 1040941203\n",
        "hidden_initializer = random_uniform(seed=SEED)\n",
        "\n",
        "\n",
        "# create model\n",
        "cnn3 = Sequential()\n",
        "cnn3.add(Conv1D(64, 3, input_shape=(46, 1)))\n",
        "cnn3.add(LeakyReLU(alpha=0.1))\n",
        "cnn3.add(MaxPooling1D(pool_size=2))\n",
        "cnn3.add(Dropout(0.3))\n",
        "\n",
        "cnn3.add(Conv1D(64, 1))\n",
        "cnn3.add(LeakyReLU(alpha=0.1))\n",
        "cnn3.add(MaxPooling1D(pool_size=2))\n",
        "cnn3.add(Dropout(0.3))\n",
        "\n",
        "cnn3.add(Conv1D(64, 1))\n",
        "cnn3.add(LeakyReLU(alpha=0.1))\n",
        "cnn3.add(MaxPooling1D(pool_size=2))\n",
        "cnn3.add(Dropout(0.3))\n",
        "\n",
        "cnn3.add(Flatten())\n",
        "cnn3.add(Dense(64, input_dim=15, kernel_initializer=hidden_initializer))\n",
        "cnn3.add(LeakyReLU(alpha=0.1))\n",
        "cnn3.add(Dense(32))\n",
        "cnn3.add(LeakyReLU(alpha=0.1))\n",
        "cnn3.add(Dense(34, activation='softmax'))\n",
        "\n",
        "cnn3.summary()"
      ],
      "id": "freelance-conversion"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "olympic-climate",
        "outputId": "020e3959-5b56-4e68-9d6f-6fcde0035755",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "280/280 [==============================] - 56s 191ms/step - loss: 0.2557 - accuracy: 0.9001 - val_loss: 0.2258 - val_accuracy: 0.9064\n",
            "Epoch 2/30\n",
            "280/280 [==============================] - 35s 125ms/step - loss: 0.2508 - accuracy: 0.9022 - val_loss: 0.2176 - val_accuracy: 0.9005\n",
            "Epoch 3/30\n",
            "280/280 [==============================] - 29s 102ms/step - loss: 0.2433 - accuracy: 0.9035 - val_loss: 0.2041 - val_accuracy: 0.9158\n",
            "Epoch 4/30\n",
            "280/280 [==============================] - 25s 91ms/step - loss: 0.2393 - accuracy: 0.9040 - val_loss: 0.2020 - val_accuracy: 0.9156\n",
            "Epoch 5/30\n",
            "280/280 [==============================] - 26s 94ms/step - loss: 0.2390 - accuracy: 0.9054 - val_loss: 0.1996 - val_accuracy: 0.9137\n",
            "Epoch 6/30\n",
            "280/280 [==============================] - 27s 97ms/step - loss: 0.2322 - accuracy: 0.9061 - val_loss: 0.2084 - val_accuracy: 0.9108\n",
            "Epoch 7/30\n",
            "280/280 [==============================] - 27s 97ms/step - loss: 0.2352 - accuracy: 0.9057 - val_loss: 0.2033 - val_accuracy: 0.9124\n",
            "Epoch 8/30\n",
            "280/280 [==============================] - 27s 94ms/step - loss: 0.2298 - accuracy: 0.9072 - val_loss: 0.1941 - val_accuracy: 0.9199\n",
            "Epoch 9/30\n",
            "280/280 [==============================] - 26s 93ms/step - loss: 0.2248 - accuracy: 0.9093 - val_loss: 0.1895 - val_accuracy: 0.9230\n",
            "Epoch 10/30\n",
            "280/280 [==============================] - 25s 89ms/step - loss: 0.2217 - accuracy: 0.9104 - val_loss: 0.1845 - val_accuracy: 0.9223\n",
            "Epoch 11/30\n",
            "280/280 [==============================] - 27s 97ms/step - loss: 0.2144 - accuracy: 0.9137 - val_loss: 0.1802 - val_accuracy: 0.9222\n",
            "Epoch 12/30\n",
            "280/280 [==============================] - 26s 92ms/step - loss: 0.2153 - accuracy: 0.9156 - val_loss: 0.1658 - val_accuracy: 0.9306\n",
            "Epoch 13/30\n",
            "280/280 [==============================] - 25s 89ms/step - loss: 0.2020 - accuracy: 0.9225 - val_loss: 0.1361 - val_accuracy: 0.9501\n",
            "Epoch 14/30\n",
            "280/280 [==============================] - 26s 93ms/step - loss: 0.1874 - accuracy: 0.9300 - val_loss: 0.1427 - val_accuracy: 0.9336\n",
            "Epoch 15/30\n",
            "280/280 [==============================] - 26s 92ms/step - loss: 0.1709 - accuracy: 0.9374 - val_loss: 0.1172 - val_accuracy: 0.9598\n",
            "Epoch 16/30\n",
            "280/280 [==============================] - 26s 92ms/step - loss: 0.1578 - accuracy: 0.9441 - val_loss: 0.1081 - val_accuracy: 0.9557\n",
            "Epoch 17/30\n",
            "280/280 [==============================] - 25s 89ms/step - loss: 0.1529 - accuracy: 0.9461 - val_loss: 0.1047 - val_accuracy: 0.9615\n",
            "Epoch 18/30\n",
            "280/280 [==============================] - 25s 88ms/step - loss: 0.1485 - accuracy: 0.9476 - val_loss: 0.1029 - val_accuracy: 0.9628\n",
            "Epoch 19/30\n",
            "280/280 [==============================] - 27s 96ms/step - loss: 0.1447 - accuracy: 0.9495 - val_loss: 0.1043 - val_accuracy: 0.9627\n",
            "Epoch 20/30\n",
            "280/280 [==============================] - 26s 92ms/step - loss: 0.1355 - accuracy: 0.9520 - val_loss: 0.1022 - val_accuracy: 0.9637\n",
            "Epoch 21/30\n",
            "280/280 [==============================] - 25s 90ms/step - loss: 0.1360 - accuracy: 0.9515 - val_loss: 0.1038 - val_accuracy: 0.9627\n",
            "Epoch 22/30\n",
            "280/280 [==============================] - 25s 89ms/step - loss: 0.1387 - accuracy: 0.9506 - val_loss: 0.1004 - val_accuracy: 0.9629\n",
            "Epoch 23/30\n",
            "280/280 [==============================] - 27s 97ms/step - loss: 0.1309 - accuracy: 0.9535 - val_loss: 0.1008 - val_accuracy: 0.9631\n",
            "Epoch 24/30\n",
            "280/280 [==============================] - 26s 93ms/step - loss: 0.1288 - accuracy: 0.9534 - val_loss: 0.0982 - val_accuracy: 0.9639\n",
            "Epoch 25/30\n",
            "280/280 [==============================] - 25s 90ms/step - loss: 0.1313 - accuracy: 0.9536 - val_loss: 0.1001 - val_accuracy: 0.9630\n",
            "Epoch 26/30\n",
            "280/280 [==============================] - 26s 93ms/step - loss: 0.1290 - accuracy: 0.9543 - val_loss: 0.0970 - val_accuracy: 0.9642\n",
            "Epoch 27/30\n",
            "280/280 [==============================] - 26s 93ms/step - loss: 0.1257 - accuracy: 0.9548 - val_loss: 0.0968 - val_accuracy: 0.9628\n",
            "Epoch 28/30\n",
            "280/280 [==============================] - 27s 97ms/step - loss: 0.1226 - accuracy: 0.9553 - val_loss: 0.0962 - val_accuracy: 0.9640\n",
            "Epoch 29/30\n",
            "280/280 [==============================] - 25s 91ms/step - loss: 0.1214 - accuracy: 0.9558 - val_loss: 0.0968 - val_accuracy: 0.9637\n",
            "Epoch 30/30\n",
            "280/280 [==============================] - 25s 88ms/step - loss: 0.1230 - accuracy: 0.9557 - val_loss: 0.0940 - val_accuracy: 0.9640\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7e2708275300>"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cnn3.compile(loss = 'categorical_crossentropy', optimizer= 'adam', metrics = ['accuracy'])\n",
        "cnn3.fit(X_train, y_train, epochs=30, batch_size=512,\n",
        "         validation_data=(X_val,y_val),callbacks=[tensorboard_callback, eary_stop_callback])"
      ],
      "id": "olympic-climate"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "afraid-parking",
        "outputId": "d99a1c51-feb6-4a14-a8e5-162b99d3061b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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n888/Z8iQIezZs4eAgABWrlzJtm3bmDVrFq6urgC8+OKLXLlyhYcffpjq1atz5swZ5syZQ4sWLUzzhRo1akTnzp1p3bo1np6e7N69m5UrV5r1iM2bN4/27dvTtGlThg0bRq1atUhMTCQ6Oppz587x77//WnyugnJzc2P+/Pk899xztGrVin79+lGlShXi4uJYu3Yt7dq1Y+7cuYW6xrp163KcwP/ggw+aesJAHfJr3749I0aMICMjg1mzZlG5cmVGjx5tajN9+nR69OhBSEgIL7zwgmkZvLu7u9l+cJMnT2bDhg106tSJ4cOH07BhQ+Lj4/nxxx/ZunUrHh4ehXpPQgCyDF6I+8ltGXzjxo1zbL9t2zblgQceUBwdHZWqVasqo0ePVv744w8FUDZv3mxql9sy+JyWeXPPMvHclsGPHDky22tr1qypDB482OxYZGSk0rJlS8XOzk6pXbu28uWXXypvvfWW4uDgoCiKuszY29tbmTZtWo7v0Wjt2rUKoPj5+ZktiTeeY/LkyUrNmjUVe3t7pWXLlspvv/2W7X3n9P5yYvx8crt99913iqIoSmJiojJ06FDFy8tLsbOzU5o2bap88803ZudauXKl0q1bN8Xb21uxs7NTatSoobz00ktKfHy8qc3//vc/JSgoSPHw8FAcHR2VBg0aKB9//LGSmZlpdq5Tp04pgwYNUnx9fRVbW1ulWrVqymOPPaasXLky3+e6l3EZ/I8//njfdsa2YWFhiru7u+Lg4KDUrl1bGTJkiLJ7925Tm8GDByvOzs55nsvofsvgAdPnevd395NPPlH8/f0Ve3t7pUOHDsq///6b7bx//vmn0q5dO8XR0VFxc3NTevbsqRw+fDhbuzNnziiDBg1SqlSpotjb2yu1atVSRo4caSqvYIzv3qXyxs/t7r9vQuREpyil6L+nQghN9O7d27RUe+fOnQQHB3Po0CEaNWqkdWiilDt9+jSBgYFMnz6dt99+W+twhLCYzAESooK5efOm2eMTJ07w+++/07lzZ9OxyZMnS/IjhCjXZA6QEBVMrVq1GDJkCLVq1eLMmTPMnz8fOzs701yNoKAggoKCNI5SCCGKlyRAQlQw3bt3Z9myZSQkJGBvb09ISAiTJ0/OsZCdEEKUVzIHSAghhBAVjswBEkIIIUSFIwmQEEIIISocmQOUA4PBwIULF3B1dc22D5IQQgghSidFUbh+/TpVq1bNc987SYBycOHCBfz9/bUOQwghhBAFcPbsWapXr37fNpIA5cBYMv/s2bO4ublpHI0QQgghLJGSkoK/v7/p9/j9SAKUA+Owl5ubmyRAQgghRBljyfQVmQQthBBCiAqnVCRA8+bNIyAgAAcHB4KDg9m5c2eubRcuXEiHDh2oVKkSlSpVIjQ0NMf2R44c4fHHH8fd3R1nZ2fatm1LXFxccb4NIYQQQpQRmidAK1asIDw8nAkTJhATE0Pz5s0JCwsjKSkpx/ZRUVH079+fzZs3Ex0djb+/P926deP8+fOmNqdOnaJ9+/Y0aNCAqKgo9u/fz7hx43BwcCiptyWEEEKIUkzzStDBwcG0bduWuXPnAuoSdH9/f1577TXefffdPF+v1+upVKkSc+fOZdCgQQD069cPW1tbvvvuuwLFlJKSgru7O8nJyTIHSAghyii9Xk9WVpbWYYgiZGtri7W1da7P5+f3t6aToDMzM9mzZw9jx441HbOysiI0NJTo6GiLzpGWlkZWVhaenp6AmkCtXbuW0aNHExYWxt69ewkMDGTs2LH07t07x3NkZGSQkZFhepySklLwNyWEEEJTiqKQkJDAtWvXtA5FFAMPDw98fX0LXadP0wTo0qVL6PV6fHx8zI77+Phw9OhRi84xZswYqlatSmhoKABJSUncuHGDKVOm8L///Y+pU6eyfv16nnzySTZv3kynTp2ynSMiIoJJkyYV/g0JIYTQnDH58fb2xsnJSQralhOKopCWlmaaIuPn51eo85XpZfBTpkxh+fLlREVFmeb3GAwGAHr16sWbb74JQIsWLfjnn39YsGBBjgnQ2LFjCQ8PNz021hEQQghRtuj1elPyU7lyZa3DEUXM0dERUDs7vL297zsclhdNEyAvLy+sra1JTEw0O56YmIivr+99XztjxgymTJnCn3/+SbNmzczOaWNjQ6NGjczaN2zYkK1bt+Z4Lnt7e+zt7Qv4LoQQQpQWxjk/Tk5OGkciiovxzzYrK6tQCZCmq8Ds7Oxo3bo1kZGRpmMGg4HIyEhCQkJyfd20adP46KOPWL9+PW3atMl2zrZt23Ls2DGz48ePH6dmzZpF+waEEEKUSjLsVX4V1Z+t5kNg4eHhDB48mDZt2hAUFMSsWbNITU1l6NChAAwaNIhq1aoREREBwNSpUxk/fjxLly4lICCAhIQEAFxcXHBxcQHgnXfeoW/fvnTs2JGHHnqI9evXs2bNGqKiojR5j0Z6g8LO2CskXU/H29WBoEBPrK3kL6kQQghR0jRPgPr27cvFixcZP348CQkJtGjRgvXr15smRsfFxZnt6Dp//nwyMzN5+umnzc4zYcIEJk6cCMATTzzBggULiIiI4PXXX6d+/fr89NNPtG/fvsTe173WH4xn0prDxCenm475uTswoWcjujcp3EQuIYQQIicBAQGMGjWKUaNGaR1KqaN5HaDSqKjrAK0/GM+IJTHc+0Eb+37mD2wlSZAQQhSB9PR0YmNjCQwMLHTx25Lstc9rWOfu/+Tnx8WLF3F2di6SOVGnT58mMDCQvXv30qJFi0Kfr6Du92dcZuoAVQR6g8KkNYezJT8ACmoSNGnNYbo28pXhMCGEKCVKutc+Pj7edH/FihWMHz/ebC6rcYoHqMvB9Xo9NjZ5/wqvUqVK0QZajmi+FUZ5tzP2itlfoHspQHxyOjtjr5RcUEIIIXJl7LW/99/uhOR0RiyJYf3B+FxeWXC+vr6mm7u7OzqdzvT46NGjuLq6sm7dOlq3bo29vT1bt27l1KlT9OrVCx8fH1xcXGjbti1//vmn2XkDAgKYNWuW6bFOp+PLL7/kiSeewMnJibp16/Lrr78WyXvIyMjg9ddfx9vbGwcHB9q3b8+uXbtMz1+9epUBAwZQpUoVHB0dqVu3Lt988w2gFkZ+9dVX8fPzw8HBgZo1a5rm/hYXSYCKWdL13JOfgrQTQgiRP4qikJZ5y6Lb9fQsJvx6KNdee4CJvx7menqWRecrylkm7777LlOmTOHIkSM0a9aMGzdu8MgjjxAZGcnevXvp3r07PXv2zHPj70mTJtGnTx/279/PI488woABA7hypfD/CR89ejQ//fQTixcvJiYmhjp16hAWFmY697hx4zh8+DDr1q3jyJEjzJ8/Hy8vLwBmz57Nr7/+yg8//MCxY8f4/vvvCQgIKHRM9yNDYMXM29WyMWhL2wkhhMifm1l6Go3/o0jOpQAJKek0nbjBovaHPwzDya5oftV++OGHdO3a1fTY09OT5s2bmx5/9NFHrF69ml9//ZVXX3011/MMGTKE/v37AzB58mRmz57Nzp076d69e4FjS01NZf78+SxatIgePXoAsHDhQjZu3MhXX33FO++8Q1xcHC1btjSVr7k7wYmLi6Nu3bq0b98enU5XImVrpAeomAUFeuLn7kBes3v+PnGRjFv6EolJCCFE2XNv3bsbN27w9ttv07BhQzw8PHBxceHIkSN59gDdXTzY2dkZNzc30/YSBXXq1CmysrJo166d6ZitrS1BQUEcOXIEgBEjRrB8+XJatGjB6NGj+eeff0xthwwZwr59+6hfvz6vv/46GzZYlmAWhvQAFTNrKx0TejZixJIYdJBjtyrAZ1Gn2HA4kWlPN6NVjUolGaIQQpRrjrbWHP4wzKK2O2OvMOSbXXm2WzS0LUGBnhZdu6g4OzubPX777bfZuHEjM2bMoE6dOjg6OvL000+TmZl53/PY2tqaPdbpdKZtpIpTjx49OHPmDL///jsbN26kS5cujBw5khkzZtCqVStiY2NZt24df/75J3369CE0NJSVK1cWWzzSA1QCujfxY/7AVvi6mw9z+bk7sGBgKxYMbIWXiz0nk27w1Px/+N9vh7mZKb1BQghRFHQ6HU52NhbdOtStct9eex3qv90d6lax6HzFWZF627ZtDBkyhCeeeIKmTZvi6+vL6dOni+1691O7dm3s7OzYtm2b6VhWVha7du0y25qqSpUqDB48mCVLljBr1iy++OIL03Nubm707duXhQsXsmLFCn766acimZuUG+kBKiHdm/jRtZFvrjUlHqhVmQ9/O8yqmPN8uTWWjUcSmfpUMx6opW7mJ1WkhRCi+N2v1974L+6Eno1Kxb+/devWZdWqVfTs2ROdTse4ceNKpCfn3q2mABo3bsyIESN455138PT0pEaNGkybNo20tDReeOEFAMaPH0/r1q1p3LgxGRkZ/PbbbzRs2BCAmTNn4ufnR8uWLbGysuLHH3/E19cXDw+PYnsfkgCVIGsrHSG1c96d2MPJjpl9WtCzWVXeW32AM5fT6PfFdp57oCatalZi2vqjUkVaCCFKgLHX/t46QL6l7N/dmTNn8vzzz/Pggw/i5eXFmDFjSElJKfbr9uvXL9uxs2fPMmXKFAwGA8899xzXr1+nTZs2/PHHH1SqpE7rsLOzY+zYsZw+fRpHR0c6dOjA8uXLAXB1dWXatGmcOHECa2tr2rZty++//262E0RRk0rQOSjqStD5vn56FhG/H2XZztwnskkVaSGEyK6sVoIWliuqStAyB6gUcnOwJeLJpnz3fBDWufxdM2atk9YcRm+QHFYIIYqasde+V4tqhNSuLMlPOSMJUClmY22F/j65jVSRFkIIUVRefvllXFxccry9/PLLWodX5GQOUCkmVaSFEEKUlA8//JC33347x+e0mA5S3CQBKsWkirQQQoiS4u3tjbe3t9ZhlBgZAivFLKkiXcXF3qJiXEIIIYS4QxKgUsxYjwLINQm6maXnRNL1kgtKCCGEKAckASrlcqsi7eNmTw1PJ25k3OLZhTs4mlD8tR+EEEKI8kLmAJUBuVWRvpF+i4Ff7eDA+WSeXbiDZcMeoL6vq9bhCiGEEKWe9ACVETnVo3B3smXJC8E0rebOldRMnl24nWMJMhwmhBBC5EUSoDLOmAQ1qebGZUmChBCiwurcuTOjRo3SOowyQxKgcsDdyZbvX3jALAk6nihJkBBClBU9e/ake/fuOT73999/o9Pp2L9/f5FdT5IlSYDKjZx6giQJEkKIAtgcAVum5fzclmnq80XshRdeYOPGjZw7dy7bc9988w1t2rShWbNmRX7dikwSoHLEw8mOJS8E07iqG5duqEnQCUmChBAif6ysYfPH2ZOgLdPU41bWRX7Jxx57jCpVqrBo0SKz4zdu3ODHH3+kd+/e9O/fn2rVquHk5ETTpk1ZtmxZkcdh9NNPP9G4cWPs7e0JCAjgk08+MXv+s88+o27dujg4OODj48PTTz9tem7lypU0bdoUR0dHKleuTGhoKKmpqcUWa0FJAlTOeDjZ8f2Ld5Kg/reTIL1BIfrUZX7Zd57oU5dlA1UhRMWhKJCZavktZCR0fEdNdjb9Tz226X/q447vqM9bei7Fsn9rbWxsGDRoEIsWLUK56zU//vgjer2egQMH0rp1a9auXcvBgwcZPnw4zz33HDt37izyj2vPnj306dOHfv36ceDAASZOnMi4ceNMydnu3bt5/fXX+fDDDzl27Bjr16+nY8eOAMTHx9O/f3+ef/55jhw5QlRUFE8++aTZeyotdEppjEpjKSkpuLu7k5ycXGb3P7mWlsmAL3dw6EIKrg422NtYcelGpul5P3cHJvRsRPcmfhpGKYQQRSs9PZ3Y2FgCAwNxcLhdPy0zFSZX1Sag9y6AnbNFTY8ePUrDhg3ZvHkznTt3BqBjx47UrFmT7777Llv7xx57jAYNGjBjxgxAndfTokULZs2alee17td2wIABXLx4kQ0bNpiOjR49mrVr13Lo0CFWrVrF0KFDOXfuHK6u5qVXYmJiaN26NadPn6ZmzZoWve/8yvHP+Lb8/P6WHqByytgT5F/Jkevpt8ySH4CE5HRGLIlh/cF4jSIUQghxtwYNGvDggw/y9ddfA3Dy5En+/vtvXnjhBfR6PR999BFNmzbF09MTFxcX/vjjD+Li4oo8jiNHjtCuXTuzY+3atePEiRPo9Xq6du1KzZo1qVWrFs899xzff/89aWlpADRv3pwuXbrQtGlTnnnmGRYuXMjVq1eLPMaiIIUQyzFXB1sy9YYcn1NQt9eYtOYwXRv5Ym11vx3HhBCiDLN1Unti8mvr/8Ff08HaDvSZ6vBX+zfzf+18eOGFF3jttdeYN28e33zzDbVr16ZTp05MnTqVTz/9lFmzZtG0aVOcnZ0ZNWoUmZmZeZ+0iLm6uhITE0NUVBQbNmxg/PjxTJw4kV27duHh4cHGjRv5559/2LBhA3PmzOH9999nx44dBAYGlnis9yM9QOXYztgrJKZk5Pq8AsQnp7Mz9krJBSWEECVNp1OHofJzi56nJj8PvQ/jLqo//5quHs/PeXT5+89lnz59sLKyYunSpXz77bc8//zz6HQ6tm3bRq9evRg4cCDNmzenVq1aHD9+vFg+roYNG7Jt2zazY9u2baNevXpYW6sTwG1sbAgNDWXatGns37+f06dPs2nTJgB0Oh3t2rVj0qRJ7N27Fzs7O1avXl0ssRaG9ACVY0nX04u0nRBCVAjG1V4PvQ+dRqvHjD83f2z+uIi5uLjQt29fxo4dS0pKCkOGDAGgbt26rFy5kn/++YdKlSoxc+ZMEhMTadSoUYGvdfHiRfbt22d2zM/Pj7feeou2bdvy0Ucf0bdvX6Kjo5k7dy6fffYZAL/99hv//fcfHTt2pFKlSvz+++8YDAbq16/Pjh07iIyMpFu3bnh7e7Njxw4uXrxIw4YNCxxncZEeoHLM29Uh70bA3rhrZN7KeahMCCEqHIPePPkx6jRaPW7QF+vlX3jhBa5evUpYWBhVq6qTtz/44ANatWpFWFgYnTt3xtfXl969exfqOkuXLqVly5Zmt4ULF9KqVSt++OEHli9fTpMmTRg/fjwffvihKRnz8PBg1apVPPzwwzRs2JAFCxawbNkyGjdujJubG3/99RePPPII9erV44MPPuCTTz6hR48ehfxUil6pWAU2b948pk+fTkJCAs2bN2fOnDkEBQXl2HbhwoV8++23HDx4EIDWrVszefLkXNu//PLLfP755/zf//2fxVUvy8MqMAC9QaH91E0kJKeT1x9yDU8n3upWj57NqmIl84GEEGXU/VYIifKh3KwCW7FiBeHh4UyYMIGYmBiaN29OWFgYSUlJObaPioqif//+bN68mejoaPz9/enWrRvnz5/P1nb16tVs377dlEFXNNZWOib0VLtH701pdLdv/YP88XKxJ+5KGm8s30fPuVvZcvyiWc0GqSEkhBCivNG8Byg4OJi2bdsyd+5cAAwGA/7+/rz22mu8++67eb5er9dTqVIl5s6dy6BBg0zHz58/T3BwMH/88QePPvooo0aNqnA9QEbrD8Yzac1h4pPvzPW5uw5QWuYtvt4ay4It/3Ej4xYAD9auzJjuDYhPvnnf11pCb1DYGXuFpOvpeLs6EBToKavOhBDFQnqA1L3D7jfkdOPGjRKMpugVVQ+QppOgMzMz2bNnD2PHjjUds7KyIjQ0lOjoaIvOkZaWRlZWFp6enqZjBoOB5557jnfeeYfGjRsXedxlTfcmfnRt5JtrEuJkZ8OrD9fl2eCazNt8ku+iz/DPqcv0mrctx/MZawjNH9gqzyQor+RLCCFE0WrTpk22yc0iO00ToEuXLqHX6/Hx8TE77uPjw9GjRy06x5gxY6hatSqhoaGmY1OnTsXGxobXX3/donNkZGSQkXFnuXhKSopFrytLrK10hNSufN82ns52jHusEUPbBfDJhmOs3ptz3QxLawitPxjPiCUx2eYf5SeBEkIIkT+Ojo7UqVNH6zBKvTK9DH7KlCksX76cqKgoUzfYnj17+PTTT4mJiUFnYf2FiIgIJk2aVJyhlinVKznRp02NXBMguFNDqNv/bcHb1QEnO2sc7KxxsrXGyc4ae1trlu6Iy3HytRRhFEIIoTVNEyAvLy+sra1JTEw0O56YmIivr+99XztjxgymTJnCn3/+SbNmzUzH//77b5KSkqhRo4bpmF6v56233mLWrFmcPn0627nGjh1LeHi46XFKSgr+/v4FfFflg6W1gU5dTOXUxfzv8nt3Eca8eqaEECK/DAYp7VFeFdWfraYJkJ2dHa1btyYyMtJUz8BgMBAZGcmrr76a6+umTZvGxx9/zB9//EGbNm3MnnvuuefMhsMAwsLCeO655xg6dGiO57O3t8fe3r5wb6acsbSG0Nvd6lGjsjM3M2+RlqknLVPPzUw9B84ns+X4xTxfL0UYhRBFyc7ODisrKy5cuECVKlWws7OzeDRAlG6KopCZmcnFixexsrLCzs6uUOfTfAgsPDycwYMH06ZNG4KCgpg1axapqammZGXQoEFUq1aNiIgIQJ3fM378eJYuXUpAQAAJCQmAWj3TxcWFypUrU7myeY+Cra0tvr6+1K9fv2TfXBkWFOiJn7tDrjWEdICvuwMjOtfJcQgr+tRlixIgSxMtIYSwhJWVFYGBgcTHx3PhQgH2/xKlnpOTEzVq1MDKqnCVfDRPgPr27cvFixcZP348CQkJtGjRgvXr15smRsfFxZm9yfnz55OZmcnTTz9tdp4JEyYwceLEkgy9XDPWEBqxJAYdmCVBxnRnQs9Guc7fySuBAnC2s6ZNzUpFGLUQQqi9QDVq1ODWrVvo9cVbtVmULGtra2xsbIqkV0/zOkClUXmrA1QYhVnGblwFBuSaBD3evCrTn2mGvY11UYVsRmoQCSFExZGf39+SAOVAEiBzhUkickugujfx4bvoOG4ZFNrVqcyCga1xdbAt0rilBpEQQlQskgAVkiRARSu3BOqv4xd5ecke0jL1NK7qxqKhQVRxLZrJ6LnVIDKmbVKDSAghyp8ytReYKP+MRRh7tahGSO3Kpt6jjvWqsHz4A1R2tuPQhRSemv8PsZfyv6T+XnqDwqQ1h3OtQQRqDSLZ00wIISouSYCEpppV9+CnEQ9Sw9OJuCtpPD3/H/afu1aoc+6MvWI27HWvu2sQCSGEqJgkARKaC/ByZuWIEBpXdeNyaib9vtjOXxYsob9XasYtlu+MY8xP+y1qLzWIhBCi4tJ8GbwQoNYDWvFSCC9/t4etJy/x/KJdTH+mGY83r3bfCdiKorD/XDLLd8Xx674LpGZavuS1iosUvxRCiIpKJkHnQCZBayfzloG3f/yXX/9VC5i5OthwPf2W6XnjKq6Q2l78su88y3ae5Uj8nc1ra3k580yb6nyz7TQXr2fkuvweoH2dyszs20KKMQohRDkhq8AKSRIgbRkMCsO+3U3k0aRc29hY6bh1exKznY0VjzTxpV9QDYIDPdHpdLnWIDIWdbS11pGlV/BysWP6M815qL538b0hIYQQJSI/v79lCEyUOgpw+K5enZzcMijU83ahf3ANnmhZDQ8n8z1hujfxY/7AVtnqAPne7kGqXcWF15bt5WjCdYZ+s4sX2gcyunv9YivIKIQQonSRHqAcSA+QtqJPXab/wu15tls2LJiQ2l73bXO/Io7pWXqmrDvKon9OA9C4qhuz+7ekdhWXQr8HIYQQJU96gESZZunqrKTrGXm2MdYgyomDrTUTH29Mh7pevLNyP4cupPDY7K1MfLwRfdr4o9PpZCsNIYQopyQBEqWOpZOSi2rycpeGPqx7owPhP+xj28nLjPnpAH+duMTDDbyZ8ccx2UpDCCHKIakDJEod407yufWz6FATkaBAzyK7po+bA989H8yY7g2wsdKxdn88b/3wb7aCignJ6YxYEsP6g/FFdm0hhBAlTxIgUepYW+mY0LMRQLYkyPh4Qs9GRT4UZWWlY0Tn2qx4KQRrXc7nlq00hBCifJAESJRKxlVcvu7mw1y+7g7FvpFp5i0D+vusDZCtNIQQouyTOUCi1OrexI+ujXxLfBKy5ZOwZSsNIYQoqyQBEqXa/VZxFZeSnoQthBCi5MkQmBD3yGsSttGO2Mtk6Q0lEpMQQoiiJQmQEPe43yTsu8368wSPz93GwfPJJROYEEKIIiMJUEnYHAFbpuX83JZp6vOiVMltErafuwPzB7Ti034t8HCy5Uh8Cr3nbeOTDcfIuGX5TvRCCCG0JXOASoKVNWz+WL3fafSd41umqccfel+buMR95TUJ+8HaXoz/5SDrDiYwZ9NJ/jiUwPSnm9Pc3wO4/zYcQgghtCV7geWgWPYCMyY7D4yAbpPh7xl3kp+7kyJR5vx+IJ5xPx/kcmomVjoY3rE2jfxciVh3VKpICyFECcrP729JgHJQbJuh/vwK7PsedFagGCT5KUeupGYy4ddDrPn3Qq5tjH0/xV3HSAghKqr8/P6WOUAlqdZD6k/FADprSX7KEU9nO+b0b8n8Aa3IbZRLqkgLIUTpIQlQSboae+e+oodl/bWLRRQLDyc77pfbSBVpIYQoHSQBKil3T3ju8JZ67Njv8OPz2sYlipTFVaRTpIq0EEJoSVaBlYS7k59Oo0FRIPUixHwLh34CWwfo/ZnWUYoiYGl16E82HudG5i16t6iGs33Ofw1lFZkQQhQfmQSdgyKfBL05Ql0Kf/ecH/0t+HEwHP0NrOxg2J/g17zw1xKa0hsU2k/dREJyOpb8xXKxt+GpVtUY+EBN6vq4mo6vPxjPpDWHZRWZEELkg6wCK6RiWwV2r6x0WPIUnNkKzlXg+T+gcu3iu54oEesPxjNiSQyAWRJk7LuZ8UwzrqZl8f2OOGIvpZqeDw70ZOADNQF4fdnebAmUrCITQoj7kwSokEosAQJIT4ZvHoXEA1ApQE2CXH2L95qi2FnSg2MwKGw7dYkl28+w8XCiafK0lY5cJ1LrAF93B7aOeViGw4QQ4h6SABVSiSZAANcT4etucPU0+DSFoWvBwb34ryuKVX7m8MQn32TZzrN8+89prt3MyvPcy4Y9QEjtykUdshBClGllrg7QvHnzCAgIwMHBgeDgYHbu3Jlr24ULF9KhQwcqVapEpUqVCA0NNWuflZXFmDFjaNq0Kc7OzlStWpVBgwZx4ULuBeo05+oDz60GZ2+1J2hZf3V4TJRp1lY6QmpXpleLaoTUrnzfHhs/d0fCu9Zj/O1NWPNi6WozIYQQOdM8AVqxYgXh4eFMmDCBmJgYmjdvTlhYGElJSTm2j4qKon///mzevJno6Gj8/f3p1q0b58+fByAtLY2YmBjGjRtHTEwMq1at4tixYzz++OMl+bbyz7MWDPwJ7N3gzDb46QV1orSoUPzcHS1qZ+lqMyGEEDnTfAgsODiYtm3bMnfuXAAMBgP+/v689tprvPvuu3m+Xq/XU6lSJebOncugQYNybLNr1y6CgoI4c+YMNWrUyPOcJT4EdrfTW+G7J0GfAS2fg8fngE7melQUlqwi85M5QEIIkaMyMwSWmZnJnj17CA0NNR2zsrIiNDSU6Ohoi86RlpZGVlYWnp6eubZJTk5Gp9Ph4eGR4/MZGRmkpKSY3TQT0B6e/lrdL2zvdxD5oXaxiBJnbaVjwu1hsNzSm2eDakjyI4QQhaRpAnTp0iX0ej0+Pj5mx318fEhISLDoHGPGjKFq1apmSdTd0tPTGTNmDP379881G4yIiMDd3d108/f3z98bKWoNH4PHZqn3t86EaCmSWJF0b+LH/IGt8HU3H+ays1H/un4WdYp/Tl3SIjQhhCg3ynQl6ClTprB8+XKioqJwcMg+JyIrK4s+ffqgKArz58/P9Txjx44lPDzc9DglJUX7JKj1YEi7pPYA/TEWnL2gWR/zNlumgUEPD43VJkZRbLo38aNrI1+zVWTNqrsz4vsY/jp+kecX7eLLQW1pX9dL61CFEKJM0rQHyMvLC2traxITE82OJyYm4ut7/1o4M2bMYMqUKWzYsIFmzZple96Y/Jw5c4aNGzfedyzQ3t4eNzc3s1up0D4cqgep91e/BCc23nnOuL2GlbU2sYlid+8qMmd7G754rjUPN/AmPcvA84t3EXUs58UCQggh7k/TBMjOzo7WrVsTGRlpOmYwGIiMjCQkJCTX102bNo2PPvqI9evX06ZNm2zPG5OfEydO8Oeff1K5chmtl6LTqYURvRuDYoBl/eDszux7i4kKw8HWmvkDW9G1kQ+ZtwwM/3YPkUcS836hEEIIM5ovgw8PD2fhwoUsXryYI0eOMGLECFJTUxk6dCgAgwYNYuzYO0M8U6dOZdy4cXz99dcEBASQkJBAQkICN27cANTk5+mnn2b37t18//336PV6U5vMzExN3mOhWFnBS1vAszYYbsFXXSX5qeDsbayZ92wrujf2JVNv4OUle/jjkGVz5oQQQqg0XwYPMHfuXKZPn05CQgItWrRg9uzZBAcHA9C5c2cCAgJYtGgRAAEBAZw5cybbOSZMmMDEiRM5ffo0gYGBOV5n8+bNdO7cOc94NF0Gn5vMVJhcDVDUFWITrmodkdBYlt7AqBX7WLs/HhsrHbP7t+SRpkWzR5jsRC+EKItkK4xCKpUJkHHYy6j1UOg5S7NwROlwS2/grR//5Zd9F7C20jGrbwt6Nq9aqHPKTvRCiLKqzNQBEha6e85P82fVY3u+gaip2sYlNGdjbcXMPi14qlV19AaFN5bvZfXec+gNCtGnLvPLvvNEn7qMPrfdVe9h3Mn+7uQHICE5nRFLYlh/ML443oYQQpQ46QHKQanqAbp3wnPyeZjTCm7d/gUlc4EE6s7yY1cdYMXuswC4O9qSfNemqpb04BirUN+b/BjJTvRCiNJOeoDKE4PePMlxrwYhr6r3HSuBvgxO7BZFzspKR8STTelwuy5Q8j07yt/bg3Mj4xbHE6+z+VgS3+84w7T1Rxn01Y5ckx8ABYhPTmdn7JViex9CCFFSynQhxAohpyKH7UdBzGJIvajuIC8EaoJyIulGrs8BvLp0L462/3I9Q1/g68hO9EKI8kB6gMoie1fofDsxioqAm9c0DUeUDjtjr5Bwnx4cgFsGxZT8uDnY0NDPjdCG3gwKqUn/IMuqn8tO9EKI8kB6gMqqVoNhx+dw6Zi6X1hX2TS1orO0Z2ZsjwY8G1wDVwdbs+N6g0LUsYu57kRvnAMUFJj7xsNCCFFWSA9QWWVtA90+Uu9vXwBXs9dGEhWLpT0zzap7ZEt+wLKd6Cf0bCQToIUQ5YIkQGVZ3W4Q2BH0GbDpI62jERoLCvTEz90h1+RFh7oa7H49OLntRA/wSFM/qQMkhCg3JAEqy3Q66PY/QAcHfoTze7SOSGjofj04xseW9OB0b+LH1jEPs2zYA3zarwWjQusCEHk0Mc85RkIIUVZIAlTW+TWH5v3U+xvGgZR1qtBy68HxdXdg/sBWFvfg3L0T/Rtd6tKmZiXSswxM/+NYcYQthBAlTgoh5qBUFUK0RPI5mNNaLY7Ybyk0eFTriITGinovr31nr9F73jYAfnutPU2quRdVqEIIUWSkEGJF414dQkaq9zeOB33W/duLcu/uHpyQ2pULPXG5hb8HvVqoe4z9b+1h5P9NQoiyThKg8qLdKHCuApdPwu5vtI5GlEOjuzfA3saK7f9dYePhRK3DEUKIQpEEqLxwcDMvjpierG08hbU5Qt0HLSdbpqnPixJVzcORFzsEAhCx7iiZtwwaRySEEAUnCVB50moweNWDm1fg75laR1M4VtbqJrD3JkHGzWGtrLWJq4Ib0bkOXi52xF5KZcl2qT0lhCi7JAEqT6xtoKuxOOJ8uBanbTyF0Wm0ugns3UmQMfm5e3NYUaJc7G0I71ofgE8jT3AtTTbjFUKUTZIAlTf1wiCgg1ocMbKMF0e8Owma6KH+7PyeJD8a69OmOvV9XEm+mcWcTSe1DkcIIQpEEqDyxlQcETjwQ9kvjhjQ4fad26uOEvbDjYuahSPAxtqK9x9tCMC30aeJvZSqcURCCJF/kgCVR1VbQLNyUBzxSiwseeL2g9vLuI/+Bp8Fw+FfNQtLQMd6VehcvwpZeoUp645oHY4QQuSbJEDl1cMfgI0DnNkGx37XOpr8u3kNvuwCWTfBxRfeOw9tXlCfS7sMPzwHq4bDzauahlmRvfdIQ6x08MehRHb8d1nrcIQQIl8kASqvPPzhgVfU+2WtOKI+C77orCY6dq4wPArsnOGxmdBpzO1GOti/Aj57EE7+qWGwFVc9H1f6B9UA4H9rj2AwlNGeRiFEhSQJUHnW/k1w8lKLI+5ZpHU0llEUWDcarsaClS0MXQtud+1f9dB76sTolgOhch24fgGWPAVrRkHGDc3Crqje7FoPF3sbDpxP5ud957UORwghLCYJUHnm4AbVWqn3cyqOWBoLCu5YALu/BnTQZ7G62eu9Oo2GXnPhpb8h+GX12J5vYP6DcOafEg23ovNysWfkQ3UAmLb+GDcz9RpHJIQQlpEEqLyrejsBSrsMW//vzvHSWFDw+B/wx3vq/a4f5r2pq50T9JgKg34Fd3+4dga+eQT+eB+y0os/XgHA0HYBVPNwJCElnS///k/rcIQQwiKSAJV3D42Fps+o97fNVosjlsaCggkHYeXzoBig1SB48DXLX1urE4z4B1o+BygQPRdmNlKHxXJSGnu+yjAHW2vG9GgAwPwtp0hKkeRTCFH6SQJUETy5EDxqgqKHWc3U5Kf1UOjwttaRqa4nwtK+kHlDrfvzyCdqPaP8cHBTh8X6rwAXH7h5WR0WW/So+QTw0tjzVQ70bOZHyxoepGXq+WTDca3DEUKIPOkUpawWiSk+KSkpuLu7k5ycjJubm9bhFI34f+HzjubHnCpD7Yehdhf1p6uP+fObI9REIadeoi3TwKBXe5gKI+ummqSc36NOan7xT3CsVLhzpl2BtW/BoVXqYxcfGPQLHFlT+nq+ypE9Z67y1Hx1DtaUJ5viaGeNt6sDQYGeWFvlM6EVQogCyM/vb5sSiklo7fgf6k8razVxsbZT5wUd+FG9Afg2hTqhakLkH3xnQ1IwTxjuHkIrDIMBfh6hJj+OleDZHwqf/AA4ecIz30DDx+DnV+BGInz2gPqcJD/FpnXNSrSu6cGeM9d4d9UB03E/dwcm9GxE9yZ+93m1EEKULBkCqwjuTljGX1F/6jPVpeQd3rqz0irhgDpRevFjMC0QLuyFet2Lb0PSqMlwaLW63L3vEqhcu3Dnu1eTp+CN/ZiqSAMEDSvaawiT9Qfj2XPmWrbjCcnpjFgSw/qD8SUflBBC5EJ6gMq7nBIW40/j8Zf+UvfXOrUJTkXCyUhIu2ReQXrzx+pSesUAncYWPvn5dwX8NV2933MWBLQv3PlyE7MY0z5iAF88BK/tkTlARUxvUJi05nCOzymoKeikNYfp2shXhsOEEKVCqegBmjdvHgEBATg4OBAcHMzOnTtzbbtw4UI6dOhApUqVqFSpEqGhodnaK4rC+PHj8fPzw9HRkdDQUE6cOFHcb6N0Muhz7q0x7rRuuF23xaUKNO8LT34Bb5+A4Vvg4XFQsx1Y3c6TFYP6c9dC+PV1NVEqSIXpM9Hw66vq/Xaj1J6o4nB38vfS3+r7uBqrLpUXRWpn7BXik3Nf/aUA8cnp7Iy9UnJBCSHEfWieAK1YsYLw8HAmTJhATEwMzZs3JywsjKSkpBzbR0VF0b9/fzZv3kx0dDT+/v5069aN8+fvVKGdNm0as2fPZsGCBezYsQNnZ2fCwsJIT6+Ay3Mfuk9vTafROU9itrJSN1Tt+DYM/R3avaEe193+uqRdUntWljwJ0+uo82yO/wG3MvKO50osrBigDsE17AldJhTobeXp3p4vv2ZqcgdwdjusGFQ8162gkq5b9nfL0nZCCFHcNE+AZs6cybBhwxg6dCiNGjViwYIFODk58fXXX+fY/vvvv+eVV16hRYsWNGjQgC+//BKDwUBkZCSg9v7MmjWLDz74gF69etGsWTO+/fZbLly4wM8//1yC76yc2DIN/v5ETSQmXIVO76rHq7ZUt9lIvwb7voelfdRk6KdhcOQ3dXXX5og7c4dA3eB0aV918rWLL1SuqyZbxSGnnq8mT0H7cPX+0d/UOU6iSHi7OhRpOyGEKG6aJkCZmZns2bOH0NBQ0zErKytCQ0OJjo626BxpaWlkZWXh6ekJQGxsLAkJCWbndHd3Jzg42OJzittymj/00Fj18YW96oTiIWshaLia0GSkwIEf1B6eabXh8C/q6zf9Tx0q+3EIXDqmbnB6IwFsHYsv9tx6vh7+AOp2U2siLR+ozn0ShRYU6ImfuwO5ze7Roa4GCwr0LMmwhBAiV5omQJcuXUKv1+PjY15/xsfHh4SEBIvOMWbMGKpWrWpKeIyvy885MzIySElJMbsJ8p4/pCjq5OVHpkP4EXh+AzwwEtyqQ1YqXDyitv9rulqZ+b/N6oqvzOvaLUe3soanvlRrDqWcgx8Gwa3Mko+jnLG20jGhZyOAHJMgBZjQs5FMgBZClBqaD4EVxpQpU1i+fDmrV6/GwaHgXesRERG4u7ubbv7+/kUYZRmWn/lDVlZQIxi6T4Y3D8KwTercoUoB6vOpt+d0GbK0r8Xj4A79loG9G8T9A+vf1S6WcqR7Ez/mD2yFr3v2v4su9jZ0qFtFg6iEECJnmiZAXl5eWFtbk5iYaHY8MTERX1/f+752xowZTJkyhQ0bNtCsWTPTcePr8nPOsWPHkpycbLqdPXu2IG9HGOl0UK21uqHp6/vUFVi628vOre1KRyHCKvXULULQwe6vYM8irSMqF7o38WPrmIdZNuwBPu3XgiUvBFHT05EbGbdY9M9prcMTQggTTRMgOzs7WrdubZrADJgmNIeEhOT6umnTpvHRRx+xfv162rRpY/ZcYGAgvr6+ZudMSUlhx44duZ7T3t4eNzc3s5soIjodHF+vzrmxtlNXf909MVpL9burc4IA1r4Ncdu1jaecsLbSEVK7Mr1aVKN93Sq82bU+AF/89R8p6QUomyCEEMVA8yGw8PBwFi5cyOLFizly5AgjRowgNTWVoUOHAjBo0CDGjr0z1DJ16lTGjRvH119/TUBAAAkJCSQkJHDjxg0AdDodo0aN4n//+x+//vorBw4cYNCgQVStWpXevXtr8RYrtrsnUo+7qP68u7K01jq8BY16q0NzK56D5PN5vkTkT8/mVanr7ULyzSy++jtW63CEEAIoBZWg+/bty8WLFxk/fjwJCQm0aNGC9evXmyYxx8XFYXXXUun58+eTmZnJ008/bXaeCRMmMHHiRABGjx5Namoqw4cP59q1a7Rv357169cXap6QKIC8qlDf/VgrOh30/gwun4TEg+oKtqHrineFWgVjbaVjVGg9Ri6N4eutsQxtF4CHk53WYQkhKjjZDT4H5XI3eC2UxG7yReXqaXWbjJtXoFk/eGKBmhyJImEwKDwy+2+OJlznlc61Gd29gdYhCSHKofz8/pYEKAeSAFVQsX/Bt73V+UphkyFkpNYRlSsbDycy7NvdONpa8/eYh/Bysdc6JCFEOZOf39+azwESotQI7KgmPgAbPlA3hxVFJrShN82ru3MzS8+CqFNahyOEqOAkARLibsEvQYuB6savy/rDlf+yt9kyTR3eE/mi0+l4s2s9AL7bfobEFNkXTAihHUmAhLibTgePzQTXqnArHb7qBhk37jxvnNhtZa1djGVYp3pVaF2zEhm3DHy2+aTW4QghKjBJgIS4l429WsnazgVSL8LCh8FgyHlVm8gXnU7HW7d7gZbtPMv5azc1jkgIUVFJAiRETtz84Lmf1QrWl47BR5Ul+SkiD9bxIqRWZTL1BuZuOqF1OEKICkoSICFy498Wes1T7ysG9aetk7qzvSiUt7qpvUA/7j5H3OU0jaMRQlREkgAJcT/Jxn3hbtcE2vA+fN4JzkRrFlJ50CbAk471qnDLoPBppPQCCSFKniRAQuTm7jk/469A/UfV40mH4JvusHoE3LiobYxlWPjtuUCr957j1MUbebQWQoiiJQmQEDm5d8KzlRX0Xwrt37zT5t+lMLc17FyoVrUW+dLC34PQhj4YFJj1p/QCCSFKliRAQuTEoM95wnPoRPV4y+fAtxmkJ8Pvb6srxc7v0STUsszYC/Tb/gscS7iucTRCiIpEtsLIgWyFISxi0MOur2DT/yAjGdBB6yFg7wb2LmVjD7RS4JXv9/D7gQS6N/ZlwXOttQ5HCFGGyVYYQpQEK2sIHg6v7VY3UEWBPd/Ari/U4bOoqebtpYhijkaF1kOng/WHEjh4PlnrcIQQFYQkQEIUlos3PPk5DPkdqjSErNvF/aImw9q31PtSRDFX9Xxcebx5VQBmbjyucTRCiIpChsByIENgosD0WbB9PkRNgaxU9ZjOSq0jJMlPrv67eIOu//cXeoPCqlcepFWNSlqHJIQog2QITAitWNtCu9fh1V3QqLd6zFhEsVZnraIq9WpVceHJltUAmLnhGNGnLvPLvvNEn7qM3iD/RxNCFD0brQMQolxyrwY+jeHwz3eOfdUN2r0BD72n7jcmzLzepS4/xZxj68nLbD152XTcz92BCT0b0b2Jn4bRCSHKG+kBEqI43D3nZ8wZ8G0KKLBtFnzRGeL/1TjA0ufQhWRy6uxJSE5nxJIY1h+ML/mghBDlliRAQhS1eyc8O3rAy1uhydPq80mH1bpBW6bJvmK36Q0Kk9YczvE5Y040ac1hGQ4TQhQZSYCEKGq5FVF8+itoNwq86oHhlpokfdUVko5qEmZpsjP2CvHJ6bk+rwDxyensjL1SckEJIco1SYCEKGoPjc19tVfXSTByJzz5JTi4w4W98HlH+GdOhd5OI+l67slPQdoJIUReJAESoqTpdNDsGXhlB9TpCvoM2PABLHoUrvyndXSa8HZ1KNJ2QgiRF0mAhNCKmx8M+BF6zgY7F4iLhvnt4PtnsleRNtoyDTZHlGycJSAo0BM/dwd0uTyvQ10NFhToWZJhCSHKMUmAhNCSTgetB8OIfyCgA2SlwYkNahXpP943b1uOt9KwttIxoWcjgFyToAk9G2FtlduzQgiRP5IACVEaVKoJg36F7lPA5vYwT/RcWD4QFKVCbKXRvYkf8we2wtc9+zDXtKebSR0gIUSRkq0wciBbYQhNXToBq1+G87tvH9ABSrlOfu6mNyjsjL1CUko6n0ae4L9LqbwZWo83QutqHZoQopSTrTCEKMu86sLzf0CX8bcP3P4/Ssp5uJGkWVglxdpKR0jtyvRqWY03u9YD4Nvo06RnVdxVckKIoicJkBClkbXNnWXxutt/Tfcsgtmt4O+ZkFUxloP3aOJLNQ9HLqdmsirmvNbhCCHKEUmAhCiN7p7zM+EqtHxOPZ55HSInwby2cHCVOj+oHLOxtuKF9oEAfPn3fxikErQQoohIAiREaZPThOdec6Hze+p9O1e4Fgcrh8LXYXBuj3axloA+bf1xc7Dhv0upRB4t/0OAQoiSIQmQEKVNbltpdB6jHg8ariZDtk5wdgd8+TD89CJcO6tNvMXMxd6GAQ/UBGDhXxWzUKQQouhpngDNmzePgIAAHBwcCA4OZufOnbm2PXToEE899RQBAQHodDpmzZqVrY1er2fcuHEEBgbi6OhI7dq1+eijj5DFbqLMuN9WGp1GQ+h4NRl6LQZaDAB0cOBHmNsGvu4OkR/l/NoyXERxyIMB2Frr2Hn6CnvjrmodjhCiHNA0AVqxYgXh4eFMmDCBmJgYmjdvTlhYGElJOXdzp6WlUatWLaZMmYKvr2+ObaZOncr8+fOZO3cuR44cYerUqUybNo05c+YU51sRouS5+UHvz2B4FNRsD7fS1WrSf8+AZc+a7y1Wxoso+rg50KtFNQC+/DtW42iEEOWBpnWAgoODadu2LXPnzgXAYDDg7+/Pa6+9xrvvvnvf1wYEBDBq1ChGjRpldvyxxx7Dx8eHr776ynTsqaeewtHRkSVLllgUl9QBEmWOosDRtbBx3J39xJy94emv1aSoHBRRPJZwnbBZf2Glg6i3H6JGZSetQxJClDJlog5QZmYme/bsITQ09E4wVlaEhoYSHR1d4PM++OCDREZGcvz4cQD+/fdftm7dSo8ePXJ9TUZGBikpKWY3IcoUnQ4aPqZusBo2GWzsITUJFj9WLpIfgPq+rnSqVwWDAl9tlblAQojC0SwBunTpEnq9Hh8fH7PjPj4+JCQkFPi87777Lv369aNBgwbY2trSsmVLRo0axYABA3J9TUREBO7u7qabv79/ga8vhKZs7CBkJIQfVZMiAHRlPvkxGt6xFgA/7D7H1dRMjaMRQpRlBUqAFi9ezNq1a02PR48ejYeHBw8++CBnzpwpsuAK4ocffuD7779n6dKlxMTEsHjxYmbMmMHixYtzfc3YsWNJTk423c6eLZ+raUQFsuvLu2oEKfDzCE3DKSoP1q5MIz83bmbp+X6Htv/WCCHKtgIlQJMnT8bR0RGA6Oho5s2bx7Rp0/Dy8uLNN9+06BxeXl5YW1uTmJhodjwxMTHXCc6WeOedd0y9QE2bNuW5557jzTffJCIi99Uv9vb2uLm5md2EKLPuriMU9JJ6bN9SiJqqbVxFQKfTmXqBFv1zRrbHEEIUWIESoLNnz1KnTh0Afv75Z5566imGDx9OREQEf//9t0XnsLOzo3Xr1kRGRpqOGQwGIiMjCQkJKUhYgLpSzMrK/G1ZW1tjMBgKfE4hyox7iyh2eAts1P+sEDVZfb6Me7SZH37uDly6kcEv+2R7DCFEwRQoAXJxceHy5csAbNiwga5duwLg4ODAzZs3LT5PeHg4CxcuZPHixRw5coQRI0aQmprK0KFDARg0aBBjx441tc/MzGTfvn3s27ePzMxMzp8/z759+zh58qSpTc+ePfn4449Zu3Ytp0+fZvXq1cycOZMnnniiIG9ViLLl3iKKrj4QPFy97+IN+lvaxVZEbK2teL6duj3Gwr9jZXsMIUSB2BTkRV27duXFF1+kZcuWHD9+nEceeQRQCxUGBARYfJ6+ffty8eJFxo8fT0JCAi1atGD9+vWmidFxcXFmvTkXLlygZcuWpsczZsxgxowZdOrUiaioKADmzJnDuHHjeOWVV0hKSqJq1aq89NJLjB8/HiHKvYfGZj/WbhTs+lrdSd6nYYmHVBz6BfkzO/IEJ5NuEHU8iYcb+OT9IiGEuEuB6gBdu3aNDz74gLNnzzJixAi6d+8OwIQJE7Czs+P9998v8kBLktQBEuXO5gjYMgW86sEr28tsQcS7Tf79CF/89R8P1PJk+fCCD5sLIcqP/Pz+1rQQYmklCZAod9JT4NNmcPMq9F4ALfprHVGhXbh2k47TNnPLoPDrq+1oVt1D65CEEBor9kKI69evZ+vWrabH8+bNo0WLFjz77LNcvSr79AhR6ji4qUNhAFERcKvs19Cp6uFIz+ZVAXUukBBC5EeBEqB33nnHVC35wIEDvPXWWzzyyCPExsYSHh5epAEKIYpI0HB1e4xrZ2Dvd1pHUyRe7KBOhv79QDxnr6RpHI0QoiwpUAIUGxtLo0aNAPjpp5947LHHmDx5MvPmzWPdunVFGqAQoojYOUHHt9X7f02HLMtXbJZWjau6076OF3qDwjfbTmsdjhCiDClQAmRnZ0damvq/rT///JNu3boB4OnpKftoCVGatR4C7v5wPR52f611NEVi2O3CiMt3xZGclqVxNEKIsqJACVD79u0JDw/no48+YufOnTz66KMAHD9+nOrVqxdpgEKIImRjf6dG0N8zIeOGtvEUgY51vajv40papp6lO+O0DkcIUUYUKAGaO3cuNjY2rFy5kvnz51OtWjUA1q1bZ1oSL4QopZo/C561IO0S7FigdTSFptPpTL1A32yLJfOWVH0XQuRNlsHnQJbBi3Jv/4+w6kVwcIc39oOjh9YRFUrmLQMdpm0iMSWDlzvVpqGfK96uDgQFemJtpdM6PCFECcnP7+8CVYIG0Ov1/Pzzzxw5cgSAxo0b8/jjj2NtXfYLrAlR7jV5CrbOhKTD8M8c6DJO64gKxc7GipBaXvy87zwLtpwyHfdzd2BCz0Z0b+KnYXRCiNKoQENgJ0+epGHDhgwaNIhVq1axatUqBg4cSOPGjTl16lTeJxBCaMvKSt0zDGD7fEi9pG08hbT+YHyOG6MmJKczYkkM6w/GaxCVEKI0K1AC9Prrr1O7dm3Onj1LTEwMMTExxMXFERgYyOuvv17UMQohikODR6FqS8hKha3/p3U0BaY3KExac5icxvKNxyatOYxeNk0VQtylQAnQli1bmDZtGp6enqZjlStXZsqUKWzZsqXIghNCFCOdDh7+QL2/cyGkXNA2ngLaGXuF+OT0XJ9XgPjkdHbGXim5oIQQpV6BEiB7e3uuX7+e7fiNGzews7MrdFBCiBJSuwvUeBD0GWpxxDIo6XruyU9B2gkhKoYCJUCPPfYYw4cPZ8eOHSiKgqIobN++nZdffpnHH3+8qGMUQhSXu3uBYr6Fq6c1DacgvF0dirSdEKJiKFACNHv2bGrXrk1ISAgODg44ODjw4IMPUqdOHWbNmlXEIQohilVAO6j9MBhuQdRUraPJt6BAT/zcHchtsbsOdTVYUKBnLi2EEBVRoeoAnTx50rQMvmHDhtSpU6fIAtOS1AESFc75PbDwYdBZwSvboUp9rSPKl/UH4xmxJAYgx8nQCwa2kqXwQlQAxVIHKK9d3jdv3my6P3PmTEtPK4QoDaq1hgaPwdHfICoCnlmkdUT50r2JH/MHtmLSmsPZJkQ72FrRNkB6f4QQ5ixOgPbu3WtRO51Oqq4KUSY99B4cXQuHVkP7cPBrpnVE+dK9iR9dG/myM/YKSdfT8XKx5+O1hzkcf52ZG4/z8RNNtQ5RCFGKyFYYOZAhMFFhrXwBDq6Eet3h2RVaR1NoO/67TN8vtmOlg7Wvd6Chn/x9FqI8y8/v7wJNghZClFNOnoAOjq+Hs7vMn9syDTZHaBJWQQXXqswjTX0xKPDRb4eR/+8JIYwkARJC3OFcBdM04k0f3Tm+ZRps/hisyt5ef2N7NMTOxop/Tl1m4+FErcMRQpQSkgAJIe7oNBpCRqr3Y7dA7F93kp+H3lefL2P8PZ0Y1iEQgI9/P0LGLb3GEQkhSgNJgIQQ5sImQ7U26v3Fj5fp5MdoROc6VHG158zlNBZtO611OEKIUkASICFEdr3n376jgLVdmU5+AFzsbRgdptY2mrPpJBevZ2gckRBCa5IACSGyO7T6zn19pjoMVsY91ao6zaq7cyPjFjM3HtM6HCGExiQBEkKY2zINoiaDdyP1cUAHdRisjCdBVlY6xj+mvqflu85y6EKyxhEJIbQkCZAQ4o67JzwHDVePKYr6uBwkQW0CPHmsmR+KAh+ukWXxQlRkkgAJIe4w6O9MeA7ooB47twsefF09bij7K6jGPtIQexsrdsReYf3BBK3DEUJoRBIgIcQdD429M+G5cm1w9QN9hpoEdRqtPl/GVfNw5KWOtQCYvO4I6VllP6kTQuSfJEBCiJzpdBDQXr1/+m9tYyliL3eujY+bPWev3OTrbbFahyOE0IAkQEKI3JkSoK3axlHEnOxsGNO9AQDzNp0kKSU9j1cIIcobzROgefPmERAQgIODA8HBwezcuTPXtocOHeKpp54iICAAnU7HrFmzcmx3/vx5Bg4cSOXKlXF0dKRp06bs3r27mN6BEOXY3fOAsm5qG0sR692iGs39PUjN1DP9D1kWL0RFo2kCtGLFCsLDw5kwYQIxMTE0b96csLAwkpKScmyflpZGrVq1mDJlCr6+vjm2uXr1Ku3atcPW1pZ169Zx+PBhPvnkEypVqlScb0WI8smzFrhWVWsBnc39PydlkZWVjgk91WXxK2POceCcLIsXoiLRNAGaOXMmw4YNY+jQoTRq1IgFCxbg5OTE119/nWP7tm3bMn36dPr164e9vX2ObaZOnYq/vz/ffPMNQUFBBAYG0q1bN2rXrl2cb0WI8kmng8DbvUDlbB4QQKsalejVoqq6LP63Q7IsXogKRLMEKDMzkz179hAaGnonGCsrQkNDiY6OLvB5f/31V9q0acMzzzyDt7c3LVu2ZOHChfd9TUZGBikpKWY3IcRt5XQekNGY7g1wsLVi1+mrrPn3AtGnLvPLvvNEn7qM3iAJkRDllY1WF7506RJ6vR4fHx+z4z4+Phw9erTA5/3vv/+YP38+4eHhvPfee+zatYvXX38dOzs7Bg8enONrIiIimDRpUoGvKUS5ZpoHtBsy08DOSdt4ilhVD0de7lSbWX+eYNSKfdyd8/i5OzChZyO6N/HTLkAhRLHQfBJ0UTMYDLRq1YrJkyfTsmVLhg8fzrBhw1iwYEGurxk7dizJycmm29mzZ0swYiFKuUoB4FYdDFlwdofW0RSLQC9nAO7t8ElITmfEkhjWH4zXICohRHHSLAHy8vLC2tqaxMREs+OJiYm5TnC2hJ+fH40aNTI71rBhQ+Li4nJ9jb29PW5ubmY3IcRtZvWAyt8wmN6gMGVdzr3Oxnxo0prDMhwmRDmjWQJkZ2dH69atiYyMNB0zGAxERkYSEhJS4PO2a9eOY8fMl7QeP36cmjVrFvicQlR45Xgi9M7YK8Qn514HSAHik9PZGXul5IISQhQ7zeYAAYSHhzN48GDatGlDUFAQs2bNIjU1laFDhwIwaNAgqlWrRkREBKBOnD58+LDp/vnz59m3bx8uLi7UqVMHgDfffJMHH3yQyZMn06dPH3bu3MkXX3zBF198oc2bFKI8MPYAnd8Dmalg56xtPEUo6bplRRAtbSeEKBs0TYD69u3LxYsXGT9+PAkJCbRo0YL169ebJkbHxcVhZXWnk+rChQu0bNnS9HjGjBnMmDGDTp06ERUVBahL5VevXs3YsWP58MMPCQwMZNasWQwYMKBE35sQ5UqlAHCvAclxELcd6nTROqIi4+3qUKTthBBlg06RwhfZpKSk4O7uTnJysswHEsJo9Qj4dym0D4fQCVpHU2T0BoX2UzeRkJxObv8YWlvp+P6FYB6oXblEYxNC5E9+fn+Xu1VgQohiUk7nAVnfVRFal0sbvUGh38LtjP/lIDcybpVccEKIYiMJkBDCMjXbqT/Px0DGDW1jKWLdm/gxf2ArfN3Nh7n83B34pE9z+rX1B+Db6DOE/d9f/HX8ohZhCiGKkAyB5UCGwITIxaymcC0OBvwEdUPzbl/G6A0KO2OvkHQ9HW9XB4ICPbG2UvuFtp64xLur9nPuqrop7DOtq/PBo41wd7LVMmQhxF1kCEwIUTwCOqo/y9kwmJG1lY6Q2pXp1aIaIbUrm5IfgPZ1vfhjVEeGPBiATgc/7jlH6P9t4Y9DCYCaPMk2GkKUHZquAhNClDEB7WHfknJZENESzvY2THy8MY8182P0T/v572IqL323h1Y1PTh35SZJ1zNMbWUbDSFKN+kBEkJYzlgP6MJeyLiubSwaahPgye+vd2BE59pY6SDmzDWz5AdkGw0hSjtJgIQQlvPwV2sCKXq1HlAF5mBrzdvd6lPJ2S7H52UbDSFKN0mAhBD5Y9wdPvYvbeMoBXbGXuHyjcxcn5dtNIQovSQBEkLkjzEBqqDzgO5m8TYaKbKNhhCljSRAQoj8Mc4Dit8H6cmahqI1S7fH+PqfWGIvpRZzNEKI/JAESAiRP+7VwLMWKIYKPw8oKNATP3eHXCtIG/17Npmw//uLqeuPkiqVpIUoFSQBEkLkn7EXqILPA7rfNhq627eJjzeiU70qZOoNzI86RZdPtvDrvxeQGrRCaEsSICFE/pkKIso8oNy20fB1d2D+wFYMeTCQRUPbsnBQG/w9HUlISef1ZXvp98V2jsSnmNpLIUUhSpZshZED2QpDiDykXICZDUFnBaNjwdFD64g0d79tNIzSs/R88dd/zNt8koxbBqx0MCgkgKbV3Jmx4RjxyXcmS0shRSHyLz+/vyUByoEkQEJYYHYruHIK+i+H+j20jqZMOXc1jY/XHmHdwYRc2xhTp/kDW0kSJISFZC8wIUTxC5Tl8AVVvZIT8we25tuhQdl6iYykkKIQxUsSICFEwUhBxEKztbG6b3IjhRSFKD6SAAkhCsa4EizhANy8qm0sZZTFhRQtbCeEsJwkQEKIgnH1hcp1AQXO/KN1NGWSpYUUF/9zhoPnK3bRSSGKmiRAQoiCk3lAhWJpIcWYuKs8Nmcrw7/dzaELkggJURQkARJCFJypIOLf2sZRRllSSHFCz0b0alEVnQ42HE7k0dlbeem73WY1hEDqCAmRX7IMPgeyDF4IC11PhE/qAToY/R84eWodUZm0/mA8k9Ycvm8doJNJ1/k08iS/7b+A8V/tHk18eSO0Lqcvpeb5eiEqAqkDVEiSAAmRD3OD4NIx6Ps9NHxM62jKLEsKKQIcT7zO7MgTrD0Qz/3+9ZY6QqIikjpAQoiSYxwGOy3DYIVhbaUjpHZlerWoRkjtyrnWB6rn48rcZ1ux/o2OPNLEN9fzSR0hIe5PEiAhROHIRGhN1Pd15bmQgPu2kTpCQuROEiAhROHUvN0DlHgQUi8X/DybI2DLtJyf2zJNfV6YkTpCQhScJEBCiMJxqQJVGqr3z2wr+HmsrGHzx9mToC3T1ONW1gU/dzllaR0hS9sJUZFIAiSEKLyimAfUaTQ89L6a7GwcD1k37yQ/D72vPi/MWFJHyM9dnVAthDAnCZAQovBMCVAh5wF1Gg31H4Vtn8LHfpL85OF+dYSMhnesleuEaiEqMkmAhBCFZ0yAkg5D6qWCnUNR1Hk+x9YaD4C1rSQ/eejexI/5A1vh624+zGVnrSY930afIflmlhahCVGqlYoEaN68eQQEBODg4EBwcDA7d+7Mte2hQ4d46qmnCAgIQKfTMWvWrPuee8qUKeh0OkaNGlW0QQsh7nD2Am+1J6JAvUD6LPjlVdgyJfvx3CZGC5PuTfzYOuZhlg17gE/7tWDZsAf4e8zDVHV3IPZSKqOW75Wl8ELcQ/MEaMWKFYSHhzNhwgRiYmJo3rw5YWFhJCUl5dg+LS2NWrVqMWXKFHx9c6+BAbBr1y4+//xzmjVrVhyhCyHuFlDA5fDpKbC0D+xbgmkgJ7CT+rNy3ZwnRots7q0j5OPmwOfPtcHexorNxy4yc+MxrUMUolTRPAGaOXMmw4YNY+jQoTRq1IgFCxbg5OTE119/nWP7tm3bMn36dPr164e9vX2u571x4wYDBgxg4cKFVKpUqbjCF0IYFWQidEo8fPMInNoEVraAos75Cfv49vPnoeNoSYIKqGl1d6Y+pf4HcN7mU6zdH69xRNnJHmZCKzZaXjwzM5M9e/YwduxY0zErKytCQ0OJjo4u1LlHjhzJo48+SmhoKP/73//u2zYjI4OMjAzT45SUlPu0FkLkyJgAXTwKNy6qy+PvJ+kILHkaUs6BcxWo/wi4V1fn/CgKuFVTEyD/IDUpMuiL/z2UQ71bVuPQhWQW/h3L2z/+S60qzjT0Kx1b/FiyB5oQxUXTHqBLly6h1+vx8fExO+7j40NCQkKBz7t8+XJiYmKIiLCscFpERATu7u6mm7+/f4GvLUSF5eQJPk3U+3n1AsX+DV+FqclP5TrwwkZ4fPadCc86HdTtpt4//sftJfJjcz+fuK8x3RvQoa4XN7P0DP9uN1dTM7UOifUH4xmxJMYs+QFISE5nxJIY1h8sfb1VonzRfAisqJ09e5Y33niD77//HgcHy4p/jR07luTkZNPt7NmzxRylEOWUJcvhD6yEJU9CRjL4P6AmP56B2dvVC1N/nviD++76KfJkY23FnP4tqeHpxNkrN3l1WQy39AbN4tEbFCatOUxOf6qyh5koKZomQF5eXlhbW5OYmGh2PDExMc8JzrnZs2cPSUlJtGrVChsbG2xsbNiyZQuzZ8/GxsYGvT57N7q9vT1ubm5mNyFEAZgmQufQA6QosHUW/PQC6DOh4eMw6Ge15ygngR3B2h6uxcFFmcBbWB5OdnwxqDVOdtZsO3mZiHVHNYtlZ+yVbD0/d5M9zERJ0DQBsrOzo3Xr1kRGRpqOGQwGIiMjCQkJKdA5u3TpwoEDB9i3b5/p1qZNGwYMGMC+ffuwtpZy+kIUm5oPAjq4dByu3/UfG4Mefn8b/pygPn5gJDyzGGwdcz+XnbOaBAEcX19sIVckDXzd+OSZ5gB8tTWWVTHnSjyG1IxbrD1wwaK2soeZKE6aToIGCA8PZ/DgwbRp04agoCBmzZpFamoqQ4cOBWDQoEFUq1bNNJ8nMzOTw4cPm+6fP3+effv24eLiQp06dXB1daVJkyZm13B2dqZy5crZjgshipiTJ/g2gYQDai9Q06chMw1+evF2gUMdhE2GkFcsO1+9MDi5EU5sgPajijPyCqNHUz9ee7gOczad5N1VB6jj7UKz6h6FOqfeoLAz9gpJ19PxdlW33ri7+nSW3sDWE5dYvfc8Gw8ncjPLsgntsoeZKE6aJ0B9+/bl4sWLjB8/noSEBFq0aMH69etNE6Pj4uKwsrrTUXXhwgVatmxpejxjxgxmzJhBp06diIqKKunwhRBGmyPUDUsDOtxOgLZCrc6wtC+c3w06a3jmG2jUy/JzGidCx22Hm1fBUUpaFIU3Q+tx+EIKkUeTeOm7Pax+pR2xl1JzTWDuJ7eVXOMfa4SfhyM/7z3Pmn8vcPmuidcBlZ24dCOTGxm3cj2vt6u97GEmipVOUWR24b1SUlJwd3cnOTlZ5gMJYSnjxqVNnoGDP4JbdXUri6ux6vOtBsHjc/J/3nnB6tL6p7+GJk8VbcwVWEp6Fr3nbeO/i6nYWuvI0t/5VWDpUnTjSi5LfolUdrajZ/OqPNGyGs2qu/PHoQRGLIkByPH1lZ3tWPNae6p63GeYVIh75Of3d7lbBSaE0IhxN/eDP6qPU87dSX6CXy5Y8gN3VoMd/6PwMQoTNwdbBoXUBDBLfiDvpeiZtwzEX7vJBz8fzDP5eby5H98Mbcv297ow8fHGNPf3QKfT5bqHmY+bPVVc7LicmsmAL3fIPCBRbKQHKAfSAyREIRh7gozavQFdPyz4+U5vg0WPgKMnvHNSHWYThaY3KLSfuum+q7Gc7azp2siHq2lZXEvL5EpaJldTs+47dHWvZcMeIKR25fvGce/8oYSUdPosiOb8tZvU83Fh+fAQPJ3t8vX+RMUkPUBCCO10Gn17WwvA2q5wyQ+AfzA4uMPNK3B+T+HjE0DeS9EBUjP1/LzvAluOX+Tfc8mcvXLTlPxYNkMo75Vc9+5hZm2lo5qHI8uGPYCPmz3HE28w8MsdJKfJjvaiaGk+CVoIUc5smQaGLDX50Weqj40VngvC2gZqd4FDq9Tl8P5BRRdrBWbp0FKvFlVpX8cLT2c7PJzs8HS2o5KTLYcvpPDslzvyfH1BV3LVqOzE9y8+QL8vojkcn8Lgb3ay5MVgXOzl15YoGtIDJIQoOsbhr4feh3EX1Z9FsZFpve7qz+MbCh+jACxPTPq1rcEzbfzp0tCH1jUrEejljIeTHcG1KuPn7pBrT5AOdTJ1YVZy1fF24bsXgvFwsmXf2Ws8v2gXNzNlTzhRNCQBEkIUjbuTH2OPj3FidGGToDqhgA4SD0Dy+SIJt6ILCvQsVAJjbaVjQs9Gprb3vhZgQs9GFi+nz01DPze+ez4YV3sbdsZeYfh3u0m3sI6QEPcjCZAQomgY9ObJj5ExCSrMbu7OlaF6W/X+CekFKgpFkcDktpLL192B+QNbFdmO7k2ru7Po+bY42Vnz94lLjPw+hsxb2u1lZqQ3KESfuswv+84Tfeqy7F1WxsgqsBzIKjAhSqG/psOm/0G9HvDscq2jKTdyK2RoSR0go7wqQReVf05dYug3u8i4ZeCRpr7M7tcSnU5XIte+V1F8bqLo5ef3tyRAOZAESIhSKOEALGgPtk4wOhZsZZuEolJSCUxRiDqWxPBv95CpNxAU6Enc5TQSUko2CcmtAKTxEyvK3i+RP7IMXghR/vg0AbdqkJWmbrMhikxOS9FLq871vZn7bEusdOpS/ruTH8i7iGNh6Q0Kk9YczrEApPHYpDWHZTisDJAESAhRNuh0ULerev+EVIWuyLo09MHNwTbH54o7CcmrfpICxCenszP2SpFfWxQtSYCEEGVHXeO2GOtBRu8rrJ2xV7h2M/fCiMWVhCiKwq7Tlp0zMeX+dZZkArX2pKKUEKLsqNUJrO3hWhxcPAbeDbSOSGjA0iKO205eonXNStjZ5Px/fUvmPimKwr/nkvn9QDxr98dz/tpNi649/peDRJ+6TNdGPrSv64WD7Z0tXGQCdekgk6BzIJOghSjFljwFJ/9Ut9ho94bW0QgNRJ+6TP+F2y1q62Jvw4O1K9O5vjed6leh2u3d5e+XhIQ19uXfc8ms3X+B3w8kmCU9jrZWKEB6Vu7L8HWY73DvaGtNh7pedGvsi6IojF65XyZQFxNZBVZIkgAJUYrt+ALWvQM128PQtVpHIzRg3Mg1ITk9193oHW2tcbS14so9e4jV9XahhqcTkUeTcj2/p5MdV9IyTY+d7Kx5uIE3jzXzo1M9b7YcT2LEkhjAPNExJjGz+7fEw8mWjYcT+fNwIhfy2HPt7tf7ujuwdczDpXoiemkmCVAhSQIkRCl29TR82hx01jD6FDhW0joioQHjUnTIOQmZP7AV3Rr5cvBCMlHHLrLl+EX2xl3F0qk2jrZWhDby5dGmvnSq542jnbXZ85YOYymKwqELKWw4nMjPe88TdyUtz2svG/YAIbUrWxaoMCMJUCFJAiREKTcvGC4ehae/hiZPaR2N0Eh+59JcS8vk622xzI48mee5Fz/flk71vO/bJr/1k37Zd543lu/L89qf9mtBrxbV8mwnssvP72+ZBC2EKHvqdlMToOMbJAGqwLo38aNrI1+LkxAPJztqV3Gx6NzX0nJfZWZkrJ9kKUs3oP1x9zlqV3GhSTV3i88t8k8SICFE2VMvDP6ZDSc3qnuMWVnn/RpRLhVXEmJpu/wwbkB7v7lLAFtPXuKxOVt5oJYnL7avxcMNvLG6J6krS9W7SytJgIQQZY9/MNi7Q9plOL8H/IO0jkiUEXklIcaJyEGBnkV+beMGtCOWxGRbKWZMXcb0aMCR+BR+2x/P9v+usP2/KwR6OfN8+0CeblUdRztrWUZfRGQOUA5kDpAQZcCPQ+HQKujwNnQZp3U0ogyxZAJ1ce8lllcCc+HaTRZHn2bpjjiup98CwMPJlgdqebL+YGK2c8oyepVMgi4kSYCEKAP+XQ6rXwLfpvCy7A0m8kfrXhRLh7BSM27x4+6zfL3tdJ4ryGQZvSRAhSYJkBBlQOolmF4HUCD8CLhV1ToiUcaUpXk0eoPC3E0n+L8/T+TZtiIvo5dVYEKI8s/ZC6q3gXO74Pgf0Gao1hGJMia/E6i1ZG2lI8DL2aK2lm4VUtHJZqhCiLKr3u3NUU9s0DYOIUqApSvTFm87zeajSaV2g9XSshGs9AAJIcquumGw6X/wXxRkpYNt0S9dFqK0sHQZfczZawxdtIvqlRzpH1SDvm398XKxN2uj1fCf1nOv7iZzgHIgc4CEKCMUBWY2gusXYMBPUDdU64iEKFZ5rWCb8Hgj4i7fZOWes6TcXj1ma62jexM/BgTXIDjQkz8OJWiShBhjL86NYGUSdCFJAiREGfLr6xCzGIKGwyPTtY5GiGJnSS9KepaeNf9eYMmOOP49e+1OOzcH4lOyzxHKTxJSkN4j4wa28blsDFtUK9gkASokSYCEKEOO/g7L+4NHTXjjX9CVzlU8QhSl/CQhB88n8/2OM6yOOU/6LUOu57QkCcnvEFZ6lp6D55P5ed95lmyPy/N9FXYFmyRAhSQJkBBlSGYqTA0EfQaM3AlV6msdkRClUuSRRF5YvDvPdo829aVTPW/q+rhQx9sFVwdbIO8hrHnPtqK+nyv74q6x76x6OxKfwq18THIu7EawsgxeCFFx2DlDQHs4FQnH10sCJEQubmTcsqjd2gMJrD2QYHrs5+5A7SrOxMRdy3HytfHYyKXZkyOAKq721PB0Ys+Zq3leuzj2YMtNqVgGP2/ePAICAnBwcCA4OJidO3fm2vbQoUM89dRTBAQEoNPpmDVrVrY2ERERtG3bFldXV7y9venduzfHjh0rxncghNCUcTn8cVkOL0RuLE0uwhr70K5OZbxd1ZVj8cnpbD15mbRM/X1fpwC2Vjra1KzEi+0DmftsS7aOeYid73Xhh5dC8HN3ILcBah1qolUce7DlRvMeoBUrVhAeHs6CBQsIDg5m1qxZhIWFcezYMby9vbO1T0tLo1atWjzzzDO8+eabOZ5zy5YtjBw5krZt23Lr1i3ee+89unXrxuHDh3F2tqyQlBCiDKnbDdaNhrhouHkNHD20jkiIUsfSjWA/G9DaNAcoOS2Lkxev8+PucyzfdTbPa0x9uhlPtqqe7bi1jjw3gp3Qs1GJVuLWvAdo5syZDBs2jKFDh9KoUSMWLFiAk5MTX3/9dY7t27Zty/Tp0+nXrx/29vY5tlm/fj1DhgyhcePGNG/enEWLFhEXF8eePXuK860IIbTiGQhe9UHRw6lNWkcjRKlk3I0eyNYTk1sS4u5kS+uanhbPy/Fzd8z1ue5N/Jg/sBW+7uY9Ub7uDpps4qppD1BmZiZ79uxh7NixpmNWVlaEhoYSHR1dZNdJTk4GwNMz5661jIwMMjIyTI9TUlKK7NpCiBJSrxtcOqZui9HkSa2jEaJUMiYh967k8s2jDpClvUd5DWF1b+JH10a+pWIPNk0ToEuXLqHX6/Hx8TE77uPjw9GjR4vkGgaDgVGjRtGuXTuaNGmSY5uIiAgmTZpUJNcTQmikbhj8MwdObgSDHqystY5IiFKpIEmIsfeoKIawSssebJoPgRW3kSNHcvDgQZYvX55rm7Fjx5KcnGy6nT2b9zinEKKUqfEA2LtD2mU4H6N1NEKUasYkpFeLaoTUrmxR4lLahrAKS9MeIC8vL6ytrUlMTDQ7npiYiK+vb6HP/+qrr/Lbb7/x119/Ub169klZRvb29rnOJxJClAGbI9QenzoPw6HVcOIP8G+rPrdlmtoj9NDY+59DCJGn0jSEVVia9gDZ2dnRunVrIiMjTccMBgORkZGEhIQU+LyKovDqq6+yevVqNm3aRGBgYFGEK4QoraysYfPHoL9d5+T4evXnlmnqcRkOE6LIFKT3qDTSfBl8eHg4gwcPpk2bNgQFBTFr1ixSU1MZOnQoAIMGDaJatWpEREQA6sTpw4cPm+6fP3+effv24eLiQp06dQB12Gvp0qX88ssvuLq6kpCgFnRyd3fH0TH3GepCiDKq02j15+aP1Z8JB2DDB+qcoIfev/O8EELcViq2wpg7dy7Tp08nISGBFi1aMHv2bIKDgwHo3LkzAQEBLFq0CIDTp0/n2KPTqVMnoqKiANDlshfQN998w5AhQ/KMR7bCEKKMMvb4GEnyI0SFInuBFZIkQEKUYR9WBsPtobAnPofm/bSNRwhRYvLz+7vcrwITQlQgW6apyY/u9pyfn1+Bc3lv/iiEqHgkARJClA/G4a+H3odxl6ByPbUy9LePQ8oFraMTQpQykgAJIcq+u5OfTqPBygqGbwLnKpCZCgsfhqybWkcphChFJAESQpR9Bn32Cc/2rvDin2DjCNfj4ZdXQaY8CiFukwRICFH2PTQ259VelQJg4EqwsoGDK2HrzBIPTQhROkkCJIQo3wLawyPT1fuRH8HR37WNRwhRKkgCJIQo/9o8D21fBBRYNQwSD2sdkRBCY5IACSEqhu5TIKADZN6AZf0g9bLWEQkhNCQJkBCiYrC2hT7fqvOCrp2BHweDPkvrqIQQGpEESAhRcTh5Qv/lYOcCp/+GdWO0jkgIoRFJgIQQFYt3Q3jqS0AHu7+CXV9qHZEQQgOSAAkhKp76PaDLePX+ujEQ+7e28QghSpwkQEKIiqn9m9D0GXXvsB8GwZVYrSMSQpQgSYCEEBWTTgePz4GqreDmFVj+LGRc1zoqIUQJkQRICFFx2TpCv+/B1hmSDsOq4WAwmLfZMg02R2gTnxCi2EgCJISo2NyqQrM+6v1jv8Pm/915zrjJqpW1NrEJIYqNjdYBCCGE5nrOgrTLcORX+PsT8KwFyechanL2TVaFEOWCTlFke+R7paSk4O7uTnJyMm5ublqHI4QoKV+HQdz2O48bP6kumZceICHKhPz8/pYhMCGEMBryO+ju+mfx0CqY01qtFZR1U7u4hBBFThIgIYQw+vsTUAxgbac+tnGAq7Gw9i34vybqnKC0K9rGKIQoEpIACSEE3Jnw/ND7MO6i+vNWOtTtBu41IO2S+vz/NVaLJ149o3XEQohCkEnQQghxd/JjnPBs/Ln5Y+g0FrzqwLZZkHAAdiyAnQuhyZPqvmJuVXOeKL1lGhj08NDYEnsrQgjLSAIkhBAGfc6rvYyPDXpo+jQ0eQr+2wzbPoX/ouDAj3faXo2FXp+pBRbBPKkqTpsj1EnakoAJkS+SAAkhxP0ShLsTC50Oaj+s3i7sg39mw6HV6ryhfUvh5CYI+xiSjsDfM0pmCb2VtZpo3RtrSSRgknyJMkzmAAkhREFUbQFPfw2v74Wg4WBlAzcS4KcX1OTHxVetLXRoNVxPyP08myPUZCEnllSh7jQaOrytJjsrBkHiIYiamn1IrzgYk69745cCkqIMkB4gIYQojEoB8Mh06PQuzKgLil49fiNBnSu0Y8GddjVCoMYD6k+vemqPUn56cAwGdagt8dDt20H159XbG7ke+UW9Afg2Uws6pl0BJ8/iee+dRoM+U43z3G7o/C6c/LNkki8hCkkKIeZACiEKIfLNmLBY26lJQaPe4OINcdGQcBC4559aR8/bydADcPkUxCy+kzQYz9VyoJrIGBOdpCOQlZbz9V184EZS9uugg6otoU4XqN0FqrcF69v/983vEJbBAFdOwbldd26Jh+8kfUZN+8ATn4OVDDKIkpWf39+SAOVAEiAhRL7cu4rs3sfpyWqyELddvZ3bDbfuKaxoZQOGW2qv0P3+WbZxgCoNwKcJ+DS+c9v9tXkC5h+s7m6fdNj89fbuUKujmgxdPgHR87L31hjjbx8OAe3UeM/tUn+mX8sek1s1SLmAWfLlVQ9CXoVmfcHWIb+fqBAFIglQIUkCJISwWE5L6O93HOBWJiTsV3uH4rarP9MuZz+3R407iY53I/W+Z607PTi5Xevuxy0HwqlNcDJSXcF286r5ax094eYVaNoXHhyp9godXwdOlXOOycZB7VGq3kbtTarWBvZ9fzv5sgV9Fljbgz5Dbe9cBYJegrYvFN9QnBC3SQJUSJIACSEsVhQroRQF1r+rzheyslZf0+Et6DI+7+vnJwEz6NXVa6ci1YTo3K7sw1f38qylJjrV26pJj08TNdHJ7TrGx7W7wMVjkHJObWfjqCZjIa+o5ywsWYEmclDm9gKbN28eAQEBODg4EBwczM6dO3Nte+jQIZ566ikCAgLQ6XTMmjWr0OcUQogCe2hs7pN9O4227JfwX9PV5Oeh92H8FfXn35/kvjrsbverYfTQ++rzRlbWUL21+twLf8Do/6DPd9B6yF0v1EHHd+DZH+Cd/9RVbk9+AUHD1J6f+yU/d1/3VKSa8Dz5Jfg2VYf8di1U91b7YRD88lrhVr9puQKtsCv3RKmg+SqwFStWEB4ezoIFCwgODmbWrFmEhYVx7NgxvL29s7VPS0ujVq1aPPPMM7z55ptFck4hhNBMXlWo736cE0trGOXE0QMaPQ4Xj6qPjfOHrO2gXljesVtSQLLZM2oRydi/4J85cHIjHP7lTtuLx+DJhXcmTFtav6jD23dWoN28CqGT1ErdJbn8H0q+9pIoMpoPgQUHB9O2bVvmzp0LgMFgwN/fn9dee4133333vq8NCAhg1KhRjBo1qsjOCTIEJoQoQVoP5eQ1gbuoJR5WJ17vXwGGLPWYYyV44BU4/beaKFVrrU6izrwBmamQmXb7p/FxKmSl5nz+Zv2g11zznqriUNKfm7BIfn5/a9oDlJmZyZ49exg79s5fbisrK0JDQ4mOji415xRCiGJTmB6cwips71NB+DSC3vPg4Q9g5xewfZ7ag2O8HsD5PeqtIPYvhxMb1J6txk9CQPuiHw4zGKD+I3B2pxp3VIRaDVySnzJF0wTo0qVL6PV6fHx8zI77+Phw9OjREjtnRkYGGRkZpscpKSkFurYQQpQplgxhFRc3PwidoE72nlJDnYyts1J7guyc1Zutk7rZrPFxtvtO8M9ciJp8p4yArZO6qm3PIvXm7A2Neqkb1/o/oA61FaT+UdIhOL1VvZ3ZZr6aTjGoP8/8A0d+g3rds6/UE6WO/AkBERERTJo0SeswhBCiZGnZ+2S0/TM1+THOP3Jwt/zaW6apyc+9w1DN+6tDYEfWQGqSOvl610JwrQqNe6tDajGL1HPkNIen83sQv9884bm3/pGdi1ro8sp/d2o3/bdZvblVhzZDoNVgtY0olTRdBebl5YW1tTWJiYlmxxMTE/H19S2xc44dO5bk5GTT7ezZswW6thBCiHy4ewhu3EX1Z04ru/J67b0r0P5dBu7+8PYJGLASmj+rFoC8fkFNuGIWqY83fwxrRqk9Pr+Fq48r11WH5T7vAH+MhWNr1eTHzgXqdFUnW78YqRZ5vPKfer0J1+CBkWoMto7q0v9N/4OZjWDlC3Am+v7FLYUmNO0BsrOzo3Xr1kRGRtK7d29AnbAcGRnJq6++WmLntLe3x97evkDXE0IIUQCFnX9kyfCdtS3U7arebmWoBSEP/gTH1kFGstpuzzfqzejyCfWnnSvUDFHnENVsD37N7wxrbZkGW6aYX7/7ZHVV3eaPoeHjcD1erbN0cKV682miFoO8dlZNkgo66V3LSfNaT9gvYpoPgYWHhzN48GDatGlDUFAQs2bNIjU1laFDhwIwaNAgqlWrRkSEWlchMzOTw4cPm+6fP3+effv24eLiQp06dSw6pxBCCI0Vdv5RfofvbOyhfg/1lnUTTmyEQ6vg0Oo7beqGqQlPQDvwbZ77PB5LYu/7nVp0cteXcGClup/bb2/eqZKdegkeuauny9Il9IVdgl+YJKa8Lf9XSoE5c+YoNWrUUOzs7JSgoCBl+/btpuc6deqkDB482PQ4NjZWQd1wxuzWqVMni8+Zl+TkZAVQkpOTC/vWhBBClFZRUxVlgpuiTKqs/oyaWjzXSbuiKP/MVZRPW6jXMd7+r6mi/PuDovz8ivp4zZuKknhEUS6fUpRrZxXlepKi3LymKJk3FcVgyB63Md57H1vynu9ta+k5CnPtEpCf39+a1wEqjaQOkBBClHNa1PExGNRJ0ru+gmO/Y7Z5rCWsbNWeLOOE8cwbgE49T5WGau+Vs5e6j9vdN2cvdc83Gzv1PLm9987vQchISL2o9lClXrzrdtfjpMPqT+O1Q16FsI9zj7sEyV5ghSQJkBBClGMF2cC2qF2Lg09b3N6LTadufKvPVOcqGX8aC0UWFXt3dUNap8rqxO7LJ++sYLN3v33dmwU4sQ5qdYIWA6DBY2p5Ao2UmUKIQgghRInTsv6R0b/LzZf/txyYPR6DQU2C7k6K9Bmgz4Idn8Pur+7UP6rTFaq2UHtq0i5D2hVIM96/rNYqykhWb1dj71zD2AdinBQO6sa1LlXA2Xjzuut+FXUj3f3L71wbBf6LUm92rtDkCTUZ8g9WE6xSShIgIYQQFYvW9Y9yG4K69/pWVmBlrw573fv63V9lf71/EPSclf16BoPa42NMhtIuw94l6jCcMYlpPQTav6kmOHbO9499//Ls1w7ooPZqXTsDMd+qN89a0OJZdXsSD/9St4pMEiAhhBCipBR2+X9BXm9ldXvoyxOoq57j2O/Zkxi3aoW7duf31HlI+5aqq+uu/KfWQ9r0sTpEZu8OR37JHqNGq8gkARJCCCFKSmGH3wr7+sIkYJZcO6CdeusxVa3Eve97dZPb/6LUNtZ26nWSz0LP2fDXdM02kZVJ0DmQSdBCCCHKJS2Goa6eUec87fteHSIzub2KrAiTH1kFVkiSAAkhhBBFzGCAuGh1iGzfEvWYtZ26DUoRyc/vb033AhNCCCFEBWFlpQ6PVaqpPjaugLNk77fiCEeTqwohhBCi4inMBrhFTCZBCyGEEKL4FXYFXBGTBEgIIYQQxa80FKC8i0yCzoFMghZCCCHKHpkELYQQQghxH5IACSGEEKLCkQRICCGEEBWOJEBCCCGEqHAkARJCCCFEhSMJkBBCCCEqHEmAhBBCCFHhSAIkhBBCiApHEiAhhBBCVDiSAAkhhBCiwpG9wHJg3B0kJSVF40iEEEIIYSnj721LdvmSBCgH169fB8Df31/jSIQQQgiRX9evX8fd3f2+bWQz1BwYDAYuXLiAq6srOp3O7LmUlBT8/f05e/asbJSaD/K5FYx8bvknn1nByOdWMPK5FUxxfW6KonD9+nWqVq2KldX9Z/lID1AOrKysqF69+n3buLm5yZe9AORzKxj53PJPPrOCkc+tYORzK5ji+Nzy6vkxkknQQgghhKhwJAESQgghRIUjCVA+2dvbM2HCBOzt7bUOpUyRz61g5HPLP/nMCkY+t4KRz61gSsPnJpOghRBCCFHhSA+QEEIIISocSYCEEEIIUeFIAiSEEEKICkcSICGEEEJUOJIA5dO8efMICAjAwcGB4OBgdu7cqXVIpdrEiRPR6XRmtwYNGmgdVqny119/0bNnT6pWrYpOp+Pnn382e15RFMaPH4+fnx+Ojo6EhoZy4sQJbYItRfL63IYMGZLtu9e9e3dtgi0lIiIiaNu2La6urnh7e9O7d2+OHTtm1iY9PZ2RI0dSuXJlXFxceOqpp0hMTNQo4tLBks+tc+fO2b5vL7/8skYRlw7z58+nWbNmpmKHISEhrFu3zvS81t81SYDyYcWKFYSHhzNhwgRiYmJo3rw5YWFhJCUlaR1aqda4cWPi4+NNt61bt2odUqmSmppK8+bNmTdvXo7PT5s2jdmzZ7NgwQJ27NiBs7MzYWFhpKenl3CkpUtenxtA9+7dzb57y5YtK8EIS58tW7YwcuRItm/fzsaNG8nKyqJbt26kpqaa2rz55pusWbOGH3/8kS1btnDhwgWefPJJDaPWniWfG8CwYcPMvm/Tpk3TKOLSoXr16kyZMoU9e/awe/duHn74YXr16sWhQ4eAUvBdU4TFgoKClJEjR5oe6/V6pWrVqkpERISGUZVuEyZMUJo3b651GGUGoKxevdr02GAwKL6+vsr06dNNx65du6bY29sry5Yt0yDC0unez01RFGXw4MFKr169NImnrEhKSlIAZcuWLYqiqN8tW1tb5ccffzS1OXLkiAIo0dHRWoVZ6tz7uSmKonTq1El54403tAuqjKhUqZLy5ZdflorvmvQAWSgzM5M9e/YQGhpqOmZlZUVoaCjR0dEaRlb6nThxgqpVq1KrVi0GDBhAXFyc1iGVGbGxsSQkJJh979zd3QkODpbvnQWioqLw9vamfv36jBgxgsuXL2sdUqmSnJwMgKenJwB79uwhKyvL7PvWoEEDatSoId+3u9z7uRl9//33eHl50aRJE8aOHUtaWpoW4ZVKer2e5cuXk5qaSkhISKn4rslmqBa6dOkSer0eHx8fs+M+Pj4cPXpUo6hKv+DgYBYtWkT9+vWJj49n0qRJdOjQgYMHD+Lq6qp1eKVeQkICQI7fO+NzImfdu3fnySefJDAwkFOnTvHee+/Ro0cPoqOjsba21jo8zRkMBkaNGkW7du1o0qQJoH7f7Ozs8PDwMGsr37c7cvrcAJ599llq1qxJ1apV2b9/P2PGjOHYsWOsWrVKw2i1d+DAAUJCQkhPT8fFxYXVq1fTqFEj9u3bp/l3TRIgUax69Ohhut+sWTOCg4OpWbMmP/zwAy+88IKGkYnyrl+/fqb7TZs2pVmzZtSuXZuoqCi6dOmiYWSlw8iRIzl48KDMycun3D634cOHm+43bdoUPz8/unTpwqlTp6hdu3ZJh1lq1K9fn3379pGcnMzKlSsZPHgwW7Zs0TosQCZBW8zLywtra+tsM9QTExPx9fXVKKqyx8PDg3r16nHy5EmtQykTjN8t+d4VXq1atfDy8pLvHvDqq6/y22+/sXnzZqpXr2467uvrS2ZmJteuXTNrL983VW6fW06Cg4MBKvz3zc7Ojjp16tC6dWsiIiJo3rw5n376aan4rkkCZCE7Oztat25NZGSk6ZjBYCAyMpKQkBANIytbbty4walTp/Dz89M6lDIhMDAQX19fs+9dSkoKO3bskO9dPp07d47Lly9X6O+eoii8+uqrrF69mk2bNhEYGGj2fOvWrbG1tTX7vh07doy4uLgK/X3L63PLyb59+wAq9PctJwaDgYyMjNLxXSuRqdblxPLlyxV7e3tl0aJFyuHDh5Xhw4crHh4eSkJCgtahlVpvvfWWEhUVpcTGxirbtm1TQkNDFS8vLyUpKUnr0EqN69evK3v37lX27t2rAMrMmTOVvXv3KmfOnFEURVGmTJmieHh4KL/88ouyf/9+pVevXkpgYKBy8+ZNjSPX1v0+t+vXrytvv/22Eh0drcTGxip//vmn0qpVK6Vu3bpKenq61qFrZsSIEYq7u7sSFRWlxMfHm25paWmmNi+//LJSo0YNZdOmTcru3buVkJAQJSQkRMOotZfX53by5Enlww8/VHbv3q3ExsYqv/zyi1KrVi2lY8eOGkeurXfffVfZsmWLEhsbq+zfv1959913FZ1Op2zYsEFRFO2/a5IA5dOcOXOUGjVqKHZ2dkpQUJCyfft2rUMq1fr27av4+fkpdnZ2SrVq1ZS+ffsqJ0+e1DqsUmXz5s0KkO02ePBgRVHUpfDjxo1TfHx8FHt7e6VLly7KsWPHtA26FLjf55aWlqZ069ZNqVKlimJra6vUrFlTGTZsWIX/z0pOnxegfPPNN6Y2N2/eVF555RWlUqVKipOTk/LEE08o8fHx2gVdCuT1ucXFxSkdO3ZUPD09FXt7e6VOnTrKO++8oyQnJ2sbuMaef/55pWbNmoqdnZ1SpUoVpUuXLqbkR1G0/67pFEVRSqavSQghhBCidJA5QEIIIYSocCQBEkIIIUSFIwmQEEIIISocSYCEEEIIUeFIAiSEEEKICkcSICGEEEJUOJIACSGEEKLCkQRICCEsEBUVhU6ny7Z3kRCibJIESAghhBAVjiRAQgghhKhwJAESQpQJBoOBiIgIAgMDcXR0pHnz5qxcuRK4Mzy1du1amjVrhoODAw888AAHDx40O8dPP/1E48aNsbe3JyAggE8++cTs+YyMDMaMGYO/vz/29vbUqVOHr776yqzNnj17aNOmDU5OTjz44IMcO3aseN+4EKJYSAIkhCgTIiIi+Pbbb1mwYAGHDh3izTffZODAgWzZssXU5p133uGTTz5h165dVKlShZ49e5KVlQWoiUufPn3o168fBw4cYOLEiYwbN45FixaZXj9o0CCWLVvG7NmzOXLkCJ9//jkuLi5mcbz//vt88skn7N69GxsbG55//vkSef9CiKIlm6EKIUq9jIwMPD09+fPPPwkJCTEdf/HFF0lLS2P48OE89NBDLF++nL59+wJw5coVqlevzqJFi+jTpw8DBgzg4sWLbNiwwfT60aNHs3btWg4dOsTx48epX78+GzduJDQ0NFsMUVFRPPTQQ/z555906dIFgN9//51HH32Umzdv4uDgUMyfghCiKEkPkBCi1Dt58iRpaWl07doVFxcX0+3bb7/l1KlTpnZ3J0eenp7Ur1+fI0eOAHDkyBHatWtndt527dpx4sQJ9Ho9+/btw9ramk6dOt03lmbNmpnu+/n5AZCUlFTo9yiEKFk2WgcghBB5uXHjBgBr166lWrVqZs/Z29ubJUEF5ejoaFE7W1tb032dTgeo85OEEGWL9AAJIUq9Ro0aYW9vT1xcHHXq1DG7+fv7m9pt377ddP/q1ascP36chg0bAtCwYUO2bdtmdt5t27ZRr149rK2tadq0KQaDwWxOkRCi/JIeICFEqefq6srbb7/Nm2++icFgoH379iQnJ7Nt2zbc3NyoWbMmAB9++CGVK1fGx8eH999/Hy8vL3r37g3AW2+9Rdu2bfnoo4/o27cv0dHRzJ07l88++wyAgIAABg8ezPPPP8/s2bNp3rw5Z86cISkpiT59+mj11oUQxUQSICFEmfDRRx9RpUoVIiIi+O+///Dw8KBVq1a89957piGoKVOm8MYbb3DixAlatGjBmjVrsLOzA6BVq1b88MMPjB8/no8++gg/Pz8+/PBDhgwZYrrG/Pnzee+993jllVe4fPkyNWrU4L333tPi7QohipmsAhNClHnGFVpXr17Fw8ND63CEEGWAzAESQgghRIUjCZAQQgghKhwZAhNCCCFEhSM9QEIIIYSocCQBEkIIIUSFIwmQEEIIISocSYCEEEIIUeFIAiSEEEKICkcSICGEEEJUOJIACSGEEKLCkQRICCGEEBWOJEBCCCGEqHD+H8+OVRLNgY0JAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plotgraphs(cnn3)"
      ],
      "id": "afraid-parking"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "disabled-fever",
        "outputId": "6538475e-a777-481f-84bf-7cf753619953"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1492/1492 [==============================] - 6s 4ms/step\n"
          ]
        }
      ],
      "source": [
        "predict = np.argmax(cnn3.predict(X_test),axis=1)\n",
        "\n",
        "a = np.unique(predict)\n",
        "b = np.unique(y_test)\n",
        "c = list(set(a) | set(b))"
      ],
      "id": "disabled-fever"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "enormous-soccer",
        "outputId": "f4d64d40-babb-4a22-bef0-9cd5819a2444"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "        ----------Classification Report Of Classes-------------\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00         5\n",
            "           1       0.66      0.98      0.79      1117\n",
            "           2       0.00      0.00      0.00         6\n",
            "           3       0.00      0.00      0.00         5\n",
            "           4       0.97      0.83      0.90       290\n",
            "           5       0.62      0.55      0.58        29\n",
            "           6       1.00      1.00      1.00      7110\n",
            "           7       0.93      0.96      0.94       463\n",
            "           8       1.00      1.00      1.00      4225\n",
            "           9       1.00      1.00      1.00      4180\n",
            "          10       0.99      1.00      0.99      4249\n",
            "          11       0.67      0.08      0.14        25\n",
            "          12       1.00      0.99      0.99      3602\n",
            "          13       1.00      0.99      1.00      4615\n",
            "          14       1.00      1.00      1.00      5591\n",
            "          15       0.98      0.98      0.98       295\n",
            "          16       0.34      0.12      0.18       179\n",
            "          17       0.00      0.00      0.00        13\n",
            "          18       0.74      0.74      0.74        86\n",
            "          19       0.98      0.99      0.99      2114\n",
            "          20       0.99      1.00      0.99      2756\n",
            "          21       0.99      1.00      0.99      3380\n",
            "          22       0.78      0.47      0.58       315\n",
            "          23       0.58      0.99      0.73      1007\n",
            "          24       0.54      0.01      0.02       754\n",
            "          25       0.99      0.99      0.99       965\n",
            "          26       0.52      0.24      0.33       134\n",
            "          27       0.00      0.00      0.00        88\n",
            "          29       1.00      0.02      0.05        81\n",
            "          30       0.00      0.00      0.00         8\n",
            "          31       0.00      0.00      0.00         1\n",
            "          32       0.50      0.80      0.61        49\n",
            "          33       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.96     47738\n",
            "   macro avg       0.63      0.57      0.56     47738\n",
            "weighted avg       0.96      0.96      0.96     47738\n",
            "\n",
            "\n",
            "        ----------Validation Data------------------\n",
            "Accuarcy: 96.37814738782521\n",
            "Precision: 96.1538 %\n",
            "Recall-score: 96.3781\n",
            "F1-score: 95.5149\n"
          ]
        }
      ],
      "source": [
        "report(predict,labels_test)"
      ],
      "id": "enormous-soccer"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ecological-puppy",
        "outputId": "58c108f2-be5f-4bda-f6e9-f13ead456d3a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d (Conv1D)             (None, 44, 64)            256       \n",
            "                                                                 \n",
            " leaky_re_lu (LeakyReLU)     (None, 44, 64)            0         \n",
            "                                                                 \n",
            " max_pooling1d (MaxPooling1D  (None, 22, 64)           0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 22, 64)            0         \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, 22, 64)            4160      \n",
            "                                                                 \n",
            " leaky_re_lu_1 (LeakyReLU)   (None, 22, 64)            0         \n",
            "                                                                 \n",
            " max_pooling1d_1 (MaxPooling  (None, 11, 64)           0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 11, 64)            0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 704)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                45120     \n",
            "                                                                 \n",
            " leaky_re_lu_2 (LeakyReLU)   (None, 64)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 32)                2080      \n",
            "                                                                 \n",
            " leaky_re_lu_3 (LeakyReLU)   (None, 32)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 34)                1122      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 52,738\n",
            "Trainable params: 52,738\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "#hyperparameters\n",
        "keras.backend.clear_session()\n",
        "SEED = 1040941203\n",
        "hidden_initializer = random_uniform(seed=SEED)\n",
        "\n",
        "\n",
        "# create model\n",
        "cnn2 = Sequential()\n",
        "cnn2.add(Conv1D(64, 3, input_shape=(46, 1)))\n",
        "cnn2.add(LeakyReLU(alpha=0.1))\n",
        "cnn2.add(MaxPooling1D(pool_size=2))\n",
        "cnn2.add(Dropout(0.3))\n",
        "\n",
        "cnn2.add(Conv1D(64, 1))\n",
        "cnn2.add(LeakyReLU(alpha=0.1))\n",
        "cnn2.add(MaxPooling1D(pool_size=2))\n",
        "cnn2.add(Dropout(0.3))\n",
        "\n",
        "cnn2.add(Flatten())\n",
        "cnn2.add(Dense(64, input_dim=15, kernel_initializer=hidden_initializer))\n",
        "cnn2.add(LeakyReLU(alpha=0.1))\n",
        "cnn2.add(Dense(32))\n",
        "cnn2.add(LeakyReLU(alpha=0.1))\n",
        "cnn2.add(Dense(34, activation='softmax'))\n",
        "\n",
        "cnn2.summary()"
      ],
      "id": "ecological-puppy"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "recreational-version",
        "outputId": "65cf4746-468a-4947-bec9-2d57f7c33c1a",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "560/560 [==============================] - 28s 47ms/step - loss: 27.1076 - accuracy: 0.5852 - val_loss: 1.7128 - val_accuracy: 0.7280\n",
            "Epoch 2/30\n",
            "560/560 [==============================] - 23s 42ms/step - loss: 3.2439 - accuracy: 0.7194 - val_loss: 0.9963 - val_accuracy: 0.7479\n",
            "Epoch 3/30\n",
            "560/560 [==============================] - 26s 47ms/step - loss: 2.0004 - accuracy: 0.7699 - val_loss: 0.7762 - val_accuracy: 0.8235\n",
            "Epoch 4/30\n",
            "560/560 [==============================] - 26s 46ms/step - loss: 1.2185 - accuracy: 0.8108 - val_loss: 0.6738 - val_accuracy: 0.8365\n",
            "Epoch 5/30\n",
            "560/560 [==============================] - 24s 43ms/step - loss: 1.6365 - accuracy: 0.8274 - val_loss: 0.6636 - val_accuracy: 0.8564\n",
            "Epoch 6/30\n",
            "560/560 [==============================] - 26s 47ms/step - loss: 1.1990 - accuracy: 0.8393 - val_loss: 0.4971 - val_accuracy: 0.8685\n",
            "Epoch 7/30\n",
            "560/560 [==============================] - 27s 47ms/step - loss: 0.9134 - accuracy: 0.8467 - val_loss: 0.4444 - val_accuracy: 0.8769\n",
            "Epoch 8/30\n",
            "560/560 [==============================] - 25s 45ms/step - loss: 0.7041 - accuracy: 0.8542 - val_loss: 0.4044 - val_accuracy: 0.8735\n",
            "Epoch 9/30\n",
            "560/560 [==============================] - 25s 44ms/step - loss: 0.5182 - accuracy: 0.8651 - val_loss: 0.3598 - val_accuracy: 0.8899\n",
            "Epoch 10/30\n",
            "560/560 [==============================] - 25s 46ms/step - loss: 0.4756 - accuracy: 0.8766 - val_loss: 0.3230 - val_accuracy: 0.8928\n",
            "Epoch 11/30\n",
            "560/560 [==============================] - 25s 45ms/step - loss: 0.4084 - accuracy: 0.8905 - val_loss: 0.2669 - val_accuracy: 0.9308\n",
            "Epoch 12/30\n",
            "560/560 [==============================] - 23s 41ms/step - loss: 0.4099 - accuracy: 0.8991 - val_loss: 0.2706 - val_accuracy: 0.9291\n",
            "Epoch 13/30\n",
            "560/560 [==============================] - 26s 47ms/step - loss: 0.3414 - accuracy: 0.9073 - val_loss: 0.2292 - val_accuracy: 0.9333\n",
            "Epoch 14/30\n",
            "560/560 [==============================] - 25s 45ms/step - loss: 0.3258 - accuracy: 0.9113 - val_loss: 0.2752 - val_accuracy: 0.9329\n",
            "Epoch 15/30\n",
            "560/560 [==============================] - 23s 41ms/step - loss: 0.3183 - accuracy: 0.9137 - val_loss: 0.2694 - val_accuracy: 0.9171\n",
            "Epoch 16/30\n",
            "560/560 [==============================] - 26s 47ms/step - loss: 0.3321 - accuracy: 0.9143 - val_loss: 0.2499 - val_accuracy: 0.9248\n",
            "Epoch 17/30\n",
            "560/560 [==============================] - 25s 45ms/step - loss: 0.2775 - accuracy: 0.9196 - val_loss: 0.2065 - val_accuracy: 0.9381\n",
            "Epoch 18/30\n",
            "560/560 [==============================] - 23s 42ms/step - loss: 0.2759 - accuracy: 0.9209 - val_loss: 0.2005 - val_accuracy: 0.9391\n",
            "Epoch 19/30\n",
            "560/560 [==============================] - 26s 47ms/step - loss: 0.5223 - accuracy: 0.9144 - val_loss: 0.2696 - val_accuracy: 0.9198\n",
            "Epoch 20/30\n",
            "560/560 [==============================] - 25s 45ms/step - loss: 0.2915 - accuracy: 0.9219 - val_loss: 0.1961 - val_accuracy: 0.9379\n",
            "Epoch 21/30\n",
            "560/560 [==============================] - 23s 42ms/step - loss: 0.2578 - accuracy: 0.9257 - val_loss: 0.1855 - val_accuracy: 0.9354\n",
            "Epoch 22/30\n",
            "560/560 [==============================] - 26s 47ms/step - loss: 0.2411 - accuracy: 0.9280 - val_loss: 0.1772 - val_accuracy: 0.9407\n",
            "Epoch 23/30\n",
            "560/560 [==============================] - 25s 45ms/step - loss: 0.2200 - accuracy: 0.9290 - val_loss: 0.1814 - val_accuracy: 0.9353\n",
            "Epoch 24/30\n",
            "560/560 [==============================] - 23s 41ms/step - loss: 0.2157 - accuracy: 0.9325 - val_loss: 0.1681 - val_accuracy: 0.9417\n",
            "Epoch 25/30\n",
            "560/560 [==============================] - 26s 47ms/step - loss: 0.2132 - accuracy: 0.9327 - val_loss: 0.1638 - val_accuracy: 0.9426\n",
            "Epoch 26/30\n",
            "560/560 [==============================] - 25s 44ms/step - loss: 0.4520 - accuracy: 0.9268 - val_loss: 0.1775 - val_accuracy: 0.9393\n",
            "Epoch 27/30\n",
            "560/560 [==============================] - 24s 43ms/step - loss: 0.2213 - accuracy: 0.9324 - val_loss: 0.1737 - val_accuracy: 0.9378\n",
            "Epoch 28/30\n",
            "560/560 [==============================] - 26s 47ms/step - loss: 0.2154 - accuracy: 0.9327 - val_loss: 0.1597 - val_accuracy: 0.9409\n",
            "Epoch 29/30\n",
            "560/560 [==============================] - 25s 45ms/step - loss: 0.2122 - accuracy: 0.9339 - val_loss: 0.1650 - val_accuracy: 0.9439\n",
            "Epoch 30/30\n",
            "560/560 [==============================] - 24s 43ms/step - loss: 0.1987 - accuracy: 0.9351 - val_loss: 0.1549 - val_accuracy: 0.9440\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7e270aaf7fd0>"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cnn2.compile(loss = 'categorical_crossentropy', optimizer= 'adam', metrics = ['accuracy'])\n",
        "cnn2.fit(X_train, y_train, epochs=30, batch_size=256,\n",
        "         validation_data=(X_val,y_val),callbacks=[tensorboard_callback, eary_stop_callback])"
      ],
      "id": "recreational-version"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "radical-simpson",
        "outputId": "41e2253a-a92d-4119-b9b0-1b1e46e6c284"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plotgraphs(cnn2)"
      ],
      "id": "radical-simpson"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bulgarian-champion",
        "outputId": "4865c722-700c-40c9-9cb7-37134c73c7a0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1492/1492 [==============================] - 6s 4ms/step\n"
          ]
        }
      ],
      "source": [
        "predict = np.argmax(cnn2.predict(X_test),axis=1)\n",
        "\n",
        "a = np.unique(predict)\n",
        "b = np.unique(y_test)\n",
        "c = list(set(a) | set(b))"
      ],
      "id": "bulgarian-champion"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "animated-substitute",
        "outputId": "b1085b3d-fc6d-437b-cb9e-0df6eb45a401"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "        ----------Classification Report Of Classes-------------\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00         5\n",
            "           1       0.58      0.96      0.73      1117\n",
            "           2       0.00      0.00      0.00         6\n",
            "           3       0.00      0.00      0.00         5\n",
            "           4       0.67      0.23      0.34       290\n",
            "           5       0.75      0.10      0.18        29\n",
            "           6       1.00      1.00      1.00      7110\n",
            "           7       0.47      0.97      0.64       463\n",
            "           8       1.00      1.00      1.00      4225\n",
            "           9       1.00      0.99      1.00      4180\n",
            "          10       0.99      0.96      0.97      4249\n",
            "          11       0.00      0.00      0.00        25\n",
            "          12       0.96      0.99      0.97      3602\n",
            "          13       1.00      0.99      1.00      4615\n",
            "          14       1.00      1.00      1.00      5591\n",
            "          15       0.27      0.02      0.04       295\n",
            "          16       0.40      0.01      0.02       179\n",
            "          17       0.00      0.00      0.00        13\n",
            "          18       0.56      0.17      0.27        86\n",
            "          19       0.93      0.99      0.96      2114\n",
            "          20       0.98      0.99      0.98      2756\n",
            "          21       0.99      0.99      0.99      3380\n",
            "          22       0.47      0.13      0.20       315\n",
            "          23       0.96      0.22      0.35      1007\n",
            "          24       0.48      0.96      0.64       754\n",
            "          25       1.00      0.99      0.99       965\n",
            "          26       0.46      0.16      0.24       134\n",
            "          27       0.07      0.03      0.05        88\n",
            "          29       0.32      0.12      0.18        81\n",
            "          30       0.00      0.00      0.00         8\n",
            "          31       0.00      0.00      0.00         1\n",
            "          32       0.38      0.24      0.30        49\n",
            "          33       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.94     47738\n",
            "   macro avg       0.54      0.46      0.46     47738\n",
            "weighted avg       0.95      0.94      0.93     47738\n",
            "\n",
            "\n",
            "        ----------Validation Data------------------\n",
            "Accuarcy: 94.3001382546399\n",
            "Precision: 94.7416 %\n",
            "Recall-score: 94.3001\n",
            "F1-score: 93.3686\n"
          ]
        }
      ],
      "source": [
        "report(predict,labels_test)"
      ],
      "id": "animated-substitute"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vertical-seller",
        "outputId": "a6b1a31f-8ea1-42bb-c67f-ca18d250f772"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d (Conv1D)             (None, 44, 64)            256       \n",
            "                                                                 \n",
            " leaky_re_lu (LeakyReLU)     (None, 44, 64)            0         \n",
            "                                                                 \n",
            " max_pooling1d (MaxPooling1D  (None, 22, 64)           0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 22, 64)            0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 1408)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                90176     \n",
            "                                                                 \n",
            " leaky_re_lu_1 (LeakyReLU)   (None, 64)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 32)                2080      \n",
            "                                                                 \n",
            " leaky_re_lu_2 (LeakyReLU)   (None, 32)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 34)                1122      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 93,634\n",
            "Trainable params: 93,634\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "#hyperparameters\n",
        "keras.backend.clear_session()\n",
        "SEED = 1040941203\n",
        "hidden_initializer = random_uniform(seed=SEED)\n",
        "\n",
        "\n",
        "# create model\n",
        "cnn1 = Sequential()\n",
        "cnn1.add(Conv1D(64, 3, input_shape=(46, 1)))\n",
        "cnn1.add(LeakyReLU(alpha=0.1))\n",
        "cnn1.add(MaxPooling1D(pool_size=2))\n",
        "cnn1.add(Dropout(0.3))\n",
        "\n",
        "\n",
        "cnn1.add(Flatten())\n",
        "cnn1.add(Dense(64, input_dim=15, kernel_initializer=hidden_initializer))\n",
        "cnn1.add(LeakyReLU(alpha=0.1))\n",
        "cnn1.add(Dense(32))\n",
        "cnn1.add(LeakyReLU(alpha=0.1))\n",
        "cnn1.add(Dense(34, activation='softmax'))\n",
        "\n",
        "cnn1.summary()"
      ],
      "id": "vertical-seller"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "advisory-commercial",
        "outputId": "3c20a6ba-23b6-4ce4-b2f6-e006b74c6125",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "1119/1119 [==============================] - 23s 19ms/step - loss: 26.2378 - accuracy: 0.6962 - val_loss: 5.6303 - val_accuracy: 0.7598\n",
            "Epoch 2/30\n",
            "1119/1119 [==============================] - 21s 19ms/step - loss: 4.7563 - accuracy: 0.7825 - val_loss: 1.1374 - val_accuracy: 0.8465\n",
            "Epoch 3/30\n",
            "1119/1119 [==============================] - 21s 19ms/step - loss: 0.9785 - accuracy: 0.8481 - val_loss: 0.5012 - val_accuracy: 0.8854\n",
            "Epoch 4/30\n",
            "1119/1119 [==============================] - 22s 20ms/step - loss: 0.5458 - accuracy: 0.8848 - val_loss: 0.5667 - val_accuracy: 0.9253\n",
            "Epoch 5/30\n",
            "1119/1119 [==============================] - 21s 19ms/step - loss: 0.4385 - accuracy: 0.9056 - val_loss: 0.2959 - val_accuracy: 0.9224\n",
            "Epoch 6/30\n",
            "1119/1119 [==============================] - 23s 21ms/step - loss: 0.3374 - accuracy: 0.9161 - val_loss: 0.2436 - val_accuracy: 0.9290\n",
            "Epoch 7/30\n",
            "1119/1119 [==============================] - 20s 18ms/step - loss: 0.3530 - accuracy: 0.9203 - val_loss: 0.2270 - val_accuracy: 0.9355\n",
            "Epoch 8/30\n",
            "1119/1119 [==============================] - 23s 20ms/step - loss: 0.3207 - accuracy: 0.9236 - val_loss: 0.2812 - val_accuracy: 0.9241\n",
            "Epoch 9/30\n",
            "1119/1119 [==============================] - 20s 18ms/step - loss: 0.3108 - accuracy: 0.9247 - val_loss: 0.2571 - val_accuracy: 0.9331\n",
            "Epoch 10/30\n",
            "1119/1119 [==============================] - 22s 19ms/step - loss: 0.3284 - accuracy: 0.9251 - val_loss: 0.2285 - val_accuracy: 0.9409\n",
            "Epoch 11/30\n",
            "1119/1119 [==============================] - 21s 18ms/step - loss: 0.4408 - accuracy: 0.9221 - val_loss: 0.3091 - val_accuracy: 0.9290\n",
            "Epoch 12/30\n",
            "1119/1119 [==============================] - 21s 19ms/step - loss: 0.2724 - accuracy: 0.9302 - val_loss: 0.1932 - val_accuracy: 0.9436\n",
            "Epoch 13/30\n",
            "1119/1119 [==============================] - 22s 20ms/step - loss: 0.2226 - accuracy: 0.9351 - val_loss: 0.3403 - val_accuracy: 0.9352\n",
            "Epoch 14/30\n",
            "1119/1119 [==============================] - 21s 19ms/step - loss: 0.3229 - accuracy: 0.9304 - val_loss: 0.2144 - val_accuracy: 0.9427\n",
            "Epoch 15/30\n",
            "1119/1119 [==============================] - 22s 20ms/step - loss: 0.3417 - accuracy: 0.9312 - val_loss: 0.1966 - val_accuracy: 0.9419\n",
            "Epoch 16/30\n",
            "1119/1119 [==============================] - 22s 19ms/step - loss: 0.2979 - accuracy: 0.9326 - val_loss: 0.2001 - val_accuracy: 0.9422\n",
            "Epoch 17/30\n",
            "1119/1119 [==============================] - 23s 20ms/step - loss: 0.2206 - accuracy: 0.9374 - val_loss: 0.1609 - val_accuracy: 0.9511\n",
            "Epoch 18/30\n",
            "1119/1119 [==============================] - 22s 19ms/step - loss: 0.3626 - accuracy: 0.9314 - val_loss: 0.2883 - val_accuracy: 0.9338\n",
            "Epoch 19/30\n",
            "1119/1119 [==============================] - 22s 20ms/step - loss: 0.2406 - accuracy: 0.9365 - val_loss: 0.1537 - val_accuracy: 0.9500\n",
            "Epoch 20/30\n",
            "1119/1119 [==============================] - 21s 19ms/step - loss: 0.3190 - accuracy: 0.9354 - val_loss: 0.2266 - val_accuracy: 0.9392\n",
            "Epoch 21/30\n",
            "1119/1119 [==============================] - 21s 19ms/step - loss: 0.2065 - accuracy: 0.9406 - val_loss: 0.1532 - val_accuracy: 0.9531\n",
            "Epoch 22/30\n",
            "1119/1119 [==============================] - 21s 19ms/step - loss: 0.2752 - accuracy: 0.9395 - val_loss: 0.1608 - val_accuracy: 0.9531\n",
            "Epoch 23/30\n",
            "1119/1119 [==============================] - 23s 20ms/step - loss: 0.1807 - accuracy: 0.9445 - val_loss: 0.1598 - val_accuracy: 0.9484\n",
            "Epoch 24/30\n",
            "1119/1119 [==============================] - 21s 19ms/step - loss: 0.2007 - accuracy: 0.9438 - val_loss: 0.2213 - val_accuracy: 0.9391\n",
            "Epoch 25/30\n",
            "1119/1119 [==============================] - 23s 21ms/step - loss: 0.1682 - accuracy: 0.9477 - val_loss: 0.1376 - val_accuracy: 0.9545\n",
            "Epoch 26/30\n",
            "1119/1119 [==============================] - 22s 19ms/step - loss: 0.7885 - accuracy: 0.9416 - val_loss: 0.7487 - val_accuracy: 0.9256\n",
            "Epoch 27/30\n",
            "1119/1119 [==============================] - 25s 22ms/step - loss: 0.5192 - accuracy: 0.9299 - val_loss: 0.4155 - val_accuracy: 0.9294\n",
            "Epoch 28/30\n",
            "1119/1119 [==============================] - 22s 20ms/step - loss: 0.3161 - accuracy: 0.9387 - val_loss: 0.1911 - val_accuracy: 0.9515\n",
            "Epoch 29/30\n",
            "1119/1119 [==============================] - 21s 19ms/step - loss: 0.2266 - accuracy: 0.9428 - val_loss: 0.2056 - val_accuracy: 0.9527\n",
            "Epoch 30/30\n",
            "1119/1119 [==============================] - 21s 19ms/step - loss: 0.1925 - accuracy: 0.9463 - val_loss: 0.1389 - val_accuracy: 0.9568\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7e2704aeef80>"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cnn1.compile(loss = 'categorical_crossentropy', optimizer= 'adam', metrics = ['accuracy'])\n",
        "cnn1.fit(X_train, y_train, epochs=30, batch_size=128,\n",
        "         validation_data=(X_val,y_val),callbacks=[tensorboard_callback, eary_stop_callback])"
      ],
      "id": "advisory-commercial"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "contrary-catch",
        "outputId": "38583dad-241b-439e-f742-a3912445962d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plotgraphs(cnn1)"
      ],
      "id": "contrary-catch"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sharing-receptor",
        "outputId": "a7d898d8-f56e-4978-dd9a-c4163e19c6bd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1492/1492 [==============================] - 4s 3ms/step\n"
          ]
        }
      ],
      "source": [
        "predict = np.argmax(cnn1.predict(X_test),axis=1)\n",
        "\n",
        "a = np.unique(predict)\n",
        "b = np.unique(y_test)\n",
        "c = list(set(a) | set(b))"
      ],
      "id": "sharing-receptor"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "raised-potter",
        "outputId": "fd0edfb8-855e-47fc-f573-f82e1c17ac5c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "        ----------Classification Report Of Classes-------------\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00         5\n",
            "           1       0.59      0.99      0.74      1117\n",
            "           2       0.00      0.00      0.00         6\n",
            "           3       0.00      0.00      0.00         5\n",
            "           4       0.88      0.91      0.89       290\n",
            "           5       0.44      0.55      0.49        29\n",
            "           6       1.00      1.00      1.00      7110\n",
            "           7       0.99      0.96      0.97       463\n",
            "           8       1.00      0.99      1.00      4225\n",
            "           9       1.00      0.99      1.00      4180\n",
            "          10       0.99      0.95      0.97      4249\n",
            "          11       0.00      0.00      0.00        25\n",
            "          12       0.96      0.99      0.97      3602\n",
            "          13       0.99      1.00      0.99      4615\n",
            "          14       1.00      1.00      1.00      5591\n",
            "          15       0.97      0.90      0.93       295\n",
            "          16       0.17      0.01      0.02       179\n",
            "          17       0.00      0.00      0.00        13\n",
            "          18       0.52      0.70      0.60        86\n",
            "          19       0.95      0.99      0.97      2114\n",
            "          20       0.99      0.99      0.99      2756\n",
            "          21       0.99      0.99      0.99      3380\n",
            "          22       0.63      0.13      0.21       315\n",
            "          23       0.58      0.98      0.72      1007\n",
            "          24       0.53      0.01      0.02       754\n",
            "          25       0.99      0.99      0.99       965\n",
            "          26       0.47      0.19      0.27       134\n",
            "          27       0.20      0.05      0.07        88\n",
            "          29       0.40      0.02      0.05        81\n",
            "          30       0.00      0.00      0.00         8\n",
            "          31       0.00      0.00      0.00         1\n",
            "          32       0.83      0.20      0.33        49\n",
            "          33       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.95     47738\n",
            "   macro avg       0.58      0.53      0.52     47738\n",
            "weighted avg       0.95      0.95      0.94     47738\n",
            "\n",
            "\n",
            "        ----------Validation Data------------------\n",
            "Accuarcy: 95.49206083204156\n",
            "Precision: 95.1707 %\n",
            "Recall-score: 95.4921\n",
            "F1-score: 94.4894\n"
          ]
        }
      ],
      "source": [
        "report(predict,labels_test)"
      ],
      "id": "raised-potter"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vocational-weekend"
      },
      "source": [
        "### RNN Models"
      ],
      "id": "vocational-weekend"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "prescribed-aruba"
      },
      "outputs": [],
      "source": [
        "# reshape input to be [samples, time steps, features] for RNN\n",
        "RNN_features_train = np.reshape(features_train, (features_train.shape[0],features_train.shape[1],1))\n",
        "RNN_features_test = np.reshape(features_test, (features_test.shape[0],features_test.shape[1],1))\n",
        "RNN_features_val = np.reshape(features_val, (features_val.shape[0],features_val.shape[1],1))"
      ],
      "id": "prescribed-aruba"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "regulated-lottery",
        "outputId": "9dd123f9-fbd1-47b2-9916-b89b45b0ac0a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " simple_rnn (SimpleRNN)      (None, 46, 32)            1088      \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 46, 32)            0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 1472)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 32)                47136     \n",
            "                                                                 \n",
            " leaky_re_lu (LeakyReLU)     (None, 32)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 16)                528       \n",
            "                                                                 \n",
            " leaky_re_lu_1 (LeakyReLU)   (None, 16)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 34)                578       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 49,330\n",
            "Trainable params: 49,330\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "#hyperparameters\n",
        "keras.backend.clear_session()\n",
        "SEED = 1040941203\n",
        "hidden_initializer = random_uniform(seed=SEED)\n",
        "# Initialising the RNN\n",
        "rnn1 = Sequential()\n",
        "\n",
        "# Adding the first RNN layer and some Dropout regularisation\n",
        "rnn1.add(SimpleRNN(units = 32,activation='relu', return_sequences = True,  input_shape = (46,1)))\n",
        "rnn1.add(Dropout(0.1))\n",
        "\n",
        "# Adding the output layer\n",
        "rnn1.add(Flatten())\n",
        "rnn1.add(Dense(32, input_dim=25, kernel_initializer=hidden_initializer))\n",
        "rnn1.add(LeakyReLU(alpha=0.1))\n",
        "rnn1.add(Dense(16))\n",
        "rnn1.add(LeakyReLU(alpha=0.1))\n",
        "rnn1.add(Dense(units = 34, activation='softmax'))\n",
        "\n",
        "rnn1.summary()"
      ],
      "id": "regulated-lottery"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "respective-database",
        "scrolled": true,
        "outputId": "7a8a90b4-059b-43d5-e395-be3f02597f67"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "560/560 [==============================] - 22s 36ms/step - loss: 22.6143 - accuracy: 0.5935 - val_loss: 1.3981 - val_accuracy: 0.7542\n",
            "Epoch 2/30\n",
            "560/560 [==============================] - 20s 35ms/step - loss: 1.8775 - accuracy: 0.7998 - val_loss: 1.1314 - val_accuracy: 0.8234\n",
            "Epoch 3/30\n",
            "560/560 [==============================] - 19s 35ms/step - loss: 2.0669 - accuracy: 0.8461 - val_loss: 1.0095 - val_accuracy: 0.8797\n",
            "Epoch 4/30\n",
            "560/560 [==============================] - 20s 35ms/step - loss: 1.1273 - accuracy: 0.8732 - val_loss: 0.5375 - val_accuracy: 0.8948\n",
            "Epoch 5/30\n",
            "560/560 [==============================] - 19s 33ms/step - loss: 0.7968 - accuracy: 0.8948 - val_loss: 0.3926 - val_accuracy: 0.9132\n",
            "Epoch 6/30\n",
            "560/560 [==============================] - 20s 35ms/step - loss: 0.6608 - accuracy: 0.9068 - val_loss: 0.8566 - val_accuracy: 0.9192\n",
            "Epoch 7/30\n",
            "560/560 [==============================] - 19s 35ms/step - loss: 0.6197 - accuracy: 0.9136 - val_loss: 3.7224 - val_accuracy: 0.9039\n",
            "Epoch 8/30\n",
            "560/560 [==============================] - 20s 35ms/step - loss: 1.1583 - accuracy: 0.9136 - val_loss: 0.3175 - val_accuracy: 0.9320\n",
            "Epoch 9/30\n",
            "560/560 [==============================] - 19s 34ms/step - loss: 0.6512 - accuracy: 0.9187 - val_loss: 0.4037 - val_accuracy: 0.9275\n",
            "Epoch 10/30\n",
            "560/560 [==============================] - 19s 34ms/step - loss: 0.3634 - accuracy: 0.9239 - val_loss: 0.2732 - val_accuracy: 0.9364\n",
            "Epoch 11/30\n",
            "560/560 [==============================] - 20s 35ms/step - loss: 0.3516 - accuracy: 0.9266 - val_loss: 0.2528 - val_accuracy: 0.9383\n",
            "Epoch 12/30\n",
            "560/560 [==============================] - 17s 31ms/step - loss: 0.3938 - accuracy: 0.9274 - val_loss: 0.2351 - val_accuracy: 0.9406\n",
            "Epoch 13/30\n",
            "560/560 [==============================] - 18s 32ms/step - loss: 0.2519 - accuracy: 0.9341 - val_loss: 0.2979 - val_accuracy: 0.9313\n",
            "Epoch 14/30\n",
            "560/560 [==============================] - 18s 33ms/step - loss: 0.4466 - accuracy: 0.9296 - val_loss: 0.2553 - val_accuracy: 0.9331\n",
            "Epoch 15/30\n",
            "560/560 [==============================] - 17s 30ms/step - loss: 0.2463 - accuracy: 0.9331 - val_loss: 0.2355 - val_accuracy: 0.9370\n",
            "Epoch 16/30\n",
            "560/560 [==============================] - 18s 33ms/step - loss: 0.2797 - accuracy: 0.9360 - val_loss: 0.1834 - val_accuracy: 0.9498\n",
            "Epoch 17/30\n",
            "560/560 [==============================] - 19s 33ms/step - loss: 0.1967 - accuracy: 0.9433 - val_loss: 0.1899 - val_accuracy: 0.9404\n",
            "Epoch 18/30\n",
            "560/560 [==============================] - 17s 31ms/step - loss: 0.1808 - accuracy: 0.9466 - val_loss: 0.1615 - val_accuracy: 0.9512\n",
            "Epoch 19/30\n",
            "560/560 [==============================] - 18s 33ms/step - loss: 0.1895 - accuracy: 0.9443 - val_loss: 0.1601 - val_accuracy: 0.9532\n",
            "Epoch 20/30\n",
            "560/560 [==============================] - 17s 31ms/step - loss: 0.1539 - accuracy: 0.9459 - val_loss: 0.1432 - val_accuracy: 0.9482\n",
            "Epoch 21/30\n",
            "560/560 [==============================] - 18s 32ms/step - loss: 0.1479 - accuracy: 0.9486 - val_loss: 0.1364 - val_accuracy: 0.9516\n",
            "Epoch 22/30\n",
            "560/560 [==============================] - 18s 33ms/step - loss: 0.1414 - accuracy: 0.9512 - val_loss: 0.1272 - val_accuracy: 0.9553\n",
            "Epoch 23/30\n",
            "560/560 [==============================] - 18s 33ms/step - loss: 0.1797 - accuracy: 0.9466 - val_loss: 0.1433 - val_accuracy: 0.9558\n",
            "Epoch 24/30\n",
            "560/560 [==============================] - 17s 30ms/step - loss: 0.1395 - accuracy: 0.9520 - val_loss: 0.1250 - val_accuracy: 0.9561\n",
            "Epoch 25/30\n",
            "560/560 [==============================] - 18s 32ms/step - loss: 0.1312 - accuracy: 0.9554 - val_loss: 0.1131 - val_accuracy: 0.9623\n",
            "Epoch 26/30\n",
            "560/560 [==============================] - 17s 31ms/step - loss: 0.1167 - accuracy: 0.9588 - val_loss: 0.1063 - val_accuracy: 0.9620\n",
            "Epoch 27/30\n",
            "560/560 [==============================] - 17s 30ms/step - loss: 0.1207 - accuracy: 0.9581 - val_loss: 0.1026 - val_accuracy: 0.9636\n",
            "Epoch 28/30\n",
            "560/560 [==============================] - 17s 30ms/step - loss: 0.1058 - accuracy: 0.9611 - val_loss: 0.0978 - val_accuracy: 0.9626\n",
            "Epoch 29/30\n",
            "560/560 [==============================] - 19s 35ms/step - loss: 0.1035 - accuracy: 0.9618 - val_loss: 0.0951 - val_accuracy: 0.9641\n",
            "Epoch 30/30\n",
            "560/560 [==============================] - 17s 30ms/step - loss: 0.0980 - accuracy: 0.9630 - val_loss: 0.0932 - val_accuracy: 0.9649\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7e270bb5f040>"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rnn1.compile(loss = \"categorical_crossentropy\", optimizer='adam', metrics=['accuracy'])\n",
        "rnn1.fit(RNN_features_train, y_train, epochs=30, batch_size=256,\n",
        "         validation_data=(RNN_features_val,y_val),callbacks=[tensorboard_callback, eary_stop_callback])"
      ],
      "id": "respective-database"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "understood-village",
        "outputId": "4f0144bd-eb5e-49d7-84f7-c3937733133e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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51QA5Yzabc9QevPfeeznO0a9fP7Zv387XX3+da7mzPfTQQ/z8889Mnz6d6tWrF9sEhoGBgXTt2pX//ve/ToO8U6dOFct1CuLkyZMOzyI5OZlPPvmE1q1bExwcDMAdd9zBpk2bHD5Xqamp/O9//yMsLMzexNmvXz9Onz7N+++/n+M6qtmR/KgGSCQfN998M1WrVmXIkCE888wzmEwmPv300zL1D+yECRP4+eef6dixIyNGjMBisfD+++/TvHlztm3bZs+3bNkyOnXqlGuziq+vL3369GHBggUAOYZf9+rViyVLltC3b1/uvPNOoqOjmTVrFk2bNiUlJaVQZT558iS//PILzzzzjNP9np6eREZG8uWXX/Luu+9y66238tBDD/Huu+9y4MABevTogdVq5bfffuPWW2/lqaeeomHDhrz66qu88cYbdO7cmXvuuQdPT082b95MaGiofT6dRx55hCeeeIJ+/frRvXt3tm/fzk8//ZSjFiovvXr14tNPPyUgIICmTZuyfv16Vq5caZ+aINtLL73E4sWLue+++3j44Ydp27YtiYmJfPvtt8yaNYtWrVrZ8z7wwAOMHj2ar7/+mhEjRhRrrcaMGTPo1KkTLVq04NFHH6V+/frExcWxfv16jh8/zvbt26/q/Fu3bmX+/Pk50hs0aECHDh3s7xs3bszw4cPZvHkzQUFBzJkzh7i4OObOnWvPM2bMGBYuXEjPnj155plnqFatGh9//DHR0dF89dVX9g7ugwcP5pNPPmHUqFFs2rSJzp07k5qaysqVK3nyySe5++67r+qe5BrngpFnIi6X2zD4Zs2aOc2/du1a46abbjK8vb2N0NBQY/To0fahy7/88os9X27D4N9+++0c5+SKodi5DYMfOXJkjmOvHK5tGIYRFRVl3HDDDYaHh4fRoEED46OPPjJeeOEFw8vLyzAM27DgwMBAY+rUqU7vMduyZcsMwAgJCXE6lHzy5MlGvXr1DE9PT+OGG24wvv/++xz37ez+rvTOO+8YgBEVFZVrnnnz5hmA8c033xiGYRuG//bbbxvXXXed4eHhYdSsWdPo2bOnsWXLFofj5syZY9xwww2Gp6enUbVqVaNLly7GihUr7PstFovx8ssvGzVq1DB8fHyMyMhI4+DBg7kOg9+8eXOOsp05c8YYNmyYUaNGDcPX19eIjIw09u7d6/R3k5CQYDz11FNGrVq1DA8PD6N27drGkCFDjNOnT+c47x133GEAxrp163J9LlfK7XNypUOHDhmDBw82goODDXd3d6NWrVpGr169jMWLFxfonp3Jbxj85c+iXr16xp133mn89NNPRsuWLQ1PT0/juuuuM7788kunZb333nuNKlWqGF5eXkb79u2N77//Pke+tLQ049VXXzXCw8MNd3d3Izg42Lj33nuNQ4cOOZSvIH+DUrGYDKMM/TdWRIpVnz592LVrFwcOHGDTpk1ERESwa9euYh8lJcWnb9++/PXXX3kuTltehYWF0bx5c77//ntXF0VEfYBErhXnz593eH/gwAF++OEHunbtak+bPHmygp8yLCYmhmXLlvHQQw+5uigi1zz1ARK5RtSvX5+hQ4dSv359jhw5wsyZM/Hw8GD06NEAtG/fnvbt27u4lOJMdHQ0a9eu5aOPPsLd3Z3HH3/c1UUSueYpABK5RvTo0YOFCxcSGxuLp6cnHTp0YPLkyU7n15GyZc2aNQwbNoy6devy8ccf20dDiUjJUR8gERERqXDUB0hEREQqHAVAIiIiUuGoD5ATVquVkydP4ufnV2prEImIiMjVMQyDc+fOERoaap8wMzcKgJw4efIkderUcXUxREREpAiOHTtG7dq188yjAMgJPz8/wPYA/f39XVwaERERKYjk5GTq1Klj/x7PiwIgJ7Kbvfz9/RUAiYiIlDMF6b6iTtAiIiJS4SgAEhERkQpHAZCIiIhUOAqAREREpMJRACQiIiIVjgIgERERqXAUAImIiEiFowBIREREKhwFQCIiIlLhKAASERGRCkcBkIiIiJSsX6bAmqnO962ZattfyrQWmIiISHnxyxRwM0OX0Tn3rZkKVgvcOrbsXdvNDL+8idUw2FjnEeLPpRPo50XEsY9wWz0Zbn21ZMqcBwVAIiIiheHKIORiIAE4Xn/NVFt6XoHExXJbOr/EpuhEexDSPrwa5t/ezr/cVxPEdBnNgbhkGq2ezJ9Zf7HA2o0H3FbSwf07DjR9hkbOnmUJUwAkIiJSGMUQhBQ5eMo+7vLrX37dvAKJi+We/eshJqfeZU9+pfK3PGb5PO9yX8iEGwZx9Mgh6q6ezNkLK/jTuJ7b3LbgZt7F2WotqXLmCHw5FDJTL24p9tdZ588RnpUGJnjS/Tue5DsApmXdy3tbb2Jm0xh6NA/J/folQAGQiIhIYRRDEJJvLUpmGqTEQUr8xZ+Xv44HvxDb9bLLEFAHjm+GxQ+Dpz94+tl+emW/9mOTpREHLtzGY3yOxZzKTMtdPFvpK4ZblvPNhQ40POtOs9X/gtRTkBoPqadt10o9BelnAah78TZ6VtpMTzbbb6tK4g5I3JHrbbsDmBzTLIYb71ruwQRM/G433ZsGY3YzOTm6ZJgMwzBK7WrlRHJyMgEBASQlJeHv7+/q4oiISFmTdR6+HgG7vwaTCQwDajSB4ObgVgnc3G3BjlslMLtfTDODmzsJe36l+ulN/GppzkZrU25z20ob80HSfELx8fK2BR2Z51x9hzlcwI1Ew5/TRgDXmY7iZjKwGCY+sdxOKl5kunkTWL0aiRc8SMh0JyGzEqcyKpFieJGGF6mGFw+Zf+Zp92/IMCrhabrAO1n38p7lHgAWPnoTHRpUv6oyFub7WzVAIiIiBWG1wpHfYcci2P0tZCTb0rPrEU7vs235yP6Kv8W8k1vMO+3pPmknIe2yjJW8wDfo4hZof70z2ZMT21YSaf2NLMOMu8nCCreOBN9wBy1quNnKlXHO9jPd9vpc8hli4uLwNZ3Hl/P4m87bi37UCCQBW2BzzlyFBAKIs/gTZ/HjNAGcNvxJMPw5iy8GbjxtXkJT9yP2ICbR8LMHMcTkft9Pm5fwtPs39qDnafMSXnBfDMB7lnuIP5dekN9CsVEAJCIikpe43bag568vIfmEPdnw9MeUkYzVZMbNsGBtdDtuDf4BliywXrD157FefG3Jwmq5wOI/DpOZkYEZK/ebV2M2GVgMN/554UFOGVWwVg7kvUd7YPYPtjVdmRybhJbvjGH3itcY5f5bjkBi2oYQTgz8J90jgjmSkMq+2HPsjT3HvthzbDl7hlOZGQD2/JlGJTxMF1h84ZZLAUyW80dQyc2EYTXsx+YWxAxsX4euTQKp6uNBFR93qvi4sy/mHJs+HuNwXHZ+wH58oN9NxfUbKxAFQCIiUv5cTWfighzbdijsXAzbF0HcX5f2ewZAs7s5lphKncNfOQYCBxZzwP06Gt3/htPLbjyUwOg1GwBbEGI2GfZaFF/OM9faE85BvS1WWtROxds9HS93M94eZrzdzXiY3Ti8ZAKj8ggkpi8y8Yz1HjItznu35BfATO7bnPbh1fH2MONVye3iTzMboxNZN2d0vkHMza2m5mjGql7Zk31ebkxLv3Rctvcu9gEK8HKjfXg157+vEqIASERESt/Vjoa6mpFYuR0b9Qb89m+oGg6/TgXDejG/OzS6HVr1h0aRHPj6TRod/iRHIGACRu1+lwNfYA+CElMz2XUyiZ0nkvlpVyyQfxAyc82hXIv+XKVM3jGcBxIAZpOFTIuBl7sbjYP8aBLkR5NgPxoH+rF70Ws8Yc09gPHzqkT/G+9w2hG5fXg1dhUxiDG7majddxIj5m/FBFwempmy77lvm1LtAA0KgERExBWuJoC5/JiCjMQyjIvNURfAsEDE45CVZsubcQ7Cb4EV4yF+ly3/mWjbzzoR0LI/NOsLPrYvdovVYM2+WL7NyhkIvGu5BwPw3nWSf328md0nkzmZ5Niv5crgB3LWoqwJGYZXJTPnsyy2LdNCepaFc+lZTL9wb66PJPs8r915PcM6hucIKEKbVGfajnt5/4pyv38xgLnr+uq5BiFXG8T0aB7CzEFtmPjdbmIueybBAV6M79201IfAg0aBOaVRYCJSLrhyQr7icDFgOR3Wixjf5tRJ+I0qMWuhbgcIvQEupMOFjItbuuNPy8X0c7H2IdoAmD1to66y++AYlks1OQVRrQG0GgAt7oNq4Tl2rz+UwMAPNxTqNsNrVKZZqD/Xh/hT6de3SM00ePeKIATgGfMS/L3cGPbq/5wGEgW9dl6jqZbvjMkRhIQUIgi52uMtViPnJIzFWPOjUWAiIuVFMSwvABStFuVqFbbsGSlw8k848Qcc/4P0w5vwAmoc/p4afH8p39H1tq0oLBeDowIyDFs/Y4th4jGPt7jv1rvo0SL0sv0GxxLP89eJJHaeTGLV3vgCnfeeG2oxoH1drg/xw8/L3Z6+vOYbRa5FaR9ejZAAL2KT0nFWc2HCVqOSV1+aHs1D6N40uMhByNUeb3YzXfVQ9+Li8gBoxowZvP3228TGxtKqVSvee+892rdv7zRvVlYWU6ZM4eOPP+bEiRM0adKEf/3rX/To0cOeZ8KECUycONHhuCZNmrB3794SvQ8RkSK5miAm4gnbnDG/vAkntkCd9pBwCLZ9lv+EfKVR9hsfgS0fXwx4tsCpPQ61MV7ABcMNN6y4XQxCFlhuIwN3bm1WhwYh1aGSp204eCVPW+2O/f3FtL++hK0f2/rpWLNszyTicdu8Oybzpfl3TG721z/tOc3IhdsZYf6GF9wX2zsiN0vbzBOf1eGRzmcxu5nYecLWdyfpfC5Do/JwX7s6TgORq2kKMruZGN+7aa4BFMD43k3zDUauNggpS0HM1XBpALRo0SJGjRrFrFmziIiIYPr06URGRrJv3z4CAwNz5H/ttdeYP38+H374Iddddx0//fQTffv2Zd26ddxwww32fM2aNWPlypX295UquTzOE5GyzJVNSXn1ZekwEsI6wY4vIOkYJB2HpBMXfx6HjKRL59m/3LaBLVCI2wl/zoeG3cEvqMTLfiIunhO+zbku+lP8T/1hC0g2f5TzGP/aGLXa8v7+AH5NC6Oz2w6ecV9qD0LijSq8b7mH2Ye9+L3/P/L+Ml8z1Rb8ZAd72c/Np3quwZ/FajDhh4P24OfKjsgG8N5vjs1THmY3mgT70bxWAM1C/fm/lftJTMl0SS1MWexLU165tA9QREQEN954I++//z4AVquVOnXq8PTTTzNmzJgc+UNDQ3n11VcZOXKkPa1fv354e3szf/58wFYDtHTpUrZt21bkcqkPkEgFk1vn2YIub1AMrD+Px23ddAxMmDDsP/PlXRUCakPcrtz7uoS0to1ianQ71GpjC/agaIGfYUDySYjZBie3Eb9/I5VjN1IZJ5PYefhBrRugVluo1Q5qtwO/YH7dH8/gOZtzHQ2V/T7PmYGL+DtbtSeOP+e/kqMjMjh2UN7d+Am6XR9Ei1oBNA7yw6OSmz3f8p0xjJi/1fY4Ljt3dvgyc1CbEg9ESrovTXlVLvoAZWZmsmXLFsaOvfTH5ebmRrdu3Vi/3nnbb0ZGBl5eXg5p3t7e/P777w5pBw4cIDQ0FC8vLzp06MCUKVOoW7cuucnIyCAj41KbcXJyclFuSUTKq8trYU7tg07Pw74fSif4MQz++n4Gdbd8RADYgx4TBlY3d9wCatsCnBxbHfCvBZ6+ti/92L/A7AGWTNscNr5BcOBnW5+bmG227depttqRht1swZAlA9b8X+7rUnV9Bc4eg5jt9oCHmG22taEuCrx0G/a+NGMvPMo2a0OG97ydOjX8+PtUKn8fTOXvDUeJPr2HowlpBRoNtXpffVrWDqCyp5OvKqsFbn3VtrL5oYRLgUDnlzBn77/o1LkMVu6J46ddsfy2/zRPm605gp/Lr282WbmrVSh3t67l9FdWFmphrpVmKFdyWQ3QyZMnqVWrFuvWraNDhw729NGjR7NmzRo2btyY45gHHniA7du3s3TpUho0aEBUVBR33303FovFHsD8+OOPpKSk0KRJE2JiYpg4cSInTpxg586d+Pn5OS2Ls35DgGqARCqS1ASY3R0SL5uD5aYnoceUkrvmqX0kLnqSaqf/sCddMNyoZLIyK6sXUy0D+GBQu7y/UK+s8bjyfUo8HFhhC4YO/eLYbGZy47xXEN7nY/j0QjdmXejN6Eqfc3el9aRWrktlIw3STue8psmMEXgdy04FsTGjLk1NRxhY6Ren6zvl5rlKi7EYbk7z2SYJtDL9wr24m03cULcqnRrWoFOjGrSsFUAls602Jq8RSc1CA/hpVyw/7YrljyNnKOw3XUHWpVItTNlTmBqgchUAnTp1ikcffZTvvvsOk8lEgwYN6NatG3PmzOH8+fNOr3P27Fnq1avHtGnTGD58uNM8zmqA6tSpowBIpKI4/Dt89Qicu2IhI7OHrSNv5xehcjH+bzvrPPz2Dsbv0zFZs8g0zHiYLPxf1j38x3LvpaUNsu7lS98H+P3lXPrCXAx2rF1fyX1l8ctrryxZcGyTLRg68DPE786/rG6VMAKvJ616c056N2GfWwO2ZtRi07E0dp5MzrcZK8jfk6Yh/tSv6Uv9mpWpX8OXsOo+9J25jrhcRjMB+HiYqerjzomzjk1rfl6V6FC/OtUqe/D55mMFfuQtawcQ2SyYbtcHMnTu5nxHUuX6zKVMKxdNYDVq1MBsNhMXF+eQHhcXR3BwsNNjatasydKlS0lPTychIYHQ0FDGjBlD/fr1c71OlSpVaNy4MQcPHsw1j6enJ56enkW7EREpv6wW+O0dWD3F1n/GpzqkJVwaUWTJhA0f2DoTd3oOIkaAh4/TUxW4NuDQL7BsFCT+jQk4ZA2hgVtMrk1BRgpsim7tvDbCauFA02cYvL4dMcsvzQ8TEtCOT5o+Q6PLmoEA2/w4YR0hrCOW2ybQ761FNE3dxK1u2+jmtgWTCawGLLLcyk4jnH1uDcgIuI4DJ7JIP3x5/yLnMxpfWXaAhndMctqUNCGf0UzT7m9FZLNgjiam8fvB0/x+4DTrDiWQdD6Ln3fH5TifMzeFV6NH82BubxZMaBVve3pxjKSS8s9lAZCHhwdt27YlKiqKPn36ALZO0FFRUTz11FN5Huvl5UWtWrXIysriq6++4v777881b0pKCocOHeKhhx4qzuKLSHl3LhaWPArRv9reB7ew9aO5sinJN9DWjBQ1CTZ9CF3HQusHwXzpn88CTQ6Xcgrjp1cw/fWF7fLuNfmP+3B8k/djseRsCrq8P8oLX2yjaWgAdav5UKeaN3Wr+VC3mg+7qw7muZ+2YVzRATk2KZ3bt97EzEFtuN1qcPZ8FqfOZXA6JYNT52zb9uNn2ZbsxzZuozpJdDdvsS+OedKozmeWbmAB4my14+5mE/WqV6Z+jcrUr+mLYRiY1+XflybQz7HfZraC9qOpV70y9apX5sGIelisBjtPJLFg01EWFaD259lujZ0GjmWhD4+4nktHgS1atIghQ4bw3//+l/bt2zN9+nS++OIL9u7dS1BQEIMHD6ZWrVpMmWJrg9+4cSMnTpygdevWnDhxggkTJhAdHc3WrVupUqUKAC+++CK9e/emXr16nDx5kvHjx7Nt2zZ2795NzZo1C1QujQITucYdjIIlj9n6t7hXhgb/gL3f5T6i6Pq7IeZPOHvUll6jCXSbAE16snxXLCPmb83RnJJduzDs5jq0TfiBrkffw9dIwWqY+NhyO+9cuI8UnNcmFRc3k60cuayLCeS+LlX2+8c6hzMwoh51qnrb+96Arcar079WXXVTUlH60Xyz7QTPfr4tzzwA/xnQOteOzEW9tpRt5aIJDKB///6cOnWKcePGERsbS+vWrVm+fDlBQbY5K44ePYqb26U/uPT0dF577TX+/vtvfH19ueOOO/j000/twQ/A8ePHGThwIAkJCdSsWZNOnTqxYcOGAgc/IhVaeV9aIT+WLFtA8/v/2d4HNYd758LOryCkZc77zn5vtUC/D2HzbPj1bTi9Dz4fiFGnA5knfHjKXDVHLYgBTKg0j+5b/qCWKRGAndYwxlsfJTOoNb1r2ZZG+M/KAySm5j6nTA0/T/51TwtOnD3PsTPnOZqQxrEzafx9KoXzWXkv8WC97KRVfdyp6edp23w9ybJYqb/7g/xX975uKuE1Kuc4tysn5cutVqmw+TSSqmLTWmBOqAZIKqwyMB9OiTl7DL4aDscuDrBoNxwi3wR377yPu1J6Evw+HTbMhAuXBl/MuRDJpAtDAPAkk4/d3+Ims20G+nSTF3uvfwaPm0fQKKQK7uarn1Pmmz9P8OyibfkWd+JdTRnYvp7DPDZgq/2Y++ZjJKdbi7Qu1eXlv5q1oYqiuGqf5NpTLkaBlWUKgKTcKo4aHPssxE9D90nw27/Lf/CzdxksfdK2aKanP9z1rm2F76tw6OB+ji55nVtSf8Jssv0zut1Sn/9aejG50myquKUCsMLSFqPnv7j95htzPVdRgojiWhizOCb0c0VTUlmYjFDKHgVAV0kBkJRbhanBOX8GzhyGxGg4E33x58X3yccvHnixcaOsBz+5BX4XMmDenXB8s+19aBu4d47TVb4h/y/yLIuVFbvj+HjdYTZG25q1GpqO83KlRXQ3b3E41znDmxeynuBn640lMqdMcdWCuKIGp7iU57JLySg3fYBExImrqcW5fEZjw4AbBsHqybZh3PVuhvg98L+utiAn/WwBCnPxq/XIWtsswKGtC307pcLZopwJh2DuHZBiG7JNh6fgtvFQycPpKfL6Mm1TryqfbzrGZxuPEJdsGxVldjPR7bpANh/x4LHUF2h3YS+LPN7AzWRgMUzclPE+aXgTks+6UNkK2x+luPrgXO3q3q5UnssurqcASKSsKcjq4BcybJP2JcfAuZO2tZmSYyD5hC3d098W+KyefOn4I+tyXss3CKqG22pEqoZder13GaydbltB27DC36vhf12gxX3wj9dseXPhkpE1Vy4oWq0+LB1hm8enkhfc9zE06ZHr4dnNKVfWpMQkpfPE/K2Y3cBysb9xDV8PBtxYlwci6hJaxdt+bAe33biZDPtQ8uHmH3nPck+JzilTXMO5y3Nn4PJcdnEtNYE5oSYwcTn78Ou7ILilbV2qk1ttAYvV4nx5grw0+Mdlgc5lAY9HztE9OZrLlr8CG2Zc2p89O/ItL4GPY82Gy5okUhPg+Casa/+D29FLawka/nUwDV9uWzsrF9lNSZeX2Zkb6gQwtGM4PZoH41nJ7LDvwBev02j3uzmGkh9o+gyN7n/j6u6tADScW8RGTWAi5VlmGlTytNVc7PnWtmVLuWwGXLMn+IfYFsT0CwH/0EvbwVWwdd6lxTHrdihYHx5nfYV6TAbvKrb0quG2/kJOZkfOrRYlNimdEfO35t8ptaBNf1YrJByAoxtsyzoc22h7D1w+zsliuNElfRqvHTPTIyDnKa1Wg+NnzvPt9hP5Bj8Ao3tc77ymYc1UGu1+F2vXV7i5ziM0PJdOoN9NWI81ptHqybDGr8T7T6kWRKTwFACJXMlVc+FcyIStH8Ov/77UbyWbyQx3vnMpwPELtdW+mHJZH2rrvJwzGkP+X8QXV9jOcz6cOu1h5XjbrMlRk2DTR1i7juXEsl95ymw4nQ/nGfMSjn+9FEvTPIZU59b0t+qftrl3wrvAZ/fZgh4n/ZcOWGtx3vCgpTnavrbWPamLGDE/i3/2aU5oFW/2x51jX9w5DsSlcDA+hfNZlhznyU38uVyCpIvPzK3LaDpcnt7gZdvv58rlKESkTFAAJHKlgvTBKU5WC+xYZFuPKnum4Sp1IbiVbXbi7Fqc1FPQblje53JWg3Nl/5i8gqC8ArvLj6t/K/z1pS04STqK23dPc7fVnxruyQAOQdDT5iWMcl/MO+n3sik6gQ4Najg//83P2Eam/fImCYe2cLZSDWrH/Izn+Xjb/ug1l/JW8oba7aBOeyy1I7jjq/Pcnvqd0xmNDeDVpc4v6WF2I8jfk2NnnC+mfLlcJ9Ur6DMTkTJFAZDIlS4PGC6k29aIOroRNs4s3uHgVquteeuXybaZhcHWx+eWlyD1NKx5q/C1OAWpwSkObm7Qqj80vZszaz7AY900amALfl5wX0ywKZFXLzxiD0JmZN3Fz9Z2HPx0NkeCs2jun0qYRxK+GacudeROS7CfvvrR5VzeoHPeOxjv+jdDnQhbDVRwC1IumNh1Ionvdpzk9tTp+c5o/LX/A7SqU5UmQX40DvKlUZAf9ar5YDKZCjScvCAjuUSk/FAnaCfUCVoAWP0vx1FUngHQ9C6o39XWHONbxOVVDAMOroRVb0DMdluad1Xo+By0fwzWv+/y2Zjz61SbccHCz7viWLT5GL8fPI0/qYyo9C3DzMvxMmUBtmUY3EyQZbjhbsp7yQb7eQ13Yo2q1DGdws1kkGW40SXjP8RQnRcjm1DZw8yOE0n8dTyJg6dSyP7X67lKi7EYORcUBVsNlNlkJfzef+a6LpQm1RO5NmgixKukAEgA2LUUvhyS+/6gFlC/i605qF6HSyOq8upD9M1IOLT60kSDHr7QYaRt8wrI//gC9kG6mlFBeY3kCqtRmc83HWPpthOcTcuy7+/UsDo7TyTjcz6W5yt9yb3mX3N0TzqLH+5VahFHNQ6l+7ErxZcYoxpxRlVijWrEGlU5iy9Pm7/mBffFZBiV8DRdcLra+OXlqlXFmz+OnMn3vvKbjFCT6omUfwqArpICICHjHLxzPWSes3VANizQsj9UrmmbEydup2N+s4eteaZ+F0g6DlvmOdbUnNgKi4fDmb9t7yt52YaSdxoFlYt39M7VfJHnNpLLmZAAL+5rW5v72tWhTrVLo8Cy+/xk1/x8dKEnb1/oz38G3eRw/ZSMC2yOTmTtwdOs2B3HkcS0fFcmb1OnCrc0qUnL2gE0rxVAoJ9Xsa4LpeHkIuWbAqCrpABImH27bXi1VxV4YS+se8+x+SnllK1T7t+rbVvSMcfjzZ5gyYCG3cHdC/Z8Z0s3uUHbobZ+Pv6heRahKF/GuQUwBWnKuWCx0vFfq+wzHeemR7MgBrSvS+dGNXOUp6jz4Xyz7QQHvxyXox8P4BAENbxvktNmLDVhiQhoHiCRq7PshUsrhvf7yLZauLORVC3utW2GAYl/XwqGon+9NEz74IpL5w1qDv3n57oO1eWKUotjsRpM/G6301qQ7LTnFm3jpk1HScuykppxgbRMCykZF0jLuEBqZsE6SA+5ObzY58MJ9PMi2mR12tyV/d5ssuY6Equ4ZkQWkYpDAZDI5axW2PuD7fX1d0Gj7pf25TaSymSC6g1s243DbftjttuCoahJgAFu7jBibYGKUJAJBSObBROTlM6hUykcik/h0KlUth49k++EfulZVlbvL+Qs0lcoiflw2odXY1TlQcTmUv73LfcQHODF03mMxNK6UCJSGAqARC63bb5tSLZ7ZejxVs79BRl95WaGWm1sI70wLs3js2ZqvscXpBbn6YV/UsnNxPmsgo2sutLA9nXo1LAmlT3NVPasRGWPSlT2NLMnJpknLjYj5aUk5sMproU9NSOyiBSUAiCRbKkJsGKc7fWtYyHA+ZDpArlyyHoB5/HZFJ2Yby1OlsUgy2JQyc1Eveo+NAz0pUFNXwwDZq45lG/R7mpVy2mQULuqDyEBXi6bD0fNWCJSmhQASdnkiuUoVo63zUQc2Awinij6eS4GO9aur7Cx9nDit50gsPZwIroauDkJgi5YrPxx5AxRe+L4ZtvJAl3i1TuuZ2jHMNzNl1a/slgNlm47UeQAprhqYa6GmrFEpLQoAJKyqbSXozi6Af781Pa61zQwuxf9XFYLB5o+w+D17YhZvsGeHBLQjk+aPkMjq4Wk81ms2X+KqD1xrN53iqTzWXmcMKfmtQIcgh8ongCmLNTCqBlLREqDhsE7oWHwZURuzUjFPROy5QL89xaI3wU3DIK7Z1zV6fKbS6dxkC9/n0rlgvVSjqo+7tzaJJBbmwTyzx92E5+cUeQ5bYpjQj/NhyMi5ZGGwcu1octoSIm3BT2rp4BhLZllIDbOsgU/3lWh26SrOlVenZiz7Y9LAaBhoC+3XR9It+uDaFO3qj3AcK9kuupanKttRlItjIhc6xQASdlz5jDs/Ar++soWmIAt+AFo2K1Qp8q3JiPphC24Aug20WFW5qLUgvyyNz7fTswA0/u3os8NtZ3uK45mKAUwIiJ5UwAkZcO5ONj1NexcDMc3X0o3uV0Mfi7Wh8y+HQYudJyfJxcFagr6aSxkpkDt9nDDQ4U7FjAMg0OnUojaE0/U3ng2H04s0O2arlwo6wrqDCwiUrLUB8gJ9QEqJvmN5MpMheoNbUFP9K+XankwQfgttoVC9y2zNXtFPGHrp3Mm2rb/rnehzeBcL12gJSE8d8Fn/WxrfT2+BoJbFOjYdwe2poqPB1F74lm1N56jiWmFey7kvzCniIgUnvoASdngbCRXZhosfRJ2f31pkdFstdrZlpZo1he2fpKzw/PITfDRPyD2L/j2aVvzVdcxXLnseH6TCZqAt779k8jKr9iCmogn7MFPQSYifGbhNof9HmY3bmpQnduuC6RL45oM/HCDy+bSERGRglEAJCXn8vWzzkTbRlvtXmqbFRlswU9gU2jez7ZdvkbWxWUVHGqPKnnA47/BvDvgyDpY8xYkn4Be/+cwbD2/yQQNoE/al5gyozljrs6Y47eTPmcTAGfTMvPtw2MAAd7u9GgWzD+uD6RTwxpU9rz0p+TquXRERCR/agJzQk1gxezTvnBo1aX3XgHQbrittieoWdHOuXk2/PCirdmsYXe4bx54+gLw2YYjvLp0Z66Hhpli+MnjZTxNF3gy8xl+sN5U6MtP79+aPjfkPlN0cQxFFxGRwlETmJQdW+Y5Bj9u7vDykRzNVoV243DwC4HFD8PBFRjz7mTzzf/l47/SWL4zJo8DDSZVmoen6QJ/B0Twj86P8Y/LynIwPoVZBVhOIsg/l/WwLlInZhGRsk0BkJScnUvgu+cuvc9eFPTXtws8l0+eQ9Gvu4PE+xbjvfhBvGO2EfRlb3ZnvYzFCMHdbCLLkrNy8063jdxi/osM3Kn30Ezq16iT43rfXMVyEpfTUHQRkbJLAZCUjAMrYMlj2HvBdH0Fur5c4EVBIfdmpNfvbIqPp5nPNx1j5Z5kahuv87H7v6jnFs/3PpOI6/Ux+92vY8TFlc2zAxlf0hjn/gkAx65/jIY1GuS4ZllYD0tEREqe+gA5oT5AV+nIOvj0Hrhw3va+61jbaK1sBVjSIr/lJC7Xpm4Vhrby4c6dz2GO2QaVvOHeOSy/cINDAPV6pU8ZXulHUivXpfJzm8E992Ys9eERESl/CvP9rQDICQVAV+HkNvi4N2QkQ7UG0OI+56u257Giu8Vq0Olfq/IcjWUCHupQjwcj6tEk2M+WmJkKXw6FAz/b3jfugWXA52yKTiT92J90XXMfJsMKLQdA1bB8V5PXelgiIuWLOkGLa5w+APP72YKfeh1h0Ffg7u08bx7NX/kNYwdb01TP5iGXgh8Aj8owYCEse942j9D+5Zjn3UGHod/DL/+yjRireR3s+LxAq8mrD4+IyLVLAZAUj7PH4JM+kHYaQlrDwM9zD37yEX8u/7W0cs1nrgS93wX/2rB6MhxdB9Ouh5Q4WyfsU3tLZkFVEREpV9xcXYAZM2YQFhaGl5cXERERbNq0Kde8WVlZTJo0iQYNGuDl5UWrVq1Yvnz5VZ1TikFKPHxyNyQfhxqNYdAS8Cpa06FhGGw/drZAeQP9cunDYzLZOlzfPQMw2YIfsI1AU/AjIiK4OABatGgRo0aNYvz48WzdupVWrVoRGRlJfHy80/yvvfYa//3vf3nvvffYvXs3TzzxBH379uXPP/8s8jnlKp0/a+vwnHgIAurCQ0sdVlQvjPhz6Tw8bzNz1h7OM58JW4fkfIei3zAIHlx86b3ZQ8GPiIgALu4EHRERwY033sj7778PgNVqpU6dOjz99NOMGTMmR/7Q0FBeffVVRo4caU/r168f3t7ezJ8/v0jndEadoAsoM9U2y/OxjVA5EB5eDtVzDi0viJ92xTJ2yV8kpmbiUcmNu1qF8tWW44DzoegzB7Up2Gis7BFn2XMQqQZIROSaVZjvb5fVAGVmZrJlyxa6det2qTBubnTr1o3169c7PSYjIwMvL8dmD29vb37//fcin1OK6EIGLBpkC368AuChr4sU/KRkXGD04u08/ukWElMzuT7En++e6sS/72vFzEFtCA5w/H0HB3gVPvi59VV4/ZTt5y9v2tJFRKRCc1kn6NOnT2OxWAgKCnJIDwoKYu/evU6PiYyMZNq0adxyyy00aNCAqKgolixZgsViKfI5wRZYZWRk2N8nJycX9bYqBssF+OoR2xIX7j62Zqbg5rlnz2U4+R+HE3n+i20cSzyPyQSP3VKfUd0b41nJDFzlchLO5hq6fHHWy9+LiEiFU65Ggf3nP//h0Ucf5brrrsNkMtGgQQOGDRvGnDlzruq8U6ZMYeLEicVUymvIL1PAzewYKBgGfP8s7PkWTG4w4DOo0z7XUzibUDDY34vWdQP4eVccVgNqVfFm2v2tiKifs+9QkYeiO1tNHi69t1oKf04REblmuCwAqlGjBmazmbi4OIf0uLg4goODnR5Ts2ZNli5dSnp6OgkJCYSGhjJmzBjq169f5HMCjB07llGjRtnfJycnU6dOnVzzVxhuZsfaEsOAn16FP239rWjaFxr8I9fDc5vNOTY5neU7bQHRPW1qMeGuZvh7uRdv2fOa5FA1PyIiFZ7L+gB5eHjQtm1boqKi7GlWq5WoqCg6dOiQ57FeXl7UqlWLCxcu8NVXX3H33Xdf1Tk9PT3x9/d32ARboHB5v5lf34YNM2z7rusF9+Ve82axGkz8bneeS1lU9XHn7XtbFX/wIyIikg+XNoGNGjWKIUOG0K5dO9q3b8/06dNJTU1l2LBhAAwePJhatWoxZcoUADZu3MiJEydo3bo1J06cYMKECVitVkaPHl3gc0ohXdlvBqBhd1vTVx4KMpvzmbQsNkUnarZlEREpdS4NgPr378+pU6cYN24csbGxtG7dmuXLl9s7MR89ehQ3t0uVVOnp6bz22mv8/fff+Pr6cscdd/Dpp59SpUqVAp9TCslqhYxzl96bzDBoce75L7qq2ZxFRERKmBZDdULzAF2UlQ5LR8CuJbb3JjMYuXQuvsL6QwkM/HBDvpdY+OhNqgESEZFiUS7mAZIyLi0R5t9zKfi5/i4Yn1jguXTah1ejqk/ufXsKPJuziIhICShXw+CllJw9CvPvhdP7bO9bPwB9ZtpeF3AuneNn0kjPcj7UPHsWn/G9mxZsTh8REZFipgBIHJ3cBgvuty0g6uELrQbCnf92zJPPXDppmRd4/NMtnM+yEl7Dh/OZFmKTL000GRzgxfjeTQs2m7OIiEgJUAAklxxYAV8MgaxUCGwGD34JAbWc582l5scwDEYv3sHe2HPU8PVk4aMdqOnnWbTZnEVEREqIAiCx2fIxfP+8rZNzeBfo/6ltja9C+vC3v/l+RwyV3EwOa3mpo7OIiJQlCoAqOsOw9ef59W3b+1YDofe7UMmj0Kf6/cBp3vrRtuba+N5NuTFMHZxFRKRsUgBUkV3IhO+ege0Lbe9vGQ23vgKmwjdPHUtM46mFW7EacF/b2gy6qV4xF1ZERKT4KACqqNKT4IvB8Pdq2/w+vf4P2g4p0qnOZ1p4/NMtnE3LomXtAN7o0xxTEYIoERGR0qIA6FrnbEX3pBPw2X0Qvwvc3GHgQmjUvUinNwyDsUt2sDsmmeqVPZg1qC1e7uZiKryIiEjJUAB0rbtyRfe4XbY5fs6dtKW1eajIwQ/A3LWHWbrtJGY3E+8/0IbQKt7FUGgREZGSpQDoWnf5xIVnomHP95CRbEu7aST0mFzkU68/lMCbP+wB4NU7rtdILxERKTcUAFUEXUbDuRj4Y86ltE6joNv4Ip/yxNnzPLVgKxarQd8bajGsY9jVl1NERKSUaC2wisLtsnW5zB5XFfykZ1l44tMtJKRm0jTEn8l9W6jTs4iIlCuqAaoIDAO2f2577VYJLJm2xUzzWdE9m8VqXDaTsydf/nGcv04kUcXHnf8+1BZvD3V6FhGR8kUBUEXww4uQkWQLfsYcg/Xv57uYabblO2OY+N1uYpLSHdJNwPsD21Cnmk8JFVpERKTkKAC61q2ZCps/sr1udDt4+BR4RfflO2MYMX8rhpN9BpCSkVXsxRURESkNCoCudVYL+IXYOkE36XkpPZ8V3S1Wg4nf7XYa/ICtBmjid7vp3jRYC5uKiEi5o07Q17q2Q2zBDyZo3MNxX5fRcOtYp4dtik7M0ex1OQOISUpnU3Ri8ZVVRESklCgAutbtX277Wbsd+AYW+LD4c7kHP0XJJyIiUpYoALrW7fvR9rPJHYU6LNDPq1jziYiIlCUKgK5lGSnw9xrb60IGQO3DqxESkHtwYwJCArxoH17tKgooIiLiGgqArmWHVoElA6qGQ80mhTrU7GbirtahTvdld3ke37upOkCLiEi5pADoWnZ581chZ2o+l57FN3/aFkyt7Ok40WFwgBczB7WhR/OQYimmiIhIadMw+GuV1XKpA/Tlw98L6O2f9hGbnE696j788ExndhxPujgTtK3ZSzU/IiJSnikAulYd2wTnE8GrCtTtUKhDtxw5w6cbjgAwuW8LKntW0krvIiJyTVET2LVq3w+2n40jwVzwODfzgpWxS3ZgGNCvTW06NqxRQgUUERFxHQVA1yp7/5/CNX/9d80h9selUK2yB6/deX0JFExERMT1FABdi04fgIQD4OYODW4r8GGHTqXw3qqDAIzr1ZSqlT1KqoQiIiIupQDoWpTd/BXeGbz8C3SI1WrwypK/yLRYuaVxTe7OZQi8iIjItUAB0LWoCLM/f7nlGBujE/F2N/Nmn+aYCjlsXkREpDxRAHStST0NxzbaXl+5+Gku4s+l8+ayPQCM6t6YOtV8Sqp0IiIiZYICoGvNgZ/BsEJwS6hSp0CHTPpuN8npF2hey59hHcNKtnwiIiJlgAKga012/58CNn+t2hvH9ztiMLuZeOuellQy6yMhIiLXPn3bXUuy0uHgKtvrAgx/T824wGtf7wRgeKdwmtcKKMnSiYiIlBkKgK4l0b9CVir4hUJIq3yz//vnfZxMSqd2VW+e69aoFAooIiJSNrg8AJoxYwZhYWF4eXkRERHBpk2b8sw/ffp0mjRpgre3N3Xq1OH5558nPT3dvn/ChAmYTCaH7brrrivp2ygb7M1fPfNd/HTbsbPMW3cYgDf7tsDHQ6uiiIhIxeHSb71FixYxatQoZs2aRUREBNOnTycyMpJ9+/YRGBiYI/+CBQsYM2YMc+bM4eabb2b//v0MHToUk8nEtGnT7PmaNWvGypUr7e8rVaoAX+5W62WLn+bd/yfLYmXMV7blLvq0DqVL45qlUEAREZGyw6WRwbRp03j00UcZNmwYALNmzWLZsmXMmTOHMWPG5Mi/bt06OnbsyAMPPABAWFgYAwcOZOPGjQ75KlWqRHBwcMnfQFkSsw3OxYCHr20CxCtYrAabohOJP5fOhkMJ7I09RxUfd17v1bT0yyoiIuJiLguAMjMz2bJlC2PHjrWnubm50a1bN9avX+/0mJtvvpn58+ezadMm2rdvz99//80PP/zAQw895JDvwIEDhIaG4uXlRYcOHZgyZQp169bNtSwZGRlkZGTY3ycnJ1/l3blA9uSHDW+DSp4Ou5bvjGHid7uJSUp3SL+7VSjVfR3zioiIVAQu6wN0+vRpLBYLQUFBDulBQUHExsY6PeaBBx5g0qRJdOrUCXd3dxo0aEDXrl155ZVX7HkiIiKYN28ey5cvZ+bMmURHR9O5c2fOnTuXa1mmTJlCQECAfatTp2Dz55QpuQx/X74zhhHzt+YIfgA+WX+E5TtjSqN0IiIiZYrLO0EXxurVq5k8eTIffPABW7duZcmSJSxbtow33njDnqdnz57cd999tGzZksjISH744QfOnj3LF198ket5x44dS1JSkn07duxYadxO8TlzBOJ2gskNGt1uT7ZYDSZ+txsjj0MnfrcbizWvHCIiItcelzWB1ahRA7PZTFxcnEN6XFxcrv13Xn/9dR566CEeeeQRAFq0aEFqaiqPPfYYr776Km5uOeO5KlWq0LhxYw4ePJhrWTw9PfH0LMdNQdmdn+t2AJ9q9uRN0YlOa36yGUBMUjqbohPp0KB6CRdSRESk7HBZDZCHhwdt27YlKirKnma1WomKiqJDhw5Oj0lLS8sR5JjNZgAMw3ktRkpKCocOHSIkJKSYSl4GXT78/TLx53IPfoqST0RE5Frh0lFgo0aNYsiQIbRr14727dszffp0UlNT7aPCBg8eTK1atZgyZQoAvXv3Ztq0adxwww1ERERw8OBBXn/9dXr37m0PhF588UV69+5NvXr1OHnyJOPHj8dsNjNw4ECX3WeJSk+Cw7/bXl/R/yfQz6tApyhoPhERkWuFSwOg/v37c+rUKcaNG0dsbCytW7dm+fLl9o7RR48edajxee211zCZTLz22mucOHGCmjVr0rt3b9588017nuPHjzNw4EASEhKoWbMmnTp1YsOGDdSseY3OdXNwJVgvQI0mUL2Bw6724dUICfDKtRnMBAQHeNE+vJrT/SIiItcqk5Fb21EFlpycTEBAAElJSfj7+7u6OHlbPBx2LoaOz0H3iTl2L98ZwxPzt+ZIz54neuagNvRofg03D4qISIVRmO/vcjUKTK5gyYIDK2yvc5n9uWuTQLwq5fw1Bwd4KfgREZEKqwKsEXENO7IOMpLApwbUbuc0y8+740i/YCU0wIt/39eKUykZBPrZmr3MbnmvFyYiInKtUgBUnmXP/ty4B7iZnWb5astxAPq1rc3NDWuUVslERETKNDWBlVeGcWn4+3XOm7/iktP57cApAO5pU7u0SiYiIlLmKQAqr+L3wNkjUMkL6nd1mmXpnyewGtC2XlXCa1Qu3fKJiIiUYQqAyqt9y2w/63cFj5zBjWEYfLX1YvOXan9EREQcKAAqr7L7/1wx+3O2nSeS2R+XgkclN+5sqZFeIiIil1MAVB6di4UTW2yvG/dwmiW79uf2pkEEeLuXVslERETKBQVA5VH24qe12oJfzoVjMy9Y+WbbCcA2+ktEREQcKQAqj+zNX85Hf/2yL54zaVkE+nnSWUPfRUREclAAVN5kpsLfq22vcwmAsuf+6XtDLSqZ9SsWERG5kr4dy5tDv8CFdKhSDwKvz7E7ISWDVXvjATV/iYiI5EYBUHlzefOXKedSFt9uP8kFq0GLWgE0DvIr5cKJiIiUDwqAyrpfpsCaqbbXVsulDtBNetrSf5nikP3S3D+1SrOUIiIi5YoCoLLOzQy/vGkLdo7/AWmnwSvAthDqL286rAG2L/YcO08k4242cVdrBUAiIiK50WKoZV2X0bafv7wJh1bZXvvXhjVvwa2vXtrPpdqfW5sEUq2yR2mXVEREpNxQAFQeXB4EAcTvyhH8XLBY+fpPzf0jIiJSEGoCKy/aDL702uzhEPwA/HbwNKfOZVDVx51bmwSWcuFERETKFwVA5cWPL198YQJL5qWO0Rdlz/1zd+taeFTSr1VERCQvagIrD9ZMhd1Lba9vehK8q1xqDusymqTzWfy8Ow7Qyu8iIiIFoQCorFsz1Rbs+FSHtAQI6wTXXZwB+mIQtMxrAJkXrDQO8qV5LX8XFlZERKR8UABU1lktcPOzsO4/gAnqdbClZ/cBsloum/unNiYnkyOKiIiII3UWKetuHQuhrWyvg5uDd9VL+7qMJrrFM2w5cgY3k23tLxEREcmfAqDy4PBa28+wzjl2LblY+9O5UU0C/b1Ks1QiIiLllgKg8uDw77af9To6JFutBku22ub+uVdz/4iIiBSYAqCyLuUUnN6Hrf/PzQ67NvydwImz5/HzqkT3pkGuKZ+IiEg5pACorDtysfYnqBn4VHPYtfhi81evlqF4uZuvPFJERERyoQCorMtu/grr5JCcmnGB5TtjAbi3rTo/i4iIFIYCoLLO3gHaMQD6cWcsaZkWwmtUpk3dqk4OFBERkdwoACrLUk7BqT2213Ud+/9kL31xzw21NPePiIhIISkAKsuOXKz9CWwGlavbk4+fSWP93wkA9G2j5i8REZHCUgBUlh1x3vz19cWh7x3qV6d2VZ/SLpWIiEi5pwCoLLN3gL40/49hGCz50xYA9dPcPyIiIkWitcDKqtQEiN9te12vIxarwaboRDZGJxB9OhVvdzd6Ng92bRlFRETKKZfXAM2YMYOwsDC8vLyIiIhg06ZNeeafPn06TZo0wdvbmzp16vD888+Tnp5+Vecsk7Kbv2pez/LoLDr9axUDP9zA9JUH7Fl+O3DKRYUTEREp31waAC1atIhRo0Yxfvx4tm7dSqtWrYiMjCQ+Pt5p/gULFjBmzBjGjx/Pnj17mD17NosWLeKVV14p8jnLrIvNX0f8b2DE/K3EJDkGeeezrIyYv5XlO2NcUToREZFyzaUB0LRp03j00UcZNmwYTZs2ZdasWfj4+DBnzhyn+detW0fHjh154IEHCAsL4/bbb2fgwIEONTyFPWeZdbEG6L9HQjHyyDbxu91YrHnlEBERkSu5LADKzMxky5YtdOvW7VJh3Nzo1q0b69evd3rMzTffzJYtW+wBz99//80PP/zAHXfcUeRzAmRkZJCcnOywuVRaIsTtBOCnlAa5ZjOAmKR0NkUnllLBRERErg0u6wR9+vRpLBYLQUGOi3gGBQWxd+9ep8c88MADnD59mk6dOmEYBhcuXOCJJ56wN4EV5ZwAU6ZMYeLEiVd5R8XoYu1Psl8DEtID8s0efy493zwiIiJyics7QRfG6tWrmTx5Mh988AFbt25lyZIlLFu2jDfeeOOqzjt27FiSkpLs27Fjx4qpxEV0cfmLtJAOBcoe6OdVkqURERG55risBqhGjRqYzWbi4uIc0uPi4ggOdj68+/XXX+ehhx7ikUceAaBFixakpqby2GOP8eqrrxbpnACenp54enpe5R0Vo4sdoANb3EbIES9ik9Kd9gMyAcEBXrQPr+Zkr4iIiOTGZTVAHh4etG3blqioKHua1WolKiqKDh2c13ykpaXh5uZYZLPZDNgmCCzKOcucy/r/uIV3Ynzvpk6zZa/+Nb53U8xuWgtMRESkMFzaBDZq1Cg+/PBDPv74Y/bs2cOIESNITU1l2LBhAAwePJixY8fa8/fu3ZuZM2fy+eefEx0dzYoVK3j99dfp3bu3PRDK75xl3tH1gAE1moBvID2ahzBzUBuq+rg7ZAsO8GLmoDb0aB7imnKKiIiUYy6dCbp///6cOnWKcePGERsbS+vWrVm+fLm9E/PRo0cdanxee+01TCYTr732GidOnKBmzZr07t2bN998s8DnLPOcLH/Ro3kI59Iv8NLiHTQJ8mPCXc1oH15NNT8iIiJFZDIMQ5PIXCE5OZmAgACSkpLw9/cv3YvP6gyxO+DeOdC8nz35w1//5s0f9nBXq1DeHXhD6ZZJRESkHCjM93e5GgV2zTt/BmL/sr2u57gCfEJqJgDVfT1Ku1QiIiLXHAVAZcmRi/1/qjcCP8cmu8TUDACqV1YAJCIicrUUAJUl2QughnXKsSshxVYDVK1yGRquLyIiUk4VKQDq168f//rXv3KkT506lfvuu++qC1VhHf7N9tNZAKQmMBERkWJTpADo119/ta+/dbmePXvy66+/XnWhKqTzZyFmh+11vY45didmB0BqAhMREblqRQqAUlJS8PDI+UXs7u7u+oVEy6ujG7D1/2kI/jnn9klIsfUBqqYASERE5KoVKQBq0aIFixYtypH++eef07Sp85mLJR/ZzV9Oan/SsyykZloAqO6rPkAiIiJXq0gTIb7++uvcc889HDp0iH/84x8AREVFsXDhQr788stiLWCFYe8A3TnHruzmL3ezCX8vl85dKSIick0o0rdp7969Wbp0KZMnT2bx4sV4e3vTsmVLVq5cSZcuXYq7jNe+9CSI2W57HZazBih7BFhVHw9MJs3+LCIicrWKXJ1w5513cueddxZnWSquoxvAsEK1+uAfmmN3QvYcQGr+EhERKRZF6gO0efNmNm7cmCN948aN/PHHH1ddqArHvv5XzuHvoBFgIiIixa1IAdDIkSM5duxYjvQTJ04wcuTIqy5UhZMdANVzHgBdmgRRAZCIiEhxKFIAtHv3btq0aZMj/YYbbmD37t1XXagKJT0ZYrbZXjvp/wOaBFFERKS4FSkA8vT0JC4uLkd6TEwMlSpplFKhHNto6/9TNRwCajvNonXAREREileRAqDbb7+dsWPHkpSUZE87e/Ysr7zyCt27dy+2wlUI9uUvnNf+gNYBExERKW5Fqq7597//zS233EK9evW44YYbANi2bRtBQUF8+umnxVrAa97h3Of/yaYmMBERkeJVpACoVq1a7Nixg88++4zt27fj7e3NsGHDGDhwIO7u7sVdxmtXxjk4+afttZMZoLNpFJiIiEjxKnKHncqVK9OpUyfq1q1LZqbtC/rHH38E4K677iqe0l3rjm4EwwJV6kGVOrlm0zpgIiIixatIAdDff/9N3759+euvvzCZTBiG4TBDscViKbYCXtOOZM//k3vzl9YBExERKX5F6gT97LPPEh4eTnx8PD4+PuzcuZM1a9bQrl07Vq9eXcxFvIblMwEiaB0wERGRklCkb9T169ezatUqatSogZubG2azmU6dOjFlyhSeeeYZ/vzzz+Iu57UnIwVObLW9LsAIMK0DJiIiUnyKVANksVjw8/MDoEaNGpw8eRKAevXqsW/fvuIr3bXsWHb/n7q2LRdaB0xERKT4FakGqHnz5mzfvp3w8HAiIiKYOnUqHh4e/O9//6N+/frFXcZrUz7LX2TTCDAREZHiV6QA6LXXXiM1NRWASZMm0atXLzp37kz16tVZtGhRsRbwmlWA/j+gdcBERERKQpECoMjISPvrhg0bsnfvXhITE6latar6qRREZiqczO7/k08ApEkQRUREil2xDSuqVq1acZ3q2ndsI1gvQEAdqFovz6xaB0xERKT4FakTtFwl+/IXedf+wKU+QFoHTEREpPgoAHKFAvb/ATidoiYwERGR4qYAqLRlpsGJLbbXeaz/lU2jwERERIqfAqDSdnwTWLPAvzZUDcs3+6UmMAVAIiIixUUBUGn4ZQqsmWp7bW/+6ggmky39lylOD0vPspCScQHQRIgiIiLFSQFQaXAzwy9v2oKdy/v/rJlqS3czOz1M64CJiIiUDH2rloYuo20/f3kTTBeDnfjdsGEm3Prqpf1XyA6AtA6YiIhI8VINUGnpMhpaP2Bb/wvyDX4ATqdoHTAREZGSUCYCoBkzZhAWFoaXlxcRERFs2rQp17xdu3bFZDLl2O688057nqFDh+bY36NHj9K4lbwF1Ln02uyRZ/ADGgEmIiJSUlweAC1atIhRo0Yxfvx4tm7dSqtWrYiMjCQ+Pt5p/iVLlhATE2Pfdu7cidls5r777nPI16NHD4d8CxcuLI3bydu5ONtPt0pgybzUMToXGgEmIiJSMlweAE2bNo1HH32UYcOG0bRpU2bNmoWPjw9z5sxxmr9atWoEBwfbtxUrVuDj45MjAPL09HTIV7Vq1dK4ndytmQpb59mavcYl2H5md4zOhSZBFBERKRkuDYAyMzPZsmUL3bp1s6e5ubnRrVs31q9fX6BzzJ49mwEDBlC5cmWH9NWrVxMYGEiTJk0YMWIECQkJuZ4jIyOD5ORkh61YZY/2urzPT5fR+QZBWgdMRESkZLh0FNjp06exWCwEBQU5pAcFBbF37958j9+0aRM7d+5k9uzZDuk9evTgnnvuITw8nEOHDvHKK6/Qs2dP1q9fj9mcc8j5lClTmDhx4tXdTF6sFucdnrPfWy1OD9M6YCIiIiWjXA+Dnz17Ni1atKB9+/YO6QMGDLC/btGiBS1btqRBgwasXr2a2267Lcd5xo4dy6hRo+zvk5OTqVOnTo58RXbr2Nz35TkKTE1gIiIiJcGlTWA1atTAbDYTFxfnkB4XF0dwcHCex6ampvL5558zfPjwfK9Tv359atSowcGDB53u9/T0xN/f32ErCzQKTEREpGS4NADy8PCgbdu2REVF2dOsVitRUVF06NAhz2O//PJLMjIyGDRoUL7XOX78OAkJCYSEhFx1mUuTRoGJiIiUDJePAhs1ahQffvghH3/8MXv27GHEiBGkpqYybNgwAAYPHszYsTmbkGbPnk2fPn2oXr26Q3pKSgovvfQSGzZs4PDhw0RFRXH33XfTsGFDIiMjS+WeioPDOmDqAyQiIlKsXN4HqH///pw6dYpx48YRGxtL69atWb58ub1j9NGjR3Fzc4zT9u3bx++//87PP/+c43xms5kdO3bw8ccfc/bsWUJDQ7n99tt544038PQsP4FEdu1PJTcT/t4u/zWJiIhcU0yGYRiuLkRZk5ycTEBAAElJSS7rD7TzRBK93vudQD9PNr3aLf8DREREKrjCfH+7vAlMnMteB0z9f0RERIqfAqAyKrsJrIYWQhURESl2CoDKKI0AExERKTkKgMqo7EkQFQCJiIgUPwVAZVT2OmA1NAu0iIhIsVMAVEZpHTAREZGSowCojFITmIiISMlRAFRGXRoFpgBIRESkuCkAKqM0CkxERKTkKAAqg7QOmIiISMlSAFQGaR0wERGRkqUAqAy6vPnLZDK5uDQiIiLXHgVAZVCC+v+IiIiUKAVAZVBCSvYkiOr/IyIiUhIUAJVBGgEmIiJSshQAlUFqAhMRESlZCoDKoEtNYAqARERESoICoDJI64CJiIiULAVAZZCawEREREqWAqAyKCFF64CJiIiUJAVAZZBGgYmIiJQsBUBljNYBExERKXkKgMoYrQMmIiJS8hQAlTFaB0xERKTkKQAqYzQCTEREpOQpACpjtA6YiIhIyVMAVMZoBJiIiEjJUwBUxqgJTEREpOQpACpjtA6YiIhIyVMAVMZoHTAREZGSpwCojFETmIiISMlTAFTGaB0wERGRkqcAqIzRKDAREZGSpwCoDMm4oHXARERESkOZCIBmzJhBWFgYXl5eREREsGnTplzzdu3aFZPJlGO788477XkMw2DcuHGEhITg7e1Nt27dOHDgQGncylXROmAiIiKlw+UB0KJFixg1ahTjx49n69attGrVisjISOLj453mX7JkCTExMfZt586dmM1m7rvvPnueqVOn8u677zJr1iw2btxI5cqViYyMJD09vbRuq0iy+/9oHTAREZGS5fIAaNq0aTz66KMMGzaMpk2bMmvWLHx8fJgzZ47T/NWqVSM4ONi+rVixAh8fH3sAZBgG06dP57XXXuPuu++mZcuWfPLJJ5w8eZKlS5eW4p0VnkaAiYiIlA6XBkCZmZls2bKFbt262dPc3Nzo1q0b69evL9A5Zs+ezYABA6hcuTIA0dHRxMbGOpwzICCAiIiIAp/TVRJTtQ6YiIhIaXBpR5PTp09jsVgICgpySA8KCmLv3r35Hr9p0yZ27tzJ7Nmz7WmxsbH2c1x5zux9V8rIyCAjI8P+Pjk5ucD3UJwubwITERGRkuPyJrCrMXv2bFq0aEH79u2v6jxTpkwhICDAvtWpU6eYSlg4agITEREpHS4NgGrUqIHZbCYuLs4hPS4ujuDg4DyPTU1N5fPPP2f48OEO6dnHFeacY8eOJSkpyb4dO3assLdSLBI1CaKIiEipcGkA5OHhQdu2bYmKirKnWa1WoqKi6NChQ57Hfvnll2RkZDBo0CCH9PDwcIKDgx3OmZyczMaNG3M9p6enJ/7+/g6bKyRc7AOkdcBERERKlssnmxk1ahRDhgyhXbt2tG/fnunTp5OamsqwYcMAGDx4MLVq1WLKlCkOx82ePZs+ffpQvXp1h3STycRzzz3HP//5Txo1akR4eDivv/46oaGh9OnTp7Ruq0jUBCYiIlI6XB4A9e/fn1OnTjFu3DhiY2Np3bo1y5cvt3diPnr0KG5ujhVV+/bt4/fff+fnn392es7Ro0eTmprKY489xtmzZ+nUqRPLly/Hy8urxO/namRPhFhdTWAiIiIlymQYhuHqQpQ1ycnJBAQEkJSUVKrNYc3H/0RKxgVWvdCF+jV9S+26IiIi14LCfH+X61Fg1xKtAyYiIlJ6FACVEVoHTEREpPQoACojtA6YiIhI6VEAVEZoBJiIiEjpUQBURmSvA6YRYCIiIiVPAVAZkd0Epg7QIiIiJU8BUBmhJjAREZHSowCojEi01wApABIRESlpCoDKiAR7HyA1gYmIiJQ0BUBlhJrARERESo8CoDJC64CJiIiUHgVAZUSC+gCJiIiUGgVAZYDWARMRESldCoDKAK0DJiIiUroUAJUBWgdMRESkdCkAKgM0AkxERKR0KQAqA7QOmIiISOlSAFQGaB0wERGR0qUAqAxQE5iIiEjpUgBUBmgdMBERkdKlAKgMSLDPAq0mMBERkdKgAKgMyF4IVU1gIiIipUMBUBmgdcBERERKlwKgMkB9gEREREqXAiAXy7hg4ZzWARMRESlVCoBcTOuAiYiIlD4FQC6mdcBERERKnwIgF9MkiCIiIqVPAZCLaR0wERGR0qcAyMW0DpiIiEjpUwDkYmoCExERKX0KgFxMcwCJiIiUPgVALqZ1wEREREqfAiAX0zpgIiIipc/lAdCMGTMICwvDy8uLiIgINm3alGf+s2fPMnLkSEJCQvD09KRx48b88MMP9v0TJkzAZDI5bNddd11J30aRaR0wERGR0ufSqYcXLVrEqFGjmDVrFhEREUyfPp3IyEj27dtHYGBgjvyZmZl0796dwMBAFi9eTK1atThy5AhVqlRxyNesWTNWrlxpf1+pUtmdYVl9gEREREqfSyODadOm8eijjzJs2DAAZs2axbJly5gzZw5jxozJkX/OnDkkJiaybt063N3dAQgLC8uRr1KlSgQHB5do2YuD1gETERFxDZc1gWVmZrJlyxa6det2qTBubnTr1o3169c7Pebbb7+lQ4cOjBw5kqCgIJo3b87kyZOxWCwO+Q4cOEBoaCj169fnwQcf5OjRoyV6L0WldcBERERcw2XfuqdPn8ZisRAUFOSQHhQUxN69e50e8/fff7Nq1SoefPBBfvjhBw4ePMiTTz5JVlYW48ePByAiIoJ58+bRpEkTYmJimDhxIp07d2bnzp34+fk5PW9GRgYZGRn298nJycV0l3nTOmAiIiKuUa6qHaxWK4GBgfzvf//DbDbTtm1bTpw4wdtvv20PgHr27GnP37JlSyIiIqhXrx5ffPEFw4cPd3reKVOmMHHixFK5h8tpEkQRERHXcFkTWI0aNTCbzcTFxTmkx8XF5dp/JyQkhMaNG2M2m+1p119/PbGxsWRmZjo9pkqVKjRu3JiDBw/mWpaxY8eSlJRk344dO1aEOyo8rQMmIiLiGi4LgDw8PGjbti1RUVH2NKvVSlRUFB06dHB6TMeOHTl48CBWq9Wetn//fkJCQvDwcB5EpKSkcOjQIUJCQnIti6enJ/7+/g5badA6YCIiIq7h0nmARo0axYcffsjHH3/Mnj17GDFiBKmpqfZRYYMHD2bs2LH2/CNGjCAxMZFnn32W/fv3s2zZMiZPnszIkSPteV588UXWrFnD4cOHWbduHX379sVsNjNw4MBSv7/8JKoJTERExCVc2geof//+nDp1inHjxhEbG0vr1q1Zvny5vWP00aNHcXO7FKPVqVOHn376ieeff56WLVtSq1Ytnn32WV5++WV7nuPHjzNw4EASEhKoWbMmnTp1YsOGDdSsWbPU7y8/CZoDSERExCVMhmEYri5EWZOcnExAQABJSUkl2hz2yMd/sHJPHG/2bc6DEfVK7DoiIiIVQWG+v12+FEZFZu8ErT5AIiIipUoBkAslaB0wERERl1AA5EKJKeoELSIi4goKgFzk8nXAaqgJTEREpFQpAHIRrQMmIiLiOgqAXCR7CHxVrQMmIiJS6hQAuUh2DZDmABIRESl9antxkQStAyYi1zCLxUJWVpariyHXGHd3d4f1QK+GAiAXSbCPAFMHaBG5dhiGQWxsLGfPnnV1UeQaVaVKFYKDg6+6+4gCIBdRE5iIXIuyg5/AwEB8fHzUx1GKjWEYpKWlER8fD5DnIucFoQDIRbQOmIhcaywWiz34qV69uquLI9cgb29vAOLj4wkMDLyq5jB1gnaR7Fmgq6kPkIhcI7L7/Pj4+Li4JHIty/58XW0fMwVALqJ1wETkWqVmLylJxfX5UgDkIloHTERExHUUALmI1gETEbm2hYWFMX36dFcXQ3KhAMgFtA6YiEjeLFaD9YcS+GbbCdYfSsBiNUrsWiaTKc9twoQJRTrv5s2beeyxx4q3sEBkZCRms5nNmzcX+7krEo0CcwGtAyYikrvlO2OY+N1uYpLS7WkhAV6M792UHs2vbuizMzExMfbXixYtYty4cezbt8+e5uvra39tGAYWi4VKlfL/t7tmzZrFW1Dg6NGjrFu3jqeeeoo5c+Zw4403Fvs1CiMrKwt3d3eXlqGoVAPkAloHTETEueU7Yxgxf6tD8AMQm5TOiPlbWb4zJpcjiy44ONi+BQQEYDKZ7O/37t2Ln58fP/74I23btsXT05Pff/+dQ4cOcffddxMUFISvry833ngjK1eudDjvlU1gJpOJjz76iL59++Lj40OjRo349ttvC1XWuXPn0qtXL0aMGMHChQs5f/68w/6zZ8/y+OOPExQUhJeXF82bN+f777+371+7di1du3bFx8eHqlWrEhkZyZkzZ5yWF6B169YONWAmk4mZM2dy1113UblyZd58800sFgvDhw8nPDwcb29vmjRpwn/+858cZZ8zZw7NmjXD09OTkJAQnnrqKQAefvhhevXq5ZA3KyuLwMBAZs+eXajnUxgKgFxAkyCKSEVhGAZpmRcKtJ1Lz2L8t7tw1tiVnTbh292cS8/K91yGUbxNZmPGjOGtt95iz549tGzZkpSUFO644w6ioqL4888/6dGjB7179+bo0aN5nmfixIncf//97NixgzvuuIMHH3yQxMTEApXBMAzmzp3LoEGDuO6662jYsCGLFy+277darfTs2ZO1a9cyf/58du/ezVtvvWWfK2fbtm3cdtttNG3alPXr1/P777/Tu3dvLBZLoZ7FhAkT6Nu3L3/99RcPP/wwVquV2rVr8+WXX7J7927GjRvHK6+8whdffGE/ZubMmYwcOZLHHnuMv/76i2+//ZaGDRsC8Mgjj7B8+XKHmrjvv/+etLQ0+vfvX6iyFYbaX1xA64CJSEVxPstC03E/Fcu5DCA2OZ0WE37ON+/uSZH4eBTfV9ykSZPo3r27/X21atVo1aqV/f0bb7zB119/zbfffmuv2XBm6NChDBw4EIDJkyfz7rvvsmnTJnr06JFvGVauXElaWhqRkZEADBo0iNmzZ/PQQw/Z92/atIk9e/bQuHFjAOrXr28/furUqbRr144PPvjAntasWbOC3L6DBx54gGHDhjmkTZw40f46PDyc9evX88UXX3D//fcD8M9//pMXXniBZ5991p4vu/nu5ptvpkmTJnz66aeMHj0asNV03XfffQ7Nj8VNNUAuoHXARETKl3bt2jm8T0lJ4cUXX+T666+nSpUq+Pr6smfPnnxrgFq2bGl/XblyZfz9/e1LO+Rnzpw59O/f397/aODAgaxdu5ZDhw4Bthqe2rVr24OfK2XXAF2tK58FwIwZM2jbti01a9bE19eX//3vf/ZnER8fz8mTJ/O89iOPPMLcuXMBiIuL48cff+Thhx++6rLmRTVALqAmMBGpKLzdzeyeFFmgvJuiExk6N/+RTfOG3Uj78Gr5Xrc4Va5c2eH9iy++yIoVK/j3v/9Nw4YN8fb25t577yUzMzPP81zZYdhkMmG1WvO9fmJiIl9//TVZWVnMnDnTnm6xWJgzZw5vvvmmfZmI3OS3383NLUfTobPZlq98Fp9//jkvvvgi77zzDh06dMDPz4+3336bjRs3Fui6AIMHD2bMmDGsX7+edevWER4eTufOnfM97mooAHIBBUAiUlGYTKYCN0V1blSTkAAvYpPSnfYDMgHBAV50blQTs5trB5CsXbuWoUOH0rdvX8BWI3T48OESu95nn31G7dq1Wbp0qUP6zz//zDvvvMOkSZNo2bIlx48fZ//+/U5rgVq2bElUVJRDc9Xlatas6dAPJzk5mejo6HzLtnbtWm6++WaefPJJe1p2rRSAn58fYWFhREVFceuttzo9R/Xq1enTpw9z585l/fr1OZrYSoKawFzgdIrWARMRuZLZzcT43k0BW7Bzuez343s3dXnwA9CoUSOWLFnCtm3b2L59Ow888ECBanKKavbs2dx77700b97cYRs+fDinT59m+fLldOnShVtuuYV+/fqxYsUKoqOj+fHHH1m+fDkAY8eOZfPmzTz55JPs2LGDvXv3MnPmTE6fPg3AP/7xDz799FN+++03/vrrL4YMGVKgxUYbNWrEH3/8wU8//cT+/ft5/fXXc8xRNGHCBN555x3effddDhw4wNatW3nvvfcc8jzyyCN8/PHH7NmzhyFDhhTTk8udAiAX0DpgIiLO9WgewsxBbQgO8HJIDw7wYuagNiUyD1BRTJs2japVq3LzzTfTu3dvIiMjadOmTYlca8uWLWzfvp1+/frl2BcQEMBtt91mHy7+1VdfceONNzJw4ECaNm3K6NGj7aO8GjduzM8//8z27dtp3749HTp04JtvvrH3KRo7dixdunShV69e3HnnnfTp04cGDRrkW77HH3+ce+65h/79+xMREUFCQoJDbRDAkCFDmD59Oh988AHNmjWjV69eHDhwwCFPt27dCAkJITIyktDQ0CI9q8IwGcU9VvAakJycTEBAAElJSfj7+xf7+bu+/QuHE9L48okO3BiWdzu2iEh5kZ6eTnR0NOHh4Xh5eeV/QB4sVoNN0YnEn0sn0M+L9uHVykTNj5SclJQUatWqxdy5c7nnnntyzZfX56ww39/qA+QCCVoHTEQkT2Y3Ex0aVHd1MaQUWK1WTp8+zTvvvEOVKlW46667SuW6agIrZVoHTERErvTEE0/g6+vrdHviiSdcXbwSdfToUYKCgliwYAFz5swp0DIjxUE1QKXsTKptSKHWARMRkWyTJk3ixRdfdLqvJLpilCVhYWHFPnN3QegbuJSdTrF1gNY6YCIiki0wMJDAwEBXF6NCURNYKdMcQCIiIq6nAKiU2QMgzQEkIiLiMgqASll2E5jWARMREXEdBUClTE1gIiIirqcAqJQpABIREXE9lwdAM2bMICwsDC8vLyIiIti0aVOe+c+ePcvIkSMJCQnB09OTxo0b88MPP1zVOUuT1gETEbn2dO3aleeee87VxZBCcGkAtGjRIkaNGsX48ePZunUrrVq1IjIykvj4eKf5MzMz6d69O4cPH2bx4sXs27ePDz/8kFq1ahX5nKVN64CJiJQtvXv3pkePHk73/fbbb5hMJnbs2FHs1124cCFms5mRI0cW+7klfy4NgKZNm8ajjz7KsGHDaNq0KbNmzcLHx4c5c+Y4zT9nzhwSExNZunQpHTt2JCwsjC5dutCqVasin7O0aRSYiEgefpkCa6Y637dmqm1/MRs+fDgrVqzg+PHjOfbNnTuXdu3a0bJly2K/7uzZsxk9ejQLFy4kPT292M9fGJmZmS69viu4LADKzMxky5YtdOvW7VJh3Nzo1q0b69evd3rMt99+S4cOHRg5ciRBQUE0b96cyZMn21e6Lco5ATIyMkhOTnbYSorWARMRyYObGX55M2cQtGaqLd3NXOyX7NWrFzVr1mTevHkO6SkpKXz55Zf06dOHgQMHUqtWLXx8fGjRogULFy68qmtGR0ezbt06xowZQ+PGjVmyZEmOPHPmzKFZs2Z4enoSEhLCU089Zd939uxZHn/8cYKCgvDy8qJ58+Z8//33AEyYMIHWrVs7nGv69OmEhYXZ3w8dOpQ+ffrw5ptvEhoaSpMmTQD49NNPadeuHX5+fgQHB/PAAw/kaEHZtWsXvXr1wt/fHz8/Pzp37syhQ4f49ddfcXd3JzY21iH/c889R+fOna/mcZUIlwVAp0+fxmKxEBQU5JAeFBSU4+Fl+/vvv1m8eDEWi4UffviB119/nXfeeYd//vOfRT4nwJQpUwgICLBvderUucq7c07rgIlIhWMYkJla8K3DSLjlJVuws+qftrRV/7S9v+Ul2/6CnKcQSytUqlSJwYMHM2/ePIclGb788kssFguDBg2ibdu2LFu2jJ07d/LYY4/x0EMPXVX/0rlz53LnnXcSEBDAoEGDmD17tsP+mTNnMnLkSB577DH++usvvv32Wxo2bAjYFg/t2bMna9euZf78+ezevZu33noLs7lwwWFUVBT79u1jxYoV9uApKyuLN954g+3bt7N06VIOHz7M0KFD7cecOHGCW265BU9PT1atWsWWLVt4+OGHuXDhArfccgv169fn008/tefPysris88+4+GHHy7ikyo55WopDKvVSmBgIP/73/8wm820bduWEydO8PbbbzN+/Pgin3fs2LGMGjXK/j45OblEgqDT52y1P24m2B2TRPvw6pjdtByGiFzDstJgcmjRjv31bduW2/u8vHISPCoX+FIPP/wwb7/9NmvWrKFr166ALUjp168f9erVc1in6+mnn+ann37iiy++oH379gW+Rjar1cq8efN47733ABgwYAAvvPAC0dHRhIeHA/DPf/6TF154gWeffdZ+3I033gjAypUr2bRpE3v27KFx48YA1K9fv9DlqFy5Mh999BEeHpdaJC4PVOrXr8+7777LjTfeSEpKCr6+vsyYMYOAgAA+//xz3N3dAexlAFtz4ty5c3nppZcA+O6770hPT+f+++8vdPlKmstqgGrUqIHZbCYuLs4hPS4ujuDgYKfHhISE0LhxY4co9/rrryc2NpbMzMwinRPA09MTf39/h624Ld8Zw90zfgfAasDADzfS6V+rWL4zptivJSIihXPddddx88032/uLHjx4kN9++43hw4djsVh44403aNGiBdWqVcPX15effvqJo0ePFulaK1asIDU1lTvuuAOwfR92797dfu34+HhOnjzJbbfd5vT4bdu2Ubt2bYfAoyhatGjhEPwAbNmyhd69e1O3bl38/Pzo0qULgP1et23bRufOne3Bz5WGDh3KwYMH2bBhAwDz5s3j/vvvp3LlggejpcVlNUAeHh60bduWqKgo+vTpA9ii4qioKId2zst17NiRBQsWYLVacXOzxW779+8nJCTE/kss7DlLw/KdMYyYv5UrK2Rjk9IZMX8rMwe1oUfzEJeUTUSkRLn72GpjCuv3/7PV9pg9wJJpa/7q9HzhrltIw4cP5+mnn2bGjBnMnTuXBg0a0KVLF/71r3/xn//8h+nTp9OiRQsqV67Mc889V+SOw7NnzyYxMRFvb297mtVqZceOHUycONEh3Zn89ru5ueVYXT0rKytHviuDktTUVCIjI4mMjOSzzz6jZs2aHD16lMjISPu95nftwMBAevfuzdy5cwkPD+fHH39k9erVeR7jKi4dBTZq1Cg+/PBDPv74Y/bs2cOIESNITU1l2LBhAAwePJixY8fa848YMYLExESeffZZ9u/fz7Jly5g8ebLDEML8zlnaLFaDid/tzhH8APa0id/txmIteHu1iEi5YTLZmqIKs62fYQt+bn0VXj9l+/nr27b0gp7DVPjuBffffz9ubm4sWLCATz75hIcffhiTycTatWu5++67GTRoEK1ataJ+/frs37+/SI8jISGBb775hs8//5xt27bZtz///JMzZ87w888/4+fnR1hYGFFRUU7P0bJlS44fP55rGWrWrElsbKxDELRt27Z8y7Z3714SEhJ466236Ny5M9ddd12ODtAtW7bkt99+cxpQZXvkkUdYtGgR//vf/2jQoAEdO3bM99qu4NI+QP379+fUqVOMGzeO2NhYWrduzfLly+2dmI8ePWqv6QGoU6cOP/30E88//zwtW7akVq1aPPvss7z88ssFPmdp2xSdSExS7sMbDSAmKZ1N0Yl0aFC99AomIlIWZY/2uvVV6DLalpb985c3Hd8XM19fX/r378/YsWNJTk62d/5t1KgRixcvZt26dVStWpVp06YRFxdH06ZNC32NTz/9lOrVq3P//fdjuiJIu+OOO5g9ezY9evRgwoQJPPHEEwQGBtKzZ0/OnTvH2rVrefrpp+nSpQu33HIL/fr1Y9q0aTRs2JC9e/diMpno0aMHXbt25dSpU0ydOpV7772X5cuX8+OPP+bbvaNu3bp4eHjw3nvv8cQTT7Bz507eeOMNhzxPPfUU7733HgMGDGDs2LEEBASwYcMG2rdvbx9JFhkZib+/P//85z+ZNGlSoZ9RaXH5TNBPPfUUR44cISMjg40bNxIREWHft3r16hzDEjt06MCGDRtIT0/n0KFDvPLKKzl6vud1ztIWf65gczsUNJ+IyDXNanEMfrJ1GW1Lt1pK9PLDhw/nzJkzREZGEhpq67z92muv0aZNGyIjI+natSvBwcH2bhaFNWfOHPr27Zsj+AHo168f3377LadPn2bIkCFMnz6dDz74gGbNmtGrVy8OHDhgz/vVV19x4403MnDgQJo2bcro0aPtU8Jcf/31fPDBB8yYMYNWrVqxadMmh07cucmeCuDLL7+kadOmvPXWW/z73/92yFO9enVWrVpFSkoKXbp0oW3btnz44YcOfYLc3NwYOnQoFouFwYMHF+k5lQaTcWVDoZCcnExAQABJSUlX3SF6/aEEBn64Id98Cx+9STVAIlKupaen20cyeXl5ubo44kLDhw/n1KlTfPvtt8V+7rw+Z4X5/i5Xw+DLo/bh1QgJ8CI2Kd1pPyATEBzgRfvwaqVdNBERkWKVlJTEX3/9xYIFC0ok+ClOLm8Cu9aZ3UyM721rJ76ywjP7/fjeTTUfkIjINeC3337D19c31+1ad/fdd3P77bfzxBNP0L17d1cXJ0+qASoFPZqHMHNQGyZ+t9uhQ3RwgBfjezfVEHgRkWtEu3btCjTi6lpVVoe8O6MAqJT0aB5C96bBbIpOJP5cOoF+tmYv1fyIiFw7vL297UtWSNmmAKgUmd1M6ugsIiJSBqgPkIiIFCsNLpaSVFyfLwVAIiJSLLLngklLS3NxSeRalv35ym09soJSE5iIiBQLs9lMlSpV7Msn+Pj4OJ3wT6QoDMMgLS2N+Ph4qlSpkmMS5MJSACQiIsUmODgYIMcaUiLFpUqVKvbP2dVQACQiIsXGZDIREhJCYGBgngtmihSFu7v7Vdf8ZFMAJCIixc5sNhfbF5VISVAnaBEREalwFACJiIhIhaMASERERCoc9QFyInuSpeTkZBeXRERERAoq+3u7IJMlKgBy4ty5cwDUqVPHxSURERGRwjp37hwBAQF55jEZmrM8B6vVysmTJ/Hz88sxiVdycjJ16tTh2LFj+Pv7u6iE5Y+eW9HouRWenlnR6LkVjZ5b4ZXkMzMMg3PnzhEaGoqbW969fFQD5ISbmxu1a9fOM4+/v78+7EWg51Y0em6Fp2dWNHpuRaPnVngl9czyq/nJpk7QIiIiUuEoABIREZEKRwFQIXl6ejJ+/Hg8PT1dXZRyRc+taPTcCk/PrGj03IpGz63wysozUydoERERqXBUAyQiIiIVjgIgERERqXAUAImIiEiFowBIREREKhwFQIU0Y8YMwsLC8PLyIiIigk2bNrm6SGXWhAkTMJlMDtt1113n6mKVOb/++iu9e/cmNDQUk8nE0qVLHfYbhsG4ceMICQnB29ubbt26ceDAAdcUtgzJ77kNHTo0x+evR48erilsGTFlyhRuvPFG/Pz8CAwMpE+fPuzbt88hT3p6OiNHjqR69er4+vrSr18/4uLiXFTisqEgz61r1645Pm9PPPGEi0pcNsycOZOWLVvaJzzs0KEDP/74o32/qz9rCoAKYdGiRYwaNYrx48ezdetWWrVqRWRkJPHx8a4uWpnVrFkzYmJi7Nvvv//u6iKVOampqbRq1YoZM2Y43T916lTeffddZs2axcaNG6lcuTKRkZGkp6eXcknLlvyeG0CPHj0cPn8LFy4sxRKWPWvWrGHkyJFs2LCBFStWkJWVxe23305qaqo9z/PPP893333Hl19+yZo1azh58iT33HOPC0vtegV5bgCPPvqow+dt6tSpLipx2VC7dm3eeusttmzZwh9//ME//vEP7r77bnbt2gWUgc+aIQXWvn17Y+TIkfb3FovFCA0NNaZMmeLCUpVd48ePN1q1auXqYpQrgPH111/b31utViM4ONh4++237Wlnz541PD09jYULF7qghGXTlc/NMAxjyJAhxt133+2S8pQX8fHxBmCsWbPGMAzbZ8vd3d348ssv7Xn27NljAMb69etdVcwy58rnZhiG0aVLF+PZZ591XaHKiapVqxofffRRmfisqQaogDIzM9myZQvdunWzp7m5udGtWzfWr1/vwpKVbQcOHCA0NJT69evz4IMPcvToUVcXqVyJjo4mNjbW4XMXEBBARESEPncFsHr1agIDA2nSpAkjRowgISHB1UUqU5KSkgCoVq0aAFu2bCErK8vh83bddddRt25dfd4uc+Vzy/bZZ59Ro0YNmjdvztixY0lLS3NF8coki8XC559/TmpqKh06dCgTnzUthlpAp0+fxmKxEBQU5JAeFBTE3r17XVSqsi0iIoJ58+bRpEkTYmJimDhxIp07d2bnzp34+fm5unjlQmxsLIDTz132PnGuR48e3HPPPYSHh3Po0CFeeeUVevbsyfr16zGbza4unstZrVaee+45OnbsSPPmzQHb583Dw4MqVao45NXn7RJnzw3ggQceoF69eoSGhrJjxw5efvll9u3bx5IlS1xYWtf766+/6NChA+np6fj6+vL111/TtGlTtm3b5vLPmgIgKTE9e/a0v27ZsiURERHUq1ePL774guHDh7uwZFIRDBgwwP66RYsWtGzZkgYNGrB69Wpuu+02F5asbBg5ciQ7d+5Uv7xCyu25PfbYY/bXLVq0ICQkhNtuu41Dhw7RoEGD0i5mmdGkSRO2bdtGUlISixcvZsiQIaxZs8bVxQLUCbrAatSogdlsztFDPS4ujuDgYBeVqnypUqUKjRs35uDBg64uSrmR/dnS5+7q1a9fnxo1aujzBzz11FN8//33/PLLL9SuXdueHhwcTGZmJmfPnnXIr8+bTW7PzZmIiAiACv958/DwoGHDhrRt25YpU6bQqlUr/vOf/5SJz5oCoALy8PCgbdu2REVF2dOsVitRUVF06NDBhSUrP1JSUjh06BAhISGuLkq5ER4eTnBwsMPnLjk5mY0bN+pzV0jHjx8nISGhQn/+DMPgqaee4uuvv2bVqlWEh4c77G/bti3u7u4On7d9+/Zx9OjRCv15y++5ObNt2zaACv15c8ZqtZKRkVE2Pmul0tX6GvH5558bnp6exrx584zdu3cbjz32mFGlShUjNjbW1UUrk1544QVj9erVRnR0tLF27VqjW7duRo0aNYz4+HhXF61MOXfunPHnn38af/75pwEY06ZNM/7880/jyJEjhmEYxltvvWVUqVLF+Oabb4wdO3YYd999txEeHm6cP3/exSV3rbye27lz54wXX3zRWL9+vREdHW2sXLnSaNOmjdGoUSMjPT3d1UV3mREjRhgBAQHG6tWrjZiYGPuWlpZmz/PEE08YdevWNVatWmX88ccfRocOHYwOHTq4sNSul99zO3jwoDFp0iTjjz/+MKKjo41vvvnGqF+/vnHLLbe4uOSuNWbMGGPNmjVGdHS0sWPHDmPMmDGGyWQyfv75Z8MwXP9ZUwBUSO+9955Rt25dw8PDw2jfvr2xYcMGVxepzOrfv78REhJieHh4GLVq1TL69+9vHDx40NXFKnN++eUXA8ixDRkyxDAM21D4119/3QgKCjI8PT2N2267zdi3b59rC10G5PXc0tLSjNtvv92oWbOm4e7ubtSrV8949NFHK/x/Vpw9L8CYO3euPc/58+eNJ5980qhatarh4+Nj9O3b14iJiXFdocuA/J7b0aNHjVtuucWoVq2a4enpaTRs2NB46aWXjKSkJNcW3MUefvhho169eoaHh4dRs2ZN47bbbrMHP4bh+s+ayTAMo3TqmkRERETKBvUBEhERkQpHAZCIiIhUOAqAREREpMJRACQiIiIVjgIgERERqXAUAImIiEiFowBIREREKhwFQCIiBbB69WpMJlOOtYtEpHxSACQiIiIVjgIgERERqXAUAIlIuWC1WpkyZQrh4eF4e3vTqlUrFi9eDFxqnlq2bBktW7bEy8uLm266iZ07dzqc46uvvqJZs2Z4enoSFhbGO++847A/IyODl19+mTp16uDp6UnDhg2ZPXu2Q54tW7bQrl07fHx8uPnmm9m3b1/J3riIlAgFQCJSLkyZMoVPPvmEWbNmsWvXLp5//nkGDRrEmjVr7Hleeukl3nnnHTZv3kzNmjXp3bs3WVlZgC1wuf/++xkwYAB//fUXEyZM4PXXX2fevHn24wcPHszChQt599132bNnD//973/x9fV1KMerr77KO++8wx9//EGlSpV4+OGHS+X+RaR4aTFUESnzMjIyqFatGitXrqRDhw729EceeYS0tDQee+wxbr31Vj7//HP69+8PQGJiIrVr12bevHncf//9PPjgg5w6dYqff/7Zfvzo0aNZtmwZu3btYv/+/TRp0oQVK1bQrVu3HGVYvXo1t956KytXruS2224D4IcffuDOO+/k/PnzeHl5lfBTEJHipBogESnzDh48SFpaGt27d8fX19e+ffLJJxw6dMie7/LgqFq1ajRp0oQ9e/YAsGfPHjp27Ohw3o4dO3LgwAEsFgvbtm3DbDbTpUuXPMvSsmVL++uQkBAA4uPjr/oeRaR0VXJ1AURE8pOSkgLAsmXLqFWrlsM+T09PhyCoqLy9vQuUz93d3f7aZDIBtv5JIlK+qAZIRMq8pk2b4unpydGjR2nYsKHDVqdOHXu+DRs22F+fOXOG/fv3c/311wNw/fXXs3btWofzrl27lsaNG2M2m2nRogVWq9WhT5GIXLtUAyQiZZ6fnx8vvvgizz//PFarlU6dOpGUlMTatWvx9/enXr16AEyaNInq1asTFBTEq6++So0aNejTpw8AL7zwAjfeeCNvvPEG/fv3Z/369bz//vt88MEHAISFhTFkyBAefvhh3n33XVq1asWRI0eIj4/n/vvvd9Wti0gJUQAkIuXCG2+8Qc2aNZkyZQp///03VapUoU2bNrzyyiv2Jqi33nqLZ599lgMHDtC6dWu+++47PDw8AGjTpg1ffPEF48aN44033iAkJIRJkyYxdOhQ+zVmzpzJK6+8wpNPPklCQgJ169bllVdeccXtikgJ0ygwESn3skdonTlzhipVqri6OCJSDqgPkIiIiFQ4CoBERESkwlETmIiIiFQ4qgESERGRCkcBkIiIiFQ4CoBERESkwlEAJCIiIhWOAiARERGpcBQAiYiISIWjAEhEREQqHAVAIiIiUuEoABIREZEK5/8B6m4G4oC1xW8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plotgraphs(rnn1)"
      ],
      "id": "understood-village"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "banned-funds",
        "outputId": "92e4ae64-dc13-42fb-fd29-7cdb5ba8ff6e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1492/1492 [==============================] - 8s 5ms/step\n"
          ]
        }
      ],
      "source": [
        "predict = np.argmax(rnn1.predict(X_test),axis=1)\n",
        "\n",
        "a = np.unique(predict)\n",
        "b = np.unique(y_test)\n",
        "c = list(set(a) | set(b))"
      ],
      "id": "banned-funds"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "selected-academy",
        "outputId": "8a8eeed1-f3c2-49a9-a5e4-24a836eae141"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "        ----------Classification Report Of Classes-------------\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00         5\n",
            "           1       0.66      0.96      0.78      1117\n",
            "           2       0.00      0.00      0.00         6\n",
            "           3       0.00      0.00      0.00         5\n",
            "           4       0.99      0.97      0.98       290\n",
            "           5       0.69      0.83      0.75        29\n",
            "           6       1.00      1.00      1.00      7110\n",
            "           7       0.95      0.97      0.96       463\n",
            "           8       1.00      1.00      1.00      4225\n",
            "           9       1.00      1.00      1.00      4180\n",
            "          10       1.00      0.99      1.00      4249\n",
            "          11       0.83      0.20      0.32        25\n",
            "          12       1.00      1.00      1.00      3602\n",
            "          13       1.00      1.00      1.00      4615\n",
            "          14       1.00      1.00      1.00      5591\n",
            "          15       0.97      0.99      0.98       295\n",
            "          16       0.18      0.01      0.02       179\n",
            "          17       0.00      0.00      0.00        13\n",
            "          18       0.79      0.70      0.74        86\n",
            "          19       0.98      0.99      0.99      2114\n",
            "          20       0.99      0.99      0.99      2756\n",
            "          21       1.00      1.00      1.00      3380\n",
            "          22       0.60      0.41      0.49       315\n",
            "          23       0.58      0.98      0.73      1007\n",
            "          24       0.59      0.04      0.07       754\n",
            "          25       0.99      0.99      0.99       965\n",
            "          26       0.57      0.45      0.50       134\n",
            "          27       0.50      0.02      0.04        88\n",
            "          29       0.37      0.17      0.24        81\n",
            "          30       0.00      0.00      0.00         8\n",
            "          31       0.00      0.00      0.00         1\n",
            "          32       0.69      0.59      0.64        49\n",
            "          33       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.97     47738\n",
            "   macro avg       0.63      0.58      0.58     47738\n",
            "weighted avg       0.96      0.97      0.96     47738\n",
            "\n",
            "\n",
            "        ----------Validation Data------------------\n",
            "Accuarcy: 96.52059156227743\n",
            "Precision: 96.2568 %\n",
            "Recall-score: 96.5206\n",
            "F1-score: 95.7370\n"
          ]
        }
      ],
      "source": [
        "report(predict,labels_test)"
      ],
      "id": "selected-academy"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "higher-zimbabwe",
        "outputId": "51970cf2-803c-42b0-ad80-190d24c6fedf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " simple_rnn (SimpleRNN)      (None, 46, 32)            1088      \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 46, 32)            0         \n",
            "                                                                 \n",
            " simple_rnn_1 (SimpleRNN)    (None, 46, 32)            2080      \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 46, 32)            0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 1472)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 32)                47136     \n",
            "                                                                 \n",
            " leaky_re_lu (LeakyReLU)     (None, 32)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 16)                528       \n",
            "                                                                 \n",
            " leaky_re_lu_1 (LeakyReLU)   (None, 16)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 34)                578       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 51,410\n",
            "Trainable params: 51,410\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "#hyperparameters\n",
        "keras.backend.clear_session()\n",
        "SEED = 1040941203\n",
        "hidden_initializer = random_uniform(seed=SEED)\n",
        "# Initialising the RNN\n",
        "rnn2 = Sequential()\n",
        "\n",
        "# Adding the first RNN layer and some Dropout regularisation\n",
        "rnn2.add(SimpleRNN(units = 32,activation='relu', return_sequences = True,  input_shape = (46,1)))\n",
        "rnn2.add(Dropout(0.1))\n",
        "\n",
        "# Adding a second RNN layer and some Dropout regularisation\n",
        "rnn2.add(SimpleRNN(units = 32,activation='relu', return_sequences = True))\n",
        "rnn2.add(Dropout(0.1))\n",
        "\n",
        "# Adding the output layer\n",
        "rnn2.add(Flatten())\n",
        "rnn2.add(Dense(32, input_dim=25, kernel_initializer=hidden_initializer))\n",
        "rnn2.add(LeakyReLU(alpha=0.1))\n",
        "rnn2.add(Dense(16))\n",
        "rnn2.add(LeakyReLU(alpha=0.1))\n",
        "rnn2.add(Dense(units = 34, activation='softmax'))\n",
        "\n",
        "rnn2.summary()"
      ],
      "id": "higher-zimbabwe"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "completed-pizza",
        "scrolled": true,
        "outputId": "115774b4-e8fa-4fe1-cbdc-0334bec5bf7e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "280/280 [==============================] - 30s 98ms/step - loss: 22.3398 - accuracy: 0.4944 - val_loss: 1.9739 - val_accuracy: 0.7578\n",
            "Epoch 2/30\n",
            "280/280 [==============================] - 27s 97ms/step - loss: 2.6285 - accuracy: 0.7904 - val_loss: 1.0989 - val_accuracy: 0.8662\n",
            "Epoch 3/30\n",
            "280/280 [==============================] - 27s 96ms/step - loss: 1.3657 - accuracy: 0.8482 - val_loss: 0.6755 - val_accuracy: 0.8981\n",
            "Epoch 4/30\n",
            "280/280 [==============================] - 26s 94ms/step - loss: 0.9295 - accuracy: 0.8733 - val_loss: 0.4459 - val_accuracy: 0.9036\n",
            "Epoch 5/30\n",
            "280/280 [==============================] - 27s 96ms/step - loss: 0.7289 - accuracy: 0.8907 - val_loss: 0.8121 - val_accuracy: 0.9135\n",
            "Epoch 6/30\n",
            "280/280 [==============================] - 27s 96ms/step - loss: 0.5944 - accuracy: 0.9020 - val_loss: 0.2921 - val_accuracy: 0.9240\n",
            "Epoch 7/30\n",
            "280/280 [==============================] - 26s 93ms/step - loss: 0.4183 - accuracy: 0.9124 - val_loss: 0.2691 - val_accuracy: 0.9283\n",
            "Epoch 8/30\n",
            "280/280 [==============================] - 26s 94ms/step - loss: 0.4496 - accuracy: 0.9152 - val_loss: 0.2439 - val_accuracy: 0.9286\n",
            "Epoch 9/30\n",
            "280/280 [==============================] - 26s 93ms/step - loss: 0.5044 - accuracy: 0.9174 - val_loss: 0.3322 - val_accuracy: 0.9302\n",
            "Epoch 10/30\n",
            "280/280 [==============================] - 26s 92ms/step - loss: 0.4306 - accuracy: 0.9176 - val_loss: 0.2220 - val_accuracy: 0.9306\n",
            "Epoch 11/30\n",
            "280/280 [==============================] - 25s 90ms/step - loss: 0.2993 - accuracy: 0.9232 - val_loss: 0.2102 - val_accuracy: 0.9367\n",
            "Epoch 12/30\n",
            "280/280 [==============================] - 26s 92ms/step - loss: 0.2704 - accuracy: 0.9282 - val_loss: 0.2091 - val_accuracy: 0.9375\n",
            "Epoch 13/30\n",
            "280/280 [==============================] - 25s 90ms/step - loss: 0.3011 - accuracy: 0.9295 - val_loss: 0.2066 - val_accuracy: 0.9325\n",
            "Epoch 14/30\n",
            "280/280 [==============================] - 27s 96ms/step - loss: 0.2459 - accuracy: 0.9333 - val_loss: 0.1891 - val_accuracy: 0.9410\n",
            "Epoch 15/30\n",
            "280/280 [==============================] - 27s 96ms/step - loss: 0.2386 - accuracy: 0.9348 - val_loss: 0.1854 - val_accuracy: 0.9452\n",
            "Epoch 16/30\n",
            "280/280 [==============================] - 27s 96ms/step - loss: 0.2283 - accuracy: 0.9388 - val_loss: 0.1980 - val_accuracy: 0.9380\n",
            "Epoch 17/30\n",
            "280/280 [==============================] - 27s 95ms/step - loss: 0.2115 - accuracy: 0.9394 - val_loss: 0.1666 - val_accuracy: 0.9489\n",
            "Epoch 18/30\n",
            "280/280 [==============================] - 26s 92ms/step - loss: 0.2069 - accuracy: 0.9416 - val_loss: 0.1645 - val_accuracy: 0.9498\n",
            "Epoch 19/30\n",
            "280/280 [==============================] - 27s 96ms/step - loss: 0.2053 - accuracy: 0.9415 - val_loss: 0.1638 - val_accuracy: 0.9451\n",
            "Epoch 20/30\n",
            "280/280 [==============================] - 26s 94ms/step - loss: 0.1778 - accuracy: 0.9453 - val_loss: 0.1479 - val_accuracy: 0.9532\n",
            "Epoch 21/30\n",
            "280/280 [==============================] - 27s 96ms/step - loss: 0.2320 - accuracy: 0.9434 - val_loss: 0.1734 - val_accuracy: 0.9477\n",
            "Epoch 22/30\n",
            "280/280 [==============================] - 27s 97ms/step - loss: 0.1867 - accuracy: 0.9438 - val_loss: 0.1468 - val_accuracy: 0.9534\n",
            "Epoch 23/30\n",
            "280/280 [==============================] - 27s 97ms/step - loss: 0.1675 - accuracy: 0.9472 - val_loss: 0.1522 - val_accuracy: 0.9524\n",
            "Epoch 24/30\n",
            "280/280 [==============================] - 27s 96ms/step - loss: 0.1613 - accuracy: 0.9488 - val_loss: 0.1345 - val_accuracy: 0.9546\n",
            "Epoch 25/30\n",
            "280/280 [==============================] - 26s 95ms/step - loss: 0.1492 - accuracy: 0.9506 - val_loss: 0.1354 - val_accuracy: 0.9541\n",
            "Epoch 26/30\n",
            "280/280 [==============================] - 27s 97ms/step - loss: 0.1523 - accuracy: 0.9514 - val_loss: 0.1273 - val_accuracy: 0.9551\n",
            "Epoch 27/30\n",
            "280/280 [==============================] - 27s 97ms/step - loss: 0.1328 - accuracy: 0.9537 - val_loss: 0.1152 - val_accuracy: 0.9577\n",
            "Epoch 28/30\n",
            "280/280 [==============================] - 27s 96ms/step - loss: 0.1270 - accuracy: 0.9555 - val_loss: 0.1134 - val_accuracy: 0.9592\n",
            "Epoch 29/30\n",
            "280/280 [==============================] - 27s 97ms/step - loss: 0.1214 - accuracy: 0.9564 - val_loss: 0.1075 - val_accuracy: 0.9615\n",
            "Epoch 30/30\n",
            "280/280 [==============================] - 27s 97ms/step - loss: 0.1176 - accuracy: 0.9575 - val_loss: 0.1039 - val_accuracy: 0.9613\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7e270b7c8a30>"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rnn2.compile(loss = \"categorical_crossentropy\", optimizer='adam', metrics=['accuracy'])\n",
        "rnn2.fit(RNN_features_train, y_train, epochs=30, batch_size=512,\n",
        "         validation_data=(RNN_features_val,y_val),callbacks=[tensorboard_callback, eary_stop_callback])"
      ],
      "id": "completed-pizza"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "attended-butter",
        "outputId": "149dbd7d-fb73-4bc4-a53d-da4892fc3c50"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plotgraphs(rnn2)"
      ],
      "id": "attended-butter"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "clear-joining",
        "outputId": "f9be6e33-4ab2-49dc-e204-ef197c144d3f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1492/1492 [==============================] - 12s 8ms/step\n"
          ]
        }
      ],
      "source": [
        "predict = np.argmax(rnn2.predict(X_test),axis=1)\n",
        "\n",
        "a = np.unique(predict)\n",
        "b = np.unique(y_test)\n",
        "c = list(set(a) | set(b))"
      ],
      "id": "clear-joining"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "awful-latitude",
        "outputId": "60e62cbd-0eb2-4852-a600-0eb6403fbbd8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "        ----------Classification Report Of Classes-------------\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00         5\n",
            "           1       0.62      0.98      0.76      1117\n",
            "           2       0.00      0.00      0.00         6\n",
            "           3       0.00      0.00      0.00         5\n",
            "           4       0.90      0.96      0.93       290\n",
            "           5       0.54      0.45      0.49        29\n",
            "           6       1.00      1.00      1.00      7110\n",
            "           7       0.97      0.95      0.96       463\n",
            "           8       1.00      1.00      1.00      4225\n",
            "           9       1.00      1.00      1.00      4180\n",
            "          10       0.99      0.99      0.99      4249\n",
            "          11       0.00      0.00      0.00        25\n",
            "          12       0.99      0.99      0.99      3602\n",
            "          13       1.00      1.00      1.00      4615\n",
            "          14       1.00      1.00      1.00      5591\n",
            "          15       0.97      0.95      0.96       295\n",
            "          16       0.25      0.04      0.07       179\n",
            "          17       0.00      0.00      0.00        13\n",
            "          18       0.48      0.63      0.55        86\n",
            "          19       0.98      0.99      0.99      2114\n",
            "          20       0.99      0.99      0.99      2756\n",
            "          21       0.99      1.00      0.99      3380\n",
            "          22       0.72      0.31      0.44       315\n",
            "          23       0.63      0.49      0.55      1007\n",
            "          24       0.47      0.60      0.53       754\n",
            "          25       0.99      0.99      0.99       965\n",
            "          26       0.44      0.33      0.38       134\n",
            "          27       0.18      0.03      0.06        88\n",
            "          29       0.00      0.00      0.00        81\n",
            "          30       0.00      0.00      0.00         8\n",
            "          31       0.00      0.00      0.00         1\n",
            "          32       0.57      0.16      0.25        49\n",
            "          33       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.96     47738\n",
            "   macro avg       0.57      0.54      0.54     47738\n",
            "weighted avg       0.96      0.96      0.96     47738\n",
            "\n",
            "\n",
            "        ----------Validation Data------------------\n",
            "Accuarcy: 96.00527881352382\n",
            "Precision: 95.7737 %\n",
            "Recall-score: 96.0053\n",
            "F1-score: 95.6539\n"
          ]
        }
      ],
      "source": [
        "report(predict,labels_test)"
      ],
      "id": "awful-latitude"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "alert-retirement",
        "outputId": "bae3ab47-755c-4c33-df10-466a255791a8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " simple_rnn (SimpleRNN)      (None, 46, 32)            1088      \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 46, 32)            0         \n",
            "                                                                 \n",
            " simple_rnn_1 (SimpleRNN)    (None, 46, 32)            2080      \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 46, 32)            0         \n",
            "                                                                 \n",
            " simple_rnn_2 (SimpleRNN)    (None, 46, 32)            2080      \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 46, 32)            0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 1472)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 32)                47136     \n",
            "                                                                 \n",
            " leaky_re_lu (LeakyReLU)     (None, 32)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 16)                528       \n",
            "                                                                 \n",
            " leaky_re_lu_1 (LeakyReLU)   (None, 16)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 34)                578       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 53,490\n",
            "Trainable params: 53,490\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "#hyperparameters\n",
        "keras.backend.clear_session()\n",
        "SEED = 1040941203\n",
        "hidden_initializer = random_uniform(seed=SEED)\n",
        "# Initialising the RNN\n",
        "rnn3 = Sequential()\n",
        "\n",
        "# Adding the first RNN layer and some Dropout regularisation\n",
        "rnn3.add(SimpleRNN(units = 32,activation='relu', return_sequences = True,  input_shape = (46,1)))\n",
        "rnn3.add(Dropout(0.1))\n",
        "\n",
        "# Adding a second RNN layer and some Dropout regularisation\n",
        "rnn3.add(SimpleRNN(units = 32,activation='relu', return_sequences = True))\n",
        "rnn3.add(Dropout(0.1))\n",
        "\n",
        "# Adding a third RNN layer and some Dropout regularisation\n",
        "rnn3.add(SimpleRNN(units = 32,activation='relu', return_sequences = True))\n",
        "rnn3.add(Dropout(0.1))\n",
        "\n",
        "# Adding the output layer\n",
        "rnn3.add(Flatten())\n",
        "rnn3.add(Dense(32, input_dim=25, kernel_initializer=hidden_initializer))\n",
        "rnn3.add(LeakyReLU(alpha=0.1))\n",
        "rnn3.add(Dense(16))\n",
        "rnn3.add(LeakyReLU(alpha=0.1))\n",
        "rnn3.add(Dense(units = 34, activation='softmax'))\n",
        "\n",
        "rnn3.summary()"
      ],
      "id": "alert-retirement"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "perceived-watershed",
        "outputId": "f1dd6d4f-1737-4349-983c-ca094114701e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "280/280 [==============================] - 43s 140ms/step - loss: 18.2187 - accuracy: 0.4651 - val_loss: 1.5766 - val_accuracy: 0.7326\n",
            "Epoch 2/30\n",
            "280/280 [==============================] - 38s 137ms/step - loss: 2.2849 - accuracy: 0.7551 - val_loss: 0.8092 - val_accuracy: 0.8023\n",
            "Epoch 3/30\n",
            "280/280 [==============================] - 39s 141ms/step - loss: 1.2755 - accuracy: 0.8183 - val_loss: 0.4929 - val_accuracy: 0.8689\n",
            "Epoch 4/30\n",
            "280/280 [==============================] - 37s 131ms/step - loss: 0.8248 - accuracy: 0.8538 - val_loss: 0.3970 - val_accuracy: 0.8838\n",
            "Epoch 5/30\n",
            "280/280 [==============================] - 37s 133ms/step - loss: 0.6676 - accuracy: 0.8689 - val_loss: 0.3944 - val_accuracy: 0.9053\n",
            "Epoch 6/30\n",
            "280/280 [==============================] - 38s 136ms/step - loss: 0.5333 - accuracy: 0.8891 - val_loss: 0.2808 - val_accuracy: 0.9217\n",
            "Epoch 7/30\n",
            "280/280 [==============================] - 38s 135ms/step - loss: 0.4441 - accuracy: 0.9069 - val_loss: 0.3746 - val_accuracy: 0.9076\n",
            "Epoch 8/30\n",
            "280/280 [==============================] - 39s 140ms/step - loss: 0.4371 - accuracy: 0.9144 - val_loss: 0.2263 - val_accuracy: 0.9316\n",
            "Epoch 9/30\n",
            "280/280 [==============================] - 37s 132ms/step - loss: 0.3359 - accuracy: 0.9211 - val_loss: 0.2140 - val_accuracy: 0.9376\n",
            "Epoch 10/30\n",
            "280/280 [==============================] - 37s 132ms/step - loss: 0.3101 - accuracy: 0.9240 - val_loss: 0.2154 - val_accuracy: 0.9376\n",
            "Epoch 11/30\n",
            "280/280 [==============================] - 38s 135ms/step - loss: 0.2652 - accuracy: 0.9284 - val_loss: 0.2129 - val_accuracy: 0.9286\n",
            "Epoch 12/30\n",
            "280/280 [==============================] - 38s 135ms/step - loss: 0.2611 - accuracy: 0.9300 - val_loss: 0.1995 - val_accuracy: 0.9396\n",
            "Epoch 13/30\n",
            "280/280 [==============================] - 39s 140ms/step - loss: 0.2307 - accuracy: 0.9340 - val_loss: 0.1847 - val_accuracy: 0.9400\n",
            "Epoch 14/30\n",
            "280/280 [==============================] - 37s 134ms/step - loss: 0.2864 - accuracy: 0.9290 - val_loss: 0.1858 - val_accuracy: 0.9456\n",
            "Epoch 15/30\n",
            "280/280 [==============================] - 37s 131ms/step - loss: 0.2281 - accuracy: 0.9342 - val_loss: 0.1820 - val_accuracy: 0.9477\n",
            "Epoch 16/30\n",
            "280/280 [==============================] - 38s 136ms/step - loss: 0.2258 - accuracy: 0.9356 - val_loss: 0.1794 - val_accuracy: 0.9428\n",
            "Epoch 17/30\n",
            "280/280 [==============================] - 38s 136ms/step - loss: 0.2163 - accuracy: 0.9392 - val_loss: 0.1671 - val_accuracy: 0.9487\n",
            "Epoch 18/30\n",
            "280/280 [==============================] - 38s 135ms/step - loss: 0.1907 - accuracy: 0.9432 - val_loss: 0.1580 - val_accuracy: 0.9508\n",
            "Epoch 19/30\n",
            "280/280 [==============================] - 39s 139ms/step - loss: 0.1849 - accuracy: 0.9438 - val_loss: 0.1543 - val_accuracy: 0.9506\n",
            "Epoch 20/30\n",
            "280/280 [==============================] - 39s 139ms/step - loss: 0.2318 - accuracy: 0.9380 - val_loss: 0.1572 - val_accuracy: 0.9506\n",
            "Epoch 21/30\n",
            "280/280 [==============================] - 36s 130ms/step - loss: 0.1851 - accuracy: 0.9436 - val_loss: 0.1453 - val_accuracy: 0.9513\n",
            "Epoch 22/30\n",
            "280/280 [==============================] - 38s 134ms/step - loss: 0.1664 - accuracy: 0.9466 - val_loss: 0.1388 - val_accuracy: 0.9518\n",
            "Epoch 23/30\n",
            "280/280 [==============================] - 38s 135ms/step - loss: 0.1655 - accuracy: 0.9472 - val_loss: 0.1409 - val_accuracy: 0.9518\n",
            "Epoch 24/30\n",
            "280/280 [==============================] - 38s 135ms/step - loss: 0.1554 - accuracy: 0.9485 - val_loss: 0.1311 - val_accuracy: 0.9533\n",
            "Epoch 25/30\n",
            "280/280 [==============================] - 39s 139ms/step - loss: 0.1467 - accuracy: 0.9511 - val_loss: 0.1256 - val_accuracy: 0.9567\n",
            "Epoch 26/30\n",
            "280/280 [==============================] - 39s 139ms/step - loss: 0.1436 - accuracy: 0.9512 - val_loss: 0.1250 - val_accuracy: 0.9568\n",
            "Epoch 27/30\n",
            "280/280 [==============================] - 39s 140ms/step - loss: 0.1373 - accuracy: 0.9526 - val_loss: 0.1206 - val_accuracy: 0.9583\n",
            "Epoch 28/30\n",
            "280/280 [==============================] - 37s 131ms/step - loss: 0.1323 - accuracy: 0.9543 - val_loss: 0.1201 - val_accuracy: 0.9559\n",
            "Epoch 29/30\n",
            "280/280 [==============================] - 38s 135ms/step - loss: 0.1297 - accuracy: 0.9551 - val_loss: 0.1138 - val_accuracy: 0.9583\n",
            "Epoch 30/30\n",
            "280/280 [==============================] - 38s 136ms/step - loss: 0.1291 - accuracy: 0.9553 - val_loss: 0.1130 - val_accuracy: 0.9592\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7e270ae6f1c0>"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rnn3.compile(loss = \"categorical_crossentropy\", optimizer='adam', metrics=['accuracy'])\n",
        "rnn3.fit(RNN_features_train, y_train, epochs=30, batch_size=512,\n",
        "         validation_data=(RNN_features_val,y_val),callbacks=[tensorboard_callback, eary_stop_callback])"
      ],
      "id": "perceived-watershed"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "occupied-thanksgiving",
        "outputId": "29908b56-873e-4276-d53c-b4baf67b6bc4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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3CA8Px7Fjx1C9enWsWrUK+/btw8yZM+Hq6goAeOWVV3D37l08/fTTqFKlCq5cuYLZs2ejSZMm8nihevXqoXPnzmjevDk8PT1x9OhRrFq1yqRHbO7cuWjfvj0aNmyIESNGoEaNGrhx4wYOHDiAa9eu4cSJE8XeV0m5ublh3rx5eOmll9CsWTO8+OKL8PLyQlxcHDZs2IB27dphzpw5pTrGpk2bChzA37ZtW7knDDCc8mvfvj1GjRqFzMxMzJw5ExUrVsR7770nbzNjxgyEhYWhTZs2GD58uDwN3t3d3eR+cJ9++im2bNmCTp06YeTIkahbty7i4+OxcuVK7N27Fx4eHqV6TUQAOA2eqCiFTYOvX79+gdvv27dP/Oc//xGOjo7C399fvPfee+Kvv/4SAMTOnTvl7QqbBl/QNG88NE28sGnwo0ePztc2ICBADB061GTd9u3bRdOmTYW9vb0ICgoSP/74oxg3bpxwcHAQQhimGXt7e4vp06cX+BqNNmzYIAAIPz8/kynxxn18+umnIiAgQGi1WtG0aVPx559/5nvdBb2+ghjfn8KWX3/9VQghxI0bN8TLL78sKlWqJOzt7UXDhg3FggULTPa1atUq0b17d+Ht7S3s7e1FtWrVxP/93/+J+Ph4eZtPPvlEtGrVSnh4eAhHR0dRp04d8b///U9kZWWZ7CsmJkYMGTJE+Pr6Cjs7O1G5cmXxzDPPiFWrVj32vh5mnAa/cuXKIrczbhsSEiLc3d2Fg4ODCAoKEuHh4eLo0aPyNkOHDhXOzs6P3JdRUdPgAcjva97P7pdffimqVq0qtFqt6NChgzhx4kS+/W7btk20a9dOODo6Cjc3N9GrVy9x9uzZfNtduXJFDBkyRHh5eQmtVitq1KghRo8eLV9ewVjfw1Plje9b3v/eiAoiCWFDf54SkSL69OkjT9U+fPgwWrdujTNnzqBevXpKl0Y27vLlywgMDMSMGTPwzjvvKF0OUbFxDBBROXP//n2TxxcvXsTGjRvRuXNned2nn37K8ENETzSOASIqZ2rUqIHw8HDUqFEDV65cwbx582Bvby+P1WjVqhVatWqlcJVERJbFAERUzoSGhmLp0qVISEiAVqtFmzZt8OmnnxZ4ITsioicVxwARERFRucMxQERERFTuMAARERFRucMxQAXQ6/W4fv06XF1d890HiYiIiGyTEAIpKSnw9/d/5H3vGIAKcP36dVStWlXpMoiIiKgErl69iipVqhS5DQNQAYyXzL969Src3NwUroaIiIiKIzk5GVWrVpW/x4vCAFQA42kvNzc3BiAiIqIypjjDVzgImoiIiModBiAiIiIqdxiAiIiIqNzhGCAiInoi6XQ6ZGdnK10GmZGdnR3UarVZ9sUARERETxQhBBISEpCYmKh0KWQBHh4e8PX1LfV1+hiAiIjoiWIMP97e3nBycuIFbZ8QQgikp6fj5s2bAAA/P79S7Y8BiIiInhg6nU4OPxUrVlS6HDIzR0dHAMDNmzfh7e1dqtNhHARNRERPDOOYHycnJ4UrIUsx/m5LO76LAYiIiJ44PO315DLX75anwKxIpxc4HHsXN1My4O3qgFaBnlCr+B8pERGRtTEAWcnm0/GYsv4s4pMy5HV+7g6Y1KseQhuUbiAXERFRQapXr4633noLb731ltKl2ByeArOCzafjMWpxpEn4AYCEpAyMWhyJzafjFaqMiIgKo9MLHIi5g3VR/+JAzB3o9MJix5Ikqchl8uTJJdrvkSNHMHLkSLPUePnyZUiShKioKLPsT2nsAbIwnV5gyvqzKOg/GwFAAjBl/Vl0q+fL02FERDbC2r328fEP/hBevnw5Jk6ciAsXLsjrXFxc5J+FENDpdNBoHv0V7uXlZd5CnyDsAbKww7F38/X85CUAxCdl4HDsXesVRUREhVKi197X11de3N3dIUmS/Pj8+fNwdXXFpk2b0Lx5c2i1WuzduxcxMTHo3bs3fHx84OLigpYtW2Lbtm0m+61evTpmzpwpP5YkCT/++CP69u0LJycnBAcH448//jDLa8jMzMQbb7wBb29vODg4oH379jhy5Ij8/L179zBo0CB4eXnB0dERwcHBWLBgAQAgKysLY8aMgZ+fHxwcHBAQEICIiAiz1FUYBiALu5lSePgpyXZERPR4hBBIz8op1pKSkY1Jf5wptNceACb/cRYpGdnF2p8Q5jtt9sEHH+Czzz7DuXPn0KhRI6SmpqJHjx7Yvn07jh8/jtDQUPTq1QtxcXFF7mfKlCno378/Tp48iR49emDQoEG4e7f0f4S/9957WL16NRYtWoTIyEjUrFkTISEh8r4nTJiAs2fPYtOmTTh37hzmzZuHSpUqAQBmzZqFP/74AytWrMCFCxfw22+/oXr16qWuqSg8BWZh3q4OZt2OiIgez/1sHepN/Mss+xIAEpIz0HDylmJtf3ZqCJzszfNVO3XqVHTr1k1+7OnpicaNG8uPp02bht9//x1//PEHxowZU+h+wsPDMXDgQADAp59+ilmzZuHw4cMIDQ0tcW1paWmYN28eFi5ciLCwMADADz/8gK1bt+Knn37Cu+++i7i4ODRt2hQtWrQAAJOAExcXh+DgYLRv3x6SJCEgIKDEtRQXe4AsrFWgJ/zcHVDY6B4JhvPKrQI9rVkWERGVMcbgYJSamop33nkHdevWhYeHB1xcXHDu3LlH9gA1atRI/tnZ2Rlubm7y7SVKKiYmBtnZ2WjXrp28zs7ODq1atcK5c+cAAKNGjcKyZcvQpEkTvPfee9i/f7+8bXh4OKKiolC7dm288cYb2LKleAGzNNgDZGFqlYRJveph1OLIfM8ZQ9GkXvU4AJqIyEIc7dQ4OzWkWNsejr2L8AVHHrndwpdbFusPV0c789y5HDCElbzeeecdbN26FV988QVq1qwJR0dHvPDCC8jKyipyP3Z2diaPJUmCXq83W52FCQsLw5UrV7Bx40Zs3boVXbp0wejRo/HFF1+gWbNmiI2NxaZNm7Bt2zb0798fXbt2xapVqyxWD3uArCC0gR/mDW6Gis72Jut93R0wb3AzXgeIiMiCJEmCk72mWEuHYK9i9dp3CPYq1v4seUXqffv2ITw8HH379kXDhg3h6+uLy5cvW+x4RQkKCoK9vT327dsnr8vOzsaRI0dQr149eZ2XlxeGDh2KxYsXY+bMmfj+++/l59zc3DBgwAD88MMPWL58OVavXm2WsUmFYQ+QlYQ28EOVCk54ZvZeuGrV+H5IS14JmojIxuTttZcAk8HQttZrHxwcjDVr1qBXr16QJAkTJkywSk9O3un5RvXr18eoUaPw7rvvwtPTE9WqVcP06dORnp6O4cOHAwAmTpyI5s2bo379+sjMzMSff/6JunXrAgC++uor+Pn5oWnTplCpVFi5ciV8fX3h4eFhsdfBAGRF7o6GbsdsvUCbIN6lmIjIFhl77R++DpCvjV29/6uvvsKwYcPQtm1bVKpUCe+//z6Sk5MtftwXX3wx37qrV6/is88+g16vx0svvYSUlBS0aNECf/31FypUqAAAsLe3x/jx43H58mU4OjqiQ4cOWLZsGQDA1dUV06dPx8WLF6FWq9GyZUts3LgRKpXlTlRJwpxz9J4QycnJcHd3R1JSEtzc3My233tpWWg6bSsAIPp/YdCoeQaSiMicMjIyEBsbi8DAQDg4lG52Le/faJuK+h0/zvc3e4CsyFn74O1Oy9TB3YkBiIjIVqlVEnvrn2CKfgPv2bMHvXr1gr+/PyRJwtq1a02eL+yeKDNmzCh0n5MnT863fZ06dSz8SorHXqOCfW6vT2pWjsLVEBERPfDqq6/CxcWlwOXVV19VujyzU7QHKC0tDY0bN8awYcPw3HPP5Xs+771RAGDTpk0YPnw4nn/++SL3W79+fZPLgRfnfinW4uKgwd20LKRmMAAREZHtmDp1Kt55550CnzPncBBboWgyCAsLk68YWRBfX1+Tx+vWrcNTTz2FGjVqFLlfjUaTr62tcNaqcTcNSM1kACIiItvh7e0Nb29vpcuwmjIzCOXGjRvYsGGDPJ2uKBcvXoS/vz9q1KiBQYMGPfKqmJmZmUhOTjZZLMVFa5gJxgBERESknDITgBYtWgRXV9cCT5Xl1bp1ayxcuBCbN2/GvHnzEBsbiw4dOiAlJaXQNhEREXB3d5eXqlWrmrt8mYvWcFXQNAYgIiIixZSZAPTzzz9j0KBBj5zWGBYWhn79+qFRo0YICQnBxo0bkZiYiBUrVhTaZvz48UhKSpKXq1evmrt8mUvuTDCOASIiIlKO7YwOLsLff/+NCxcuYPny5Y/d1sPDA7Vq1UJ0dHSh22i1Wmi12tKUWGzGqfA8BUZERKScMtED9NNPP6F58+Zo3LjxY7dNTU1FTEwM/Pxs48qdrg4MQEREREpTNAClpqYiKioKUVFRAIDY2FhERUWZDFpOTk7GypUr8corrxS4jy5dumDOnDny43feeQe7d+/G5cuXsX//fvTt2xdqtRoDBw606GspLmd7QwDiGCAiIjKnzp0746233lK6jDJD0QB09OhRNG3aFE2bNgUAjB07Fk2bNsXEiRPlbZYtWwYhRKEBJiYmBrdv35YfX7t2DQMHDkTt2rXRv39/VKxYEQcPHoSXl5dlX0wxueT2AKUwABERUa5evXohNDS0wOf+/vtvSJKEkydPmu14DEsKjwHq3LkzHnUrspEjR2LkyJGFPn/58mWTx8Ybq9kq4yBo9gAREdmonRGASg10ei//c7unA3od8NR4sx7SeJHfa9euoUqVKibPLViwAC1atECjRo3MeszyrkyMAXqScBYYEZGNU6mBnf8zhJ28dk83rFepzX7IZ555Bl5eXli4cKHJ+tTUVKxcuRJ9+vTBwIEDUblyZTg5OaFhw4ZYunSp2eswWr16NerXrw+tVovq1avjyy+/NHn+22+/RXBwMBwcHODj44MXXnhBfm7VqlVo2LAhHB0dUbFiRXTt2hVpaWkWq7WkysQssCcJZ4EREVmZEEB2evG3bzMa0GUZwo4uC2j/NrD3a2DPDKDju4bns4r5hW7nBEiPvoO8RqPBkCFDsHDhQnz00UeQctusXLkSOp0OgwcPxsqVK/H+++/Dzc0NGzZswEsvvYSgoCC0atWq+K+tGI4dO4b+/ftj8uTJGDBgAPbv34/XXnsNFStWRHh4OI4ePYo33ngDv/76K9q2bYu7d+/i77//BmC4hdXAgQMxffp09O3bFykpKfj7778febZHCQxAViafAuPNUImIrCM7HfjUv2Rt98wwLIU9fpQPrwP2zsXadNiwYZgxYwZ2796Nzp07AzCc/nr++ecREBBgcp+u119/HX/99RdWrFhh9gD01VdfoUuXLpgwYQIAoFatWjh79ixmzJiB8PBwxMXFwdnZGc888wxcXV0REBAgj+WNj49HTk4OnnvuOQQEBAAAGjZsaNb6zIWnwKzMOAiap8CIiCivOnXqoG3btvj5558BANHR0fj7778xfPhw6HQ6TJs2DQ0bNoSnpydcXFzw119/PfJWTyVx7tw5tGvXzmRdu3btcPHiReh0OnTr1g0BAQGoUaMGXnrpJfz2229ITzf0sDVu3BhdunRBw4YN0a9fP/zwww+4d++e2Ws0B/YAWZlxGnxqpk7hSoiIygk7J0NPzOMynvZS2xtOhXV813A67HGP/RiGDx+O119/HXPnzsWCBQsQFBSETp064fPPP8c333yDmTNnomHDhnB2dsZbb72FrKysx6vHDFxdXREZGYldu3Zhy5YtmDhxIiZPnowjR47Aw8MDW7duxf79+7FlyxbMnj0bH330EQ4dOoTAwECr11oU9gBZ2YMLIWYrXAkRUTkhSYbTUI+zHJhrCD9PfQRMuGX4d88Mw/rH2U8xxv/k1b9/f6hUKixZsgS//PILhg0bBkmSsG/fPvTu3RuDBw9G48aNUaNGDfzzzz8Webvq1q2Lffv2mazbt28fatWqBbXaMABco9Gga9eumD59Ok6ePInLly9jx44dAABJktCuXTtMmTIFx48fh729PX7//XeL1Foa7AGyMuMg6IxsPXJ0emjUzKBERDbFONvrqY8eTIU3/rvzf6aPzczFxQUDBgzA+PHjkZycjPDwcABAcHAwVq1ahf3796NChQr46quvcOPGDdSrV6/Ex7p165Z8IWIjPz8/jBs3Di1btsS0adMwYMAAHDhwAHPmzMG3334LAPjzzz9x6dIldOzYERUqVMDGjRuh1+tRu3ZtHDp0CNu3b0f37t3h7e2NQ4cO4datW6hbt26J67QUBiArc9Y+mD6ZlqmDuxMDEBGRTdHrTMOPkfGx3rJDGIYPH46ffvoJPXr0gL+/YfD2xx9/jEuXLiEkJAROTk4YOXIk+vTpg6SkpBIfZ8mSJViyZInJumnTpuHjjz/GihUrMHHiREybNg1+fn6YOnWqHMY8PDywZs0aTJ48GRkZGQgODsbSpUtRv359nDt3Dnv27MHMmTORnJyMgIAAfPnllwgLCytxnZYiCVucm6aw5ORkuLu7IykpCW5ubmbff62PNiFLp8e+D55GZQ9Hs++fiKi8ysjIQGxsLAIDA+Hg4KB0OWQBRf2OH+f7m90PCuBMMCIiImUxACnAeBqMF0MkIiJz+/vvv+Hi4lLoQgYcA6QAF60dgPsMQEREZHYtWrTIN7iZ8mMAUoBLbg8Qb4hKRETm5ujoiJo1aypdhs3jKTAF8IaoREREymIAUgBviEpEZFl6vV7pEshCzPW75SkwBcg3RGUAIiIyK3t7e6hUKly/fh1eXl6wt7eX76xOZZsQAllZWbh16xZUKhXs7e1LtT8GIAW4sAeIiMgiVCoVAgMDER8fj+vXS3D/L7J5Tk5OqFatGlSq0p3EYgBSAE+BERFZjr29PapVq4acnBzodLzx9JNErVZDo9GYpVePAUgBD26IygBERGQJkiTBzs4OdnZ2SpdCNoqDoBXgzDFAREREimIAUoBxDFAKp8ETEREpggFIAfIssCwGICIiIiUwACmAN0MlIiJSFgOQApztjYOgOTuBiIhICQxACngwCyxb4UqIiIjKJwYgBRhngWVk65Gj4+XaiYiIrI0BSAHOuXeDB4A0ngYjIiKyOgYgBWg1atirDW99KmeCERERWR0DkEKMM8F4MUQiIiLrYwBSiPE0GC+GSEREZH0MQAoxToVnDxAREZH1MQAphDdEJSIiUg4DkEKMU+EZgIiIiKyPAUghxvuB8XYYRERE1scApBD5hqjsASIiIrI6RQPQnj170KtXL/j7+0OSJKxdu9bk+fDwcEiSZLKEhoY+cr9z585F9erV4eDggNatW+Pw4cMWegUl58JTYERERIpRNAClpaWhcePGmDt3bqHbhIaGIj4+Xl6WLl1a5D6XL1+OsWPHYtKkSYiMjETjxo0REhKCmzdvmrv8UuEYICIiIuVolDx4WFgYwsLCitxGq9XC19e32Pv86quvMGLECLz88ssAgPnz52PDhg34+eef8cEHH5SqXnPiLDAiIiLl2PwYoF27dsHb2xu1a9fGqFGjcOfOnUK3zcrKwrFjx9C1a1d5nUqlQteuXXHgwAFrlFtszhwDREREpBhFe4AeJTQ0FM899xwCAwMRExODDz/8EGFhYThw4ADUanW+7W/fvg2dTgcfHx+T9T4+Pjh//nyhx8nMzERmZqb8ODk52XwvohDGMUC8EjQREZH12XQAevHFF+WfGzZsiEaNGiEoKAi7du1Cly5dzHaciIgITJkyxWz7Kw55FhhvhkpERGR1Nn8KLK8aNWqgUqVKiI6OLvD5SpUqQa1W48aNGybrb9y4UeQ4ovHjxyMpKUlerl69ata6C/LgZqg6ix+LiIiITJWpAHTt2jXcuXMHfn5+BT5vb2+P5s2bY/v27fI6vV6P7du3o02bNoXuV6vVws3NzWSxNOO9wHgKjIiIyPoUDUCpqamIiopCVFQUACA2NhZRUVGIi4tDamoq3n33XRw8eBCXL1/G9u3b0bt3b9SsWRMhISHyPrp06YI5c+bIj8eOHYsffvgBixYtwrlz5zBq1CikpaXJs8JsBS+ESEREpBxFxwAdPXoUTz31lPx47NixAIChQ4di3rx5OHnyJBYtWoTExET4+/uje/fumDZtGrRardwmJiYGt2/flh8PGDAAt27dwsSJE5GQkIAmTZpg8+bN+QZGK814Cux+tg45Oj006jLVGUdERFSmSUIIoXQRtiY5ORnu7u5ISkqy2OmwzBwdan+8GQBwYlJ3uDvaWeQ4RERE5cXjfH+z20EhWo0a9rm9PrwYIhERkXUxACnIWWu4lhHHAREREVkXA5CCjOOAOBOMiIjIuhiAFGScCs8eICIiIutiAFIQb4hKRESkDAYgBRlviMoAREREZF0MQAoyXgwxlWOAiIiIrIoBSEG8GjQREZEyGIAUJPcA8Y7wREREVsUApCBnngIjIiJSBAOQgngKjIiISBkMQApy4TR4IiIiRTAAKYjT4ImIiJTBAKQgVwYgIiIiRTAAKchZHgOkU7gSIiKi8oUBSEHGQdC8GSoREZF1MQApiLPAiIiIlMEApCDjLLD72Trk6PQKV0NERFR+MAApyFmrln9Oy+I4ICIiImthAFKQVqOGvdrwK+BMMCIiIuthAFKYsReI44CIiIishwFIYbwaNBERkfUxACnM2Z43RCUiIrI2BiCFcSo8ERGR9TEAKcx4CiyFAYiIiMhqGIAU5sweICIiIqtjAFKYfENUjgEiIiKyGgYghRl7gFKzGICIiIishQFIYS7sASIiIrI6BiCFcRYYERGR9TEAKYwXQiQiIrI+BiCFyWOAGICIiIishgFIYa4MQERERFbHAKSwB9cB0ilcCRERUfnBAKQwF/YAERERWR0DkMI4DZ6IiMj6FA1Ae/bsQa9eveDv7w9JkrB27Vr5uezsbLz//vto2LAhnJ2d4e/vjyFDhuD69etF7nPy5MmQJMlkqVOnjoVfSckZZ4Hdz9ZBpxcKV0NERFQ+KBqA0tLS0LhxY8ydOzffc+np6YiMjMSECRMQGRmJNWvW4MKFC3j22Wcfud/69esjPj5eXvbu3WuJ8s3CWauWf+ZpMCIiIuvQKHnwsLAwhIWFFficu7s7tm7darJuzpw5aNWqFeLi4lCtWrVC96vRaODr62vWWi1Fq1HDTi0hWyeQlpkDd0c7pUsiIiJ64pWpMUBJSUmQJAkeHh5Fbnfx4kX4+/ujRo0aGDRoEOLi4orcPjMzE8nJySaLNXEgNBERkXWVmQCUkZGB999/HwMHDoSbm1uh27Vu3RoLFy7E5s2bMW/ePMTGxqJDhw5ISUkptE1ERATc3d3lpWrVqpZ4CYXixRCJiIisq0wEoOzsbPTv3x9CCMybN6/IbcPCwtCvXz80atQIISEh2LhxIxITE7FixYpC24wfPx5JSUnycvXqVXO/hCJxJhgREZF1KToGqDiM4efKlSvYsWNHkb0/BfHw8ECtWrUQHR1d6DZarRZarba0pZYYb4hKRERkXTbdA2QMPxcvXsS2bdtQsWLFx95HamoqYmJi4OfnZ4EKzcM4FT6FAYiIiMgqFA1AqampiIqKQlRUFAAgNjYWUVFRiIuLQ3Z2Nl544QUcPXoUv/32G3Q6HRISEpCQkICsrCx5H126dMGcOXPkx++88w52796Ny5cvY//+/ejbty/UajUGDhxo7ZdXbM7sASIiIrIqRU+BHT16FE899ZT8eOzYsQCAoUOHYvLkyfjjjz8AAE2aNDFpt3PnTnTu3BkAEBMTg9u3b8vPXbt2DQMHDsSdO3fg5eWF9u3b4+DBg/Dy8rLsiykFV44BIiIisipFA1Dnzp0hROFXPy7qOaPLly+bPF62bFlpy7I6eRZYFgMQERGRNdj0GKDygoOgiYiIrIsByAZwGjwREZF1MQDZAOMssNRMncKVEBERlQ8MQDbgwZWgsxWuhIiIqHxgALIBLrl3hE9jDxAREZFVMADZABet4Q7wvBcYERGRdTAA2QDn3B4gBiAiIiLrYACyAa7GHiDOAiMiIrIKBiAbYOwBup+tg07/6Is/EhERUekwANkA4zR4gKfBiIiIrIEByAZoNWrYqSUAvBo0ERGRNTAA2Qj5atAMQERERBbHAGQjnBmAiIiIrIYByEbwhqhERETWwwBkI3hDVCIiIuthALIRD26IygBERERkaQxANoJjgIiIiKyHAchGuNhzDBAREZG1MADZCOMpsBQGICIiIotjALIRzpwFRkREZDUMQDbClbPAiIiIrIYByEY8GAStU7gSIiKiJx8DkI14MA0+W+FKiIiInnwMQDbCRasGAKSxB4iIiMjiGIBshIvWDgCvA0RERGQNDEA2wjm3B4gBiIiIyPIYgGyEa24PEKfBExERWR4DkI0w9gClZ+mg0wuFqyEiInqyMQDZCOMsMABIy2IvEBERkSUxANkIrUYNO7UEgBdDJCIisjQGIBviwtthEBERWQUDkA0xXg2aN0QlIiKyLAYgG8IeICIiIutgALIhLrwhKhERkVUwANmQBzdEZQAiIiKyJEUD0J49e9CrVy/4+/tDkiSsXbvW5HkhBCZOnAg/Pz84Ojqia9euuHjx4iP3O3fuXFSvXh0ODg5o3bo1Dh8+bKFXYF4PbojKAERERGRJigagtLQ0NG7cGHPnzi3w+enTp2PWrFmYP38+Dh06BGdnZ4SEhCAjI6PQfS5fvhxjx47FpEmTEBkZicaNGyMkJAQ3b9601MswGxd7jgEiIiKyBkUDUFhYGD755BP07ds333NCCMycORMff/wxevfujUaNGuGXX37B9evX8/UU5fXVV19hxIgRePnll1GvXj3Mnz8fTk5O+Pnnny34SszD2APEWWBERESWZbNjgGJjY5GQkICuXbvK69zd3dG6dWscOHCgwDZZWVk4duyYSRuVSoWuXbsW2gYAMjMzkZycbLIowZmzwIiIiKzCZgNQQkICAMDHx8dkvY+Pj/zcw27fvg2dTvdYbQAgIiIC7u7u8lK1atVSVl8yrnIA0ilyfCIiovLCZgOQNY0fPx5JSUnycvXqVUXqkC+EyGnwREREFmWzAcjX1xcAcOPGDZP1N27ckJ97WKVKlaBWqx+rDQBotVq4ubmZLEowjgHiKTAiIiLLstkAFBgYCF9fX2zfvl1el5ycjEOHDqFNmzYFtrG3t0fz5s1N2uj1emzfvr3QNrbERasGwGnwRERElqZR8uCpqamIjo6WH8fGxiIqKgqenp6oVq0a3nrrLXzyyScIDg5GYGAgJkyYAH9/f/Tp00du06VLF/Tt2xdjxowBAIwdOxZDhw5FixYt0KpVK8ycORNpaWl4+eWXrf3yHpuL1g4Ae4CIiIgsTdEAdPToUTz11FPy47FjxwIAhg4dioULF+K9995DWloaRo4cicTERLRv3x6bN2+Gg4OD3CYmJga3b9+WHw8YMAC3bt3CxIkTkZCQgCZNmmDz5s35BkbbIufcHiBOgyciIrIsSQghHrfRokWLUKlSJfTs2RMA8N577+H7779HvXr1sHTpUgQEBJi9UGtKTk6Gu7s7kpKSrDoe6MqdNHSasQtO9mqcnRpqteMSERE9CR7n+7tEY4A+/fRTODo6AgAOHDiAuXPnYvr06ahUqRLefvvtkuyS8OBmqOlZOuj0j51LiYiIqJhKdArs6tWrqFmzJgBg7dq1eP755zFy5Ei0a9cOnTt3Nmd95YpxGjwApGXlwM3BTsFqiIiInlwl6gFycXHBnTt3AABbtmxBt27dAAAODg64f/+++aorZ7QaFezUEgAgldcCIiIispgS9QB169YNr7zyCpo2bYp//vkHPXr0AACcOXMG1atXN2d95YokSXDWapCYns2ZYERERBZUoh6guXPnok2bNrh16xZWr16NihUrAgCOHTuGgQMHmrXA8sY4DogzwYiIiCynRD1AHh4emDNnTr71U6ZMKXVB5Z0Lb4hKRERkcSXqAdq8eTP27t0rP547dy6aNGmC//73v7h3757ZiiuPGICIiIgsr0QB6N1330VycjIA4NSpUxg3bhx69OiB2NhY+WKGVDK8ISoREZHllegUWGxsLOrVqwcAWL16NZ555hl8+umniIyMlAdEU8nwhqhERESWV6IeIHt7e6SnpwMAtm3bhu7duwMAPD095Z4hKhkXe0MA4g1RiYiILKdEPUDt27fH2LFj0a5dOxw+fBjLly8HAPzzzz+oUqWKWQssb4w9QKmZOoUrISIienKVqAdozpw50Gg0WLVqFebNm4fKlSsDADZt2oTQUN7DqjSMY4BSM7MVroSIiOjJVaIeoGrVquHPP//Mt/7rr78udUHlnUvuHeHT2ANERERkMSUKQACg0+mwdu1anDt3DgBQv359PPvss1Cr1WYrrjxy0Rru/8VZYERERJZTogAUHR2NHj164N9//0Xt2rUBABEREahatSo2bNiAoKAgsxZZnjjLPUAMQERERJZSojFAb7zxBoKCgnD16lVERkYiMjIScXFxCAwMxBtvvGHuGssVVwfOAiMiIrK0EvUA7d69GwcPHoSnp6e8rmLFivjss8/Qrl07sxVXHjnb8zpAREREllaiHiCtVouUlJR861NTU2Fvb1/qosoz4zR43gyViIjIckoUgJ555hmMHDkShw4dghACQggcPHgQr776Kp599llz11iu8F5gRERElleiADRr1iwEBQWhTZs2cHBwgIODA9q2bYuaNWti5syZZi6xfDEGoPQsHXR6oXA1RERET6YSjQHy8PDAunXrEB0dLU+Dr1u3LmrWrGnW4soj44UQASAtKwduDnYKVkNERPRkKnYAetRd3nfu3Cn//NVXX5W8onJOq1HBTi0hWyeQlskAREREZAnFDkDHjx8v1naSJJW4GDK8f85aDRLTs5GakQO4K10RERHRk6fYAShvDw9ZlosxAHEgNBERkUWUaBA0WZaLlhdDJCIisiQGIBvEqfBERESWxQBkg4wzwXhDVCIiIstgALJB7AEiIiKyLAYgG8QxQERERJbFAGSDnOUApFO4EiIioicTA5ANMt4QNTUzW+FKiIiInkwMQDbIRasGAKSxB4iIiMgiGIBskIvWcPsLzgIjIiKyDAYgG+Qs9wAxABEREVkCA5ANcs0dA5SWxQBERERkCQxANsjZPncQNE+BERERWYTNB6Dq1atDkqR8y+jRowvcfuHChfm2dXBwsHLVpfNgFhgDEBERkSUU+27wSjly5Ah0ugezoU6fPo1u3bqhX79+hbZxc3PDhQsX5MeSJFm0RnPjhRCJiIgsy+YDkJeXl8njzz77DEFBQejUqVOhbSRJgq+vr6VLsxhjAErP0kGnF1CrylaAIyIisnU2fwosr6ysLCxevBjDhg0rslcnNTUVAQEBqFq1Knr37o0zZ84Uud/MzEwkJyebLEoyXgka4EBoIiIiSyhTAWjt2rVITExEeHh4odvUrl0bP//8M9atW4fFixdDr9ejbdu2uHbtWqFtIiIi4O7uLi9Vq1a1QPXFp9WooMnt9eFUeCIiIvOThBBC6SKKKyQkBPb29li/fn2x22RnZ6Nu3boYOHAgpk2bVuA2mZmZyMzMlB8nJyejatWqSEpKgpubW6nrLokmU7cgMT0bW9/uiGAfV0VqICIiKkuSk5Ph7u5erO9vmx8DZHTlyhVs27YNa9aseax2dnZ2aNq0KaKjowvdRqvVQqvVlrZEs3K21yAxPZsDoYmIiCygzJwCW7BgAby9vdGzZ8/HaqfT6XDq1Cn4+flZqDLLcOVUeCIiIospEwFIr9djwYIFGDp0KDQa006rIUOGYPz48fLjqVOnYsuWLbh06RIiIyMxePBgXLlyBa+88oq1yy4V40BojgEiIiIyvzJxCmzbtm2Ii4vDsGHD8j0XFxcHlepBjrt37x5GjBiBhIQEVKhQAc2bN8f+/ftRr149a5Zcasap8LwhKhERkfmViQDUvXt3FDZWe9euXSaPv/76a3z99ddWqMqyXNgDREREZDFl4hRYeSQHoCzdI7YkIiKix8UAZKOceQqMiIjIYhiAbJTxhqg8BUZERGR+DEA2ykWrBsBp8ERERJbAAGSjXLR2ABiAiIiILIEByEY5G3uAOAaIiIjI7BiAbNSDWWAMQERERObGAGSjjAGIPUBERETmxwBko4zT4DkGiIiIyPwYgGwUb4ZKRERkOQxANsrYA5SepYNOX/BtQIiIiKhkGIBslHEMEMCB0ERERObGAGSjtBoVNCoJAK8GTUREZG4MQDZKkiTeDoOIiMhCGIBsmLM9b4hKRERkCQxANsxV7gHSKVwJERHRk4UByIY9uBZQtsKVEBERPVkYgGyYfDVo9gARERGZFQOQDXtwOwz2ABEREZkTA5ANM94RPi2LPUBERETmxABkw1y0dgA4C4yIiMjcGIBsmIuxB4jXASIiIjIrBiAb5sIbohIREVkEA5ANezANngGIiIjInBiAbNiDWWAMQERERObEAGTDjAGId4MnIiIyLwYgG+bCU2BEREQWwQBkw5x5CoyIiMgiGIBs2IOboTIAERERmRMDkA1zlscA6aDXC4WrISIienIwANkw4xgggAOhiYiIzIkByIZpNSpoVBIADoQmIiIyJwYgGyZJknw1aI4DIiIiMh8GIBvnbG8IQLwhKhERkfkwANk4+WKImTqFKyEiInpy2HQAmjx5MiRJMlnq1KlTZJuVK1eiTp06cHBwQMOGDbFx40YrVWsZD26Imq1wJURERE8Omw5AAFC/fn3Ex8fLy969ewvddv/+/Rg4cCCGDx+O48ePo0+fPujTpw9Onz5txYrN68ENUdkDREREZC42H4A0Gg18fX3lpVKlSoVu+8033yA0NBTvvvsu6tati2nTpqFZs2aYM2eOFSs2L1f5atDsASIiIjIXmw9AFy9ehL+/P2rUqIFBgwYhLi6u0G0PHDiArl27mqwLCQnBgQMHijxGZmYmkpOTTRZb4axVAzBcDJGIiIjMw6YDUOvWrbFw4UJs3rwZ8+bNQ2xsLDp06ICUlJQCt09ISICPj4/JOh8fHyQkJBR5nIiICLi7u8tL1apVzfYaSstFaweA1wEiIiIyJ5sOQGFhYejXrx8aNWqEkJAQbNy4EYmJiVixYoVZjzN+/HgkJSXJy9WrV826/9Jwye0B4g1RiYiIzEfz6E1sh4eHB2rVqoXo6OgCn/f19cWNGzdM1t24cQO+vr5F7ler1UKr1ZqtTnPihRCJiIjMz6Z7gB6WmpqKmJgY+Pn5Ffh8mzZtsH37dpN1W7duRZs2baxRnkUYZ4GlMAARERGZjU0HoHfeeQe7d+/G5cuXsX//fvTt2xdqtRoDBw4EAAwZMgTjx4+Xt3/zzTexefNmfPnllzh//jwmT56Mo0ePYsyYMUq9hFJ7cCFEBiAiIiJzselTYNeuXcPAgQNx584deHl5oX379jh48CC8vLwAAHFxcVCpHmS4tm3bYsmSJfj444/x4YcfIjg4GGvXrkWDBg2Uegml5iJfB4gBiIiIyFxsOgAtW7asyOd37dqVb12/fv3Qr18/C1VkfQxARERE5mfTp8Aoz5WgOQuMiIjIbBiAbBzHABEREZkfA5CNk6fBZ+mg1wuFqyEiInoyMADZOGMPEACkZbEXiIiIyBwYgGycVqOCRiUB4EBoIiIic2EAsnGSJMkDoTkOiIiIyDwYgMqAB1PheUd4IiIic2AAKgNcOBWeiIjIrBiAygDjTDCOASIiIjIPBqAywJlXgyYiIjIrBqAywJWDoImIiMyKAagMcNaqAbAHiIiIyFwYgMoAF60dAAYgIiIic2EAKgNcjD1AnAVGRERkFgxAZQAvhEhERGReDEBlgHEafAoDEBERkVkwAJUBLuwBIiIiMisGoDLAhdcBIiIiMisGoDKAF0IkIiIyLwagMoCnwIiIiMyLAagM4M1QiYiIzIsBqAwwzgJLy9JBrxcKV0NERFT2MQCVAcYeIABIy2IvEBERUWkxAJUBWo0KGpUEAEjL1ClcDRERUdnHAFQGSJKUZyZYtsLVEBERlX0MQGXEg2sBsQeIiIiotBiAygjOBCMiIjIfBqAywjgTjBdDJCIiKj0GoDKCV4MmIiIyHwagMsJFqwbAq0ETERGZAwNQGcEbohIREZkPA1AZwVNgRERE5sMAVEa48oaoREREZsMAVEY4cxo8ERGR2TAAlRGcBk9ERGQ+Nh2AIiIi0LJlS7i6usLb2xt9+vTBhQsXimyzcOFCSJJksjg4OFipYsvhIGgiIiLzsekAtHv3bowePRoHDx7E1q1bkZ2dje7duyMtLa3Idm5uboiPj5eXK1euWKliy3HhGCAiIiKz0ShdQFE2b95s8njhwoXw9vbGsWPH0LFjx0LbSZIEX19fS5dnVcYxQCkMQERERKVm0z1AD0tKSgIAeHp6FrldamoqAgICULVqVfTu3RtnzpyxRnkWxR4gIiIi8ykzAUiv1+Ott95Cu3bt0KBBg0K3q127Nn7++WesW7cOixcvhl6vR9u2bXHt2rVC22RmZiI5OdlksTWOdoYrQSemZeFAzB3o9ELhioiIiMouSQhRJr5JR40ahU2bNmHv3r2oUqVKsdtlZ2ejbt26GDhwIKZNm1bgNpMnT8aUKVPyrU9KSoKbm1uJazaXzafjMXHdGdxMyZTX+bk7YFKveght4KdgZURERLYjOTkZ7u7uxfr+LhM9QGPGjMGff/6JnTt3Plb4AQA7Ozs0bdoU0dHRhW4zfvx4JCUlycvVq1dLW7LZbD4dj1GLI03CDwAkJGVg1OJIbD4dr1BlREREZZdNByAhBMaMGYPff/8dO3bsQGBg4GPvQ6fT4dSpU/DzK7ynRKvVws3NzWSxBTq9wJT1Z1FQF51x3ZT1Z3k6jIiI6DHZdAAaPXo0Fi9ejCVLlsDV1RUJCQlISEjA/fv35W2GDBmC8ePHy4+nTp2KLVu24NKlS4iMjMTgwYNx5coVvPLKK0q8hFI5HHsX8UkZhT4vAMQnZeBw7F3rFUVERPQEsOlp8PPmzQMAdO7c2WT9ggULEB4eDgCIi4uDSvUgx927dw8jRoxAQkICKlSogObNm2P//v2oV6+etco2m5sphYefkmxHREREBjYdgIozPnvXrl0mj7/++mt8/fXXFqrIurxdi3cF6+JuR0RERAY2fQqsvGsV6Ak/dwdIRWzj6+aAVoFFXxeJiIiITDEA2TC1SsKkXoZTd4WFIG9XbZEBiYiIiPJjALJxoQ38MG9wM/i6m57mquRiD41Kwsl/kzBjS9E3iCUiIiJTNj0GiAxCG/ihWz1fHI69i5spGfB2NZz2Wn/iOt5aHoV5u2IQ7O2C55o93jWSiIiIyisGoDJCrZLQJqiiybo+TSvjnxsp+HZXDD5YfQrVKzmjWbUKClVIRERUdvAUWBn3Tvfa6FbPB1k6PUb+cgz/Jt5/dCMiIqJyjgGojFOpJMwc0AR1fF1xOzUTIxYdRXoW7xhPRERUFAYga9gZAeyeXvBzu6cbni8FZ60GPw5tgYrO9jgbn4yxy09Az9tjEBERFYoByBpUamDn//KHoN3TDetV6lIfokoFJ3z3UnPYqSVsPpOAmdv+KfU+iYiInlQMQNbQ6T3gqY8MYeeXPkBW+oPw89RHhufNoEV1T3zatyEAYNaOaPxx4rpZ9ktERPSk4Swwa+n0HvDPX8ClncCn/gCEWcOPUb8WVXHxZiq+33MJ7648gQBPJzSu6mHWYxAREZV17AGypu6f5P4gAEhA05cscpj3Q+vg6TreyMzRY8QvR5GQlAGdXuBAzB2si/oXB2LuQMcxQkREVI6xB8iaLv+d54EAvm0NjNgJVAwy62HUKgnfvNgEz327HxdvpmLA9weQma1DQnKmvI2fuwMm9aqH0AZ+Zj02ERFRWcAeIGvJO+bnzROAYwUgIwmY3x64HmX2w7k62OGnoS3hbK/GlTvpJuEHABKSMjBqcSQ2n443+7GJiIhsHQOQNTw84LlCdWD0YcDFB8hOB37qBsTuMfthK1dwhNau4BlmxhNgU9af5ekwIiIqdxiArEGvyz/g2cUbGHMU8AgAdFnA4ueBs3+Y9bCHY+/iblpWoc8LAPFJGTgce9esxyUiIrJ1DEDW8NT4gmd7ObgZeoLqPGMIQSuHAscWmu2wN1MyzLodERHRk4IBSGl2DkD/X4BmQwChB9a/Cez5AhClPy3l7epg1u2IiIieFAxAtkClBnrNAjqMMzzeMQ3460NAry/VblsFesLP3QHSI7b7YU8MLiSklOpYREREZQkDkK2QJKDLRCAk975gB78F1r4K6LJLvEu1SsKkXvUMuy9kG5UE7LhwC6Hf7MG4FSd4N3kiIioXGIBsTZvXgL7fAyoNcHI5sHQgkJVW4t2FNvDDvMHN4OtueprLz90B8wc3w9axndCjoS+EAFZHXsNTX+zC/zacxb08g6d5EUUiInrSSEKYYbDJEyY5ORnu7u5ISkqCm5ubMkX8swVYMQTIuQ+4VQZe3Qs4eZpus3t67gyz8Y/cnU4vcDj2Lm6mZMDb1QGtAj2hVj3oFzoedw+fbz6Pg5cMM8JctRq82jkIVSo44rNN5xGf9GCgNC+iSEREtuhxvr8ZgApgEwEIAOIOAb/0AnIyAadKwKt/A27+hucscDNVIQR2/3MLn206j/NFjAkyxqZ5g5sxBBERkc1gAColmwlAAHDjrOFCiVmpgNbNcOuMM2vMHn7y0usFfj9+De+tOgldIZ8OCYCvuwP2vv+0SU9SQR7V+0RERGQOj/P9zXuB2TqfesCo/cD3nYD794A5zQ3rm4cDHd+1yCFVKgn+Hk6Fhh/gwUUUv9p6Ab2bVEaNSs7QqPMPKdt8Oh5T1p/lKTQiIrIp7AEqgE31ABml3gK+CMaDm1gAqFQbaPJfoPGLgKuvWQ+3LupfvLksqtjb22tUqOPrinp+bqjn74Z6fm64eu8+xi6PwsMfsMc5hcbeIyIiKi72AD2Jji0AIAyzw/Q5hn9vXwC2TQK2TwFqdgWaDAJqhwEabakPV9yLI9b2ccG1e/eRlqXDyWtJOHkt6ZFtBAwhaMr6s+hWz7fQQMPeIyIishT2ABXA5nqAHh7wbHxcuweQfge4eujBto4VgIb9DGHIrzGw6zPDhRYLGitUxCwynV6g/ec7kJCUka8HBzAdAyQBuHovHWevJ+NsfDLOXk/G8av3cDft0dcweq6pPzrV9kZNbxcEebnAIffmrZtPx2PU4shS9R4ZX0dJe5DY+0REVLZwEHQp2VQAKmy2V9719fsCUUuAE8uAlOsPtvGuD7j5AdHbim5fyEBqYwgBTE68FSuEPO4pNMBwLcgqFRwRVMkZRy7fQ1qWruDtULwB2KXpQWLvExFR2cMAVEo2FYB2RhS/B0evA2J2AlG/Aec3ALpMw3pJZbjPWIMXgNDPgKM/A7s+ffQssp0RuHgrHUNiOucLAr8E7UKwl1Oh1yA6EHMHhxa8A51QYbbuuXzPv65eA7Wkx4mgUUjJyEH0rVQkpj/eVa9f7VgD7YIrwdfNAb7uDnB1sJOfi17+If44eQOzHjq2lHvsZxv5oOaATwvcry30PpmjPRFRecMxQE+Soi5y+HB4UamB4K6G5f494NQqQ8/QdUMvDk6vMiyA4VRZ7B7g7iXAvYrhYovuVR787OAGqNQIPjsL+zq74FDVV+Qv4tZXf4Rq1yxDgCpEq0BPnNLaY6RuGQCYhKDX1Wswzm4Vvle/iB+HtoRaJUEIgTtpWYi+mYrVkdew8ui1R7418/dcwvw9l+THzvZq+Lg7wMdVizbXbmKs3SqIh449Rr0GY+1WYd6FAbh3+S7s1CqoJQmSBKgkCQICH689XeBpv+KOXQJK34NU2vZlOXwx+BGRNbAHqAA21QNkDjfOAieWAPtnF7+N1s0QhHRZwN0YILCT4VTbxS3AhY1A7Z5AnR65G0uG81d5/wVw4t8kXNu/Ej01h/FnTmv8pu+Kp6XjGGG3EV9lv4B6Az8p8Mu8uL1H232GITNHj4SkDCRn5BS43Ti7Vfgy+wXM1j2X73Fp9Grsj1aBnqji4YjKFRxR2cMRzlrD3xOl7UEyR/uyGr7KcvBj6CRSHk+BldITF4CAB2N+1PaGUNN0MFC9I5B8DUj6F0i6BiTn/puRaNFScqBCpks1OPsGAxWqA56BQIVAw88VqkOnccRP/3sVI3XL8oWVvL1Hwz+ab/ifdGYq7t+5gsT4y0i7HYe42H9w89ol+El3UV+KRSVVCoQwZLNDutr4TdcN50VVJDpWg529A4QQ0AtALwTuZ+kwXLfskeFrZs4L+Z7zcLKDv7sDwu4sRJZOKrS9m1aFXm/OhpNWDa1GBXu1CpJk+LLR6QUW/G8kkjL0BbZ/Q70Gbg4qvPzR9wV+QZXm1J852pd23FVZDX7lOXSW9vjlNbSW5cBsy3gKjEwVNovMI6DgMUCZqQ/CkDEY7ZlhGEckSUDN7rkbCkCIPP8+tA4AhIAQArj8NwwnmAAN9NCkXgaiLxdYrtrFF/08/HHmVgDG2a1CXSkOy/RP4b/q7QhVH8VJXSD6+l6Hen5bQ3jLTIIjAMfc9jWBfJ/s3HyB1uoLaK2+AADQww4qt1qAT33DBSe96+NYhj92LVdhnJ3hVGFB4evL7BcQUt8HOj3wb+J9/HsvHckZOUhMz0Zieja6qKWi22e+gP9EbDepT6tRQasxBKEhWfpC24+1W4UvM17Ax2tPob6/O1y0GsPioIGjnRoHLtwp8tTf9/+8iEC9KPB/lDq9wI5/St4+evmHOHvyBuIfCk8JSRk4u/Rj1CwkPOn1Aon3sxG3ZiLGqPMHPwFD8Lv2+1ro6hUe/EpybHO0V/LYpRmnZ47aS3X8UtZe2vYMzGUz+JkTA9CTrqDZXsZ/d/7P9LGR1gXwqm1YjPsQ+ge9R1VaPNYtOKTd04HLewC1PSRdFtBmNBAcAtyLBe5dBu7GGn6+exnITAJSE1AhNQEVci8s3UNzGD1wWN5fI3UskBD7UM25p+zcK0Pv6o+fTmbhYoYbWknn8ILmb2QLNewkHU7oaiBb0qCu6iqc9feBm2cMyynDbpoDCLZzxjV9JYyzW4XGqkv4IacnnlXvwyDNDvyS0w1HnTpgcYgb1BqN4XpMKg1SswWup+Rg/YkE/HSgBzTQmYQY09NvfQEISLkhUQKQnaNHdg4gQeBb9IYKhhCkgh5zdX0wWr0Ob9utxlfZz2OOrg/E4Tg86BvJ61mkqXMKP3bGs5g15S/Ya9Qw/D/HOP4JyMrR4176s0h5RPsdPx5ElQpOcLBTwUGjhqO9GvYaFaRzt4oMT7PO9Mf5xceQnJGDpPvZ8pKSkQ29AF5XPzr4NZi0GZVctfBwtIeHkx3cHO3g5qCB/9mijz3v/ADcir4NlUqSe9skyfAO6vQCkedvF9l+/oUXob+RAnu1CmqVBJVKgjp3zNg2BUPnxVvpCD47Cy9kX8dsPGjbL3UJgs+uwsV6byC4gE9Jtk6PpPTsR9b+3T8vokq2Tr48hbmOX9q2pW3PwKxA6CztsS2AAehJp9cVPNvL+Fhf8FRzWWG9R3n3UZL2Dh4Ft0+/axKM9HdjIUX9ltt7JEE0GQSVexXAvbLhxrBuVQz/Ojzo6lQBqBoUj5SlH+MFzd/5xgB9lf0Cbvdbh9Aq2cDNs8CNMw/+vX0RbiINbqo0AEBXdSS6qiPlfQ/RbMWQ7K3At6ZluwCoBWAcgHF5riE5zm6V/KVe0ONHedtuDd62WyM/Hmu3GmPtVufbTm+MU8IQrXTC0As1VrMKkgRkCDuEa/7CS5ptyIEKumy14V+okQM1dFAhB2rk2Bsex+m9MM5uFd7WrIZKEvhHXxm1VNfwtWoucuLUhgUPFh3UyIYa+6W6GGe3Ci1V5/G3vhHaq06hk/oUdusaIlWvRpVzP5jENgnC8AsDkAMN/tY1wDi7VWilOo/9+gZoozqNjurT2K1riPvQ4r/69UCSBJFkeL0CEvSQcBNO2KZqKofWHfqmeEp1HN3Ukdiia47LejfELvgMIs/RBSQIYdyPJzaqWmKc3SrUkeKwWd8K3VRH8azmIH7PaYuD+mrY+81cw/skDO+X8T3ToQm06rsYZ7cK7lIaFupCMUS1BSPtNmBedi98n9EZSz9ZA2dHB2g0GthpNNBo1LDT2CM1U4/ItEeHztPLo1DV0xEqyRDgjFnoxzPt8XLO9Xxtx+a2/f5kWzRLPIDUTB1SM3OQkpGDlIxsZOboc9+FRwfmiAmbodWo4OqggauDndzr6KxVY190W7yiL/z4v57vgE9OXIfWTg07tQQ7tQp2ahVUEvDqPx0xMLvwtisudsKae/fl4Cfl+eDo9AKDLnbCgELaf5X9AlZEd8aOrBw5tOY9zVya0Fma9koeG1A2dJb22JZQJsYAzZ07FzNmzEBCQgIaN26M2bNno1WrVoVuv3LlSkyYMAGXL19GcHAwPv/8c/To0aPQ7R/2RI4BKoniXIOoqBBU2vZ5tzX2PhX3BrC57b5Xv4hP056VV3/o/IdhZlph+8nJBG7/gxPH9uNU5H78N2cdVJKAEMAtyRNuDmo4qPSGq3HrdXn+fbwp/ERGemEIcQCgkfTyeLVMoUEm7JAbE3P7DA0/66GS+w8FJDghAy5Shtw2VTggDQ6QTFojT6/jg8cSBLTIhoOULbdPE1okw9kQ9IQh6OmhQg5Uuf8+CIE6qOGH2whQ3YJeSFBJAjF6P1wS/tDnBlRDQFfleawyhFchoY4Uh4bqy9AJCWpJ4KQuEGdFgBxVH7xqY69dntPtAOpKcainijNpf0rUkOvT5/lXSIZ/s4WEltJ5tFOfxR5dA/ytb4R2qtPorD6JnbrG2KVvAhcHQ1iFpIIECVCpAEmFzByBm6lZ6CJFIkxzBBtyWuEvfUuEqI6gp+aw/LiapyOc7HP7GCQJEoC0rBzE3bmfu+0hbMhpjb/0LdFddQTPaA7hj5z/YKP+P6jt6wpXR3tIkir3RRtOjyfdz0bUtRQ8q9qH5zV7sTKnA37Xd0Bf1V700+zBypyO+F3fHm1qeKKSiyOQ27trtPnMDYTpdqG/Zg9W5HTEGn1H9FX9jQGa3Via0xkbNF3Rp2kVSCo1IBl+Z5AMi4CEXw9fRZ+cvzBM8xd+zAnDIl13DFVvwSuaTfgxJwyrNT0Q3i4Qask0fOkF8PO+WDyfsxEjNBvxQ3YPLNSFYKB6B8bYrcNX2S9gpct/i3Vz7Ud5ogZBL1++HEOGDMH8+fPRunVrzJw5EytXrsSFCxfg7e2db/v9+/ejY8eOiIiIwDPPPIMlS5bg888/R2RkJBo0aFCsYzIA5XqcaxBZon1hvUfFCUG5x9Z1eDf/+ea/Zzz62AD0uz6Haten0KnsoNZnQ9/5Q6g6v19EA0Mw2nLmX4xdegyj1Osw2u4PZAkN7KUczMvuhR91PTG9XyN0qZN77zYp/3/s287fxLgVJzBCvQFj7NbJ7edmP4sfdT3xRb9G6FIn97OfZwyWTgj0nvM3biVnYph6I/7PbgOyhBr2kg4/54Riqe5p+LhosCi8GdTCGOKMSzZ0OTkYv+o4UtIz0FN1AM9oDiFHqKCR9Nima4q9+kao4CDh9c6BUIlsw3uoywb0Obh+NwV/nboGDXRQQ4cB6l1QSwI6IWGtvr2hVEjoGOwFb1ftQ69bgh4CG0/GIy3L0DPxgnq33P53fQdIEHCyUyGkvo/hqz/P676dmolDl27JX+ghqqNQSQJ6IWG7vlnuMQSaV/OAp5Od/Ni4j8T0LJy4mih/sbZXnZbbR4pgaHK/PoMqOsBZgwfvmdAhMysbSWn3oYYOGujghnRIkmHXOhjeOyIqXN6JLktH/AdtgiqWan9PVABq3bo1WrZsiTlz5gAA9Ho9qlatitdffx0ffPBBvu0HDBiAtLQ0/Pnnn/K6//znP2jSpAnmz59frGMyANkAc/QemfP4j3nciysmIPjsrHyn3y7WewPB/adZrP3m0/E4u/Rj+TTCw6f+Crv0QGnb5711ypjc7TOFBlopB19mv4A5uuceefVu4yww42kMY/uvcusobBZYaY9dmvbFauumxd73OkENvSE0Cj0gdIBeB51Oh2dn78bt5AwMV2/EyDyh9aecUCzWdYOXixZLR7SGWjINfieu3sP7q09CBT0GqnfgJc02eazbrzldsVT3NAQkfPZ8IzSu4pHvUhU6AQz68RBupWZhkHobhmk2y8f+Jacbluuego+LGj8Mbppbe05u3YZez/PXE/H1lrNQQ4+eqkPomScw/6VrgR36plBBjxHtq6NGRUdD7UIPCD0u307BbwcvQwWB9qpT6KA+Lbf9W1cfB/T1AUgY0KoaAjydTC6vAUnC5Tv38duhKxCQ0F51Cp3VJ+X2e3X1cVhfF2pJj2cb+cDP1Q5Cr5OXG0lp2PfPzdw+LT365Qnb6/VtIAFQQY8G/q5wc9DINYvc+lPvZ+HSrVSocvu08gbmffr6hhIhUNXTCY65Y6eMX7L3s3Lw77378uenjeqs3PaIMIy5VEEPH1cttBoJUt5JJkKPrBwdEtOzcvvPBGpLcVBJhh6Wf0RVeb9uDhpo1BLk8+IwjPHLe3X9YOkaVLlh/ZLwy+2fAxztJNipAEnoc3vf9JCEgNDroNPrcl+3gGuesJ8GB/l1q/KcojUyzrQ1/pHhgCxIEpAjVKiZuVje7psXm6B3k8r5/ht9HE/MLLCsrCwcO3YM48c/+EtdpVKha9euOHDgQIFtDhw4gLFjx5qsCwkJwdq1aws9TmZmJjIzM+XHycnJpSucSq+0Y5dKoyQDxx9qH3x2FvSdP0Tbqq+gZkoGvF3/A/3VWgje9Smw29Vi7UPv/IrQ3MsEzM4wnPqbrXsOrg4ajMUy4E5tAIUfu6Tt1SoJk3rVKzQ8SQDq9fqkyO7t0AZ+2NLsIILP5m/fq7E/ghv0LLBdaY9dmvbFavvsJ1Br7PK1BQA1gNefbYezSz/GSLsN+donCRfU6/0J1N75g18DL4GkrRnol7oEL2m25Wt7S3hgpct/0aBZO+T7Rso9dnhvd5xd+jGGaTbna39buBuOHVBwYA6uKXDywA70S12CnppD+dqf0VfHSpf/4pOQp/Mdv6pe4M/ThrYd1KfztT2ir4uVLv/FuF7528rtzxjad1afzNf+sL4uljkPxpgX8ofWAL3AoDyhVS0JObTG6P0fBN7/KzgwV9ALDMzTvqP6lNz+sL7Og/ZvFhyY87Ztpz4jt92b0+BB23GFh/V+edrXtYuT22/IaV1kWD8QcwcDfzgI4MGMVGPbtTntHvTCDC24F6ao9t9lP1NkL05RbV9Xr5HbFvcm3OZi0wHo9u3b0Ol08PHxMVnv4+OD8+fPF9gmISGhwO0TEhIKPU5ERASmTJlS+oLJfB7nCtjmVtrwldte1ek9tMm7Puh9w1+ylmyf23Z4h3fR0OTUXw/g76BiH7sk7Usbvspi8FPy2GqVZJg9c3aV3EtmbCsBGGu3Cr2C/KFWdbHI6y7N8Utbe2mPbdOB2ULHbhXoCT93B/RLXVJo25Uu/0WrQM8Cj12a9qU9tqXYdACylvHjx5v0GiUnJ6Nq1apFtKAnWmnDl5Ltc9uqgfx/xT3GsUvU3kzhq6wFPyWPHezlhIv13sDKmM5AnqnFK13+i15B/oapxZZ63aU8fmlrL017Bmbrh87ShnVLsOkxQFlZWXBycsKqVavQp08fef3QoUORmJiIdevW5WtTrVo1jB07Fm+99Za8btKkSVi7di1OnDhRrONyDBARlSVKX1yuzF0RubSTJErTXuFjP+nXAXriBkG3atUKs2cb7mOl1+tRrVo1jBkzptBB0Onp6Vi/fr28rm3btmjUqBEHQRMRUbn3JF8J+okZBA0AY8eOxdChQ9GiRQu0atUKM2fORFpaGl5++WUAwJAhQ1C5cmVEREQAAN5880106tQJX375JXr27Illy5bh6NGj+P7775V8GURERDZBrZJKNd28NO1Le2xzsvkANGDAANy6dQsTJ05EQkICmjRpgs2bN8sDnePi4qBSqeTt27ZtiyVLluDjjz/Ghx9+iODgYKxdu7bY1wAiIiKiJ5/NnwJTAk+BERERlT2P8/2tKvJZIiIioicQAxARERGVOwxAREREVO4wABEREVG5wwBERERE5Q4DEBEREZU7DEBERERU7tj8hRCVYLw0UnJyssKVEBERUXEZv7eLc4lDBqACpKSkAADvCE9ERFQGpaSkwN3dvchteCXoAuj1ely/fh2urq6QJNObtCUnJ6Nq1aq4evUqrxL9GPi+lQzft8fH96xk+L6VDN+3x2fJ90wIgZSUFPj7+5vcJqsg7AEqgEqlQpUqVYrcxs3NjR/2EuD7VjJ83x4f37OS4ftWMnzfHp+l3rNH9fwYcRA0ERERlTsMQERERFTuMAA9Jq1Wi0mTJkGr1SpdSpnC961k+L49Pr5nJcP3rWT4vj0+W3nPOAiaiIiIyh32ABEREVG5wwBERERE5Q4DEBEREZU7DEBERERU7jAAPaa5c+eievXqcHBwQOvWrXH48GGlS7JZkydPhiRJJkudOnWULsvm7NmzB7169YK/vz8kScLatWtNnhdCYOLEifDz84OjoyO6du2KixcvKlOsDXnU+xYeHp7v8xcaGqpMsTYiIiICLVu2hKurK7y9vdGnTx9cuHDBZJuMjAyMHj0aFStWhIuLC55//nncuHFDoYptQ3Het86dO+f7vL366qsKVWwb5s2bh0aNGskXPGzTpg02bdokP6/0Z40B6DEsX74cY8eOxaRJkxAZGYnGjRsjJCQEN2/eVLo0m1W/fn3Ex8fLy969e5UuyeakpaWhcePGmDt3boHPT58+HbNmzcL8+fNx6NAhODs7IyQkBBkZGVau1LY86n0DgNDQUJPP39KlS61Yoe3ZvXs3Ro8ejYMHD2Lr1q3Izs5G9+7dkZaWJm/z9ttvY/369Vi5ciV2796N69ev47nnnlOwauUV530DgBEjRph83qZPn65QxbahSpUq+Oyzz3Ds2DEcPXoUTz/9NHr37o0zZ84AsIHPmqBia9WqlRg9erT8WKfTCX9/fxEREaFgVbZr0qRJonHjxkqXUaYAEL///rv8WK/XC19fXzFjxgx5XWJiotBqtWLp0qUKVGibHn7fhBBi6NChonfv3orUU1bcvHlTABC7d+8WQhg+W3Z2dmLlypXyNufOnRMAxIEDB5Qq0+Y8/L4JIUSnTp3Em2++qVxRZUSFChXEjz/+aBOfNfYAFVNWVhaOHTuGrl27yutUKhW6du2KAwcOKFiZbbt48SL8/f1Ro0YNDBo0CHFxcUqXVKbExsYiISHB5HPn7u6O1q1b83NXDLt27YK3tzdq166NUaNG4c6dO0qXZFOSkpIAAJ6engCAY8eOITs72+TzVqdOHVSrVo2ftzweft+MfvvtN1SqVAkNGjTA+PHjkZ6erkR5Nkmn02HZsmVIS0tDmzZtbOKzxpuhFtPt27eh0+ng4+Njst7Hxwfnz59XqCrb1rp1ayxcuBC1a9dGfHw8pkyZgg4dOuD06dNwdXVVurwyISEhAQAK/NwZn6OChYaG4rnnnkNgYCBiYmLw4YcfIiwsDAcOHIBarVa6PMXp9Xq89dZbaNeuHRo0aADA8Hmzt7eHh4eHybb8vD1Q0PsGAP/9738REBAAf39/nDx5Eu+//z4uXLiANWvWKFit8k6dOoU2bdogIyMDLi4u+P3331GvXj1ERUUp/lljACKLCQsLk39u1KgRWrdujYCAAKxYsQLDhw9XsDIqD1588UX554YNG6JRo0YICgrCrl270KVLFwUrsw2jR4/G6dOnOS7vMRX2vo0cOVL+uWHDhvDz80OXLl0QExODoKAga5dpM2rXro2oqCgkJSVh1apVGDp0KHbv3q10WQA4CLrYKlWqBLVanW+E+o0bN+Dr66tQVWWLh4cHatWqhejoaKVLKTOMny1+7kqvRo0aqFSpEj9/AMaMGYM///wTO3fuRJUqVeT1vr6+yMrKQmJiosn2/LwZFPa+FaR169YAUO4/b/b29qhZsyaaN2+OiIgING7cGN98841NfNYYgIrJ3t4ezZs3x/bt2+V1er0e27dvR5s2bRSsrOxITU1FTEwM/Pz8lC6lzAgMDISvr6/J5y45ORmHDh3i5+4xXbt2DXfu3CnXnz8hBMaMGYPff/8dO3bsQGBgoMnzzZs3h52dncnn7cKFC4iLiyvXn7dHvW8FiYqKAoBy/XkriF6vR2Zmpm181qwy1PoJsWzZMqHVasXChQvF2bNnxciRI4WHh4dISEhQujSbNG7cOLFr1y4RGxsr9u3bJ7p27SoqVaokbt68qXRpNiUlJUUcP35cHD9+XAAQX331lTh+/Li4cuWKEEKIzz77THh4eIh169aJkydPit69e4vAwEBx//59hStXVlHvW0pKinjnnXfEgQMHRGxsrNi2bZto1qyZCA4OFhkZGUqXrphRo0YJd3d3sWvXLhEfHy8v6enp8javvvqqqFatmtixY4c4evSoaNOmjWjTpo2CVSvvUe9bdHS0mDp1qjh69KiIjY0V69atEzVq1BAdO3ZUuHJlffDBB2L37t0iNjZWnDx5UnzwwQdCkiSxZcsWIYTynzUGoMc0e/ZsUa1aNWFvby9atWolDh48qHRJNmvAgAHCz89P2Nvbi8qVK4sBAwaI6OhopcuyOTt37hQA8i1Dhw4VQhimwk+YMEH4+PgIrVYrunTpIi5cuKBs0TagqPctPT1ddO/eXXh5eQk7OzsREBAgRowYUe7/WCno/QIgFixYIG9z//598dprr4kKFSoIJycn0bdvXxEfH69c0TbgUe9bXFyc6Nixo/D09BRarVbUrFlTvPvuuyIpKUnZwhU2bNgwERAQIOzt7YWXl5fo0qWLHH6EUP6zJgkhhHX6moiIiIhsA8cAERERUbnDAERERETlDgMQERERlTsMQERERFTuMAARERFRucMAREREROUOAxARERGVOwxARETFsGvXLkiSlO/eRURUNjEAERERUbnDAERERETlDgMQEZUJer0eERERCAwMhKOjIxo3boxVq1YBeHB6asOGDWjUqBEcHBzwn//8B6dPnzbZx+rVq1G/fn1otVpUr14dX375pcnzmZmZeP/991G1alVotVrUrFkTP/30k8k2x44dQ4sWLeDk5IS2bdviwoULln3hRGQRDEBEVCZERETgl19+wfz583HmzBm8/fbbGDx4MHbv3i1v8+677+LLL7/EkSNH4OXlhV69eiE7OxuAIbj0798fL774Ik6dOoXJkydjwoQJWLhwodx+yJAhWLp0KWbNmoVz587hu+++g4uLi0kdH330Eb788kscPXoUGo0Gw4YNs8rrJyLz4s1QicjmZWZmwtPTE9u2bUObNm3k9a+88grS09MxcuRIPPXUU1i2bBkGDBgAALh79y6qVKmChQsXon///hg0aBBu3bqFLVu2yO3fe+89bNiwAWfOnME///yD2rVrY+vWrejatWu+Gnbt2oWnnnoK27ZtQ5cuXQAAGzduRM+ePXH//n04ODhY+F0gInNiDxAR2bzo6Gikp6ejW7ducHFxkZdffvkFMTEx8nZ5w5Gnpydq166Nc+fOAQDOnTuHdu3amey3Xbt2uHjxInQ6HaKioqBWq9GpU6cia2nUqJH8s5+fHwDg5s2bpX6NRGRdGqULICJ6lNTUVADAhg0bULlyZZPntFqtSQgqKUdHx2JtZ2dnJ/8sSRIAw/gkIipb2ANERDavXr160Gq1iIuLQ82aNU2WqlWrytsdPHhQ/vnevXv4559/ULduXQBA3bp1sW/fPpP97tu3D7Vq1YJarUbDhg2h1+tNxhQR0ZOLPUBEZPNcXV3xzjvv4O2334Zer0f79u2RlJSEffv2wc3NDQEBAQCAqVOnomLFivDx8cFHH32ESpUqoU+fPgCAcePGoWXLlpg2bRoGDBiAAwcOYM6cOfj2228BANWrV8fQoUMxbNgwzJo1C40bN8aVK1dw8+ZN9O/fX6mXTkQWwgBERGXCtGnT4OXlhYiICFy6dAkeHh5o1qwZPvzwQ/kU1GeffYY333wTFy9eRJMmTbB+/XrY29sDAJo1a4YVK1Zg4sSJmDZtGvz8/DB16lSEh4fLx5g3bx4+/PBDvPbaa7hz5w6qVauGDz/8UImXS0QWxllgRFTmGWdo3bt3Dx4eHkqXQ0RlAMcAERERUbnDAERERETlDk+BERERUbnDHiAiIiIqdxiAiIiIqNxhACIiIqJyhwGIiIiIyh0GICIiIip3GICIiIio3GEAIiIionKHAYiIiIjKHQYgIiIiKnf+H58XdYtaoI71AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plotgraphs(rnn3)"
      ],
      "id": "occupied-thanksgiving"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "demonstrated-wilderness",
        "outputId": "0fed8d2c-b891-4cbb-a655-b5e78fad0613"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1492/1492 [==============================] - 16s 10ms/step\n"
          ]
        }
      ],
      "source": [
        "predict = np.argmax(rnn3.predict(X_test),axis=1)\n",
        "\n",
        "a = np.unique(predict)\n",
        "b = np.unique(y_test)\n",
        "c = list(set(a) | set(b))"
      ],
      "id": "demonstrated-wilderness"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "apparent-wyoming",
        "outputId": "8c426acd-30f6-4cb2-bd72-6cdd3ec11baf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "        ----------Classification Report Of Classes-------------\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00         5\n",
            "           1       0.58      0.98      0.73      1117\n",
            "           2       0.00      0.00      0.00         6\n",
            "           3       0.00      0.00      0.00         5\n",
            "           4       0.84      0.96      0.90       290\n",
            "           5       0.57      0.41      0.48        29\n",
            "           6       1.00      1.00      1.00      7110\n",
            "           7       0.99      0.96      0.97       463\n",
            "           8       1.00      1.00      1.00      4225\n",
            "           9       1.00      1.00      1.00      4180\n",
            "          10       0.99      0.99      0.99      4249\n",
            "          11       0.00      0.00      0.00        25\n",
            "          12       0.99      0.99      0.99      3602\n",
            "          13       0.99      1.00      0.99      4615\n",
            "          14       1.00      0.99      1.00      5591\n",
            "          15       0.98      0.96      0.97       295\n",
            "          16       0.32      0.12      0.18       179\n",
            "          17       0.00      0.00      0.00        13\n",
            "          18       0.42      0.23      0.30        86\n",
            "          19       0.99      0.98      0.99      2114\n",
            "          20       0.99      0.98      0.99      2756\n",
            "          21       0.99      0.99      0.99      3380\n",
            "          22       0.55      0.07      0.12       315\n",
            "          23       0.59      0.95      0.73      1007\n",
            "          24       0.58      0.08      0.14       754\n",
            "          25       0.99      0.99      0.99       965\n",
            "          26       0.53      0.39      0.45       134\n",
            "          27       0.25      0.02      0.04        88\n",
            "          28       0.00      0.00      0.00         0\n",
            "          29       0.42      0.06      0.11        81\n",
            "          30       0.00      0.00      0.00         8\n",
            "          31       0.00      0.00      0.00         1\n",
            "          32       0.00      0.00      0.00        49\n",
            "          33       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.96     47738\n",
            "   macro avg       0.55      0.50      0.50     47738\n",
            "weighted avg       0.96      0.96      0.95     47738\n",
            "\n",
            "\n",
            "        ----------Validation Data------------------\n",
            "Accuarcy: 95.8963509154133\n",
            "Precision: 95.6034 %\n",
            "Recall-score: 95.8964\n",
            "F1-score: 95.0397\n"
          ]
        }
      ],
      "source": [
        "report(predict,labels_test)"
      ],
      "id": "apparent-wyoming"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "crazy-basics"
      },
      "outputs": [],
      "source": [],
      "id": "crazy-basics"
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}