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"Guiding Future STEM Leaders through Innovative Research Training" ~ thinkingbeyond.education
Project: stephanie's main branch
Path: ThinkingBeyond Activities / BeyondAI-2024-Mentee-Projects / louis-jack / LstmWithAttention.ipynb~
Views: 1179Image: ubuntu2204
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "yI6AYlxlHsl6"
},
"source": [
"# LSTM-based Sentiment Analysis on IMDB Dataset\n",
"\n",
"This notebook implements a sentiment analysis model using LSTM (Long Short-Term Memory) neural networks to classify IMDB movie reviews as either positive or negative.\n",
"\n",
"Key components:\n",
"- PyTorch for deep learning implementation\n",
"- Hugging Face's datasets library for loading the IMDB dataset\n",
"- DistilBERT tokenizer for text preprocessing\n",
"- LSTM architecture for sequence processing\n",
"\n",
"The model will learn to classify movie reviews as positive (1) or negative (0) based on the text content."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XlEvp5gQHsl6"
},
"source": [
"## 1. Import Required Libraries\n",
"\n",
"We need several Python libraries:\n",
"- `torch`: Main PyTorch library for deep learning\n",
"- `transformers`: For using pre-trained tokenizers\n",
"- `datasets`: For loading the IMDB dataset\n",
"- `matplotlib`: For visualizing training progress\n",
"- `numpy`: For numerical operations"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "X2euj5VPHsl7",
"outputId": "a563e13c-c5a3-4c70-eafb-f7f02068ec41"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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"Downloading jedi-0.19.2-py2.py3-none-any.whl (1.6 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m38.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hInstalling collected packages: jedi\n",
"Successfully installed jedi-0.19.2\n",
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]
}
],
"source": [
"!pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118\n",
"!pip install transformers\n",
"!pip install datasets\n",
"!pip install scikit-learn\n",
"!pip install ipywidgets\n",
"!pip install tqdm\n",
"\n",
"import torch\n",
"import torch.nn as nn\n",
"from torch.utils.data import Dataset, DataLoader\n",
"from transformers import AutoTokenizer\n",
"from datasets import load_dataset\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Nuq0nu7MHsl7"
},
"source": [
"## 2. Custom Dataset Class\n",
"\n",
"The `SentimentDataset` class handles data preprocessing:\n",
"\n",
"1. Accepts raw text data and tokenizer\n",
"2. Converts text to token IDs using the tokenizer\n",
"3. Handles padding and truncation to ensure fixed length\n",
"4. Creates attention masks for valid tokens\n",
"5. Converts labels to tensor format\n",
"\n",
"Key parameters:\n",
"- `max_length`: Maximum sequence length (default: 128)\n",
"- `padding`: Set to 'max_length' to ensure uniform sizes\n",
"- `truncation`: True to handle reviews longer than max_length"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "voOF2BJmHsl7"
},
"outputs": [],
"source": [
"class SentimentDataset(Dataset):\n",
" def __init__(self, dataset_split, tokenizer, max_length=128):\n",
" self.data = dataset_split\n",
" self.tokenizer = tokenizer\n",
" self.max_length = max_length\n",
"\n",
" def __len__(self):\n",
" return len(self.data)\n",
"\n",
" def __getitem__(self, idx):\n",
" text = self.data[idx]['text']\n",
" label = self.data[idx]['label']\n",
"\n",
" encoding = self.tokenizer(\n",
" text,\n",
" max_length=self.max_length,\n",
" padding='max_length',\n",
" truncation=True,\n",
" return_tensors='pt'\n",
" )\n",
"\n",
" return {\n",
" 'input_ids': encoding['input_ids'].squeeze(),\n",
" 'attention_mask': encoding['attention_mask'].squeeze(),\n",
" 'labels': torch.tensor(label, dtype=torch.float)\n",
" }\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bY0SC5ogHsl8"
},
"source": [
"## 3. LSTM Model Architecture\n",
"\n",
"The `LSTMClassifier` implements a neural network with:\n",
"\n",
"1. **Embedding Layer**: Converts token IDs to dense vectors\n",
" - Input: Vocabulary size\n",
" - Output: 300-dimensional embeddings\n",
"\n",
"2. **LSTM Layer**: Processes the sequence\n",
" - Input: 300-dimensional vectors\n",
" - Hidden size: 256 dimensions\n",
" - 2 stacked LSTM layers\n",
" - Includes dropout for regularization\n",
"\n",
"3. **Output Layer**: Final classification\n",
" - Linear layer converting to single score\n",
" - Sigmoid activation (implicit through loss function)\n",
"\n",
"The model incorporates attention masks to handle variable-length sequences properly."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "OHdAuet6Hsl8"
},
"outputs": [],
"source": [
"class LSTMClassifier(nn.Module):\n",
" def __init__(self, vocab_size, embedding_dim=300, hidden_dim=256, n_layers=2, dropout=0.3):\n",
" super().__init__()\n",
"\n",
" self.embedding = nn.Embedding(vocab_size, embedding_dim)\n",
" self.lstm = nn.LSTM(embedding_dim, hidden_dim, n_layers,\n",
" batch_first=True, dropout=dropout if n_layers > 1 else 0)\n",
" self.dropout = nn.Dropout(dropout)\n",
" self.fc = nn.Linear(hidden_dim, 1)\n",
"\n",
" def forward(self, input_ids, attention_mask):\n",
" embedded = self.embedding(input_ids)\n",
"\n",
" # Apply attention mask\n",
" embedded = embedded * attention_mask.unsqueeze(-1)\n",
"\n",
" # LSTM forward pass\n",
" lstm_out, _ = self.lstm(embedded)\n",
"\n",
" # Get final hidden state\n",
" final_hidden_state = lstm_out[:, -1, :]\n",
"\n",
" # Apply dropout and classification layer\n",
" output = self.dropout(final_hidden_state)\n",
" output = self.fc(output)\n",
"\n",
" return output"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eYcZE1r2Hsl8"
},
"source": [
"## 4. Training and Evaluation Functions\n",
"\n",
"The training loop includes:\n",
"\n",
"1. **Per Epoch**:\n",
" - Training phase with gradient updates\n",
" - Validation phase without gradients\n",
" - Loss computation and optimization\n",
" - Progress tracking and metrics calculation\n",
"\n",
"2. **Key Features**:\n",
" - Gradient clipping (max norm: 1.0)\n",
" - Early saving of best model\n",
" - Training and validation loss tracking\n",
" - Accuracy monitoring\n",
"\n",
"3. **Hyperparameters**:\n",
" - Learning rate: 2e-5\n",
" - Batch size: 32\n",
" - Number of epochs: 5\n",
" - Loss function: BCEWithLogitsLoss"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "EVuy38xXHsl8"
},
"outputs": [],
"source": [
"def train_model(model, train_loader, valid_loader, criterion, optimizer, device, num_epochs=5):\n",
" best_valid_loss = float('inf')\n",
" train_losses, valid_losses = [], []\n",
"\n",
" for epoch in range(num_epochs):\n",
" # Training\n",
" model.train()\n",
" train_loss = 0\n",
" for batch in train_loader:\n",
" input_ids = batch['input_ids'].to(device)\n",
" attention_mask = batch['attention_mask'].to(device)\n",
" labels = batch['labels'].to(device)\n",
"\n",
" optimizer.zero_grad()\n",
" outputs = model(input_ids, attention_mask)\n",
" loss = criterion(outputs.squeeze(), labels)\n",
" loss.backward()\n",
" torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
" optimizer.step()\n",
" train_loss += loss.item()\n",
"\n",
" train_loss = train_loss / len(train_loader)\n",
" train_losses.append(train_loss)\n",
"\n",
" # Validation\n",
" model.eval()\n",
" valid_loss = 0\n",
" correct = 0\n",
" total = 0\n",
"\n",
" with torch.no_grad():\n",
" for batch in valid_loader:\n",
" input_ids = batch['input_ids'].to(device)\n",
" attention_mask = batch['attention_mask'].to(device)\n",
" labels = batch['labels'].to(device)\n",
"\n",
" outputs = model(input_ids, attention_mask)\n",
" loss = criterion(outputs.squeeze(), labels)\n",
" valid_loss += loss.item()\n",
"\n",
" predictions = (outputs.squeeze() > 0.5).float()\n",
" correct += (predictions == labels).sum().item()\n",
" total += labels.size(0)\n",
"\n",
" valid_loss = valid_loss / len(valid_loader)\n",
" valid_losses.append(valid_loss)\n",
" accuracy = correct / total\n",
"\n",
" print(f'Epoch {epoch+1}/{num_epochs}:')\n",
" print(f'Training Loss: {train_loss:.4f}')\n",
" print(f'Validation Loss: {valid_loss:.4f}')\n",
" print(f'Validation Accuracy: {accuracy:.4f}')\n",
"\n",
" if valid_loss < best_valid_loss:\n",
" best_valid_loss = valid_loss\n",
" torch.save(model.state_dict(), 'best_model.pt')\n",
"\n",
" return train_losses, valid_losses"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nz-W268PHsl8"
},
"source": [
"## 5. Main Training Pipeline\n",
"\n",
"The training process follows these steps:\n",
"\n",
"1. **Setup**:\n",
" - GPU/CPU device selection\n",
" - Dataset loading and preprocessing\n",
" - Model initialization\n",
"\n",
"2. **Training**:\n",
" - Batched processing of reviews\n",
" - Forward and backward passes\n",
" - Model parameter updates\n",
"\n",
"3. **Monitoring**:\n",
" - Loss tracking for both training and validation\n",
" - Learning curves visualization\n",
"\n",
"4. **Results**:\n",
" - Best model saved during training\n",
" - Final performance metrics\n",
" - Training progress plots"
]
},
{
"cell_type": "code",
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},
"id": "YKl7QK4tHsl8",
"outputId": "f953b6df-ce11-40f3-b7e7-72489eb77d9b"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"PyTorch version: 2.5.1+cu121\n",
"CUDA available: True\n",
"CUDA version: 12.1\n",
"\n",
"Successfully set device to: cuda:0\n",
"Using GPU: Tesla T4\n",
"GPU Memory: 15.84 GB\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
"You will be able to reuse this secret in all of your notebooks.\n",
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
" warnings.warn(\n"
]
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"def main():\n",
" # Detailed CUDA diagnostics\n",
" print(\"PyTorch version:\", torch.__version__)\n",
" print(\"CUDA available:\", torch.cuda.is_available())\n",
" print(\"CUDA version:\", torch.version.cuda)\n",
"\n",
" if not torch.cuda.is_available():\n",
" print(\"\\nWARNING: CUDA not available. Checking system:\")\n",
" print(\"1. Check if NVIDIA GPU is present:\")\n",
" import subprocess\n",
" try:\n",
" nvidia_smi = subprocess.check_output([\"nvidia-smi\"]).decode('utf-8')\n",
" print(nvidia_smi)\n",
" except:\n",
" print(\"nvidia-smi command failed - GPU may not be present or drivers not installed\")\n",
"\n",
" # Try to force CUDA device\n",
" try:\n",
" device = torch.device(\"cuda:0\")\n",
" torch.cuda.set_device(device)\n",
" print(f\"\\nSuccessfully set device to: {device}\")\n",
" print(f\"Using GPU: {torch.cuda.get_device_name(0)}\")\n",
" print(f\"GPU Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB\")\n",
" except Exception as e:\n",
" print(f\"\\nFailed to set CUDA device: {e}\")\n",
" print(\"Falling back to CPU\")\n",
" device = torch.device(\"cpu\")\n",
"\n",
" # Load dataset\n",
" dataset = load_dataset('imdb')\n",
" tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')\n",
"\n",
" # Create datasets\n",
" train_dataset = SentimentDataset(dataset['train'], tokenizer)\n",
" valid_dataset = SentimentDataset(dataset['test'], tokenizer)\n",
"\n",
" # Create dataloaders\n",
" batch_size = 32\n",
" train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
" valid_loader = DataLoader(valid_dataset, batch_size=batch_size)\n",
"\n",
" # Initialize model\n",
" model = LSTMClassifier(\n",
" vocab_size=tokenizer.vocab_size,\n",
" embedding_dim=300,\n",
" hidden_dim=256,\n",
" n_layers=2\n",
" ).to(device)\n",
"\n",
" # Training parameters\n",
" criterion = nn.BCEWithLogitsLoss()\n",
" optimizer = torch.optim.AdamW(model.parameters(), lr=2e-5)\n",
"\n",
" # Train model\n",
" train_losses, valid_losses = train_model(\n",
" model, train_loader, valid_loader,\n",
" criterion, optimizer, device, num_epochs=5\n",
" )\n",
"\n",
" # Plot training curves\n",
" plt.figure(figsize=(10, 6))\n",
" plt.plot(train_losses, label='Training Loss')\n",
" plt.plot(valid_losses, label='Validation Loss')\n",
" plt.xlabel('Epoch')\n",
" plt.ylabel('Loss')\n",
" plt.title('Training and Validation Loss')\n",
" plt.legend()\n",
" plt.show()\n",
"\n",
"if __name__ == '__main__':\n",
" main()\n"
]
}
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