FEATURES.md

# Advanced Features - PyTorch Neural Networks

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Project: PyTorch Neural Networks
Author: RSK World
Website: https://rskworld.in
Email: help@rskworld.in
Phone: +91 93305 39277
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This document describes all the advanced features available in this PyTorch Neural Networks project.

## Training Features

### 1. Advanced Trainer (`training/advanced_trainer.py`)
- **Gradient Clipping**: Prevents exploding gradients
- **Mixed Precision Training**: Faster training with reduced memory usage
- **Distributed Training**: Multi-GPU training support

### 2. Training Callbacks (`training/callbacks.py`)
- **Early Stopping**: Stop training when validation loss stops improving
- **Model Checkpointing**: Save best models automatically
- **Learning Rate Scheduling**: Automatic learning rate adjustment

### 3. Evaluation Metrics (`training/metrics.py`)
- **Confusion Matrix**: Visualize classification performance
- **Classification Report**: Detailed performance metrics
- **Accuracy Calculation**: Various accuracy metrics
- **Model Evaluation**: Comprehensive model evaluation utilities

## Data Features

### 1. Data Augmentation (`data/augmentation.py`)
- **Image Augmentation**:
- Random horizontal flip
- Random rotation
- Color jitter
- Random affine transformations
- **Sequence Augmentation**:
- Noise injection
- Feature dropout
- **MixUp**: Advanced augmentation technique

### 2. Custom Datasets (`data/datasets.py`)
- **ImageDataset**: Custom image dataset loader
- **SequenceDataset**: Custom sequence dataset loader
- **TabularDataset**: Custom tabular data loader

## Model Features

### 1. Transfer Learning (`models/transfer_learning.py`)
- **Pre-trained Models**:
- ResNet18, ResNet50
- VGG16
- DenseNet121
- **Fine-tuning**: Freeze/unfreeze layers for fine-tuning
- **Flexible Architecture**: Easy to adapt to different tasks

### 2. Advanced Architectures (`models/advanced.py`)
- **Residual Blocks**: ResNet-style skip connections
- **Attention Layers**: Self-attention mechanism
- **Transformer Blocks**: Complete transformer architecture

## Utility Features

### 1. Hyperparameter Tuning (`utils/hyperparameter_tuning.py`)
- **Grid Search**: Exhaustive parameter search
- **Random Search**: Efficient random parameter sampling
- **Results Logging**: Save and analyze search results

### 2. TensorBoard Integration (`utils/tensorboard_logger.py`)
- **Metric Logging**: Track training/validation metrics
- **Model Visualization**: Visualize model architecture
- **Image Logging**: Log images during training
- **Learning Rate Tracking**: Monitor learning rate changes

## Usage Examples

### Advanced Training

```python
from training.advanced_trainer import AdvancedTrainer
from training.callbacks import EarlyStopping, ModelCheckpoint

trainer = AdvancedTrainer(
model, criterion, optimizer, device,
gradient_clip=1.0,
use_mixed_precision=True
)

early_stopping = EarlyStopping(patience=5)
checkpoint = ModelCheckpoint('best_model.pth')
```

### Transfer Learning

```python
from models.transfer_learning import TransferLearningModel

model = TransferLearningModel(
model_name='resnet18',
num_classes=10,
pretrained=True,
freeze_backbone=True
)
```

### Hyperparameter Tuning

```python
from utils.hyperparameter_tuning import GridSearch

param_grid = {
'hidden_size': [64, 128, 256],
'num_layers': [1, 2, 3],
'dropout': [0.1, 0.2, 0.3]
}

grid_search = GridSearch(param_grid, model_class, train_loader, val_loader, device)
best_params, best_score, results = grid_search.search(epochs=10)
```

### TensorBoard Logging

```python
from utils.tensorboard_logger import TensorBoardLogger

logger = TensorBoardLogger(log_dir='runs/experiment')
logger.log_training_metrics({'loss': loss, 'accuracy': acc}, epoch)
logger.log_validation_metrics({'loss': val_loss, 'accuracy': val_acc}, epoch)
```

## Running Examples

1. **Advanced Features Example**:
```bash
python examples/advanced_features_example.py
```

2. **Transfer Learning Example**:
```bash
python examples/transfer_learning_example.py
```

3. **Hyperparameter Tuning Example**:
```bash
python examples/hyperparameter_tuning_example.py
```

## TensorBoard Visualization

After running training with TensorBoard logging:

```bash
tensorboard --logdir=runs
```

Then open http://localhost:6006 in your browser.

## Contact

- Website: https://rskworld.in
- Email: help@rskworld.in
- Phone: +91 93305 39277