notebooks/3.2_lstm_lastname.ipynb

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MP8iXi3aBVHS","executionInfo":{"status":"ok","timestamp":1687745239504,"user_tz":420,"elapsed":4201,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"74c2b168-0de4-48d5-a9be-72adb2b294b3"},"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","execution_count":2,"metadata":{"id":"6wTqaxXwRGBE","executionInfo":{"status":"ok","timestamp":1687745244250,"user_tz":420,"elapsed":4750,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}}},"outputs":[],"source":["import os\n","import string\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":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XTPFY-l8Brzi","executionInfo":{"status":"ok","timestamp":1687745263594,"user_tz":420,"elapsed":19353,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"7448b4ca-fd24-4766-aca3-8c7505944681"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"markdown","metadata":{"id":"cSlSeNxpELos"},"source":["## Data preprocessing"]},{"cell_type":"code","execution_count":4,"metadata":{"id":"VVVfrmyYbL0-","executionInfo":{"status":"ok","timestamp":1687745268000,"user_tz":420,"elapsed":4409,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}}},"outputs":[],"source":["train_df = pd.read_csv(\"/content/drive/MyDrive/Colab/ethnicolor/data/fl_2022_LastName_train.csv.gz\")\n","val_df = pd.read_csv(\"/content/drive/MyDrive/Colab/ethnicolor/data/fl_2022_LastName_val.csv.gz\")\n","test_df = pd.read_csv(\"/content/drive/MyDrive/Colab/ethnicolor/data/fl_2022_LastName_test.csv.gz\")"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1687745268001,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"},"user_tz":420},"id":"u0TJpyUQEAHz","outputId":"32ed2f73-52e5-444b-f6d3-70582b9fe8a6"},"outputs":[{"output_type":"stream","name":"stdout","text":["(845316, 9)\n","(105664, 9)\n","(105665, 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":"4nB7n9qlCbP3","executionInfo":{"status":"ok","timestamp":1687745268001,"user_tz":420,"elapsed":6,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"055b329b-00f1-4745-8098-2808bba90fd8"},"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["           name_last  asian  hispanic  nh_black  nh_white     other  total_n  \\\n","0  Neaman-Piotrowicz    0.0       0.0       0.0  1.000000  0.000000      1.0   \n","1              Chaet    0.0       0.0       0.0  1.000000  0.000000     13.0   \n","2        Veloz Perez    0.0       1.0       0.0  0.000000  0.000000      1.0   \n","3            Spalter    0.0       0.0       0.0  0.928571  0.071429     42.0   \n","4     Kearns-Edwards    0.0       0.0       0.0  1.000000  0.000000      1.0   \n","\n","       race  race_code  \n","0  nh_white          3  \n","1  nh_white          3  \n","2  hispanic          1  \n","3  nh_white          3  \n","4  nh_white          3  "],"text/html":["\n","  <div id=\"df-5377422b-62dd-42e1-aaf1-e6fd4cadda92\">\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>name_last</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>Neaman-Piotrowicz</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>1.0</td>\n","      <td>nh_white</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Chaet</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>13.0</td>\n","      <td>nh_white</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Veloz Perez</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.0</td>\n","      <td>hispanic</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Spalter</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.928571</td>\n","      <td>0.071429</td>\n","      <td>42.0</td>\n","      <td>nh_white</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Kearns-Edwards</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.000000</td>\n","      <td>0.000000</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-5377422b-62dd-42e1-aaf1-e6fd4cadda92')\"\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 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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":6}]},{"cell_type":"markdown","metadata":{"id":"GzHXp_2UE_H6"},"source":["## Creating Vocab"]},{"cell_type":"code","execution_count":7,"metadata":{"id":"eQma_l_7KbGw","executionInfo":{"status":"ok","timestamp":1687745268001,"user_tz":420,"elapsed":5,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}}},"outputs":[],"source":["vectorizer = CountVectorizer(analyzer='char', lowercase=False)"]},{"cell_type":"code","source":["vectorizer.fit_transform(train_df['name_last'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iccxoK7fCmM5","executionInfo":{"status":"ok","timestamp":1687745272658,"user_tz":420,"elapsed":4662,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"1566a21c-e74b-4abd-c327-0b239116cb09"},"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<845316x55 sparse matrix of type '<class 'numpy.int64'>'\n","\twith 7105885 stored elements in Compressed Sparse Row format>"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","execution_count":9,"metadata":{"executionInfo":{"elapsed":17,"status":"ok","timestamp":1687745272659,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"},"user_tz":420},"id":"acgCGLmiKePO"},"outputs":[],"source":["vocab = list(vectorizer.get_feature_names_out())"]},{"cell_type":"code","execution_count":10,"metadata":{"id":"NRIPVUoDKg6I","executionInfo":{"status":"ok","timestamp":1687745272659,"user_tz":420,"elapsed":16,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}}},"outputs":[],"source":["n_letters = len(vocab)"]},{"cell_type":"markdown","source":["# Creating Dataset"],"metadata":{"id":"jNCEhTebCz8m"}},{"cell_type":"code","execution_count":11,"metadata":{"id":"ClaG4XqwBVHb","executionInfo":{"status":"ok","timestamp":1687745273047,"user_tz":420,"elapsed":403,"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['name_last'].str.len().max()"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mslRveqMBVHc","executionInfo":{"status":"ok","timestamp":1687745273047,"user_tz":420,"elapsed":11,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"f4c27d4f-8c95-4fc3-e003-a423de53551b"},"outputs":[{"output_type":"stream","name":"stdout","text":["classes - ['asian', 'hispanic', 'nh_black', 'nh_white', 'other']\n","no of classes - 5\n","max seq len - 30\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":13,"metadata":{"id":"Y0LCplqlShEA","executionInfo":{"status":"ok","timestamp":1687745273048,"user_tz":420,"elapsed":11,"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","source":["lineToTensor('Jason')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CV-yYx6ADJVp","executionInfo":{"status":"ok","timestamp":1687745273048,"user_tz":420,"elapsed":11,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"d11f563f-a0e5-41e0-cedb-5d91a8b09866"},"execution_count":14,"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.])"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","execution_count":15,"metadata":{"id":"t0fkgqZEbbh5","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1687745273048,"user_tz":420,"elapsed":8,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"cad97c54-6006-4d1e-8af7-548d699e086f"},"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([30])\n"]}],"source":["print(lineToTensor('Jason').size())"]},{"cell_type":"code","execution_count":16,"metadata":{"executionInfo":{"elapsed":8,"status":"ok","timestamp":1687745273049,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"},"user_tz":420},"id":"4b4W2hy7b_q-"},"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('name_last')]\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":17,"metadata":{"id":"Ue_MhX-7QN9I","executionInfo":{"status":"ok","timestamp":1687745273049,"user_tz":420,"elapsed":7,"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":18,"metadata":{"id":"U9fp-gC9Mnft","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1687745273049,"user_tz":420,"elapsed":7,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"f40473b4-daa6-4b71-98e5-a58e5fec9d25"},"outputs":[{"output_type":"stream","name":"stdout","text":["0 tensor([16., 33., 29., 41., 29., 42.,  2., 18., 37., 43., 48., 46., 43., 51.,\n","        37., 31., 54., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56.,\n","        56., 56.]) tensor(3)\n","1 tensor([ 5., 36., 29., 33., 48., 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.]) tensor(3)\n","2 tensor([24., 33., 40., 43., 54.,  0., 18., 33., 46., 33., 54., 56., 56., 56.,\n","        56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56.,\n","        56., 56.]) tensor(1)\n"]}],"source":["for i in range(3):\n","  name, label = train_dataset[i]\n","  print(i, name, label)"]},{"cell_type":"code","execution_count":19,"metadata":{"id":"tGyzWNzgNzjc","executionInfo":{"status":"ok","timestamp":1687745273050,"user_tz":420,"elapsed":7,"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":20,"metadata":{"id":"C7c7-RtYlE3G","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1687745273436,"user_tz":420,"elapsed":393,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"4ac5ec21-5b7e-4259-e11b-d113503be7b7"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[tensor([[22., 49., 30.,  ..., 56., 56., 56.],\n","         [ 4., 37., 35.,  ..., 56., 56., 56.],\n","         [ 4., 49., 42.,  ..., 56., 56., 56.],\n","         ...,\n","         [20., 29., 42.,  ..., 56., 56., 56.],\n","         [15., 29., 54.,  ..., 56., 56., 56.],\n","         [16., 43., 50.,  ..., 56., 56., 56.]]),\n"," tensor([3, 1, 3, 1, 1, 0, 2, 1, 3, 3, 3, 3, 3, 1, 3, 1, 1, 0, 1, 3, 3, 3, 3, 0,\n","         3, 3, 4, 3, 1, 0, 1, 3, 3, 2, 2, 1, 4, 1, 3, 3, 1, 1, 3, 3, 2, 3, 1, 1,\n","         3, 1, 1, 3, 1, 1, 3, 3, 3, 3, 1, 2, 2, 3, 3, 1, 2, 1, 1, 3, 3, 3, 3, 3,\n","         3, 1, 2, 3, 1, 1, 2, 3, 3, 2, 2, 1, 1, 1, 3, 4, 1, 1, 3, 0, 3, 3, 1, 1,\n","         4, 1, 3, 1, 2, 3, 3, 1, 1, 1, 3, 3, 3, 1, 3, 3, 3, 1, 1, 3, 1, 3, 3, 1,\n","         1, 1, 1, 1, 1, 1, 3, 1])]"]},"metadata":{},"execution_count":20}],"source":["next(iter(train_dataloader))"]},{"cell_type":"code","execution_count":21,"metadata":{"id":"UAebM2_6kIr1","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1687745273880,"user_tz":420,"elapsed":446,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"0739bd16-35e1-4bbb-fa90-9fa924a35aa3"},"outputs":[{"output_type":"stream","name":"stdout","text":["0 torch.Size([128, 30]) torch.Size([128])\n","1 torch.Size([128, 30]) torch.Size([128])\n","2 torch.Size([128, 30]) 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":"code","execution_count":22,"metadata":{"id":"5zvwGXG2M-Gu","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1687745282138,"user_tz":420,"elapsed":8260,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"fe754655-8d5d-4f67-bf34-2be86de7752b"},"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":22}],"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":"code","execution_count":23,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"G2v1iG1PTDsO","outputId":"8ac612f2-10ef-476f-a25b-49fa4793c68b","executionInfo":{"status":"ok","timestamp":1687745282497,"user_tz":420,"elapsed":364,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([20., 43., 35., 33., 46., 47., 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.])\n","input shape :  torch.Size([30])\n","input shape w batch :  torch.Size([1, 30])\n","torch.Size([1, 5])\n","tensor([[-1.6282, -1.6088, -1.6169, -1.5790, -1.6150]], 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')\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":24,"metadata":{"executionInfo":{"elapsed":6,"status":"ok","timestamp":1687745282497,"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":25,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"RUkgRkq-TuWB","executionInfo":{"status":"ok","timestamp":1687746156276,"user_tz":420,"elapsed":873785,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"e25c06d4-dc14-4118-d368-78d11edb09e6"},"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='15' class='' max='100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      15.00% [15/100 13:39&lt;1:17:21]\n","    </div>\n","    \n","\n","Epoch 0: Training loss 0.675240 validation loss 0.620454 with lr 0.000500<p>\n","Epoch 1: Training loss 0.602570 validation loss 0.595232 with lr 0.000500<p>\n","Epoch 2: Training loss 0.576782 validation loss 0.583607 with lr 0.000500<p>\n","Epoch 3: Training loss 0.557165 validation loss 0.576611 with lr 0.000500<p>\n","Epoch 4: Training loss 0.539669 validation loss 0.571943 with lr 0.000500<p>\n","Epoch 5: Training loss 0.522223 validation loss 0.570769 with lr 0.000500<p>\n","Epoch 6: Training loss 0.504834 validation loss 0.574288 with lr 0.000500<p>\n","Epoch 7: Training loss 0.487072 validation loss 0.576429 with lr 0.000500<p>\n","Epoch 8: Training loss 0.469061 validation loss 0.581420 with lr 0.000500<p>\n","Epoch 9: Training loss 0.450799 validation loss 0.585995 with lr 0.000500<p>\n","Epoch 10: Training loss 0.432611 validation loss 0.605998 with lr 0.000500<p>\n","Epoch 11: Training loss 0.414445 validation loss 0.616261 with lr 0.000500<p>\n","Epoch 12: Training loss 0.396367 validation loss 0.628237 with lr 0.000500<p>\n","Epoch 13: Training loss 0.378896 validation loss 0.648585 with lr 0.000500<p>\n","Epoch 14: Training loss 0.361631 validation loss 0.658140 with lr 0.000500<p>\n","\n","    <div>\n","      <progress value='825' class='' max='825' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      100.00% [825/825 00:05&lt;00:00]\n","    </div>\n","    "]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure size 600x400 with 1 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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 --> 0.620454).  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 (0.620454 --> 0.595232).  Saving model ...\n","Validation loss decreased (0.595232 --> 0.583607).  Saving model ...\n","Validation loss decreased (0.583607 --> 0.576611).  Saving model ...\n","Validation loss decreased (0.576611 --> 0.571943).  Saving model ...\n","Validation loss decreased (0.571943 --> 0.570769).  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(), '/content/drive/MyDrive/Colab/ethnicolor/lstm_lastname.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":26,"metadata":{"id":"1DoKR-M_qedg","executionInfo":{"status":"ok","timestamp":1687746156277,"user_tz":420,"elapsed":16,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}}},"outputs":[],"source":["torch.save(rnn.state_dict(), '/content/drive/MyDrive/Colab/ethnicolor/lstm_LastName_after_train.pt')"]},{"cell_type":"code","execution_count":27,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":563},"id":"eqEQPWdGBVHr","executionInfo":{"status":"ok","timestamp":1687746156690,"user_tz":420,"elapsed":14,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"f00fd22c-503a-463b-e130-4af21426b254"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0, 0.5, 'loss')"]},"metadata":{},"execution_count":27},{"output_type":"display_data","data":{"text/plain":["<Figure size 600x600 with 1 Axes>"],"image/png":"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\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":28,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WUHmb7RGBVHs","executionInfo":{"status":"ok","timestamp":1687746156690,"user_tz":420,"elapsed":10,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"18c6d7a1-17f0-4937-9580-753a1ff4ef47"},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([0.3450])\n","tensor([0.6848])\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":29,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WClV6_m9yFsG","executionInfo":{"status":"ok","timestamp":1687746156690,"user_tz":420,"elapsed":9,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"119af993-6bbc-416d-ca2a-8c1b310fa5f7"},"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":30,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eDUpH4eNgb5O","executionInfo":{"status":"ok","timestamp":1687746173336,"user_tz":420,"elapsed":16653,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"98469c9e-9c89-4b48-eea2-6c6f528a96e3"},"outputs":[{"output_type":"stream","name":"stderr","text":["Testing: 100%|██████████| 825/825 [00:14<00:00, 56.05it/s]\n"]},{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","       asian       0.47      0.42      0.45      3646\n","    hispanic       0.87      0.85      0.86     32436\n","    nh_black       0.62      0.54      0.58     10394\n","    nh_white       0.82      0.88      0.85     56236\n","       other       0.24      0.07      0.11      2888\n","\n","    accuracy                           0.80    105600\n","   macro avg       0.60      0.55      0.57    105600\n","weighted avg       0.79      0.80      0.79    105600\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":31,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oKsIjrK_8KEJ","executionInfo":{"status":"ok","timestamp":1687746173336,"user_tz":420,"elapsed":28,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"856d58d5-cc1c-4c7a-aea6-650f43ab488c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.69226381984624"]},"metadata":{},"execution_count":31}],"source":["test_loss/len(test_dataloader)"]},{"cell_type":"code","execution_count":32,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":702},"id":"TrS2WIqxyJPL","executionInfo":{"status":"ok","timestamp":1687746173337,"user_tz":420,"elapsed":26,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"d068b78f-5416-44df-9f97-e7c8a31057cb"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7eff6237ec20>"]},"metadata":{},"execution_count":32},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x800 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=(8,8))\n","cmp.plot(ax=ax, xticks_rotation='vertical')"]},{"cell_type":"code","execution_count":33,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Kjz7DX4o9qlO","executionInfo":{"status":"ok","timestamp":1687746173337,"user_tz":420,"elapsed":24,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"3c33c48d-949d-4080-a085-dde669952534"},"outputs":[{"output_type":"stream","name":"stdout","text":["Test Loss: 0.692264\n","\n","Test Accuracy of asian: 42% (1539/3646)\n","Test Accuracy of hispanic: 85% (27720/32436)\n","Test Accuracy of nh_black: 54% (5662/10394)\n","Test Accuracy of nh_white: 88% (49603/56236)\n","Test Accuracy of other:  7% (206/2888)\n","\n","Test Accuracy (Overall): 80% (84730/105600)\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":34,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":23,"status":"ok","timestamp":1687746173338,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"},"user_tz":420},"id":"Hy8oB6Kg-YM8","outputId":"72da3c0a-a4f2-4db2-d36e-3107b0fe5de6"},"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([30])\n","torch.Size([1, 5])\n","tensor(1, device='cuda:0')\n","hispanic\n"]}],"source":["name = \"Veloz Perez\"\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":35,"metadata":{"id":"TL-PMZkSAyNm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1687746173338,"user_tz":420,"elapsed":22,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"04b57f75-721c-4226-be0f-65fa6aaf7591"},"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":35}],"source":["test_rnn = LSTM(vocab_size, n_hidden, n_categories, num_layers=2)\n","test_rnn.load_state_dict(torch.load('/content/drive/MyDrive/Colab/ethnicolor/lstm_lastname.pt'))\n","test_rnn.to(device)"]},{"cell_type":"code","execution_count":36,"metadata":{"id":"u82VG3fG8pBT","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1687746190227,"user_tz":420,"elapsed":16910,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"460932b4-c90b-4ad1-be82-205534756da4"},"outputs":[{"output_type":"stream","name":"stderr","text":["Testing: 100%|██████████| 825/825 [00:14<00:00, 55.54it/s]\n"]},{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","       asian       0.55      0.41      0.47      3647\n","    hispanic       0.89      0.85      0.87     32445\n","    nh_black       0.67      0.51      0.58     10399\n","    nh_white       0.81      0.92      0.86     56221\n","       other       0.42      0.04      0.08      2888\n","\n","    accuracy                           0.81    105600\n","   macro avg       0.67      0.55      0.57    105600\n","weighted avg       0.80      0.81      0.80    105600\n","\n","Test Loss: 0.573202\n","\n","Test Accuracy of asian: 40% (1495/3647)\n","Test Accuracy of hispanic: 84% (27539/32445)\n","Test Accuracy of nh_black: 50% (5298/10399)\n","Test Accuracy of nh_white: 91% (51595/56221)\n","Test Accuracy of other:  4% (121/2888)\n","\n","Test Accuracy (Overall): 81% (86048/105600)\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":37,"metadata":{"id":"96QvegHxBvsb","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1687746191351,"user_tz":420,"elapsed":5,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"5ca47094-fa25-4fef-a547-89b49772efe6"},"outputs":[{"output_type":"stream","name":"stdout","text":["4.1M\t/content/drive/MyDrive/Colab/ethnicolor/lstm_lastname.pt\n"]}],"source":["!du -sh /content/drive/MyDrive/Colab/ethnicolor/lstm_lastname.pt"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8J-ROrB5BVHv"},"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}