notebooks/3.1_lstm_fullname.ipynb

{"cells":[{"cell_type":"markdown","source":["# LSTM model to work on Fullname"],"metadata":{"id":"Du5qiFElYU6L"}},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d4_eoBacYSpd","executionInfo":{"status":"ok","timestamp":1687542989081,"user_tz":420,"elapsed":4183,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"212859fc-fa56-4fce-a394-ac9332402743"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: fastprogress in /usr/local/lib/python3.10/dist-packages (1.0.3)\n"]}],"source":["!pip install fastprogress"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Q76qchsoYtvw","executionInfo":{"status":"ok","timestamp":1687542779798,"user_tz":420,"elapsed":25199,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"dbdc85b5-e652-4ee2-dfb9-12247de206c8"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":54,"metadata":{"id":"6wTqaxXwRGBE","executionInfo":{"status":"ok","timestamp":1687544577988,"user_tz":420,"elapsed":3,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}}},"outputs":[],"source":["import string\n","import os\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from torch.optim.adamw import AdamW\n","\n","from tqdm import tqdm\n","tqdm.pandas()\n","\n","import pandas as pd\n","import numpy as np\n","\n","from fastprogress import master_bar, progress_bar\n","\n","from sklearn.feature_extraction.text import CountVectorizer"]},{"cell_type":"markdown","metadata":{"id":"cSlSeNxpELos"},"source":["## Data preprocessing"]},{"cell_type":"code","execution_count":3,"metadata":{"id":"VVVfrmyYbL0-","executionInfo":{"status":"ok","timestamp":1687542895867,"user_tz":420,"elapsed":13845,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}}},"outputs":[],"source":["train_df = pd.read_csv(\"/content/drive/MyDrive/Colab/ethnicolor/data/fl_2022_FullName_train.csv.gz\")\n","val_df = pd.read_csv(\"/content/drive/MyDrive/Colab/ethnicolor/data/fl_2022_FullName_val.csv.gz\")\n","test_df = pd.read_csv(\"/content/drive/MyDrive/Colab/ethnicolor/data/fl_2022_FullName_test.csv.gz\")"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":18,"status":"ok","timestamp":1687542895868,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"},"user_tz":420},"id":"u0TJpyUQEAHz","outputId":"7def8fda-0466-46b8-b9c8-4463958b50e4"},"outputs":[{"output_type":"stream","name":"stdout","text":["(7214894, 9)\n","(901862, 9)\n","(901862, 9)\n"]}],"source":["print(train_df.shape)\n","print(val_df.shape)\n","print(test_df.shape)"]},{"cell_type":"code","source":["train_df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"iwdO0P4PbfHI","executionInfo":{"status":"ok","timestamp":1687543519484,"user_tz":420,"elapsed":7,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"436dbedc-04a0-4374-f226-ff52f2f97743"},"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["          full_name  asian  hispanic  nh_black  nh_white  other  total_n  \\\n","0        Veit Karen    0.0       0.0       0.0       1.0    0.0      2.0   \n","1  Barahona Octavio    0.0       1.0       0.0       0.0    0.0      1.0   \n","2    Albert Jurieal    0.0       0.0       1.0       0.0    0.0      1.0   \n","3        Foco Kevin    0.0       0.0       0.0       1.0    0.0      1.0   \n","4      Ewen Deborah    0.0       0.0       0.0       1.0    0.0      1.0   \n","\n","       race  race_code  \n","0  nh_white          3  \n","1  hispanic          1  \n","2  nh_black          2  \n","3  nh_white          3  \n","4  nh_white          3  "],"text/html":["\n","  <div id=\"df-7299bc17-eaf5-4ce2-9cab-733b0b2ae4f3\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>full_name</th>\n","      <th>asian</th>\n","      <th>hispanic</th>\n","      <th>nh_black</th>\n","      <th>nh_white</th>\n","      <th>other</th>\n","      <th>total_n</th>\n","      <th>race</th>\n","      <th>race_code</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Veit Karen</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>2.0</td>\n","      <td>nh_white</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Barahona Octavio</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>hispanic</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Albert Jurieal</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>nh_black</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Foco Kevin</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>nh_white</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Ewen Deborah</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>nh_white</td>\n","      <td>3</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7299bc17-eaf5-4ce2-9cab-733b0b2ae4f3')\"\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","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\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","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\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","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-7299bc17-eaf5-4ce2-9cab-733b0b2ae4f3 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-7299bc17-eaf5-4ce2-9cab-733b0b2ae4f3');\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","  "]},"metadata":{},"execution_count":11}]},{"cell_type":"markdown","source":["## Creating Vocab"],"metadata":{"id":"WitJjk39ZW3r"}},{"cell_type":"code","source":["vectorizer = CountVectorizer(analyzer='char', lowercase=False)"],"metadata":{"id":"mGdsr5-1ZWMc","executionInfo":{"status":"ok","timestamp":1687543443342,"user_tz":420,"elapsed":471,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["vectorizer.fit_transform(train_df['full_name'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JCGmPOeDbXUE","executionInfo":{"status":"ok","timestamp":1687543518248,"user_tz":420,"elapsed":46778,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"aac1ad7d-f582-495d-d3a0-71c502ed2a25"},"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<7214894x55 sparse matrix of type '<class 'numpy.int64'>'\n","\twith 79471282 stored elements in Compressed Sparse Row format>"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["vocab = list(vectorizer.get_feature_names_out())"],"metadata":{"id":"renBhCNHbekD","executionInfo":{"status":"ok","timestamp":1687543568008,"user_tz":420,"elapsed":436,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}}},"execution_count":14,"outputs":[]},{"cell_type":"code","source":["n_letters = len(vocab)"],"metadata":{"id":"1e0oppKIb2I7","executionInfo":{"status":"ok","timestamp":1687543588603,"user_tz":420,"elapsed":7,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}}},"execution_count":16,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"GzHXp_2UE_H6"},"source":["## Creating Dataset"]},{"cell_type":"code","execution_count":21,"metadata":{"id":"a2UdQLOKYSpn","executionInfo":{"status":"ok","timestamp":1687543831808,"user_tz":420,"elapsed":2325,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}}},"outputs":[],"source":["race_id_df = train_df[['race', 'race_code']].drop_duplicates().sort_values('race_code')\n","race_to_id = dict(race_id_df.values)\n","id_to_race = dict(race_id_df[['race_code', 'race']].values)\n","all_categories = list(id_to_race.values())\n","n_categories = len(all_categories)\n","seq_len = train_df['full_name'].str.len().max()"]},{"cell_type":"code","execution_count":22,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ft1gqGeqYSpp","executionInfo":{"status":"ok","timestamp":1687543831808,"user_tz":420,"elapsed":5,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"10bf2abd-0816-46c6-ef80-78e1ea06616d"},"outputs":[{"output_type":"stream","name":"stdout","text":["classes - ['asian', 'hispanic', 'nh_black', 'nh_white', 'other']\n","no of classes - 5\n","max seq len - 47\n"]}],"source":["print(f\"classes - {all_categories}\")\n","print(f\"no of classes - {n_categories}\")\n","print(f\"max seq len - {seq_len}\")"]},{"cell_type":"code","execution_count":31,"metadata":{"id":"RI-qV--Da6I_","executionInfo":{"status":"ok","timestamp":1687544029065,"user_tz":420,"elapsed":4,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}}},"outputs":[],"source":["# helper methods used for transform in dataset\n","\n","all_letters = ''.join(vocab)\n","oob = n_letters + 1\n","\n","def letterToIndex(letter):\n","    return all_letters.find(letter)\n","\n","def lineToTensor(line):\n","    tensor = torch.ones(seq_len) * oob\n","    for li, letter in enumerate(line):\n","        tensor[li] = letterToIndex(letter)\n","    return tensor"]},{"cell_type":"code","execution_count":32,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":269,"status":"ok","timestamp":1687544032641,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"},"user_tz":420},"id":"uRYxmYbkcIVZ","outputId":"f9a9cae4-7857-498c-b9c6-0732c43c60f1"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor([12., 29., 47., 43., 42., 56., 56., 56., 56., 56., 56., 56., 56., 56.,\n","        56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56.,\n","        56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56.,\n","        56., 56., 56., 56., 56.])"]},"metadata":{},"execution_count":32}],"source":["lineToTensor('Jason')"]},{"cell_type":"code","execution_count":33,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1687544034222,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"},"user_tz":420},"id":"NPAYYcHAK59U","outputId":"8cc92173-6c0b-4376-fa98-1f684db8dfd2"},"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([47])\n"]}],"source":["print(lineToTensor('Jason').size())"]},{"cell_type":"code","execution_count":41,"metadata":{"id":"kKwuNTwjEzkV","executionInfo":{"status":"ok","timestamp":1687544331388,"user_tz":420,"elapsed":534,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}}},"outputs":[],"source":["# A custom Dataset class must implement three functions: __init__, __len__, and __getitem__\n","\n","class EthniColorDataset(Dataset):\n","  def __init__(self, data_df, transform=None):\n","    self.df = data_df\n","    self.transform = transform\n","\n","  def __len__(self):\n","    return len(self.df)\n","\n","  def __getitem__(self, idx):\n","    if torch.is_tensor(idx):\n","      idx = idx.tolist()\n","    name = self.df.iloc[idx, train_df.columns.get_loc('full_name')]\n","    if self.transform:\n","      name = self.transform(name)\n","    label = self.df.iloc[idx, train_df.columns.get_loc('race_code')]\n","    #target = getTarget(label)\n","    target = torch.tensor(label, dtype=torch.int64)\n","    return name, target"]},{"cell_type":"code","execution_count":42,"metadata":{"id":"Ue_MhX-7QN9I","executionInfo":{"status":"ok","timestamp":1687544334420,"user_tz":420,"elapsed":230,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}}},"outputs":[],"source":["train_dataset = EthniColorDataset(train_df, lineToTensor)\n","val_dataset = EthniColorDataset(val_df, lineToTensor)\n","test_dataset = EthniColorDataset(test_df, lineToTensor)"]},{"cell_type":"code","execution_count":43,"metadata":{"id":"U9fp-gC9Mnft","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1687544337340,"user_tz":420,"elapsed":231,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"1145f860-7dd2-40af-ec9c-9259a5802773"},"outputs":[{"output_type":"stream","name":"stdout","text":["0 tensor([24., 33., 37., 48.,  0., 13., 29., 46., 33., 42., 56., 56., 56., 56.,\n","        56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56.,\n","        56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56.,\n","        56., 56., 56., 56., 56.]) tensor(3)\n","1 tensor([ 4., 29., 46., 29., 36., 43., 42., 29.,  0., 17., 31., 48., 29., 50.,\n","        37., 43., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56.,\n","        56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56.,\n","        56., 56., 56., 56., 56.]) tensor(1)\n","2 tensor([ 3., 40., 30., 33., 46., 48.,  0., 12., 49., 46., 37., 33., 29., 40.,\n","        56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56.,\n","        56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56.,\n","        56., 56., 56., 56., 56.]) tensor(2)\n"]}],"source":["for i in range(3):\n","  name, label = train_dataset[i]\n","  print(i, name, label)"]},{"cell_type":"code","source":["num_workers = !lscpu | grep \"^CPU(s):\" | awk '{print $2}'"],"metadata":{"id":"nbNgheP8e12B","executionInfo":{"status":"ok","timestamp":1687544507154,"user_tz":420,"elapsed":9,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}}},"execution_count":50,"outputs":[]},{"cell_type":"code","execution_count":55,"metadata":{"id":"tGyzWNzgNzjc","executionInfo":{"status":"ok","timestamp":1687544613634,"user_tz":420,"elapsed":6,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}}},"outputs":[],"source":["# The Dataset retrieves our dataset’s features and labels one sample at a time.\n","# While training a model, we typically want to pass samples in “minibatches”,\n","# reshuffle the data at every epoch to reduce model overfitting, and\n","# use Python’s multiprocessing to speed up data retrieval.\n","\n","batch_size=128\n","\n","train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=os.cpu_count())\n","val_dataloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=os.cpu_count())\n","test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=os.cpu_count())"]},{"cell_type":"code","execution_count":56,"metadata":{"id":"C7c7-RtYlE3G","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1687544618299,"user_tz":420,"elapsed":1515,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"b8f39dbc-d206-4277-db91-c1842cacd480"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[tensor([[24., 29., 40.,  ..., 56., 56., 56.],\n","         [18., 43., 31.,  ..., 56., 56., 56.],\n","         [ 9., 46., 37.,  ..., 56., 56., 56.],\n","         ...,\n","         [13., 29., 46.,  ..., 56., 56., 56.],\n","         [ 6., 29., 40.,  ..., 56., 56., 56.],\n","         [21., 37., 40.,  ..., 56., 56., 56.]]),\n"," tensor([3, 1, 3, 2, 0, 3, 3, 1, 3, 3, 3, 3, 3, 3, 2, 3, 2, 2, 3, 1, 2, 1, 1, 3,\n","         3, 3, 2, 3, 3, 1, 3, 2, 1, 0, 3, 1, 1, 3, 3, 3, 2, 3, 3, 3, 1, 4, 4, 3,\n","         2, 2, 3, 3, 3, 1, 2, 3, 1, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 2, 0, 3, 3, 4,\n","         3, 3, 3, 3, 3, 1, 3, 3, 3, 2, 3, 2, 3, 3, 1, 2, 3, 3, 0, 2, 3, 3, 2, 1,\n","         3, 3, 1, 2, 1, 3, 1, 3, 3, 3, 2, 4, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n","         2, 3, 3, 3, 3, 3, 3, 2])]"]},"metadata":{},"execution_count":56}],"source":["next(iter(train_dataloader))"]},{"cell_type":"code","execution_count":57,"metadata":{"id":"UAebM2_6kIr1","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1687544626622,"user_tz":420,"elapsed":1092,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"a07d1f0e-9279-4ad2-a223-b852540f832c"},"outputs":[{"output_type":"stream","name":"stdout","text":["0 torch.Size([128, 47]) torch.Size([128])\n","1 torch.Size([128, 47]) torch.Size([128])\n","2 torch.Size([128, 47]) torch.Size([128])\n"]}],"source":["for i_batch, sample_batched in enumerate(train_dataloader):\n","    print(i_batch, sample_batched[0].size(), sample_batched[1].size())\n","    if i_batch == 2:\n","        break"]},{"cell_type":"markdown","source":["## Define LSTM Model"],"metadata":{"id":"-YDe9RIDgnVS"}},{"cell_type":"code","execution_count":59,"metadata":{"id":"5zvwGXG2M-Gu","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1687544785150,"user_tz":420,"elapsed":8091,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"4d436435-33fd-4b50-a44a-a93b3f034913"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["LSTM(\n","  (embedding): Embedding(57, 256)\n","  (lstm): LSTM(256, 256, num_layers=2, batch_first=True)\n","  (fc): Linear(in_features=256, out_features=5, bias=True)\n","  (softmax): LogSoftmax(dim=1)\n",")"]},"metadata":{},"execution_count":59}],"source":["# Set the random seed for reproducible results\n","torch.manual_seed(42)\n","device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n","\n","class LSTM(nn.Module):\n","    def __init__(self, input_size, hidden_size, output_size, num_layers=1):\n","        super(LSTM, self).__init__()\n","        self.hidden_size = hidden_size\n","        self.num_layers = num_layers\n","\n","        # The nn.Embedding layer returns a new tensor with dimension (sequence_length, 1, hidden_size)\n","        self.embedding = nn.Embedding(input_size, hidden_size)\n","        # LSTM layer expects a tensor of dimension (batch_size, sequence_length, hidden_size).\n","        self.lstm = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True)\n","        self.fc = nn.Linear(hidden_size, output_size)\n","        self.softmax = nn.LogSoftmax(dim=1)\n","\n","    def forward(self, input):\n","        embedded = self.embedding(input.type(torch.IntTensor).to(input.device))\n","        # embedded = embedded.view(embedded.shape[0],-1,embedded.shape[3])\n","        h0 = torch.zeros(self.num_layers, embedded.size(0), self.hidden_size).to(input.device)\n","        c0 = torch.zeros(self.num_layers, embedded.size(0), self.hidden_size).to(input.device)\n","        out, _ = self.lstm(embedded, (h0, c0))\n","        out = out[:, -1, :]  # get the output of the last time step\n","        out = self.fc(out)\n","        out = self.softmax(out)\n","        return out\n","\n","\n","n_hidden = 256\n","seq_len = seq_len\n","vocab_size = n_letters + 1 + 1 # vocab + oob + 1\n","\n","rnn = LSTM(vocab_size, n_hidden, n_categories, num_layers=2)\n","rnn.to(device)"]},{"cell_type":"markdown","source":["## Verify with sample data"],"metadata":{"id":"NIsVBTBmgr9N"}},{"cell_type":"code","execution_count":65,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"G2v1iG1PTDsO","outputId":"afbe6f10-4d9e-43ba-d1cc-8a21f9f154ed","executionInfo":{"status":"ok","timestamp":1687544974215,"user_tz":420,"elapsed":8,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([20., 43., 35., 33., 46., 47.,  0., 20., 33., 42., 33., 33., 56., 56.,\n","        56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56.,\n","        56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56.,\n","        56., 56., 56., 56., 56.])\n","input shape :  torch.Size([47])\n","input shape w batch :  torch.Size([1, 47])\n","torch.Size([1, 5])\n","tensor([[-1.6279, -1.6090, -1.6168, -1.5790, -1.6152]], device='cuda:0',\n","       grad_fn=<LogSoftmaxBackward0>)\n","tensor(3, device='cuda:0')\n","model predicted w/o train - nh_white\n"]}],"source":["input = lineToTensor('Rogers Renee')\n","\n","print(input)\n","print(\"input shape : \",input.shape)\n","print(\"input shape w batch : \", input.unsqueeze(0).shape)\n","#print(hidden.shape)\n","\n","# sending with batch 1\n","output = rnn(input.unsqueeze(0).to(device))\n","# print(output) - has 47x8 log values\n","print(output.shape)\n","print(output)\n","print(torch.argmax(output))\n","print(f\"model predicted w/o train - {all_categories[torch.argmax(output).item()]}\")"]},{"cell_type":"markdown","metadata":{"id":"_qsEnfPKgYgt"},"source":["## Training\n"]},{"cell_type":"code","execution_count":66,"metadata":{"executionInfo":{"elapsed":553,"status":"ok","timestamp":1687545004648,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"},"user_tz":420},"id":"g6MYfcuxZm0_"},"outputs":[],"source":["class EarlyStopper:\n","    def __init__(self, patience=1, min_delta=0):\n","        self.patience = patience\n","        self.min_delta = min_delta\n","        self.counter = 0\n","        self.min_validation_loss = np.inf\n","\n","    def early_stop(self, validation_loss):\n","        if validation_loss < self.min_validation_loss:\n","            self.min_validation_loss = validation_loss\n","            self.counter = 0\n","        elif validation_loss > (self.min_validation_loss + self.min_delta):\n","            self.counter += 1\n","            if self.counter >= self.patience:\n","                return True\n","        return False"]},{"cell_type":"code","execution_count":67,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"RUkgRkq-TuWB","executionInfo":{"status":"ok","timestamp":1687566427448,"user_tz":420,"elapsed":21359794,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"199ae3e5-0c54-43f7-fa62-0078b3e0aea9"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","<style>\n","    /* Turns off some styling */\n","    progress {\n","        /* gets rid of default border in Firefox and Opera. */\n","        border: none;\n","        /* Needs to be in here for Safari polyfill so background images work as expected. */\n","        background-size: auto;\n","    }\n","    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n","        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n","    }\n","    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n","        background: #F44336;\n","    }\n","</style>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      <progress value='40' class='' max='100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      40.00% [40/100 5:47:21&lt;8:41:02]\n","    </div>\n","    \n","\n","Epoch 0: Training loss 0.567343 validation loss 0.506261 with lr 0.000500<p>\n","Epoch 1: Training loss 0.491842 validation loss 0.488012 with lr 0.000500<p>\n","Epoch 2: Training loss 0.476384 validation loss 0.478004 with lr 0.000500<p>\n","Epoch 3: Training loss 0.468368 validation loss 0.475072 with lr 0.000500<p>\n","Epoch 4: Training loss 0.463265 validation loss 0.470858 with lr 0.000500<p>\n","Epoch 5: Training loss 0.459581 validation loss 0.471248 with lr 0.000500<p>\n","Epoch 6: Training loss 0.456834 validation loss 0.469685 with lr 0.000500<p>\n","Epoch 7: Training loss 0.454580 validation loss 0.466651 with lr 0.000500<p>\n","Epoch 8: Training loss 0.452533 validation loss 0.465881 with lr 0.000500<p>\n","Epoch 9: Training loss 0.450915 validation loss 0.467296 with lr 0.000500<p>\n","Epoch 10: Training loss 0.449386 validation loss 0.465318 with lr 0.000500<p>\n","Epoch 11: Training loss 0.448085 validation loss 0.464154 with lr 0.000500<p>\n","Epoch 12: Training loss 0.446738 validation loss 0.463602 with lr 0.000500<p>\n","Epoch 13: Training loss 0.445465 validation loss 0.462078 with lr 0.000500<p>\n","Epoch 14: Training loss 0.444172 validation loss 0.462372 with lr 0.000500<p>\n","Epoch 15: Training loss 0.442931 validation loss 0.462036 with lr 0.000500<p>\n","Epoch 16: Training loss 0.441652 validation loss 0.461024 with lr 0.000500<p>\n","Epoch 17: Training loss 0.440437 validation loss 0.461316 with lr 0.000500<p>\n","Epoch 18: Training loss 0.439180 validation loss 0.460302 with lr 0.000500<p>\n","Epoch 19: Training loss 0.437911 validation loss 0.460933 with lr 0.000500<p>\n","Epoch 20: Training loss 0.436562 validation loss 0.459551 with lr 0.000500<p>\n","Epoch 21: Training loss 0.435215 validation loss 0.458612 with lr 0.000500<p>\n","Epoch 22: Training loss 0.433869 validation loss 0.457829 with lr 0.000500<p>\n","Epoch 23: Training loss 0.432689 validation loss 0.456861 with lr 0.000500<p>\n","Epoch 24: Training loss 0.431165 validation loss 0.457985 with lr 0.000500<p>\n","Epoch 25: Training loss 0.429698 validation loss 0.456737 with lr 0.000500<p>\n","Epoch 26: Training loss 0.428245 validation loss 0.456832 with lr 0.000500<p>\n","Epoch 27: Training loss 0.426707 validation loss 0.456080 with lr 0.000500<p>\n","Epoch 28: Training loss 0.425094 validation loss 0.457397 with lr 0.000500<p>\n","Epoch 29: Training loss 0.423537 validation loss 0.455768 with lr 0.000500<p>\n","Epoch 30: Training loss 0.422009 validation loss 0.454129 with lr 0.000500<p>\n","Epoch 31: Training loss 0.420291 validation loss 0.457450 with lr 0.000500<p>\n","Epoch 32: Training loss 0.418638 validation loss 0.455352 with lr 0.000500<p>\n","Epoch 33: Training loss 0.416686 validation loss 0.454849 with lr 0.000500<p>\n","Epoch 34: Training loss 0.414951 validation loss 0.455856 with lr 0.000500<p>\n","Epoch 35: Training loss 0.413048 validation loss 0.455977 with lr 0.000500<p>\n","Epoch 36: Training loss 0.411177 validation loss 0.457864 with lr 0.000500<p>\n","Epoch 37: Training loss 0.409163 validation loss 0.454961 with lr 0.000500<p>\n","Epoch 38: Training loss 0.407244 validation loss 0.455775 with lr 0.000500<p>\n","Epoch 39: Training loss 0.405224 validation loss 0.457419 with lr 0.000500<p>\n","\n","    <div>\n","      <progress value='7045' class='' max='7045' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      100.00% [7045/7045 00:46&lt;00:00]\n","    </div>\n","    "]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure size 600x400 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/numpy/core/shape_base.py:65: FutureWarning: The input object of type 'Tensor' is an array-like implementing one of the corresponding protocols (`__array__`, `__array_interface__` or `__array_struct__`); but not a sequence (or 0-D). In the future, this object will be coerced as if it was first converted using `np.array(obj)`. To retain the old behaviour, you have to either modify the type 'Tensor', or assign to an empty array created with `np.empty(correct_shape, dtype=object)`.\n","  ary = asanyarray(ary)\n"]},{"output_type":"stream","name":"stdout","text":["Validation loss decreased (inf --> 0.506261).  Saving model ...\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/numpy/core/shape_base.py:65: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n","  ary = asanyarray(ary)\n"]},{"output_type":"stream","name":"stdout","text":["Validation loss decreased (0.506261 --> 0.488012).  Saving model ...\n","Validation loss decreased (0.488012 --> 0.478004).  Saving model ...\n","Validation loss decreased (0.478004 --> 0.475072).  Saving model ...\n","Validation loss decreased (0.475072 --> 0.470858).  Saving model ...\n","Validation loss decreased (0.470858 --> 0.469685).  Saving model ...\n","Validation loss decreased (0.469685 --> 0.466651).  Saving model ...\n","Validation loss decreased (0.466651 --> 0.465881).  Saving model ...\n","Validation loss decreased (0.465881 --> 0.465318).  Saving model ...\n","Validation loss decreased (0.465318 --> 0.464154).  Saving model ...\n","Validation loss decreased (0.464154 --> 0.463602).  Saving model ...\n","Validation loss decreased (0.463602 --> 0.462078).  Saving model ...\n","Validation loss decreased (0.462078 --> 0.462036).  Saving model ...\n","Validation loss decreased (0.462036 --> 0.461024).  Saving model ...\n","Validation loss decreased (0.461024 --> 0.460302).  Saving model ...\n","Validation loss decreased (0.460302 --> 0.459551).  Saving model ...\n","Validation loss decreased (0.459551 --> 0.458612).  Saving model ...\n","Validation loss decreased (0.458612 --> 0.457829).  Saving model ...\n","Validation loss decreased (0.457829 --> 0.456861).  Saving model ...\n","Validation loss decreased (0.456861 --> 0.456737).  Saving model ...\n","Validation loss decreased (0.456737 --> 0.456080).  Saving model ...\n","Validation loss decreased (0.456080 --> 0.455768).  Saving model ...\n","Validation loss decreased (0.455768 --> 0.454129).  Saving model ...\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 600x400 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["epochs = 100\n","lr = 0.0005 # 0.005 is very slow\n","\n","\n","# CrossEntropyLoss expects raw prediction values while NLLLoss expects log probabilities.\n","# criterion = nn.CrossEntropyLoss() # nn.NLLLoss()\n","# since we are using nn.LogSoftmax as final layer at model\n","criterion = nn.NLLLoss()\n","\n","optimizer = AdamW(rnn.parameters(), lr)\n","scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=60, eta_min=1e-5)\n","\n","\n","early_stopper = EarlyStopper(patience=10)\n","\n","\n","mb = master_bar(range(epochs))\n","mb.names = ['Training loss', 'Validation loss']\n","\n","x = []\n","training_losses = []\n","validation_losses = []\n","\n","valid_mean_min = np.Inf\n","\n","till_batch = 1000\n","\n","for epoch in mb:\n","  x.append(epoch)\n","  # Train\n","  i = 0\n","  rnn.train()\n","  total_loss = torch.Tensor([0.0]).to(device)\n","  for batch in progress_bar(train_dataloader, parent=mb):\n","    rnn.zero_grad()\n","    input = batch[0].to(device)\n","    label = batch[1].to(device)\n","    output = rnn(input)\n","    loss = criterion(output, label)\n","    # backward propagation\n","    loss.backward()\n","    # `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.\n","    #torch.nn.utils.clip_grad_norm_(rnn.parameters(), clip)\n","    optimizer.step()\n","    with torch.no_grad():\n","      total_loss += loss.item()\n","    i += 1\n","    #if i == till_batch:\n","    #  break\n","\n","  # decay lr\n","  scheduler.step()\n","\n","  mean = total_loss / len(train_dataloader)\n","  #mean = total_loss / till_batch\n","  training_losses.append(mean.cpu())\n","\n","  # Evaluate\n","  i = 0\n","  rnn.eval()\n","  validation_loss = torch.Tensor([0.0]).to(device)\n","  with torch.no_grad():\n","    for batch in progress_bar(val_dataloader, parent=mb):\n","      input = batch[0].to(device)\n","      label = batch[1].to(device)\n","      output = rnn(input)\n","      loss = criterion(output, label)\n","      validation_loss += loss.item()\n","      i += 1\n","      #if i == till_batch:\n","      #  break\n","\n","  val_mean = validation_loss / len(val_dataloader)\n","  #val_mean = validation_loss / till_batch\n","  validation_losses.append(val_mean.cpu())\n","  # Update training chart\n","  mb.update_graph([[x, training_losses], [x, validation_losses]], [0,epochs])\n","  mb.write(f\"\\nEpoch {epoch}: Training loss {mean.item():.6f} validation loss {val_mean.item():.6f} with lr {lr:.6f}\")\n","  # save model if validation loss has decreased\n","  if val_mean.item() <= valid_mean_min:\n","      print('Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...'.format(\n","      valid_mean_min,\n","      val_mean.item()))\n","      torch.save(rnn.state_dict(), 'simple_rnn_ethni.pt')\n","      valid_mean_min = val_mean.item()\n","\n","  if early_stopper.early_stop(val_mean.item()):\n","      break"]},{"cell_type":"markdown","metadata":{"id":"e6ylXlQwqfkj"},"source":["## Save Model"]},{"cell_type":"code","execution_count":68,"metadata":{"id":"1DoKR-M_qedg","executionInfo":{"status":"ok","timestamp":1687566428422,"user_tz":420,"elapsed":17,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}}},"outputs":[],"source":["torch.save(rnn.state_dict(), 'lstm_FullName_pytorch.pt')"]},{"cell_type":"code","execution_count":69,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":563},"id":"nFk3kN8mYSp-","executionInfo":{"status":"ok","timestamp":1687566428422,"user_tz":420,"elapsed":16,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"aa6e3483-0381-4781-92cc-9bc3c271c93f"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0, 0.5, 'loss')"]},"metadata":{},"execution_count":69},{"output_type":"display_data","data":{"text/plain":["<Figure size 600x600 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAikAAAIRCAYAAACPui3eAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB1oUlEQVR4nO3dd3xUZaL/8c9MKiWVkAaB0EmQZoBsEFGWKKCLoHhFRUGlqD/RFdbGuqLiveLqrhdRVryuiK6usHZXV6QjQgQkREIvQkJJoaVC2sz5/XHIhJEWIMNMMt/363VemZz6HA4yX5/zFIthGAYiIiIiHsbq7gKIiIiInIlCioiIiHgkhRQRERHxSAopIiIi4pEUUkRERMQjKaSIiIiIR1JIEREREY+kkCIiIiIeSSFFREREPJJCioiIiHgkt4eUWbNmER8fT2BgIMnJyaxdu/as+86dOxeLxeK0BAYGOu3z6+3VyyuvvOLYJz4+/rTtL730ksvuUURERC6crzsvPn/+fCZPnszs2bNJTk5mxowZDBo0iO3btxMZGXnGY4KDg9m+fbvjd4vF4rQ9JyfH6fdvv/2WsWPHMmLECKf106ZNY/z48Y7fg4KCLvV2REREpA65NaS8+uqrjB8/nnvvvReA2bNn88033zBnzhyeeuqpMx5jsViIjo4+6zl/ve3LL79kwIABtG3b1ml9UFDQOc9zPna7nYMHDxIUFHRaUBIREZGzMwyD4uJiYmNjsVrP8VLHcJPy8nLDx8fH+Pzzz53Wjx492rjpppvOeMy7775r+Pj4GK1atTJatmxp3HTTTcamTZvOeo3c3FzD19fX+PDDD53Wt27d2oiKijLCw8ONHj16GC+//LJRWVl5zvKWlZUZhYWFjmXLli0GoEWLFi1atGi5yGXfvn3n/O51W03K4cOHsdlsREVFOa2Piopi27ZtZzymU6dOzJkzh27dulFYWMhf/vIX+vbty+bNm2nZsuVp+7/33nsEBQVxyy23OK1/5JFHuPLKKwkPD2f16tVMmTKFnJwcXn311bOWd/r06Tz//POnrd+3bx/BwcG1uWUREREBioqKiIuLO29TC4thGMZlKpOTgwcP0qJFC1avXk1KSopj/RNPPMGKFStYs2bNec9RWVlJQkICd9xxBy+88MJp2zt37sx1113H66+/fs7zzJkzh/vvv5+SkhICAgLOuE95eTnl5eWO36v/gAsLCxVSRERELkBRUREhISHn/Q51W01KREQEPj4+5OXlOa3Py8urdVsRPz8/evbsya5du07btnLlSrZv3878+fPPe57k5GSqqqrYu3cvnTp1OuM+AQEBZw0wIiIiUvfc1gXZ39+fpKQklixZ4lhnt9tZsmSJU83KudhsNjIzM4mJiTlt2zvvvENSUhLdu3c/73kyMjKwWq1n7VEkIiIil59be/dMnjyZMWPG0KtXL/r06cOMGTMoLS119PYZPXo0LVq0YPr06YDZbfg3v/kN7du3p6CggFdeeYWsrCzGjRvndN6ioiI+/vhj/vrXv552zbS0NNasWcOAAQMICgoiLS2NSZMmcddddxEWFub6mxYREZFacWtIGTlyJIcOHWLq1Knk5ubSo0cPFixY4GhMm52d7dQ16dixY4wfP57c3FzCwsJISkpi9erVJCYmOp133rx5GIbBHXfccdo1AwICmDdvHs899xzl5eW0adOGSZMmMXnyZNferIiIhzEMg6qqKmw2m7uLIg2Mj48Pvr6+lzxEh9saztZ3tW30IyLiiSoqKsjJyeH48ePuLoo0UI0bNyYmJgZ/f//Ttnl8w1kREXEPu93Onj178PHxITY2Fn9/fw1KKXXGMAwqKio4dOgQe/bsoUOHDucesO0cFFJERLxMRUUFdruduLg4Gjdu7O7iSAPUqFEj/Pz8yMrKoqKi4rR59mrL7RMMioiIe1zs/92K1EZd/P3S31ARERHxSAopIiIi4pEUUkRExKvFx8czY8aMWu+/fPlyLBYLBQUFLiuTmBRSRESkXrBYLOdcnnvuuYs677p165gwYUKt9+/bty85OTmEhIRc1PVqS2FIvXtERKSeyMnJcXyeP38+U6dOZfv27Y51TZs2dXw2DAObzYav7/m/5po3b35B5fD396/1HHNyaVSTIiIiGIbB8Yoqtyy1HVM0OjrasYSEhGCxWBy/b9u2jaCgIL799luSkpIICAjghx9+YPfu3QwbNoyoqCiaNm1K7969Wbx4sdN5f/26x2Kx8Pe//52bb76Zxo0b06FDB7766ivH9l/XcMydO5fQ0FC+++47EhISaNq0KYMHD3YKVVVVVTzyyCOEhobSrFkznnzyScaMGcPw4cMv+pkdO3aM0aNHExYWRuPGjRkyZAg7d+50bM/KymLo0KGEhYXRpEkTunTpwn/+8x/HsaNGjaJ58+Y0atSIDh068O677150WVxFNSkiIsKJShuJU79zy7W3TBtEY/+6+Tp66qmn+Mtf/kLbtm0JCwtj37593HDDDfzP//wPAQEBvP/++wwdOpTt27fTqlWrs57n+eef5+WXX+aVV17h9ddfZ9SoUWRlZREeHn7G/Y8fP85f/vIX/vGPf2C1Wrnrrrt47LHH+PDDDwH485//zIcffsi7775LQkICr732Gl988QUDBgy46Hu955572LlzJ1999RXBwcE8+eST3HDDDWzZsgU/Pz8eeughKioq+P7772nSpAlbtmxx1DY988wzbNmyhW+//ZaIiAh27drFiRMnLrosrqKQIiIiDca0adO47rrrHL+Hh4fTvXt3x+8vvPACn3/+OV999RUTJ04863nuuecex/xvL774IjNnzmTt2rUMHjz4jPtXVlYye/Zs2rVrB8DEiROZNm2aY/vrr7/OlClTuPnmmwF44403HLUaF6M6nKxatYq+ffsC8OGHHxIXF8cXX3zBf/3Xf5Gdnc2IESPo2rUrAG3btnUcn52dTc+ePenVqxdg1iZ5IoUUD3G4pJz0rGP4+1q5tlOku4sjIl6mkZ8PW6YNctu160r1l261kpISnnvuOb755htycnKoqqrixIkTZGdnn/M83bp1c3xu0qQJwcHB5Ofnn3X/xo0bOwIKQExMjGP/wsJC8vLy6NOnj2O7j48PSUlJ2O32C7q/alu3bsXX15fk5GTHumbNmtGpUye2bt0KwCOPPMKDDz7IwoULSU1NZcSIEY77evDBBxkxYgTp6elcf/31DB8+3BF2PInapHiIrTlFTPjHel76dpu7iyIiXshisdDY39ctS13OG9SkSROn3x977DE+//xzXnzxRVauXElGRgZdu3aloqLinOfx8/M77c/nXIHiTPu7e/7ecePG8csvv3D33XeTmZlJr169eP311wEYMmQIWVlZTJo0iYMHDzJw4EAee+wxt5b3TBRSPETTALNSq7isys0lERFpOFatWsU999zDzTffTNeuXYmOjmbv3r2XtQwhISFERUWxbt06xzqbzUZ6evpFnzMhIYGqqirWrFnjWHfkyBG2b99OYmKiY11cXBwPPPAAn332GX/4wx94++23HduaN2/OmDFj+OCDD5gxYwb/93//d9HlcRW97vEQQYHmoygpV0gREakrHTp04LPPPmPo0KFYLBaeeeaZi37Fcikefvhhpk+fTvv27encuTOvv/46x44dq1UtUmZmJkFBQY7fLRYL3bt3Z9iwYYwfP5633nqLoKAgnnrqKVq0aMGwYcMAePTRRxkyZAgdO3bk2LFjLFu2jISEBACmTp1KUlISXbp0oby8nK+//tqxzZMopHiIpgFmVWFJudkdT9Omi4hculdffZX77ruPvn37EhERwZNPPklRUdFlL8eTTz5Jbm4uo0ePxsfHhwkTJjBo0CB8fM7fHqd///5Ov/v4+FBVVcW7777L73//e373u99RUVFB//79+c9//uN49WSz2XjooYfYv38/wcHBDB48mP/93/8FzLFepkyZwt69e2nUqBFXX3018+bNq/sbv0QWw90vzeqpoqIiQkJCKCwsJDg4+JLPV1JexRXPmt3/tk4bTCP/umtIJiJyqrKyMvbs2UObNm0IDAx0d3G8kt1uJyEhgdtuu40XXnjB3cVxiXP9Pavtd6hqUjxEYz8fLBYwDCgur1RIERFpQLKysli4cCHXXHMN5eXlvPHGG+zZs4c777zT3UXzaGo46yGsVgtNTw5mVKLGsyIiDYrVamXu3Ln07t2bq666iszMTBYvXuyR7UA8iWpSPEjTQF+Ky6vUeFZEpIGJi4tj1apV7i5GvaOaFA9S3Q1ZNSkiIiIKKR6l6cluyMWqSREREVFI8SSqSREREamhkOJBNKCbiIhIDYUUD+KoSVFIERERUUjxJNWjzmr+HhEREYUUj9LU8bqn0s0lERFpuK699loeffRRx+/x8fHMmDHjnMdYLBa++OKLS752XZ3HWyikeJAgNZwVETmroUOHMnjw4DNuW7lyJRaLhY0bN17wedetW8eECRMutXhOnnvuOXr06HHa+pycHIYMGVKn1/q1uXPnEhoa6tJrXC4KKR6kqRrOioic1dixY1m0aBH79+8/bdu7775Lr1696Nat2wWft3nz5jRu3Lguinhe0dHRBAQEXJZrNQQKKR6kuuGs2qSIyGVnGFBR6p6llvPc/u53v6N58+bMnTvXaX1JSQkff/wxY8eO5ciRI9xxxx20aNGCxo0b07VrVz766KNznvfXr3t27txJ//79CQwMJDExkUWLFp12zJNPPknHjh1p3Lgxbdu25ZlnnqGy0nxVP3fuXJ5//nl+/vlnLBYLFovFUeZfv+7JzMzkt7/9LY0aNaJZs2ZMmDCBkpISx/Z77rmH4cOH85e//IWYmBiaNWvGQw895LjWxcjOzmbYsGE0bdqU4OBgbrvtNvLy8hzbf/75ZwYMGEBQUBDBwcEkJSXx008/AeYcREOHDiUsLIwmTZrQpUsX/vOf/1x0Wc5Hw+J7ENWkiIjbVB6HF2Pdc+0/HgT/JufdzdfXl9GjRzN37lyefvppLBYLAB9//DE2m4077riDkpISkpKSePLJJwkODuabb77h7rvvpl27dvTp0+e817Db7dxyyy1ERUWxZs0aCgsLndqvVAsKCmLu3LnExsaSmZnJ+PHjCQoK4oknnmDkyJFs2rSJBQsWsHjxYgBCQkJOO0dpaSmDBg0iJSWFdevWkZ+fz7hx45g4caJTEFu2bBkxMTEsW7aMXbt2MXLkSHr06MH48ePPez9nur/qgLJixQqqqqp46KGHGDlyJMuXLwdg1KhR9OzZkzfffBMfHx8yMjLw8zM7djz00ENUVFTw/fff06RJE7Zs2ULTpk0vuBy1pZDiQYLUBVlE5Jzuu+8+XnnlFVasWMG1114LmK96RowYQUhICCEhITz22GOO/R9++GG+++47/vWvf9UqpCxevJht27bx3XffERtrhrYXX3zxtHYkf/rTnxyf4+Pjeeyxx5g3bx5PPPEEjRo1omnTpvj6+hIdHX3Wa/3zn/+krKyM999/nyZNzJD2xhtvMHToUP785z8TFRUFQFhYGG+88QY+Pj507tyZG2+8kSVLllxUSFmyZAmZmZns2bOHuLg4AN5//326dOnCunXr6N27N9nZ2Tz++ON07twZgA4dOjiOz87OZsSIEXTt2hWAtm3bXnAZLoRCigdx1KTodY+IXG5+jc0aDXddu5Y6d+5M3759mTNnDtdeey27du1i5cqVTJs2DQCbzcaLL77Iv/71Lw4cOEBFRQXl5eW1bnOydetW4uLiHAEFICUl5bT95s+fz8yZM9m9ezclJSVUVVURHBxc6/uovlb37t0dAQXgqquuwm63s337dkdI6dKlCz4+Po59YmJiyMzMvKBrnXrNuLg4R0ABSExMJDQ0lK1bt9K7d28mT57MuHHj+Mc//kFqair/9V//Rbt27QB45JFHePDBB1m4cCGpqamMGDHiotoB1ZbapHgQR5sU1aSIyOVmsZivXNyxnHxtU1tjx47l008/pbi4mHfffZd27dpxzTXXAPDKK6/w2muv8eSTT7Js2TIyMjIYNGgQFRUVdfZHlZaWxqhRo7jhhhv4+uuv2bBhA08//XSdXuNU1a9aqlksFux2u0uuBWbPpM2bN3PjjTeydOlSEhMT+fzzzwEYN24cv/zyC3fffTeZmZn06tWL119/3WVlUUjxIEEnB3OrqLJTXmVzc2lERDzTbbfdhtVq5Z///Cfvv/8+9913n6N9yqpVqxg2bBh33XUX3bt3p23btuzYsaPW505ISGDfvn3k5OQ41v34449O+6xevZrWrVvz9NNP06tXLzp06EBWVpbTPv7+/ths5/53PCEhgZ9//pnS0lLHulWrVmG1WunUqVOty3whqu9v3759jnVbtmyhoKCAxMREx7qOHTsyadIkFi5cyC233MK7777r2BYXF8cDDzzAZ599xh/+8Afefvttl5QVFFI8SpOAmuq80nKFFBGRM2natCkjR45kypQp5OTkcM899zi2dejQgUWLFrF69Wq2bt3K/fff79Rz5XxSU1Pp2LEjY8aM4eeff2blypU8/fTTTvt06NCB7Oxs5s2bx+7du5k5c6ajpqFafHw8e/bsISMjg8OHD1NeXn7atUaNGkVgYCBjxoxh06ZNLFu2jIcffpi7777b8arnYtlsNjIyMpyWrVu3kpqaSteuXRk1ahTp6emsXbuW0aNHc80119CrVy9OnDjBxIkTWb58OVlZWaxatYp169aRkJAAwKOPPsp3333Hnj17SE9PZ9myZY5trqCQ4kF8faw08jODitqliIic3dixYzl27BiDBg1yaj/ypz/9iSuvvJJBgwZx7bXXEh0dzfDhw2t9XqvVyueff86JEyfo06cP48aN43/+53+c9rnpppuYNGkSEydOpEePHqxevZpnnnnGaZ8RI0YwePBgBgwYQPPmzc/YDbpx48Z89913HD16lN69e3PrrbcycOBA3njjjQv7wziDkpISevbs6bQMHToUi8XCl19+SVhYGP379yc1NZW2bdsyf/58AHx8fDhy5AijR4+mY8eO3HbbbQwZMoTnn38eMMPPQw89REJCAoMHD6Zjx4787W9/u+Tyno3FMGrZQV2cFBUVERISQmFh4QU3ljqX3v+zmEPF5XzzSD+6xJ7eZU1E5FKVlZWxZ88e2rRpQ2BgoLuLIw3Uuf6e1fY7VDUpHkZD44uIiJgUUjyMBnQTERExKaR4mKYa0E1ERARQSPE4mr9HRETEpJDiYfS6R0QuF/WbEFeqi79fCikeRg1nRcTVqkcwPX78uJtLIg1Z9d+vX4+YeyE0d4+HCQo0H6ZqUkTEVXx8fAgNDSU/Px8wx+uwXODQ9CJnYxgGx48fJz8/n9DQUKd5hy6UQoqHqX7dozYpIuJK1bPzVgcVkboWGhp6zlmga0MhxcPU9O6pdHNJRKQhs1gsxMTEEBkZSWWl/r2RuuXn53dJNSjVFFI8TJAazorIZeTj41MnXyYirqCGsx6mqRrOioiIAAopHscxTopqUkRExMsppHgYxzgpqkkREREvp5DiYYIC1AVZREQEFFI8TnVNyvEKGza7RoMUERHv5faQMmvWLOLj4wkMDCQ5OZm1a9eedd+5c+disViclsDAQKd97rnnntP2GTx4sNM+R48eZdSoUQQHBxMaGsrYsWMpKSlxyf1dqCYBNa3sVZsiIiLezK0hZf78+UyePJlnn32W9PR0unfvzqBBg845uFBwcDA5OTmOJSsr67R9Bg8e7LTPRx995LR91KhRbN68mUWLFvH111/z/fffM2HChDq/v4sR4OuDv6/5WBRSRETEm7k1pLz66quMHz+ee++9l8TERGbPnk3jxo2ZM2fOWY+xWCxER0c7lqioqNP2CQgIcNonLCzMsW3r1q0sWLCAv//97yQnJ9OvXz9ef/115s2bx8GDB11ynxdK8/eIiIi4MaRUVFSwfv16UlNTawpjtZKamkpaWtpZjyspKaF169bExcUxbNgwNm/efNo+y5cvJzIykk6dOvHggw9y5MgRx7a0tDRCQ0Pp1auXY11qaipWq5U1a9ac9brl5eUUFRU5La5SMxOyRoEUERHv5baQcvjwYWw222k1IVFRUeTm5p7xmE6dOjFnzhy+/PJLPvjgA+x2O3379mX//v2OfQYPHsz777/PkiVL+POf/8yKFSsYMmQINpsNgNzcXCIjI53O6+vrS3h4+FmvCzB9+nRCQkIcS1xc3MXe+nk5xkpRTYqIiHixejUsfkpKCikpKY7f+/btS0JCAm+99RYvvPACALfffrtje9euXenWrRvt2rVj+fLlDBw48KKvPWXKFCZPnuz4vaioyGVBpWb+HoUUERHxXm6rSYmIiMDHx4e8vDyn9Xl5ebWeNdHPz4+ePXuya9eus+7Ttm1bIiIiHPtER0ef1jC3qqqKo0ePnvO6AQEBBAcHOy2uEqQB3URERNwXUvz9/UlKSmLJkiWOdXa7nSVLljjVlpyLzWYjMzOTmJiYs+6zf/9+jhw54tgnJSWFgoIC1q9f79hn6dKl2O12kpOTL/Ju6pZqUkRERNzcu2fy5Mm8/fbbvPfee2zdupUHH3yQ0tJS7r33XgBGjx7NlClTHPtPmzaNhQsX8ssvv5Cens5dd91FVlYW48aNA8xGtY8//jg//vgje/fuZcmSJQwbNoz27dszaNAgABISEhg8eDDjx49n7dq1rFq1iokTJ3L77bcTGxt7+f8QzqC64azapIiIiDdza5uUkSNHcujQIaZOnUpubi49evRgwYIFjsa02dnZWK01OerYsWOMHz+e3NxcwsLCSEpKYvXq1SQmJgLmlOMbN27kvffeo6CggNjYWK6//npeeOEFAgICHOf58MMPmThxIgMHDsRqtTJixAhmzpx5eW/+HJpqaHwREREshmFo7PWLUFRUREhICIWFhXXePmXWsl288t12RvaK48+3dqvTc4uIiLhbbb9D3T4svpxObVJEREQUUjySY5wUhRQREfFiCikeyDHibJlGnBUREe+lkOKBgvS6R0RERCHFEzXVYG4iIiIKKZ5IbVJEREQUUjxSzSzIVaiHuIiIeCuFFA8UdHIwN8OA4xU2N5dGRETEPRRSPFCgnxUfqwVQ41kREfFeCikeyGKx1LRLUeNZERHxUgopHkqjzoqIiLdTSPFQQeqGLCIiXk4hxUPV1KRo1FkREfFOCikeqrobstqkiIiIt1JI8VBqkyIiIt5OIcVDqU2KiIh4O4UUD6WaFBER8XYKKR6q6clRZzV/j4iIeCuFFA+lmZBFRMTbKaR4qCC97hERES+nkOKhVJMiIiLeTiHFQznm7lFNioiIeCmFFA/lqEnRiLMiIuKlFFI8lKNNil73iIiIl1JI8VA1NSlVGIbh5tKIiIhcfgopHqq6TUqlzaC8yu7m0oiIiFx+Cikeqom/r+OzuiGLiIg3UkjxUFarpWZofLVLERERL6SQ4sE0f4+IiHgzhRQPVt14tlg1KSIi4oUUUjyYalJERMSbKaR4sCAN6CYiIl5MIcWDqeGsiIh4M4UUD6b5e0RExJsppHgwzYQsIiLeTCHFgwWp4ayIiHgxhRQPppoUERHxZgopHqxpgB+gNikiIuKdFFI8mGpSRETEmymkeDC1SREREW+mkOLBaobF12BuIiLifRRSPJiGxRcREW+mkOLBHIO5qU2KiIh4IYUUD1Y9d095lZ2KKrubSyMiInJ5KaR4sCYna1IASvXKR0REvIxCigfz87ES6Gc+IrVLERERb6OQ4uEcA7qpXYqIiHgZhRQPV90uRTUpIiLibRRSPFxNN2SNlSIiIt5FIcXDqRuyiIh4K4UUD9dUr3tERMRLKaR4OMf8PapJERERL6OQ4uFUkyIiIt7K7SFl1qxZxMfHExgYSHJyMmvXrj3rvnPnzsVisTgtgYGBju2VlZU8+eSTdO3alSZNmhAbG8vo0aM5ePCg03ni4+NPO89LL73ksnu8FGqTIiIi3sqtIWX+/PlMnjyZZ599lvT0dLp3786gQYPIz88/6zHBwcHk5OQ4lqysLMe248ePk56ezjPPPEN6ejqfffYZ27dv56abbjrtPNOmTXM6z8MPP+ySe7xUqkkRERFv5Xv+XVzn1VdfZfz48dx7770AzJ49m2+++YY5c+bw1FNPnfEYi8VCdHT0GbeFhISwaNEip3VvvPEGffr0ITs7m1atWjnWBwUFnfU8nkRtUkRExFu5rSaloqKC9evXk5qaWlMYq5XU1FTS0tLOelxJSQmtW7cmLi6OYcOGsXnz5nNep7CwEIvFQmhoqNP6l156iWbNmtGzZ09eeeUVqqrOHQLKy8spKipyWi4H1aSIiIi3cltIOXz4MDabjaioKKf1UVFR5ObmnvGYTp06MWfOHL788ks++OAD7HY7ffv2Zf/+/Wfcv6ysjCeffJI77riD4OBgx/pHHnmEefPmsWzZMu6//35efPFFnnjiiXOWd/r06YSEhDiWuLi4C7zji+MYFl8hRUREvIxbX/dcqJSUFFJSUhy/9+3bl4SEBN566y1eeOEFp30rKyu57bbbMAyDN99802nb5MmTHZ+7deuGv78/999/P9OnTycgIOCM154yZYrTcUVFRZclqDhGnC3TiLMiIuJd3BZSIiIi8PHxIS8vz2l9Xl5erduK+Pn50bNnT3bt2uW0vjqgZGVlsXTpUqdalDNJTk6mqqqKvXv30qlTpzPuExAQcNYA40qau0dERLyV2173+Pv7k5SUxJIlSxzr7HY7S5YscaotORebzUZmZiYxMTGOddUBZefOnSxevJhmzZqd9zwZGRlYrVYiIyMv/EZcrKkazoqIiJdy6+ueyZMnM2bMGHr16kWfPn2YMWMGpaWljt4+o0ePpkWLFkyfPh0wuw3/5je/oX379hQUFPDKK6+QlZXFuHHjADOg3HrrraSnp/P1119js9kc7VvCw8Px9/cnLS2NNWvWMGDAAIKCgkhLS2PSpEncddddhIWFuecP4hyqG86WVtiw2Q18rBY3l0hEROTycGtIGTlyJIcOHWLq1Knk5ubSo0cPFixY4GhMm52djdVaU9lz7Ngxxo8fT25uLmFhYSQlJbF69WoSExMBOHDgAF999RUAPXr0cLrWsmXLuPbaawkICGDevHk899xzlJeX06ZNGyZNmuTU3sSTVNekAJRWVBEc6OfG0oiIiFw+FsMwDHcXoj4qKioiJCSEwsLC87Z5uRSGYdDxT99SaTNY/dRviQ1t5LJriYiIXA61/Q51+7D4cm4Wi6WmXYoaz4qIiBdRSKkHqtulaP4eERHxJgop9UD1gG6qSREREW+ikFIPaP4eERHxRgop9UDN/D0adVZERLyHQko9UN1wVm1SRETEmyik1AOaCVlERLyRQko9oDYpIiLijRRS6gGNkyIiIt5IIaUecIyTopAiIiJeRCGlHtBMyCIi4o0UUuqBIDWcFRERL6SQUg8EnZz5WDUpIiLiTRRS6gE1nBUREW+kkFIP1EwwqBFnRUTEeyik1ANBp9SkGIbh5tKIiIhcHgop9UB1TYrdgBOVNjeXRkRE5PJQSKkHGvn5YLWYn9V4VkREvIVCSj1gsVhqJhlU41kREfESCin1hLohi4iIt1FIqSfUDVlERLyNQko9UdMNWSFFRES8g0JKPaGaFBER8TYKKfVEdU1KiQZ0ExERL6GQUk8EqSZFRES8jEJKPaEuyCIi4m0UUuqJmtc9CikiIuIdFFLqCTWcFRERb6OQUk8EqSZFRES8jEJKPdE0wBxxVm1SRETEWyik1BNqkyIiIt5GIaWeUJsUERHxNgop9YSjTYpCioiIeAmFlHrCUZOi1z0iIuIlFFLqieo2KRU2O+VVNjeXRkRExPUUUuqJJv6+js+qTREREW+gkFJP+FgtNPH3AdQuRUREvINCSj1S/cqnWDUpIiLiBRRS6hF1QxYREW+ikOJJThyDw7vOurlpoDnqrNqkiIiIN1BI8RSbP4c/x8O/HznrLkGqSRERES+ikOIpIruYPw+sB1vlGXepft2j+XtERMQbKKR4imbtoVEYVJVB7sYz7qL5e0RExJsopHgKqxVa9jE/71t7xl1qGs6euaZFRESkIVFI8SRx1SFlzRk3B6kmRUREvIhCiieJSzZ/nqcmRW1SRETEGyikeJIWV4LFB4oOQOH+0zarTYqIiHgThRRP4t8Eoruan8/wykeDuYmIiDdRSPE053jl42iTopAiIiJeQCHF05yj8WzTAI04KyIi3kMhxdNU16TkbISKUqdNajgrIiLeRCHF04S0hKBYMGxwcIPTJnVBFhERb6KQ4mkslrO+8qmuSTlRaaPKZr/cJRMREbms3B5SZs2aRXx8PIGBgSQnJ7N27ZnHCAGYO3cuFovFaQkMDHTaxzAMpk6dSkxMDI0aNSI1NZWdO3c67XP06FFGjRpFcHAwoaGhjB07lpKSEpfc30U5S+PZJidDCkBpue1ylkhEROSyc2tImT9/PpMnT+bZZ58lPT2d7t27M2jQIPLz8896THBwMDk5OY4lKyvLafvLL7/MzJkzmT17NmvWrKFJkyYMGjSIsrIyxz6jRo1i8+bNLFq0iK+//prvv/+eCRMmuOw+L5gjpKwBw3Cs9ve1EuBrPrJiDY0vIiINnFtDyquvvsr48eO59957SUxMZPbs2TRu3Jg5c+ac9RiLxUJ0dLRjiYqKcmwzDIMZM2bwpz/9iWHDhtGtWzfef/99Dh48yBdffAHA1q1bWbBgAX//+99JTk6mX79+vP7668ybN4+DBw+6+pZrJ7or+AbCiWNwZJfTJnVDFhERb+G2kFJRUcH69etJTU2tKYzVSmpqKmlpaWc9rqSkhNatWxMXF8ewYcPYvHmzY9uePXvIzc11OmdISAjJycmOc6alpREaGkqvXr0c+6SmpmK1Wlmz5sxz5gCUl5dTVFTktLiMrz/EXml+Pku7FDWeFRGRhs5tIeXw4cPYbDanmhCAqKgocnNzz3hMp06dmDNnDl9++SUffPABdrudvn37sn+/OYR89XHnOmdubi6RkZFO2319fQkPDz/rdQGmT59OSEiIY4mLi7uwG75QZ2s8G6huyCIi4h3c3nD2QqSkpDB69Gh69OjBNddcw2effUbz5s156623XH7tKVOmUFhY6Fj27dvn2guepfGsalJERMRbuC2kRERE4OPjQ15entP6vLw8oqOja3UOPz8/evbsya5dZruN6uPOdc7o6OjTGuZWVVVx9OjRc143ICCA4OBgp8WlqmtSDm0z26ac5Bh1VjUpIiLSwLktpPj7+5OUlMSSJUsc6+x2O0uWLCElJaVW57DZbGRmZhITEwNAmzZtiI6OdjpnUVERa9ascZwzJSWFgoIC1q9f79hn6dKl2O12kpOT6+LW6kaTCAhvZ37e/5NjtQZ0ExERb+HW1z2TJ0/m7bff5r333mPr1q08+OCDlJaWcu+99wIwevRopkyZ4th/2rRpLFy4kF9++YX09HTuuususrKyGDduHGD2/Hn00Uf57//+b7766isyMzMZPXo0sbGxDB8+HICEhAQGDx7M+PHjWbt2LatWrWLixIncfvvtxMbGXvY/g3M6tSvySRoaX0REvIXv+XdxnZEjR3Lo0CGmTp1Kbm4uPXr0YMGCBY6Gr9nZ2VitNTnq2LFjjB8/ntzcXMLCwkhKSmL16tUkJiY69nniiScoLS1lwoQJFBQU0K9fPxYsWOA06NuHH37IxIkTGThwIFarlREjRjBz5szLd+O1FdcHfv6nc0hRTYqIiHgJi2GcMlqY1FpRUREhISEUFha6rn1K3hZ4MwX8msBT2eDjy6xlu3jlu+3c1qslL9/a3TXXFRERcaHafofWq949Xqd5ZwgIhspSyN8CaDA3ERHxHgopnsxqhZYnB507+crH0SZFr3tERKSBU0jxdL8aL8UxTopqUkREpIFTSPF0vxp5Vg1nRUTEWyikeLoWvQALFGRBcS5BGsxNRES8hEKKpwsMhqgu5ud9a1WTIiIiXkMhpT445ZWPo01KRRV2u3qPi4hIw6WQUh+c0ni2uguyYcDxSpsbCyUiIuJaCin1QXVNSk4GAVTga7UAeuUjIiINm0JKfRDWBpo0B1sFlpyNNe1SyivdXDARERHXUUipDywWp8kGNaCbiIh4A4WU+uJMjWfVDVlERBowhZT64tTGswE+gNqkiIhIw6aQUl/E9ACrH5Tm08bnMAAFJ9QmRUREGi6FlPrCLxBiewDQL3A3ABuyj7mxQCIiIq6lkFKfnHzl08tnJwA/7DyMYWhANxERaZgUUuqTk41nows34u9j5WBhGXsOl7q5UCIiIq6hkFKftDRDivXQFq5u5Q/AD7sOu7NEIiIiLqOQUp8Ex0BoKzDs3NQ8FzBf+YiIiDRECin1zcl2Kcm+uwBI232EKpvdnSUSERFxCYWU+uZkSIkq/JmQRn4Ul1ex8UChmwslIiJS9y4qpLz33nt88803jt+feOIJQkND6du3L1lZWXVWODmDk41nLfvXcVXbMECvfEREpGG6qJDy4osv0qhRIwDS0tKYNWsWL7/8MhEREUyaNKlOCyi/EtkF/JpAeRE3xJg1KGo8KyIiDZHvxRy0b98+2rdvD8AXX3zBiBEjmDBhAldddRXXXnttXZZPfs3HF1omwZ7v6ctGoBMbso9RWl5Fk4CLepwiIiIe6aJqUpo2bcqRI0cAWLhwIddddx0AgYGBnDhxou5KJ2eWOByA8K0fEhcWSKXNYO2eo+4tk4iISB27qJBy3XXXMW7cOMaNG8eOHTu44YYbANi8eTPx8fF1WT45k263gX8QHNnFmGizDZBe+YiISENzUSFl1qxZpKSkcOjQIT799FOaNWsGwPr167njjjvqtIByBgFB0H0kADeW/wdQ41kREWl4LIYmf7koRUVFhISEUFhYSHBw8OUvQN4WeDMFw+JDStlr5BrhrH16IJFBgZe/LCIiIhegtt+hF1WTsmDBAn744QfH77NmzaJHjx7ceeedHDummXkvi6hEaH0VFsPGI6GrAFilVz4iItKAXFRIefzxxykqKgIgMzOTP/zhD9xwww3s2bOHyZMn12kB5Rx63QfA0KpF+FLFDzuPuLlAIiIideei+qzu2bOHxMREAD799FN+97vf8eKLL5Kenu5oRCuXQcJN0KQ5QaWHuM66nlW7mmIYBhaLxd0lExERuWQXVZPi7+/P8ePHAVi8eDHXX389AOHh4Y4aFrkMfP3hyjEAjPZdTG5RGbsPlbi5UCIiInXjokJKv379mDx5Mi+88AJr167lxhtvBGDHjh20bNmyTgso55F0D1ispFg3085yQL18RESkwbiokPLGG2/g6+vLJ598wptvvkmLFi0A+Pbbbxk8eHCdFlDOIzQOOpp/5nf5LNZ4KSIi0mCoC/JFcnsX5FPtWgwfjKDIaMxAZrN66lD8fDTBtYiIeKbafode9GQvNpuNL774gq1btwLQpUsXbrrpJnx8fC72lHKx2v4WI6wNwcf28NvKlfy872p6xYe7u1QiIiKX5KL+d3vXrl0kJCQwevRoPvvsMz777DPuuusuunTpwu7du+u6jHI+ViuWk92R7/ZZxA87D7m5QCIiIpfuokLKI488Qrt27di3bx/p6emkp6eTnZ1NmzZteOSRR+q6jFIbPe/CZvXnCute8raucndpRERELtlFhZQVK1bw8ssvEx5e80qhWbNmvPTSS6xYsaLOCicXoHE4ZR2HAdD70GcUl1W6uUAiIiKX5qJCSkBAAMXFxaetLykpwd/f/5ILJRenSb8HALjR+iPp2/TaTURE6reLCim/+93vmDBhAmvWrMEwDAzD4Mcff+SBBx7gpptuqusySm21SOJAo44EWCopX/cPd5dGRETkklxUSJk5cybt2rUjJSWFwMBAAgMD6du3L+3bt2fGjBl1XESpNYuFowl3A3DFwU/AbndzgURERC7eJY2TsmvXLkcX5ISEBNq3b19nBfN0HjVOyikKCo5h/d9Egi3HOXrzR4R311xKIiLiWep8nJTzzW68bNkyx+dXX321tqeVOhYaGsZXja7jprIvKVv9f6CQIiIi9VStQ8qGDRtqtZ9m4HW//E53ws9fEp23AgqyIbSVu4skIiJywWodUk6tKRHPltitF6vSu3CVz2aMn+ZiSZ3q7iKJiIhcME3w0gBd2SqM+VwPgG39e1BV4eYSiYiIXDiFlAYo0M+HotbXkWeE4nviMGz9yt1FEhERuWAKKQ1U347RfGT7rfnLqtfgxDH3FkhEROQCKaQ0UFe1j+Cjqt9y3AiA3I3w5lWwZ6W7iyUiIlJrCikNVEJ0MFVNohlZ8QxlwfFQdADeGwqLnlUbFRERqRcUUhooq9VC3/YRZBpteTthLlw5BjBg1Qx4JxUO7XBzCUVERM5NIaUBu7p9BABL9xyHm2bCyA+gUTjk/Axv9Yd178DFDzgsIiLiUm4PKbNmzSI+Pp7AwECSk5NZu3ZtrY6bN28eFouF4cOHO623WCxnXF555RXHPvHx8adtf+mll+rytjxCvw5mSNmQXcDWnCJIGAoProa2A6DqBHwzGT66A0oPu7mkIiIip3NrSJk/fz6TJ0/m2WefJT09ne7duzNo0CDy8/PPedzevXt57LHHuPrqq0/blpOT47TMmTMHi8XCiBEjnPabNm2a034PP/xwnd6bJ4gNbcTvusUA8Oqik693gmPgrs9g0Ivg4w87voW/pcDOxW4sqYiIyOncGlJeffVVxo8fz7333ktiYiKzZ8+mcePGzJkz56zH2Gw2Ro0axfPPP0/btm1P2x4dHe20fPnllwwYMOC0fYOCgpz2a9KkSZ3fnyd4NLUjVgss2pLHz/sKzJVWK6Q8BOOXQvMEKM2HD0fAt09CZZlbyysiIlLNbSGloqKC9evXk5qaWlMYq5XU1FTS0tLOety0adOIjIxk7Nix571GXl4e33zzzRn3femll2jWrBk9e/bklVdeoaqq6pznKi8vp6ioyGmpD9pHNuXmni0B+OuiXzWWje4KE5ZBn/vN39fMhnl3KqiIiIhHcFtIOXz4MDabjaioKKf1UVFR5ObmnvGYH374gXfeeYe33367Vtd47733CAoK4pZbbnFa/8gjjzBv3jyWLVvG/fffz4svvsgTTzxxznNNnz6dkJAQxxIXF1erMniC3w/sgK/Vwvc7DrF2z1HnjX6N4IaX4c6Pwa8x7F4C/xoNVeXuKayIiMhJbm84W1vFxcXcfffdvP3220RERNTqmDlz5jBq1CgCAwOd1k+ePJlrr72Wbt268cADD/DXv/6V119/nfLys38xT5kyhcLCQseyb9++S7qfy6lVs8b8Vy8zVP1l4XaMM/Xo6Xg93Pkv8G0EO7+Dj+8FW+VlLqmIiEgNt4WUiIgIfHx8yMvLc1qfl5dHdHT0afvv3r2bvXv3MnToUHx9ffH19eX999/nq6++wtfXl927dzvtv3LlSrZv3864cePOW5bk5GSqqqrYu3fvWfcJCAggODjYaalPHv5te/x9rKzdc5RVu46ceac2V8MdH4FvIGz/Bj65T0FFRETcxm0hxd/fn6SkJJYsWeJYZ7fbWbJkCSkpKaft37lzZzIzM8nIyHAsN910EwMGDCAjI+O01y/vvPMOSUlJdO/e/bxlycjIwGq1EhkZeek35qFiQxtxZ3Ir4By1KQDtBsDtH5o9f7Z+BZ9NANu52+uIiIi4gq87Lz558mTGjBlDr1696NOnDzNmzKC0tJR7770XgNGjR9OiRQumT59OYGAgV1xxhdPxoaGhAKetLyoq4uOPP+avf/3raddMS0tjzZo1DBgwgKCgINLS0pg0aRJ33XUXYWFhrrlRD/H/BrRj3rpsMvYVsHRbPgMTos68Y/tUc+C3eaNg82dg9YWbZ4PV5/IWWEREvJpbQ8rIkSM5dOgQU6dOJTc3lx49erBgwQJHY9rs7Gys1guv7Jk3bx6GYXDHHXecti0gIIB58+bx3HPPUV5eTps2bZg0aRKTJ0++5PvxdJFBgYzpG89bK37h1UU7GNApEqvVcuadOw6C294zG9Fm/gt8/OCmN8zuyyIiIpeBxThrvb+cS1FRESEhIRQWFtar9inHSiu4+uVllJRX8eaoKxnSNebcB2z50mxEa9jgytHwu9cUVERE5JLU9jtU3zZeJqyJP/f1awOYo9Da7OfJqInDYMTbYLFC+vvwn8c034+IiFwWbn3dI+4xtl8b3lu9l535Jfz754MM79ni3AdcMcJsPPv5/fDTO2YblSF/Bsspr4oMA04cg5I8cyk++fPEMYhLNtu5+Oivm4iI1J6+NbxQSCM/JvRvyyvfbWfG4h3c2C0GP5/zVKp1Hwn2KvjyIVj7FhzZZbZTKcmDknxzsZ+ju3KTSPMcPe6CyM51e0MiItIgqU3KRaqvbVKqlZZX0f/lZRwpreDPI7oysner2h24/j349yNn394oDJpG1Sy+/rB9ARw/ZablFknQY5RZQ9Mo9JLuQ0RE6p/afocqpFyk+h5SAP6+8hf++5uttAhtxNLHriHAt5ZdjH9ZAfvWQtPmJ8NIpPmzSXPwDTh9/6oK2LkQMv5pjmZrPznuik8AJPzODCxtr1UXZxERL6GQ4mINIaSUVdq45pVl5BWVM21YF0anxLv+oiX5sPFfkPEh5G+pWR/cwuw9dNXvzfmERESkwVLvHjmvQD8fJv62AwCvL93FiQqb6y/aNBL6ToQHV8P4ZdB7HASGQNEBWD4d3h4Ih7a7vhwiIuLxFFK83MhecbQIbcSh4nI++DHr8l3YYoEWV8KNf4U/7IBb3jZfF+Vvhv+7FtL/oa7OIiJeTiHFy/n7Wvl9qlmb8uaK3ZSUu2GeHr9A6HYbPLDKbJtSeRy+mgifjoOyostfHhER8QgKKcItPVvQJqIJR0sreHXhDvcVJCgK7vocBk4Fiw9s+gTe6g8H0t1XJhERcRuFFMHXx8ofb0gAYM6qPXy+Yb/7CmO1wtV/gHu/hZA4OLYH3rkeVr8Bdrv7yiUiIpedQooAcF1iFA8NaAfAk59msnF/gXsL1CoZHlgJCUPNQeIWPg0fjYTSw+c/9kKVFcLmz2HPSrWDERHxIOqCfJEaQhfkX7PbDca9/xNLt+UTExLIVxP70TzoDOOeXE6GYQ7Fv+CPYCuHoBizkW2bqy/tvBWlsP1b2PQZ7FoEtgpzfWxPsyan042aSFFExEU0ToqLNcSQAlBUVsnwWav45VApvVqH8c/xv8Hf1wO+rHM3wSf3wuEdgAU63QCxPSDqCojuCiEtnecSOpPKMjOQbPoUdnxnNtCt1qw9FB6AqhPm7807Q7/J5qi4mnNIRKROKaS4WEMNKQC7D5Uw/I1VFJdXcWdyK168uau7i2SqKIVvn4ANH5y+LTDUDCvRXWuCS/POZnDZvQw2fwZbv4aK4ppjwtqYIeSKWyAyEY4fgR/fhLX/B+UnexWFtoZ+j5qj4p5pNF0REblgCiku1pBDCsCybfnc9946DAP+e/gV3PWb1u4uUo1962DfGsjbBLmZcGhbzVD7p7L6gm8j52AS3AK63GyGk9ieZ659KSuEtW/Dj38zgwuYr5lSJkKve8G/iWvuS0TESyikuFhDDykAf1u+i5cXbMfXauGf439Dnzbh7i7SmVWVm6PU5mbWBJfcjWbYAHOQuOpg0rJP7duaVByH9Pdg1UwoPmiuaxQOv3kQut4K4W1dcz8iIg2cQoqLeUNIMQyDhz/awNcbc4ho6s9XE/sRG1pP5tUxDCjcb86+HNX10tqVVJXDz/Pgh/81u0RXC28L7VPNJb6falhERGpJIcXFvCGkAByvqGLEm2lszSniihbBfPJAXwL9vHS2YlsVbPkC1s+F7DTnV0w+/tC6b01oqW4PIyIip1FIcTFvCSkA+44eZ9isVRwtrWB4j1j+d2QPLN7+BVxWBHtXwq7FsHMxFGY7bw9uCe0HmsElrA2ExZuTK3r7n5uICAopLudNIQUgbfcR7npnDTa7wdM3JDC+v9pjOBgGHN5pBpZdi2HvD+aYLr/m19gMK07LyQAT2sqcw0hExAsopLiYt4UUgPfT9jL1y81YLTD33j7079jc3UXyTBXHIWu1GVjyNsGxLCjaD8a5hvW3mO1aetwJCTdBQNPLVlwRkctNIcXFvDGkGIbBU59mMv+nfQQF+vLOmN6e2+PH01RVQOE+s+Htsb1w9OTPY1nmuoqSmn39GptBpccdEH81WL20DZCINFgKKS7mjSEFoLzKxt3vrGXtnqP4+1p5bWQPhnSNcXex6jfDMANL5ifw80dwdHfNtuAW0O026H4nNO/otiKKiNQlhRQX89aQAnCiwsYj8zawaEseFgs8N7QLY/rGu7tYDYNhwP51ZljZ9GnNWC8AsVear4PaXmvWvJw4VrMcP+b8+4ljUFYAvoHQJAIaR5z8GX7K51PWBYaqUa+IXDYKKS7mzSEFwGY3mPrlJj5cY/ZqeeCadjwxqBNWq77o6kxlGexYYI7RsmvRmUfVrSt+jaFlb7NdTOuroEWSGvKKiMsopLiYt4cUMNuozFq2i78s3AHAzT1b8OcR3TxjQsKGpuQQbDr5OujwTrPmo1GYWQvS6ORnxxJu/gwMgcoT5oB2pYfNIf6PHzn5+ZR1p7aHqeYTAC17mYEl/iozwNR2sLqqCrMWp6rM7Iqt2aRF5FcUUlxMIaXGxz/t46nPMrHZDa7uEMGbdyXRNEAzB9cblWVw9BfIWmUue1dBab7zPlZf83VT/FXQuNnJV0xHf/WKqcD8eepcSY3CzbFiqsNO1BUX3xC4sgyKcyAkTjNTi9RzCikuppDibPn2fP7fh+kcr7DRJTaYd+/tTWSQXhfUS4YBR3ZD1g9mYMlaBUUHLvAkFjPY2CudVweGQKuUmtAS3d05cBiGGUQO74QjO+HwrpM/d0JBNmCYczElDDXnY2p9lXo/idRDCikuppByuo37C7hv7joOl1TQMqwR793Xh3bNNd5HvWcYUJBlBpbsNHMuo8bhv3rF9KslMATsNsjJqKmdyf7RuZYFwD8IWiWbxxzeCUd2nfn1UzWLDxi2mt+bRJ4SWPoqsIjUEwopLqaQcmZZR0oZM2cte48cJ7SxH++M6U1S6zB3F0s8ga3KnJ3aEVpWO/deqmbxMUfhjegAzdqf/NnB/NkoDPZ8D5s/h63/Ntu+VGsSCYk3mYGlVYoCi4gHU0hxMYWUsztSUs597/3Ez/sKCPC18ucR3RjWI1bz/Ygzux3yN5uj81YerwkiYW3A1//8x9sqYc+Kk4Hla+fA0jQKOg2BVn3NmprQ1upiLeJBFFJcTCHl3I5XVDHxnxtYus1sgDmoSxQvDL9C7VTENWyV8MvJwLLtV4EFzNAS1wfiks0lpjv4BrilqCKikOJyCinnV2WzM2vZbl5fupMqu0FoYz+ev6kLN3VXrYq4UFWFWcPyy3LYtwYOZpzegNfHH2J71gSX2CshOFa1LSKXiUKKiymk1N6Wg0U8/snPbD5YBMD1iVH8982qVZHLpPKEGVT2rYF9a82fxw+fvl/jZhDd9eTS3fwZ0UFtW0RcQCHFxRRSLkylzc6by81alUqbQUgjs1ZFbVXksjMMc1yYfWtqgsuh7c69hqr5NoKoLmZgielWE15q02bmfGU4sht+WWZeO64PtE81e02Jdyg6CAX7zGfvSf8G2qrMyVCP7oYjv5j/rRzdbTZI73FnnV1GIcXFFFIuztYcs1Zl0wGzViU1IYoXb76CyGDVqogbVZ6A/K1m76PcTMjZCHmbzAa9v+bbyByNt9VvzKVlHwisxb8Bx4+ar6F2L4Xdy6Ew23m7xcfsldRpMHQcAhHt6+TW6kxZkTnqsDtqlqoq4NBWiOpa/0cwzt0Eq183R5C2V5kzng997fIH1JJ88+/60V/MwFwdRo5lnf56FKDXffC7/62zyyukuJhCysWrtNl5a8VuXlti1qoEB/ry3E1duLlnC9WqiOew28x/uE8NLgfTzVF1T2WxmrUtrVJOBpcUs31LVYU5WeTupeZycANwyj+3Vj9z/8gE2PsD5G9xPm+zDjWBJS758o+yaxhmLc+2f5vdvXN+Nqc56HmXuYTGXZ5yFOyD+aPM60cmwrVPQeeh9SusGIYZUFfNhN1LTtlgAQxoGg3D/wbtB7q2HHYb7FoM6+fCju/OXHsI5rQY4W3NpdnJny2SzAbndUQhxcUUUi7d9txiHvv4ZzIPmGNl/LZzJM/8LpE2EbWcI0bkcrPbzRFws9PMwemy0+DY3tP3C4kza04qS53XN0+AdgOg3W/NwedOnQ/p2F7YvgB2fGuGllMnlGwUBh2uN8OQrfLkUmEu9qqaz9Xr7TYIb2PWPERfYY434+N3/vszDDOIbf232a37yM6z7GgxX08ljYGOg2t37oux9wf415jT2xBFdYUBU6DTDZ71quTXbFWw5QtY9ZoZdsEMtQk3wVWPmJ8/HV/z55z8AKQ+B36N6rYchfthwweQ/g8o2l+zPqKjGYarg0h4O2jWDoJiXR4CFVJcTCGlblTZ7Lz1/S+8tngnFTY7vlYLdya34pGBHYhoqi6iUg8U5cC+H2tCS24mGHZzW5Pm0PZaM5S0vdasYamNskLYtcScBXvnwtNrby6Ujz8072zOnRR9hfkz6gpo0sz8Is1OM4PJtm+cv8R8/M1yJww17yErDdLfg70ra/ZpGmW2VbhytPlFVxcMA9a+Dd9NMUNYdDezpmHrvyHtbzUjF8d0hwFPmwHOk8JKeQmkvw8//s1s3wHma8Ked0HKQ2aArFZxHBZNhXVvm7837wy3vG22gboUtipz9vT1c82/Q9V/JxuFQfc7zYDZvNOlXeMSKKS4mEJK3dqZV8z0b7c5xlVp4u/D/de0Y9zVbWjsr8nkpB4pLzZf7QSGnpxQ8RL/j9RWZTbw3fkdFOeawcGx+J35s2GHwzvMdjV5m88+1UBQjFnzcvxIzTq/JtDhOjOYdLj+zO1tjuw2w0rGP6H0UM36Nv3hyjHmsRc7Dk1lGXzzB8j4wPy9621mmw3/xubvx49C2hvw4+yamqoWSTDgj9BuoGvCyvGjZk2XrcKc3bvq5E/H7+U1n4vz4Od/1oym3DgCku+HXmPNUHg2OxbClw+Zk3ta/WDgM5Ay8cLbABXsgw3/MGtNig/WrI+/GpLugc6/Az/3twFUSHExhRTXWL37MC99u42N+83/wJsHBTAptSO39WqJr089egct4insdnPupbxNZqPNvJPLqa+pGoWZr04Shpo1J7V93VBVYb6eWv+e2e6mus1No/CTtStjoHnH2pe18AD86244sN58FXLdC2bNw5mCR+kRWP2aWeNS3cA5LhmunWLew6WEleLcmukbslabjXYvVHg76DsRut9R+z/P0sPw1SOw/Rvz9/irYfibZ2//U15sPtOcDLPNTs7PZgPw6ufQuFnNc4jocOH34EIKKS6mkOI6drvB15k5vPLdNvYdPQFAu+ZNeGpIAqkJkWpcK1IXyoshb4tZ69Ky96U3zD2WBRkfmm0fTp01u1Vf8//gE28695d19o8w/26zJqFRGNz6rtl+53xK8s02H+v+btZkgDm1Qngbs21QaCtzCYkzv+yDYk6vnSjIrpnxO2u12cvl14JizPL7BJhd0H0DzVor30Cz1sg3wNzmF2i+Gut0w8X1hDIMsybk26fMmqKAELjxr2btVu7GmjByMMOckJMzfIW36V9Ta+KhIysrpLiYQorrlVfZ+PDHbF5fupNjx80ucX3iw5lyQ2d6ttKkhSIeyVZl9mBZP9dsU1PdFiIwxKxVuHIMRCU6H/PTHPjPE2bX18gucPuHzu02aqM4F374X/jpXbCVn30/qy8EtzCDS6NQ88u+ut2Ig8UcD6f1VWYD59Z9oUnEhZXnUh3ZDZ9NgAM/nXu/oFiI7WG2z4npYX4Oir4MBbw0CikuppBy+RSVVTJ7+W7e+WEP5VXmP3jXJ0bxyMAOXNEixM2lE5GzKjoIGz40G5GeOi5My94n/0//Rlj8nBlowBwwbNgs515PF6r0sNkOp3CfWUNSsK/mc9EB515T1Sw+5jQJrftCfD/ztVGj0IsvQ12xVcHKv8CKl83uwiGtILb7yUDS02xc2zTS3aW8KAopLqaQcvnlFJ7g1YU7+CR9P9V/a1MTovj9wA50bamwIuKx7DbYvQzS58L2b2uCgsV6sqbFAqnPwlWPuraXjt1m1rgU7jPDS2m+OU5Nyz4Q0NR1171UJ46Zr4Ea0IjECikuppDiPjvzinl96S7+vfGgI6wM7BzJIwM70D0u1K1lE5HzKM4z266kv2c23g0MgRFzoEOqu0sml5FCiosppLjfrvwSZi3bxZcZB7Cf/Ft8bafm/H5gB7VZEfF0drvZ3iIkDoJj3F0aucwUUlxMIcVz/HKohDeW7eLLjIPYTqaV/h3NsJLUWmFFRMTTKKS4mEKK59l7uJRZy3bx2YYDjrDSr30Eo1NaM6BzJH4aZ0VExCMopLiYQornyj5ynFnLdvFp+n6qToaV5kEB/FdSS27rFUe85gYSEXErhRQXU0jxfPuOHueDH7P4ZP1+jpRWONantG3G7X3iGNQlmkA/N0w7LyLi5RRSXEwhpf6oqLKzdFse89btY8WOQ44eQSGN/Li5Zwtu7xNH52g9QxGRy6W236Fuf0k/a9Ys4uPjCQwMJDk5mbVr19bquHnz5mGxWBg+fLjT+nvuuQeLxeK0DB482Gmfo0ePMmrUKIKDgwkNDWXs2LGUlJxlAi6p9/x9rQy+Ioa59/bhhyd/y6OpHYgNCaTwRCVzV+9l8IyVDJu1ig/XZFF4otLdxRURkZPcWpMyf/58Ro8ezezZs0lOTmbGjBl8/PHHbN++ncjIs4+it3fvXvr160fbtm0JDw/niy++cGy75557yMvL491333WsCwgIICysppfHkCFDyMnJ4a233qKyspJ7772X3r17889//rPWZVdNSv1msxus3HmI+ev2sWhLnqPtir+vlesSo7j1ypZc3SFCkxqKiLhAvXjdk5ycTO/evXnjjTcAsNvtxMXF8fDDD/PUU0+d8RibzUb//v257777WLlyJQUFBaeFlF+vO9XWrVtJTExk3bp19OrVC4AFCxZwww03sH//fmJjY2tVdoWUhuNQcTmfb9jPJ+v3syOvpkYtomkAw3vEMiKpJQkxesYiInXF41/3VFRUsH79elJTa0YZtFqtpKamkpaWdtbjpk2bRmRkJGPHjj3rPsuXLycyMpJOnTrx4IMPcuTIEce2tLQ0QkNDHQEFIDU1FavVypo1a856zvLycoqKipwWaRiaBwUwoX87vnu0P/+e2I97+sYT3sSfwyXl/P2HPQx5bSU3vLaSd37Yw+GSc0xcJiIideoS5+a+eIcPH8ZmsxEVFeW0Pioqim3btp3xmB9++IF33nmHjIyMs5538ODB3HLLLbRp04bdu3fzxz/+kSFDhpCWloaPjw+5ubmnvUry9fUlPDyc3Nzcs553+vTpPP/887W/Qal3LBYLXVuG0LVlCH+8IYHl2/P5LP0AS7blsSWniC1fb+HF/2zl2o7NuaFrDAMTIglt7O/uYouINFhuCykXqri4mLvvvpu3336biIizT5l9++23Oz537dqVbt260a5dO5YvX87AgQMv+vpTpkxh8uTJjt+LioqIi4u76POJZ/P3tXJ9l2iu7xLNsdIK/r3xIJ+mH+DnfQUs2ZbPkm35+FgtJLcJZ1CXaK5LjCI2tJG7iy0i0qC4LaRERETg4+NDXl6e0/q8vDyio6NP23/37t3s3buXoUOHOtbZ7XbArAnZvn077dq1O+24tm3bEhERwa5duxg4cCDR0dHk5+c77VNVVcXRo0fPeN1qAQEBBAQEXNA9SsMQ1sSf0SnxjE6JZ1d+MV9lHGThljy25RazevcRVu8+wrNfbaZbyxAGdYnm+sQo2kc2xeLK2VxFRLyA20KKv78/SUlJLFmyxNGN2G63s2TJEiZOnHja/p07dyYzM9Np3Z/+9CeKi4t57bXXzlqrsX//fo4cOUJMjDmBVUpKCgUFBaxfv56kpCQAli5dit1uJzk5uQ7vUBqi9pFBTL6+E5Ov78Tew6Us2pLHd5tzWZ99jI37C9m4v5BXvttO24gmXNclikFdounRMhSrVYFFRORCub0L8pgxY3jrrbfo06cPM2bM4F//+hfbtm0jKiqK0aNH06JFC6ZPn37G43/dk6ekpITnn3+eESNGEB0dze7du3niiScoLi4mMzPTURMyZMgQ8vLymD17tqMLcq9evdQFWS7aoeJyFm/NY+HmXFbtOkKFze7YFhMSyKAu0dzQNYak1mH4KLCIiJer7XeoW9ukjBw5kkOHDjF16lRyc3Pp0aMHCxYscDSmzc7OxmqtfQckHx8fNm7cyHvvvUdBQQGxsbFcf/31vPDCC06vaj788EMmTpzIwIEDsVqtjBgxgpkzZ9b5/Yn3aB4UwB19WnFHn1YUl1WyYschvtucx9KteeQUljF39V7mrt5LRNMABl8RxZArYkhuE65xWEREzkHD4l8k1aRIbZRV2vhh52H+symHxVvyKCqrcmwLa+zH9YnRDOkaTd92Efj7KrCIiHeoF4O51WcKKXKhKqrsrN59mAWbclm4JY+jp0x6GBzoy287R3JdYjT9O0YQFOjnxpKKiLiWQoqLKaTIpaiy2Vm75yjfbsplweZcDhXXDBLn72PlN+2acV1iFNclRBEdEujGkoqI1D2FFBdTSJG6YrMbbMg+xqIteSzakscvh0udtndrGcJ1CVFc1yWKTlFB6tosIvWeQoqLKaSIq+zKLzkZWHLZsK+AU/8LjQtvxHUJ0VzfJYpercPU8FZE6iWFFBdTSJHL4VBxOUu3mTUsK3cepryqpmtzWGM/BiZEcX1iFFd3aE4jfx83llREpPYUUlxMIUUut+MVVazceZhFW/JYvDWPguOVjm2Bflb6d2jO9V2iGdg5krAmmlNIRDyXQoqLKaSIO1XZ7Kzbe4yFW3JZuDmPAwUnHNt8rBZ6x4dxfaI5p1BceGM3llRE5HQKKS6mkCKewjAMtuQUsXBzHgu35LE1p8hpe6eoIAYmRDIwIZIecRrxVkTcTyHFxRRSxFPtO3qchdVzCmUdw2av+U+8WRN/ru0USWpCJFd3bE7TgHozEbqINCAKKS6mkCL1QcHxCpZvP8TirXms2HGI4lNGvPX3sZLcNpyBnSMZmKDXQiJy+SikuJhCitQ3lTY76/YeZcnWfJZszWPvkeNO2ztHB3F9YhTXd4mmS2ywxmMREZdRSHExhRSpzwzDYPehUpZszWPJ1nx+yjrKKW+FiA0JJDUxiusSo0hu00zzColInVJIcTGFFGlIjpZWsHRbPou25PL9jsOcqLQ5tgUF+jKgUyTXJUZxbafmmldIRC6ZQoqLKaRIQ1U9c/OiLXks2ZbH4ZKaiRD9fCz8pm0zfts5kt92jqR1syZuLKmI1FcKKS6mkCLewGY3yNh3jIWbzzyvUNvmTRjYOZIBnSPpHR+On4bpF5FaUEhxMYUU8Ua78ktYti2fJdvy+GnvMapOacgSFOBL/47NGdA5kms7NSeiaYAbSyoinkwhxcUUUsTbFZVVsnLHYZZsy2PF9kMcKa15LWSxQPeWoaQmRJKaqNmbRcSZQoqLKaSI1LDbDX7eX3CyliWfzQedR71tGdaI1ASzt1CfNnotJOLtFFJcTCFF5Ozyisoc47H8sMt59uagQF/HqLfXdookpJF6C4l4G4UUF1NIEamd4xVV/LDzMIu35rF0W75TbyFfq4U+bcIdtSwa9VbEOyikuJhCisiFs9sNMvYXsHhLHou35rEjr8Rpe+foIEdg6doiBKsmQxRpkBRSXEwhReTSZR0pZdHJwLJur/NkiFHBAQxMiOK6hChS2jUj0M/HjSUVkbqkkOJiCikidavgeAXLtuezeEs+y7fnU1pRM+ptY38frunYnNSEKAYmRBLa2N+NJRWRS6WQ4mIKKSKuU15l48dfjrJoSy6Lt+STW1Tm2OZrtZDSrhk3dI3h+sQommk8FpF6RyHFxRRSRC4PwzDYdKCIRVvzWLg5l225xY5tVgskt2nGDV2jGdQlmsjgQDeWVERqSyHFxRRSRNxjz+FSvt2Uw7eZuWQeKHSst1igV+swBl8Rw5ArookNbeTGUorIuSikuJhCioj77Tt6nAWbcvnPphw2ZBc4beseF8oNV0Qz5IoYWjVT12YRT6KQ4mIKKSKeJafwBAs25fJtZi7rso5y6r9sXWKDGXJFNEO6xtCueVP3FVJEAIUUl1NIEfFc+cVlfLc5jwWbcvjxl6NOXZs7RjVlyBUxDOkarTmFRNxEIcXFFFJE6oejpRUs2pLLfzJzWb37MJW2mn/y2kY0YfDJV0JXtAhWYBG5TBRSXEwhRaT+KTxeyeKteXy7KZfvdx6i4pQ5hVqGNXK8EurRMlSj3Yq4kEKKiymkiNRvJeVVLN2Wz7eZOSzffogTlTWDx0UHB56sYYmmV3w4PgosInVKIcXFFFJEGo4TFTZW7MjnP5m5LN2WT0l5lWNbRNMABnWJYsgVMfymbTi+PlY3llSkYVBIcTGFFJGGqazSxqpdh/lPZi6LtuRSVFYTWEIb+3F1h+YM6NSc/h2bE6HRbkUuikKKiymkiDR8FVV20n45woJNOXy3OY+jpRVO27u1DOHajs25plMkPeJC9VpIpJYUUlxMIUXEu1TZ7GzYV8Dy7fks336IzQeLnLarlkWk9hRSXEwhRcS75ReVsWLHIZbvOMTKHYecXguBOUT/LVe25MZuMYQ08nNTKUU8k0KKiymkiEi1KpudjH0FLN9+iOU78tl0oKaWxd/XynWJUYy4sgVXd2iOnxreiiikuJpCioicTV5RGV9mHODT9QfYnlcza3NEU3+G9WjBLVe2oEtsiBtLKOJeCikuppAiIudjGAabDxbxWfoBvsw4wJFTGt52jg5ixJUtGdYjlsjgQDeWUuTyU0hxMYUUEbkQlTY73+84xKfp+1m8JZ8KmznardUCV7WPYFiPFgy+IpqmAb5uLqmI6ymkuJhCiohcrMLjlfx740E+S99PenaBY32Ar5XUxCiG92jBNR2b4++r9ivSMCmkuJhCiojUhb2HS/ky4yBfZhzgl8OljvWhjf24oWsMw3u0oFfrMM0lJA2KQoqLKaSISF0yDIPMA4V8mXGQr34+yKHicse2FqGNuKlHLMN7tKBTdJAbSylSNxRSXEwhRURcxWY3SNt9hC8yDrBgU67TXEKdo4O4qUcsQ7vFEhfe2I2lFLl4CikuppAiIpdDWaWNJVvz+SLjACu2H3I0uAVzwLhhPWK5oWsMzTTCrdQjCikuppAiIpdb4fFKFmzO4cuMg6T9coTqf719rBau7hDBsB6xXJeoHkLi+RRSXEwhRUTcKa+ojH//bLZf2bi/0LE+0M9KakIUt1zZgv4dmuOrEW7FAymkuJhCioh4il8OlfDVzwf5MuMge07pIRTRNIDhPWK5tVdLOkfr3ynxHAopLqaQIiKexjAMNh0o4vMNp49w2yU2mFuTWnJT91i1XxG3U0hxMYUUEfFklTY7y7cf4tP1+1myLY9Km/lPva/VwoDOkdya1JIBnSI1YJy4hUKKiymkiEh9cay0gq9+Psin6fud2q+EN/Hnpu6xjOwdR0KM/h2Ty0chxcUUUkSkPtqRV8yn6/fz+YYD5J8yYFz3liGM7N2Kod1jCAr0c2MJxRvU9jvU7fV8s2bNIj4+nsDAQJKTk1m7dm2tjps3bx4Wi4Xhw4c71lVWVvLkk0/StWtXmjRpQmxsLKNHj+bgwYNOx8bHx2OxWJyWl156qS5vS0TEI3WMCmLKDQmsfuq3vHtvb27oGo2fj4Wf9xfyx88z6fM/S3j8459Zn3UU/T+suJtba1Lmz5/P6NGjmT17NsnJycyYMYOPP/6Y7du3ExkZedbj9u7dS79+/Wjbti3h4eF88cUXABQWFnLrrbcyfvx4unfvzrFjx/j973+PzWbjp59+chwfHx/P2LFjGT9+vGNdUFAQTZo0qXXZVZMiIg3F4ZJyPk8/wLx12ew+VNM7qH1kU27vHcfNPVuosa3UqXrxuic5OZnevXvzxhtvAGC324mLi+Phhx/mqaeeOuMxNpuN/v37c99997Fy5UoKCgocIeVM1q1bR58+fcjKyqJVq1aAGVIeffRRHn300Ysuu0KKiDQ0hmGwPusY89bt45uNOZyotAHg52PhusQobusVx9UdmuOjyQ7lEnn8656KigrWr19PampqTWGsVlJTU0lLSzvrcdOmTSMyMpKxY8fW6jqFhYVYLBZCQ0Od1r/00ks0a9aMnj178sorr1BVVXXmE5xUXl5OUVGR0yIi0pBYLBZ6xYfzl//qzpqnB/I/N19B95YhVNoM/pOZyz3vrqPvS0v484Jt7D5U4u7iihdw29jJhw8fxmazERUV5bQ+KiqKbdu2nfGYH374gXfeeYeMjIxaXaOsrIwnn3ySO+64wympPfLII1x55ZWEh4ezevVqpkyZQk5ODq+++upZzzV9+nSef/75Wl1XRKS+Cw70Y1Rya0Ylt2bLwSL+9dM+vsg4QF5ROW8u382by3dzZatQ/qtXHDd2iyFYjW3FBdz2uufgwYO0aNGC1atXk5KS4lj/xBNPsGLFCtasWeO0f3FxMd26deNvf/sbQ4YMAeCee+456+ueyspKRowYwf79+1m+fPk5q5PmzJnD/fffT0lJCQEBZ37vWl5eTnl5TUv4oqIi4uLi9LpHRLxGeZWNpVvz+WT9fpbvOITNbn59BPpZGdwlmluT4ujbrhlWvQ6S86jt6x631aRERETg4+NDXl6e0/q8vDyio6NP23/37t3s3buXoUOHOtbZ7eZsoL6+vmzfvp127doBZkC57bbbyMrKYunSpecNEcnJyVRVVbF37146dep0xn0CAgLOGmBERLxBgK8PQ7rGMKRrDPlFZXyRcYCPf9rPzvwSvsg4yBcZB4kNCWREUktuubIlbSJq3xlB5EzcFlL8/f1JSkpiyZIljm7EdrudJUuWMHHixNP279y5M5mZmU7r/vSnP1FcXMxrr71GXFwcUBNQdu7cybJly2jWrNl5y5KRkYHVaj1njyIREakRGRzIhP7tGH91WzbuL+ST9fv5MuMABwvLeH3pLl5fuosrW4UyIqklv+sWS0gjvQ6SC+fW+bwnT57MmDFj6NWrF3369GHGjBmUlpZy7733AjB69GhatGjB9OnTCQwM5IorrnA6vroxbPX6yspKbr31VtLT0/n666+x2Wzk5uYCEB4ejr+/P2lpaaxZs4YBAwYQFBREWloakyZN4q677iIsLOzy3byISANgsVjoHhdK97hQnr4xgUVb8vgsfT8rdhwiPbuA9OwCnv/3Fq5LiGJEkmZmlgvj1pAycuRIDh06xNSpU8nNzaVHjx4sWLDA0Zg2Ozsbq7X2f5kPHDjAV199BUCPHj2cti1btoxrr72WgIAA5s2bx3PPPUd5eTlt2rRh0qRJTJ48uc7uS0TEGwX6+TC0eyxDu8eSX1zGlxvMofi35RbzTWYO32TmENE0gGE9YhlxZUsSY9WeT85Nw+JfJI2TIiJyfoZhsCWniE/Xnz4zc0JMMLf1Mtuv6HWQd6kXg7nVZwopIiIXptJm5/sdh/g0fT+Lt+RTYTM7PzTy82F4zxbc/ZvWql3xEgopLqaQIiJy8QqOmzMzf/BjFjvyagaG69U6jLtTWjP4imgCfH3cWEJxJYUUF1NIERG5dIZhsHbPUf7xYxYLNuVSdXLslYim/ozsHcedya1pEdrIzaWUuqaQ4mIKKSIidSu/uIx5a/fxzzXZ5BaVAWC1wG87RzE6pTX92kdooLgGQiHFxRRSRERco8pmZ/HWPP7xYxardh1xrG8V3pjb+8TxX0lxNA/S4Jr1mUKKiymkiIi43q78Ej74MYtP0/dTXGZOBOtrtXB9lyju6NOKq9qpdqU+UkhxMYUUEZHL50SFja83HuSjtdmkZxc41qt2pX5SSHExhRQREffYmlPEvLXZfLbhwGm1K3f2aa1JDusBhRQXU0gREXGv6tqVf67NZsMptSttI5pw/zVtublnS/x9NQS/J1JIcTGFFBERz7E1p4iP1mbzefoBisvN2pXo4EDGXd2GO/q0okmAW2eBkV9RSHExhRQREc9TWl7FR2uzeXvlL+QVlQMQ2tiPMSnx3NM3nrAm/m4uoYBCissppIiIeK7yKhufpR/grRW72XvkOACN/X24o08rxl/dluiQQDeX0LsppLiYQoqIiOez2Q2+3ZTD35btZktOEQB+PhZu6dmS+69pS9vmTd1cQu+kkOJiCikiIvWHYRis2HGIvy3fzdo9RwGwWGCgRrN1C4UUF1NIERGpn9ZnHeVvy3azZFu+Y13biCbc9ZvW3NqrJcGBfm4snXdQSHExhRQRkfqtejTbT9bvp+Rkj6DG/j4M79mC0Smt6Rytf9tdRSHFxRRSREQahpLyKj7fcIB/pO1lR16JY32fNuGMTmnNoC7R+PlovJW6pJDiYgopIiINi2EY/PjLUf7x416+25yHzW5+PUYFB3Bnn9bcndKacHVhrhMKKS6mkCIi0nDlFJ7gozXZ/HPtPg6XmOOtBPpZGdkrjnFXtyUuvLGbS1i/KaS4mEKKiEjDV1Fl59tNOby98hc2HTC7MFstcGO3WO7v35YrWoS4uYT1k0KKiymkiIh4D8MwSNt9hNnf/8L3Ow451vdrH8GE/m25ukMEFou6MNeWQoqLKaSIiHinLQeL+L/vd/PvjTmOdisJMcE8cE1bbuwag68a2Z6XQoqLKaSIiHi3/ceO884Pe5i3dh8nKm0AtAhtxNh+bbitdxxNNanhWSmkuJhCioiIABQcr+CDH7N4d9VejpRWABAc6Muo37Tmnr7xRAVrnqBfU0hxMYUUERE5VVmljU/T9/POyj38crgUMOcJGtajBeOvbkun6CA3l9BzKKS4mEKKiIicid1usGRbPm9//wtr9x51rL+mY3Mm9G9L33bNvL6RrUKKiymkiIjI+WzIPsbfV+7h2005nGxjS2JMMBP6t+XGbjFeO5KtQoqLKaSIiEhtZR85zpxVe5i/rqaRbWxIIBP6t+X2Pq0I9PNxcwkvL4UUF1NIERGRC1VwvIIP12Tz7qq9jpFsmwcFMOHqtoz6TSsa+3tHjyCFFBdTSBERkYtVVmnjk/X7eXP5bg4UnAAgvIk/Y/u1YXRKa4IC/dxcQtdSSHExhRQREblUlTY7n6cfYNbyXWQdOQ6Y3ZfvvaoN913VhpDGDTOsKKS4mEKKiIjUlSqbnX9vPMgbS3ex+5DZfblpgC93p7RmXL82NGsa4OYS1i2FFBdTSBERkbpmsxss2JTL60t3si23GIBGfj7c0acV465uQ2xoIzeXsG4opLiYQoqIiLiK3W6weGsebyzbxcb9hQD4Wi3c1COWB65pR8eo+j0wnEKKiymkiIiIqxmGwfc7DzN7+W7SfjniWD+wcyQPXNuO3vHhbizdxVNIcTGFFBERuZwy9hXw1ordLNicS/U3d1LrMB64ph0DO0ditdafUWwVUlxMIUVERNzhl0MlvL3yFz5df4AKmx2ADpFNmdC/LcN6tMDf1/NHsVVIcTGFFBERcaf8ojLmrNrLhz9mUVxeBUDMyVFs7/DwUWwVUlxMIUVERDxBUVkl/1yTzZwf9pBfbI5iG9E0gAn92zAquTVNAjxvFFuFFBdTSBEREU9SXmWOYvu3ZTWj2IY19mPc1W09bhRbhRQXU0gRERFPVGmz8/mGA/xt2S72njKK7X392nBvX88YxVYhxcUUUkRExJNV2ex8vTGHN5btYld+CWCOYjumb2vG9mtLeBN/t5VNIcXFFFJERKQ+sNkNvt2UwxtLdzlGsW3s78Pdv2nNhP5t3TLkvkKKiymkiIhIfWK3GyzamsfrS3ey6UARYIaVe/rGM/7qtoRdxpoVhRQXU0gREZH6yDAMlm3P538X7STzgDnkftMAX+67Kp6xV7clpJHr26wopLiYQoqIiNRnhmGweGs+ry7awdYcs2YlKNCX8Ve35d6r4l3aG0ghxcUUUkREpCGw2w0WbsnlfxftZHue2WYltLEf469uyz19410yzopCiosppIiISENitxt8k5nDjMU72H2oFIDwJv7c378td6e0prF/3YUVhRQXU0gREZGGyGY3+PfPB3ltyU72HDbDyrWdmjP33j51do3afod63li5IiIi4jY+VgvDe7bgd91i+CLjIDOX7GRMSrxbyqKQIiIiIqfx9bFya1JLhvWIxddqcU8Z3HJVERERqRf8fKxuu7b7riwiIiJyDm4PKbNmzSI+Pp7AwECSk5NZu3ZtrY6bN28eFouF4cOHO603DIOpU6cSExNDo0aNSE1NZefOnU77HD16lFGjRhEcHExoaChjx46lpKSkrm5JRERE6oBbQ8r8+fOZPHkyzz77LOnp6XTv3p1BgwaRn59/zuP27t3LY489xtVXX33atpdffpmZM2cye/Zs1qxZQ5MmTRg0aBBlZWWOfUaNGsXmzZtZtGgRX3/9Nd9//z0TJkyo8/sTERGRi+fWLsjJycn07t2bN954AwC73U5cXBwPP/wwTz311BmPsdls9O/fn/vuu4+VK1dSUFDAF198AZi1KLGxsfzhD3/gscceA6CwsJCoqCjmzp3L7bffztatW0lMTGTdunX06tULgAULFnDDDTewf/9+YmNja1V2dUEWERG5OLX9DnVbTUpFRQXr168nNTW1pjBWK6mpqaSlpZ31uGnTphEZGcnYsWNP27Znzx5yc3OdzhkSEkJycrLjnGlpaYSGhjoCCkBqaipWq5U1a9ac9brl5eUUFRU5LSIiIuI6bgsphw8fxmazERUV5bQ+KiqK3NzcMx7zww8/8M477/D222+fcXv1cec6Z25uLpGRkU7bfX19CQ8PP+t1AaZPn05ISIhjiYuLO/cNioiIyCVxe8PZ2iouLubuu+/m7bffJiIi4rJff8qUKRQWFjqWffv2XfYyiIiIeBO3jZMSERGBj48PeXl5Tuvz8vKIjo4+bf/du3ezd+9ehg4d6lhnt9sBsyZk+/btjuPy8vKIiYlxOmePHj0AiI6OPq1hblVVFUePHj3jdasFBAQQEBBwYTcpIiIiF81tNSn+/v4kJSWxZMkSxzq73c6SJUtISUk5bf/OnTuTmZlJRkaGY7npppsYMGAAGRkZxMXF0aZNG6Kjo53OWVRUxJo1axznTElJoaCggPXr1zv2Wbp0KXa7neTkZBfesYiIiFwIt444O3nyZMaMGUOvXr3o06cPM2bMoLS0lHvvvReA0aNH06JFC6ZPn05gYCBXXHGF0/GhoaEATusfffRR/vu//5sOHTrQpk0bnnnmGWJjYx3jqSQkJDB48GDGjx/P7NmzqaysZOLEidx+++217tkjIiIirufWkDJy5EgOHTrE1KlTyc3NpUePHixYsMDR8DU7Oxur9cIqe5544glKS0uZMGECBQUF9OvXjwULFhAYGOjY58MPP2TixIkMHDgQq9XKiBEjmDlzZp3em4iIiFwat46TUp9pnBQREZGL4/HjpIiIiIici0KKiIiIeCSFFBEREfFICikiIiLikRRSRERExCO5tQtyfVbdKUoTDYqIiFyY6u/O83UwVki5SMXFxQCaaFBEROQiFRcXExISctbtGiflItntdg4ePEhQUBAWi6VOzllUVERcXBz79u1rMGOvNLR7amj3A7qn+kL3VD/onmrHMAyKi4uJjY0956Ctqkm5SFarlZYtW7rk3MHBwQ3mL3e1hnZPDe1+QPdUX+ie6gfd0/mdqwalmhrOioiIiEdSSBERERGPpJDiQQICAnj22WcJCAhwd1HqTEO7p4Z2P6B7qi90T/WD7qluqeGsiIiIeCTVpIiIiIhHUkgRERERj6SQIiIiIh5JIUVEREQ8kkKKh5g1axbx8fEEBgaSnJzM2rVr3V2ki/bcc89hsVicls6dO7u7WBfk+++/Z+jQocTGxmKxWPjiiy+cthuGwdSpU4mJiaFRo0akpqayc+dO9xS2ls53T/fcc89pz23w4MHuKWwtTJ8+nd69exMUFERkZCTDhw9n+/btTvuUlZXx0EMP0axZM5o2bcqIESPIy8tzU4nPrzb3dO211572nB544AE3lfj83nzzTbp16+YYCCwlJYVvv/3Wsb2+PSM4/z3Vt2f0ay+99BIWi4VHH33Usc5dz0khxQPMnz+fyZMn8+yzz5Kenk737t0ZNGgQ+fn57i7aRevSpQs5OTmO5YcffnB3kS5IaWkp3bt3Z9asWWfc/vLLLzNz5kxmz57NmjVraNKkCYMGDaKsrOwyl7T2zndPAIMHD3Z6bh999NFlLOGFWbFiBQ899BA//vgjixYtorKykuuvv57S0lLHPpMmTeLf//43H3/8MStWrODgwYPccsstbiz1udXmngDGjx/v9JxefvllN5X4/Fq2bMlLL73E+vXr+emnn/jtb3/LsGHD2Lx5M1D/nhGc/56gfj2jU61bt4633nqLbt26Oa1323MyxO369OljPPTQQ47fbTabERsba0yfPt2Npbp4zz77rNG9e3d3F6POAMbnn3/u+N1utxvR0dHGK6+84lhXUFBgBAQEGB999JEbSnjhfn1PhmEYY8aMMYYNG+aW8tSF/Px8AzBWrFhhGIb5TPz8/IyPP/7Ysc/WrVsNwEhLS3NXMS/Ir+/JMAzjmmuuMX7/+9+7r1B1ICwszPj73//eIJ5Rtep7Moz6+4yKi4uNDh06GIsWLXK6B3c+J9WkuFlFRQXr168nNTXVsc5qtZKamkpaWpobS3Zpdu7cSWxsLG3btmXUqFFkZ2e7u0h1Zs+ePeTm5jo9s5CQEJKTk+v1MwNYvnw5kZGRdOrUiQcffJAjR464u0i1VlhYCEB4eDgA69evp7Ky0uk5de7cmVatWtWb5/Tre6r24YcfEhERwRVXXMGUKVM4fvy4O4p3wWw2G/PmzaO0tJSUlJQG8Yx+fU/V6uMzeuihh7jxxhudnge4978lTTDoZocPH8ZmsxEVFeW0Pioqim3btrmpVJcmOTmZuXPn0qlTJ3Jycnj++ee5+uqr2bRpE0FBQe4u3iXLzc0FOOMzq95WHw0ePJhbbrmFNm3asHv3bv74xz8yZMgQ0tLS8PHxcXfxzslut/Poo49y1VVXccUVVwDmc/L39yc0NNRp3/rynM50TwB33nknrVu3JjY2lo0bN/Lkk0+yfft2PvvsMzeW9twyMzNJSUmhrKyMpk2b8vnnn5OYmEhGRka9fUZnuyeon89o3rx5pKens27dutO2ufO/JYUUqXNDhgxxfO7WrRvJycm0bt2af/3rX4wdO9aNJZNzuf322x2fu3btSrdu3WjXrh3Lly9n4MCBbizZ+T300ENs2rSp3rV9Opez3dOECRMcn7t27UpMTAwDBw5k9+7dtGvX7nIXs1Y6depERkYGhYWFfPLJJ4wZM4YVK1a4u1iX5Gz3lJiYWO+e0b59+/j973/PokWLCAwMdHdxnOh1j5tFRETg4+NzWivpvLw8oqOj3VSquhUaGkrHjh3ZtWuXu4tSJ6qfS0N+ZgBt27YlIiLC45/bxIkT+frrr1m2bBktW7Z0rI+OjqaiooKCggKn/evDczrbPZ1JcnIygEc/J39/f9q3b09SUhLTp0+ne/fuvPbaa/X6GZ3tns7E05/R+vXryc/P58orr8TX1xdfX19WrFjBzJkz8fX1JSoqym3PSSHFzfz9/UlKSmLJkiWOdXa7nSVLlji936zPSkpK2L17NzExMe4uSp1o06YN0dHRTs+sqKiINWvWNJhnBrB//36OHDnisc/NMAwmTpzI559/ztKlS2nTpo3T9qSkJPz8/Jye0/bt28nOzvbY53S+ezqTjIwMAI99Tmdit9spLy+vl8/obKrv6Uw8/RkNHDiQzMxMMjIyHEuvXr0YNWqU47PbnpNLm+VKrcybN88ICAgw5s6da2zZssWYMGGCERoaauTm5rq7aBflD3/4g7F8+XJjz549xqpVq4zU1FQjIiLCyM/Pd3fRaq24uNjYsGGDsWHDBgMwXn31VWPDhg1GVlaWYRiG8dJLLxmhoaHGl19+aWzcuNEYNmyY0aZNG+PEiRNuLvnZneueiouLjccee8xIS0sz9uzZYyxevNi48sorjQ4dOhhlZWXuLvoZPfjgg0ZISIixfPlyIycnx7EcP37csc8DDzxgtGrVyli6dKnx008/GSkpKUZKSoobS31u57unXbt2GdOmTTN++uknY8+ePcaXX35ptG3b1ujfv7+bS352Tz31lLFixQpjz549xsaNG42nnnrKsFgsxsKFCw3DqH/PyDDOfU/18Rmdya97KLnrOSmkeIjXX3/daNWqleHv72/06dPH+PHHH91dpIs2cuRIIyYmxvD39zdatGhhjBw50ti1a5e7i3VBli1bZgCnLWPGjDEMw+yG/MwzzxhRUVFGQECAMXDgQGP79u3uLfR5nOuejh8/blx//fVG8+bNDT8/P6N169bG+PHjPToon+leAOPdd9917HPixAnj//2//2eEhYUZjRs3Nm6++WYjJyfHfYU+j/PdU3Z2ttG/f38jPDzcCAgIMNq3b288/vjjRmFhoXsLfg733Xef0bp1a8Pf399o3ry5MXDgQEdAMYz694wM49z3VB+f0Zn8OqS46zlZDMMwXFtXIyIiInLh1CZFREREPJJCioiIiHgkhRQRERHxSAopIiIi4pEUUkRERMQjKaSIiIiIR1JIEREREY+kkCIiXmv58uVYLJbT5iQREc+gkCIiIiIeSSFFREREPJJCioi4jd1uZ/r06bRp04ZGjRrRvXt3PvnkE6DmVcw333xDt27dCAwM5De/+Q2bNm1yOsenn35Kly5dCAgIID4+nr/+9a9O28vLy3nyySeJi4sjICCA9u3b88477zjts379enr16kXjxo3p27cv27dvd2z7+eefGTBgAEFBQQQHB5OUlMRPP/3koj8RETmVQoqIuM306dN5//33mT17Nps3b2bSpEncddddrFixwrHP448/zl//+lfWrVtH8+bNGTp0KJWVlYAZLm677TZuv/12MjMzee6553jmmWeYO3eu4/jRo0fz0UcfMXPmTLZu3cpbb71F06ZNncrx9NNP89e//pWffvoJX19f7rvvPse2UaNG0bJlS9atW8f69et56qmn8PPzc+0fjIiYXD6FoYjIGZSVlRmNGzc2Vq9e7bR+7Nixxh133OGYtXnevHmObUeOHDEaNWpkzJ8/3zAMw7jzzjuN6667zun4xx9/3EhMTDQMwzC2b99uAMaiRYvOWIbqayxevNix7ptvvjEA48SJE4ZhGEZQUJAxd+7cS79hEblgqkkREbfYtWsXx48f57rrrqNp06aO5f3332f37t2O/VJSUhyfw8PD6dSpE1u3bgVg69atXHXVVU7nveqqq9i5cyc2m42MjAx8fHy45pprzlmWbt26OT7HxMQAkJ+fD8DkyZMZN24cqampvPTSS05lExHXUkgREbcoKSkB4JtvviEjI8OxbNmyxdEu5VI1atSoVvud+vrGYrEAZnsZgOeee47Nmzdz4403snTpUhITE/n888/rpHwicm4KKSLiFomJiQQEBJCdnU379u2dlri4OMd+P/74o+PzsWPH2LFjBwkJCQAkJCSwatUqp/OuWrWKjh074uPjQ9euXbHb7U5tXC5Gx44dmTRpEgsXLuSWW27h3XffvaTziUjt+Lq7ACLinYKCgnjssceYNGkSdrudfv36UVhYyKpVqwgODqZ169YATJs2jWbNmhEVFcXTTz9NREQEw4cPB+APf/gDvXv35oUXXmDkyJGkpaXxxhtv8Le//Q2A+Ph4xowZw3333cfMmTPp3r07WVlZ5Ofnc9ttt523jCdOnODxxx/n1ltvpU2bNuzfv59169YxYsQIl/25iMgp3N0oRkS8l91uN2bMmGF06tTJ8PPzM5o3b24MGjTIWLFihaNR67///W+jS5cuhr+/v9GnTx/j559/djrHJ598YiQmJhp+fn5Gq1atjFdeecVp+4kTJ4xJkyYZMTExhr+/v9G+fXtjzpw5hmHUNJw9duyYY/8NGzYYgLFnzx6jvLzcuP322424uDjD39/fiI2NNSZOnOhoVCsirmUxDMNwc04SETnN8uXLGTBgAMeOHSM0NNTdxRERN1CbFBEREfFICikiIiLikfS6R0RERDySalJERETEIymkiIiIiEdSSBERERGPpJAiIiIiHkkhRURERDySQoqIiIh4JIUUERER8UgKKSIiIuKRFFJERETEI/1/qUEYCOgWzeYAAAAASUVORK5CYII=\n"},"metadata":{}}],"source":["import matplotlib.pyplot as plt\n","\n","fig, ax = plt.subplots(figsize=(6, 6))\n","ax.plot(x, training_losses, validation_losses)\n","ax.legend(['Training Loss', 'Validation Loss'])\n","plt.xlabel(\"epochs\")\n","plt.ylabel(\"loss\")"]},{"cell_type":"code","execution_count":70,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sPoPdvmJYSp-","executionInfo":{"status":"ok","timestamp":1687567460019,"user_tz":420,"elapsed":517,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"d7a3ed90-5102-4c1b-82ea-c9ac023f7c99"},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([0.4031])\n","tensor([0.4580])\n"]}],"source":["# last epoch losses\n","print(training_losses[-1])\n","print(validation_losses[-1])"]},{"cell_type":"markdown","metadata":{"id":"YkVR5WaPBRin"},"source":["## Testing"]},{"cell_type":"code","execution_count":71,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WClV6_m9yFsG","executionInfo":{"status":"ok","timestamp":1687567463710,"user_tz":420,"elapsed":278,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"3a86dbf9-2be4-4406-b1ef-36ddca8cde3e"},"outputs":[{"output_type":"stream","name":"stdout","text":["CUDA is available!  Training on GPU ...\n"]}],"source":["# check if CUDA is available\n","train_on_gpu = torch.cuda.is_available()\n","\n","if not train_on_gpu:\n","    print('CUDA is not available.  Training on CPU ...')\n","else:\n","    print('CUDA is available!  Training on GPU ...')"]},{"cell_type":"code","execution_count":72,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eDUpH4eNgb5O","executionInfo":{"status":"ok","timestamp":1687567606522,"user_tz":420,"elapsed":140431,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"b74118fe-39fc-4d6f-b614-827c00e2e5ab"},"outputs":[{"output_type":"stream","name":"stderr","text":["Testing: 100%|██████████| 7045/7045 [02:06<00:00, 55.77it/s]\n"]},{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","       asian       0.65      0.63      0.64     25754\n","    hispanic       0.85      0.86      0.85    163509\n","    nh_black       0.78      0.75      0.76    133457\n","    nh_white       0.89      0.93      0.91    552672\n","       other       0.40      0.07      0.12     26368\n","\n","    accuracy                           0.85    901760\n","   macro avg       0.71      0.65      0.66    901760\n","weighted avg       0.84      0.85      0.84    901760\n","\n"]}],"source":["from sklearn.metrics import confusion_matrix, classification_report, ConfusionMatrixDisplay\n","\n","# track test loss\n","test_loss = 0.0\n","\n","\n","class_correct = list(0. for i in range(n_categories))\n","class_total = list(0. for i in range(n_categories))\n","\n","\n","actual = []\n","predictions = []\n","\n","rnn.eval()\n","# iterate over test data\n","pbar = tqdm(test_dataloader, total=len(test_dataloader), position=0, desc=\"Testing\", leave=True)\n","for batch in pbar:\n","    # move tensors to GPU if CUDA is available\n","    input = batch[0].to(device)\n","    label = batch[1].to(device)\n","    output = rnn(input)\n","    loss = criterion(output, label)\n","    test_loss += loss.item()\n","    pred = torch.argmax(output, dim=1)\n","    correct_tensor = pred.eq(label.data.view_as(pred))\n","    correct = np.squeeze(correct_tensor.numpy()) if not train_on_gpu else np.squeeze(correct_tensor.cpu().numpy())\n","    # calculate test accuracy for each object class\n","    for i in range(label.shape[0]):\n","        l = label.data[i]\n","        class_correct[l.long()] += correct[i].item()\n","        class_total[l.long()] += 1\n","        # for confusion matrix\n","        actual.append(all_categories[label.data[i].item()])\n","        predictions.append(all_categories[pred.data[i].item()])\n","\n","\n","# plot confusion matrix\n","cm = confusion_matrix(actual, predictions, labels=all_categories)\n","print(classification_report(actual, predictions))"]},{"cell_type":"code","execution_count":73,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oKsIjrK_8KEJ","executionInfo":{"status":"ok","timestamp":1687567614507,"user_tz":420,"elapsed":605,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"ecd3d007-d08b-4379-aa0f-b1775604cc4d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.46043037150962035"]},"metadata":{},"execution_count":73}],"source":["test_loss/len(test_dataloader)"]},{"cell_type":"code","execution_count":74,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":840},"id":"TrS2WIqxyJPL","executionInfo":{"status":"ok","timestamp":1687567616716,"user_tz":420,"elapsed":683,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"0ae8a8f0-5f42-4075-f08e-6088b3522642"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f00d3fd1e40>"]},"metadata":{},"execution_count":74},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x1000 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["import matplotlib.pyplot as plt\n","%matplotlib inline\n","\n","cmp = ConfusionMatrixDisplay(cm, display_labels=all_categories)\n","fig, ax = plt.subplots(figsize=(10,10))\n","cmp.plot(ax=ax, xticks_rotation='vertical')"]},{"cell_type":"code","execution_count":75,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Kjz7DX4o9qlO","executionInfo":{"status":"ok","timestamp":1687567623976,"user_tz":420,"elapsed":265,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"f2bb27fa-907f-4425-c83c-c5103d44aec4"},"outputs":[{"output_type":"stream","name":"stdout","text":["Test Loss: 0.460430\n","\n","Test Accuracy of asian: 63% (16313/25754)\n","Test Accuracy of hispanic: 86% (140670/163509)\n","Test Accuracy of nh_black: 74% (99473/133457)\n","Test Accuracy of nh_white: 92% (512077/552672)\n","Test Accuracy of other:  7% (1855/26368)\n","\n","Test Accuracy (Overall): 85% (770388/901760)\n"]}],"source":["# average test loss\n","test_loss = test_loss/len(test_dataloader)\n","print('Test Loss: {:.6f}\\n'.format(test_loss))\n","\n","for i in range(len(all_categories)):\n","    if class_total[i] > 0:\n","        print('Test Accuracy of %5s: %2d%% (%2d/%2d)' % (\n","            all_categories[i], 100 * class_correct[i] / class_total[i],\n","            np.sum(class_correct[i]), np.sum(class_total[i])))\n","    else:\n","        print('Test Accuracy of %5s: N/A (no training examples)' % (all_categories[i]))\n","\n","print('\\nTest Accuracy (Overall): %2d%% (%2d/%2d)' % (\n","    100. * np.sum(class_correct) / np.sum(class_total),\n","    np.sum(class_correct), np.sum(class_total)))"]},{"cell_type":"markdown","metadata":{"id":"m3j4uUHzpnKg"},"source":["## Inference"]},{"cell_type":"code","execution_count":76,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":434,"status":"ok","timestamp":1687567630150,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"},"user_tz":420},"id":"Hy8oB6Kg-YM8","outputId":"8eaf4f94-6729-431f-eecb-d862cd7ca0a7"},"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([47])\n","torch.Size([1, 5])\n","tensor(3, device='cuda:0')\n","nh_white\n"]}],"source":["name = \"Ward Stephanie\"\n","name_tokens = lineToTensor(name)\n","inp = name_tokens\n","print(inp.shape)\n","out = rnn(inp.unsqueeze(0).to(device))\n","print(out.shape)\n","out = torch.argmax(out)\n","print(out)\n","print(all_categories[out.item()])"]},{"cell_type":"markdown","metadata":{"id":"_cxLVsA7AyvA"},"source":["## Load and Test"]},{"cell_type":"code","execution_count":77,"metadata":{"id":"TL-PMZkSAyNm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1687567672288,"user_tz":420,"elapsed":5,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"4ec0b0c8-6339-46bd-8fd0-aac2621c68a8"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["LSTM(\n","  (embedding): Embedding(57, 256)\n","  (lstm): LSTM(256, 256, num_layers=2, batch_first=True)\n","  (fc): Linear(in_features=256, out_features=5, bias=True)\n","  (softmax): LogSoftmax(dim=1)\n",")"]},"metadata":{},"execution_count":77}],"source":["test_rnn = LSTM(vocab_size, n_hidden, n_categories, num_layers=2)\n","test_rnn.load_state_dict(torch.load('lstm_FullName_pytorch.pt'))\n","test_rnn.to(device)"]},{"cell_type":"code","execution_count":78,"metadata":{"id":"u82VG3fG8pBT","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1687567814805,"user_tz":420,"elapsed":139772,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"d86f4d0a-c84a-4d71-b5b2-afe0660d966d"},"outputs":[{"output_type":"stream","name":"stderr","text":["Testing: 100%|██████████| 7045/7045 [02:05<00:00, 56.18it/s]\n"]},{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","       asian       0.65      0.63      0.64     25752\n","    hispanic       0.85      0.86      0.85    163505\n","    nh_black       0.78      0.75      0.76    133458\n","    nh_white       0.89      0.93      0.91    552675\n","       other       0.40      0.07      0.12     26370\n","\n","    accuracy                           0.85    901760\n","   macro avg       0.71      0.65      0.66    901760\n","weighted avg       0.84      0.85      0.84    901760\n","\n","Test Loss: 0.460421\n","\n","Test Accuracy of asian: 63% (16311/25752)\n","Test Accuracy of hispanic: 86% (140668/163505)\n","Test Accuracy of nh_black: 74% (99472/133458)\n","Test Accuracy of nh_white: 92% (512082/552675)\n","Test Accuracy of other:  7% (1855/26370)\n","\n","Test Accuracy (Overall): 85% (770388/901760)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x800 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["from sklearn.metrics import confusion_matrix, classification_report, ConfusionMatrixDisplay\n","\n","# track test loss\n","test_loss = 0.0\n","\n","\n","class_correct = list(0. for i in range(n_categories))\n","class_total = list(0. for i in range(n_categories))\n","\n","\n","actual = []\n","predictions = []\n","\n","rnn.eval()\n","# iterate over test data\n","pbar = tqdm(test_dataloader, total=len(test_dataloader), position=0, desc=\"Testing\", leave=True)\n","for batch in pbar:\n","    # move tensors to GPU if CUDA is available\n","    input = batch[0].to(device)\n","    label = batch[1].to(device)\n","    output = test_rnn(input)\n","    loss = criterion(output, label)\n","    test_loss += loss.item()\n","    pred = torch.argmax(output, dim=1)\n","    correct_tensor = pred.eq(label.data.view_as(pred))\n","    correct = np.squeeze(correct_tensor.numpy()) if not train_on_gpu else np.squeeze(correct_tensor.cpu().numpy())\n","    # calculate test accuracy for each object class\n","    for i in range(label.shape[0]):\n","        l = label.data[i]\n","        class_correct[l.long()] += correct[i].item()\n","        class_total[l.long()] += 1\n","        # for confusion matrix\n","        actual.append(all_categories[label.data[i].item()])\n","        predictions.append(all_categories[pred.data[i].item()])\n","\n","\n","# plot confusion matrix\n","cm = confusion_matrix(actual, predictions, labels=all_categories)\n","print(classification_report(actual, predictions))\n","\n","import matplotlib.pyplot as plt\n","%matplotlib inline\n","\n","cmp = ConfusionMatrixDisplay(cm, display_labels=all_categories)\n","fig, ax = plt.subplots(figsize=(8,8))\n","cmp.plot(ax=ax, xticks_rotation='vertical')\n","\n","# average test loss\n","test_loss = test_loss/len(test_dataloader)\n","print('Test Loss: {:.6f}\\n'.format(test_loss))\n","\n","for i in range(len(all_categories)):\n","    if class_total[i] > 0:\n","        print('Test Accuracy of %5s: %2d%% (%2d/%2d)' % (\n","            all_categories[i], 100 * class_correct[i] / class_total[i],\n","            np.sum(class_correct[i]), np.sum(class_total[i])))\n","    else:\n","        print('Test Accuracy of %5s: N/A (no training examples)' % (all_categories[i]))\n","\n","print('\\nTest Accuracy (Overall): %2d%% (%2d/%2d)' % (\n","    100. * np.sum(class_correct) / np.sum(class_total),\n","    np.sum(class_correct), np.sum(class_total)))"]},{"cell_type":"code","execution_count":80,"metadata":{"id":"96QvegHxBvsb","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1687567823615,"user_tz":420,"elapsed":364,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"33309a3c-d20e-4c53-e553-3f8c4124cc10"},"outputs":[{"output_type":"stream","name":"stdout","text":["4.1M\tlstm_FullName_pytorch.pt\n"]}],"source":["!du -sh lstm_FullName_pytorch.pt"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"c80Xzw5iYSqD"},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","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":"3.8.10"},"colab":{"provenance":[],"machine_shape":"hm","gpuType":"A100"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0}