tp_lstm_classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "import os\n",
    "\n",
    "\n",
    "raw_data_path = 'data/news.csv'\n",
    "destination_folder = 'lstm'\n",
    "\n",
    "os.makedirs(destination_folder, exist_ok=True)\n",
    "\n",
    "train_test_ratio = 0.10\n",
    "train_valid_ratio = 0.80\n",
    "\n",
    "first_n_words = 200\n",
    "\n",
    "def trim_string(x):\n",
    "    x = x.split(maxsplit=first_n_words)\n",
    "    x = ' '.join(x[:first_n_words])\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read raw data\n",
    "df_raw = pd.read_csv(raw_data_path)\n",
    "\n",
    "# Prepare columns\n",
    "df_raw['label'] = (df_raw['label'] == 'FAKE').astype('int')\n",
    "df_raw['titletext'] = df_raw['title'] + \". \" + df_raw['text']\n",
    "df_raw = df_raw.reindex(columns=['label', 'title', 'text', 'titletext'])\n",
    "\n",
    "# Drop rows with empty text\n",
    "df_raw.drop( df_raw[df_raw.text.str.len() < 5].index, inplace=True)\n",
    "\n",
    "# Trim text and titletext to first_n_words\n",
    "df_raw['text'] = df_raw['text'].apply(trim_string)\n",
    "df_raw['titletext'] = df_raw['titletext'].apply(trim_string) \n",
    "\n",
    "# Split according to label\n",
    "df_real = df_raw[df_raw['label'] == 0]\n",
    "df_fake = df_raw[df_raw['label'] == 1]\n",
    "\n",
    "# Train-test split\n",
    "df_real_full_train, df_real_test = train_test_split(df_real, train_size = train_test_ratio, random_state = 1)\n",
    "df_fake_full_train, df_fake_test = train_test_split(df_fake, train_size = train_test_ratio, random_state = 1)\n",
    "\n",
    "# Train-valid split\n",
    "df_real_train, df_real_valid = train_test_split(df_real_full_train, train_size = train_valid_ratio, random_state = 1)\n",
    "df_fake_train, df_fake_valid = train_test_split(df_fake_full_train, train_size = train_valid_ratio, random_state = 1)\n",
    "\n",
    "# Concatenate splits of different labels\n",
    "df_train = pd.concat([df_real_train, df_fake_train], ignore_index=True, sort=False)\n",
    "df_valid = pd.concat([df_real_valid, df_fake_valid], ignore_index=True, sort=False)\n",
    "df_test = pd.concat([df_real_test, df_fake_test], ignore_index=True, sort=False)\n",
    "\n",
    "# Write preprocessed data\n",
    "df_train.to_csv(destination_folder + '/train.csv', index=False)\n",
    "df_valid.to_csv(destination_folder + '/valid.csv', index=False)\n",
    "df_test.to_csv(destination_folder + '/test.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import torch\n",
    "\n",
    "# Preliminaries\n",
    "\n",
    "from torchtext.legacy.data import Field, TabularDataset, BucketIterator\n",
    "\n",
    "# Models\n",
    "\n",
    "import torch.nn as nn\n",
    "from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\n",
    "\n",
    "# Training\n",
    "\n",
    "import torch.optim as optim\n",
    "\n",
    "# Evaluation\n",
    "\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "if torch.cuda.is_available():\n",
    "    device = torch.device('cuda')\n",
    "else:\n",
    "    device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fields\n",
    "\n",
    "label_field = Field(sequential=False, use_vocab=False, batch_first=True, dtype=torch.float)\n",
    "text_field = Field(tokenize='spacy', lower=True, include_lengths=True, batch_first=True)\n",
    "fields = [('label', label_field), ('title', text_field), ('text', text_field), ('titletext', text_field)]\n",
    "\n",
    "# TabularDataset\n",
    "\n",
    "train, valid, test = TabularDataset.splits(path=destination_folder, train='train.csv', validation='valid.csv', test='test.csv',\n",
    "                                           format='CSV', fields=fields, skip_header=True)\n",
    "\n",
    "# Iterators\n",
    "\n",
    "train_iter = BucketIterator(train, batch_size=32, sort_key=lambda x: len(x.text),\n",
    "                            device=device, sort=True, sort_within_batch=True)\n",
    "valid_iter = BucketIterator(valid, batch_size=32, sort_key=lambda x: len(x.text),\n",
    "                            device=device, sort=True, sort_within_batch=True)\n",
    "test_iter = BucketIterator(test, batch_size=32, sort_key=lambda x: len(x.text),\n",
    "                            device=device, sort=True, sort_within_batch=True)\n",
    "\n",
    "# Vocabulary\n",
    "\n",
    "text_field.build_vocab(train, min_freq=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class LSTM(nn.Module):\n",
    "\n",
    "    def __init__(self, dimension=128):\n",
    "        super(LSTM, self).__init__()\n",
    "\n",
    "        self.embedding = nn.Embedding(len(text_field.vocab), 300)\n",
    "        self.dimension = dimension\n",
    "        self.lstm = nn.LSTM(input_size=300,\n",
    "                            hidden_size=dimension,\n",
    "                            num_layers=1,\n",
    "                            batch_first=True,\n",
    "                            bidirectional=True)\n",
    "        self.drop = nn.Dropout(p=0.5)\n",
    "\n",
    "        self.fc = nn.Linear(2*dimension, 1)\n",
    "\n",
    "    def forward(self, text, text_len):\n",
    "\n",
    "        text_emb = self.embedding(text)\n",
    "\n",
    "        packed_input = pack_padded_sequence(text_emb, text_len, batch_first=True, enforce_sorted=False)\n",
    "        packed_output, _ = self.lstm(packed_input)\n",
    "        output, _ = pad_packed_sequence(packed_output, batch_first=True)\n",
    "\n",
    "        out_forward = output[range(len(output)), text_len - 1, :self.dimension]\n",
    "        out_reverse = output[:, 0, self.dimension:]\n",
    "        out_reduced = torch.cat((out_forward, out_reverse), 1)\n",
    "        text_fea = self.drop(out_reduced)\n",
    "\n",
    "        text_fea = self.fc(text_fea)\n",
    "        text_fea = torch.squeeze(text_fea, 1)\n",
    "        text_out = torch.sigmoid(text_fea)\n",
    "\n",
    "        return text_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save and Load Functions\n",
    "\n",
    "def save_checkpoint(save_path, model, optimizer, valid_loss):\n",
    "\n",
    "    if save_path == None:\n",
    "        return\n",
    "    \n",
    "    state_dict = {'model_state_dict': model.state_dict(),\n",
    "                  'optimizer_state_dict': optimizer.state_dict(),\n",
    "                  'valid_loss': valid_loss}\n",
    "    \n",
    "    torch.save(state_dict, save_path)\n",
    "    print(f'Model saved to ==> {save_path}')\n",
    "\n",
    "\n",
    "def load_checkpoint(load_path, model, optimizer):\n",
    "\n",
    "    if load_path==None:\n",
    "        return\n",
    "    \n",
    "    state_dict = torch.load(load_path, map_location=device)\n",
    "    print(f'Model loaded from <== {load_path}')\n",
    "    \n",
    "    model.load_state_dict(state_dict['model_state_dict'])\n",
    "    optimizer.load_state_dict(state_dict['optimizer_state_dict'])\n",
    "    \n",
    "    return state_dict['valid_loss']\n",
    "\n",
    "\n",
    "def save_metrics(save_path, train_loss_list, valid_loss_list, global_steps_list):\n",
    "\n",
    "    if save_path == None:\n",
    "        return\n",
    "    \n",
    "    state_dict = {'train_loss_list': train_loss_list,\n",
    "                  'valid_loss_list': valid_loss_list,\n",
    "                  'global_steps_list': global_steps_list}\n",
    "    \n",
    "    torch.save(state_dict, save_path)\n",
    "    print(f'Model saved to ==> {save_path}')\n",
    "\n",
    "\n",
    "def load_metrics(load_path):\n",
    "\n",
    "    if load_path==None:\n",
    "        return\n",
    "    \n",
    "    state_dict = torch.load(load_path, map_location=device)\n",
    "    print(f'Model loaded from <== {load_path}')\n",
    "    \n",
    "    return state_dict['train_loss_list'], state_dict['valid_loss_list'], state_dict['global_steps_list']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 69])\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "'lengths' argument should be a 1D CPU int64 tensor, but got 1D cuda:0 Long tensor",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-f9dc26e46899>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     80\u001b[0m \u001b[0moptimizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAdam\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.001\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-21-f9dc26e46899>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(model, optimizer, criterion, train_loader, valid_loader, num_epochs, eval_every, file_path, best_valid_loss)\u001b[0m\n\u001b[1;32m     25\u001b[0m             \u001b[0mtitletext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtitletext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m             \u001b[0mtitletext_len\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtitletext_len\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitletext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitletext_len\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m             \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1103\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-11-21caa93d8835>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, text, text_len)\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0mtext_emb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membedding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m         \u001b[0mpacked_input\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpack_padded_sequence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext_emb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtext_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_first\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menforce_sorted\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m         \u001b[0mpacked_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpacked_input\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpad_packed_sequence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpacked_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_first\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/torch/nn/utils/rnn.py\u001b[0m in \u001b[0;36mpack_padded_sequence\u001b[0;34m(input, lengths, batch_first, enforce_sorted)\u001b[0m\n\u001b[1;32m    247\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    248\u001b[0m     \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_sizes\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 249\u001b[0;31m         \u001b[0m_VF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pack_padded_sequence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_first\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    250\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_packed_sequence_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_sizes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msorted_indices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: 'lengths' argument should be a 1D CPU int64 tensor, but got 1D cuda:0 Long tensor"
     ]
    }
   ],
   "source": [
    "def train(model,\n",
    "          optimizer,\n",
    "          criterion = nn.BCELoss(),\n",
    "          train_loader = train_iter,\n",
    "          valid_loader = valid_iter,\n",
    "          num_epochs = 5,\n",
    "          eval_every = len(train_iter) // 2,\n",
    "          file_path = destination_folder,\n",
    "          best_valid_loss = float(\"Inf\")):\n",
    "    \n",
    "    # initialize running values\n",
    "    running_loss = 0.0\n",
    "    valid_running_loss = 0.0\n",
    "    global_step = 0\n",
    "    train_loss_list = []\n",
    "    valid_loss_list = []\n",
    "    global_steps_list = []\n",
    "\n",
    "    # training loop\n",
    "    model.train()\n",
    "    for epoch in range(num_epochs):\n",
    "        for (labels, (title, title_len), (text, text_len), (titletext, titletext_len)), _ in train_loader:           \n",
    "            labels = labels.to(device)\n",
    "            titletext = titletext.to(device)\n",
    "            titletext_len = titletext_len.to(device)\n",
    "            output = model(titletext, titletext_len)\n",
    "\n",
    "            loss = criterion(output, labels)\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            # update running values\n",
    "            running_loss += loss.item()\n",
    "            global_step += 1\n",
    "\n",
    "            # evaluation step\n",
    "            if global_step % eval_every == 0:\n",
    "                model.eval()\n",
    "                with torch.no_grad():                    \n",
    "                  # validation loop\n",
    "                  for (labels, (title, title_len), (text, text_len), (titletext, titletext_len)), _ in valid_loader:\n",
    "                      labels = labels.to(device)\n",
    "                      titletext = titletext.to(device)\n",
    "                      titletext_len = titletext_len.to(device)\n",
    "                      output = model(titletext, titletext_len)\n",
    "\n",
    "                      loss = criterion(output, labels)\n",
    "                      valid_running_loss += loss.item()\n",
    "\n",
    "                # evaluation\n",
    "                average_train_loss = running_loss / eval_every\n",
    "                average_valid_loss = valid_running_loss / len(valid_loader)\n",
    "                train_loss_list.append(average_train_loss)\n",
    "                valid_loss_list.append(average_valid_loss)\n",
    "                global_steps_list.append(global_step)\n",
    "\n",
    "                # resetting running values\n",
    "                running_loss = 0.0                \n",
    "                valid_running_loss = 0.0\n",
    "                model.train()\n",
    "\n",
    "                # print progress\n",
    "                print('Epoch [{}/{}], Step [{}/{}], Train Loss: {:.4f}, Valid Loss: {:.4f}'\n",
    "                      .format(epoch+1, num_epochs, global_step, num_epochs*len(train_loader),\n",
    "                              average_train_loss, average_valid_loss))\n",
    "                \n",
    "                # checkpoint\n",
    "                if best_valid_loss > average_valid_loss:\n",
    "                    best_valid_loss = average_valid_loss\n",
    "                    save_checkpoint(file_path + '/model.pt', model, optimizer, best_valid_loss)\n",
    "                    save_metrics(file_path + '/metrics.pt', train_loss_list, valid_loss_list, global_steps_list)\n",
    "    \n",
    "    save_metrics(file_path + '/metrics.pt', train_loss_list, valid_loss_list, global_steps_list)\n",
    "    print('Finished Training!')\n",
    "\n",
    "\n",
    "model = LSTM().to(device)\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "train(model=model, optimizer=optimizer, num_epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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