All notable changes to this project will be documented in this file.
The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.
- Text Generation Utilities:
TextVocabulary,CharacterEmbedding, sampling functionssample_with_temperature,sample_top_k,sample_nucleusargmax,softmaxhelperstext_utils_example.rsdemonstration
text_generation_advanced.rsrefactored to use new text utilities
- Linear (Dense) Layer: Fully connected layer for classification and output projections
- Forward and backward passes with gradient computation
- Xavier/Glorot weight initialization
- Works with all optimizers (SGD, Adam, RMSprop)
linear_layer_example.rsdemonstration
- Text Generation: Fixed broken text generation in advanced example
- Implemented temperature-based sampling for character selection
- Added direct network access for more efficient inference
- Text generation now works continuously for specified length
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Early Stopping: Complete early stopping implementation for all trainers
- Configurable Patience: Stop training after N epochs without improvement
- Multiple Metrics: Monitor validation loss or training loss
- Best Weight Restoration: Automatically restore best weights when stopping
- Flexible Configuration: Customizable min_delta threshold and monitoring options
- Universal Support: Works with LSTMTrainer, ScheduledLSTMTrainer, and LSTMBatchTrainer
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Enhanced Training Features:
- Visual Best Epoch Indicators: "*" markers in training logs for best epochs
- Automatic Overfitting Prevention: Stop training before performance degrades
- Comprehensive Logging: Detailed early stopping trigger information
- Weight Management: Optional best weight restoration for optimal model recovery
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Examples and Documentation:
- Complete Early Stopping Example: Demonstration of all early stopping configurations
- Multiple Scenarios: Validation loss, training loss, and custom patience examples
- Integration Examples: Shows usage with different trainer types
- Comprehensive Testing: Full test suite for early stopping functionality
- All Trainers: Added early stopping support to LSTMTrainer, ScheduledLSTMTrainer, and LSTMBatchTrainer
- Training Configuration: Extended TrainingConfig with optional early stopping settings
- Training Loops: Enhanced all training loops with early stopping logic and best epoch tracking
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Batch Processing System: Complete implementation of batch processing capabilities
- Batch Forward/Backward Passes: Efficient batch processing in LSTM cells and networks
- LSTMBatchTrainer: New trainer specifically designed for batch training
- Batch Prediction Support: Process multiple sequences simultaneously for inference
- LSTMCellBatchCache and LSTMNetworkBatchCache: Caching structures for batch training
- Batch Loss Computation: Enhanced loss functions with batch processing support
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Performance Improvements:
- 4-5x Training Speedup: Significant performance gains through batch processing
- Memory Efficient: Optimized memory usage for large batch sizes
- Scalable Architecture: Handles varying dataset sizes efficiently
- Configurable Batch Sizes: Flexible batch size configuration for different use cases
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New Training Infrastructure:
- Batch Training Functions:
create_basic_batch_trainer(),create_adam_batch_trainer() - Batch Sequence Processing:
forward_batch_sequences()for multiple sequence handling - Enhanced Gradient Computation: Batch-optimized gradient calculations
- Batch Evaluation: Efficient evaluation with batch processing
- Batch Training Functions:
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Examples and Documentation:
- Comprehensive Batch Processing Example: Complete demonstration with performance benchmarks
- Scalability Tests: Examples showing performance across different dataset sizes
- Performance Comparisons: Side-by-side comparison of single vs batch processing
- Updated Library Documentation: Enhanced documentation for new batch features
- LSTM Cell: Added
forward_batch(),forward_batch_with_cache(), andbackward_batch()methods - LSTM Network: Extended with batch processing capabilities and multi-sequence support
- Loss Functions: Enhanced MSE, MAE, and CrossEntropy with batch computation methods
- Training System: Improved training pipeline with batch processing integration
- 4.3x speedup with batch size 8
- 5.4x speedup with batch size 16
- Efficient memory utilization across different batch sizes
- Scales well from small (50 sequences) to large (500+ sequences) datasets
- Backward Compatible: All existing APIs remain unchanged
- Drop-in Replacement: Existing code continues to work without modifications
- Progressive Enhancement: Users can opt into batch processing when beneficial
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Advanced Learning Rate Scheduling: Comprehensive expansion of learning rate scheduling capabilities
- PolynomialLR: Polynomial decay with configurable power for smooth learning rate transitions
- CyclicalLR: Cyclical learning rates with triangular, triangular2, and exponential range modes
- WarmupScheduler: Generic warmup wrapper that can be applied to any base scheduler
- LRScheduleVisualizer: ASCII visualization tool for learning rate schedules
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Enhanced Scheduler Integration:
- Convenience factory methods for new schedulers in
ScheduledOptimizer - Helper functions:
polynomial,cyclical,cyclical_triangular2,cyclical_exp_range - Complete integration with existing training infrastructure
- Comprehensive test coverage for all new schedulers
- Convenience factory methods for new schedulers in
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Learning Rate Visualization:
- ASCII-based schedule visualization with customizable dimensions
- Schedule generation utilities for analysis and debugging
- Visual comparison tools for different scheduler behaviors
- Integration examples showing visualization usage
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Advanced Training Examples:
advanced_lr_scheduling.rs: Comprehensive demonstration of new schedulers- Warmup + cyclical learning rate combinations
- Best practices example with dropout + gradient clipping + advanced scheduling
- Performance comparison between different scheduling strategies
- Extended scheduler trait system to support generic warmup wrapper
- Robust cyclical learning rate computation with proper cycle handling
- Polynomial decay implementation with numerical stability
- Comprehensive error handling and edge case management
- Enhanced documentation with visual examples and mathematical formulations
- More sophisticated learning rate control for better training quality
- Modern scheduling techniques used in state-of-the-art deep learning
- Visualization capabilities for schedule analysis and debugging
- Flexible warmup support for any existing scheduler
- Production-ready implementations with comprehensive testing
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Learning Rate Scheduling System: Comprehensive scheduling framework with multiple schedulers
- StepLR, MultiStepLR, ExponentialLR, CosineAnnealingLR, OneCycleLR, ReduceLROnPlateau, LinearLR
- Convenient trainer factory functions:
create_step_lr_trainer,create_one_cycle_trainer, etc. - Automatic scheduler stepping during training with optional logging
- Support for both validation-based and epoch-based scheduling
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GRU (Gated Recurrent Unit) Implementation: Complete GRU architecture as alternative to LSTM
- Multi-layer GRU networks with configurable architecture
- Full dropout support (input, recurrent, output dropout and zoneout)
- Forward and backward propagation with gradient computation
- Training integration with existing optimizer and loss function systems
- Comprehensive example demonstrating GRU usage and comparison with LSTM
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Bidirectional LSTM Networks: Complete BiLSTM implementation with flexible output combination
- Multiple combine modes: concatenation, sum, and average of forward/backward outputs
- Multi-layer BiLSTM with proper input/output size handling
- Full dropout compatibility (input, recurrent, output dropout and zoneout)
- Sequence processing with both past and future context
- Text classification example comparing BiLSTM vs unidirectional LSTM
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Enhanced Dropout Regularization: Comprehensive dropout system with extensive testing
- Variational dropout with consistent masks across time steps
- Layer-specific dropout configuration for fine-grained control
- Zoneout implementation for RNN-specific regularization
- Automatic train/eval mode switching
- Comprehensive dropout demonstration example
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Model Persistence System: Complete model saving and loading infrastructure
- JSON and binary serialization formats
- Model metadata tracking (architecture, training info, timestamps)
- State preservation for weights, biases, and optimizer states
- Model inspection utilities for debugging and analysis
- Comprehensive persistence testing
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Enhanced Examples and Documentation:
- Learning rate scheduling comprehensive example
- GRU vs LSTM comparison example
- Bidirectional LSTM demonstration
- Enhanced dropout regularization examples
- Model inspection and analysis utilities
- Real-world applications (stock prediction, weather forecasting, text classification)
- Modular scheduler architecture with trait-based design
- Efficient bidirectional sequence processing implementation
- Enhanced gradient computation for GRU cells
- Improved testing coverage with comprehensive test suites
- Better code organization and documentation
- Performance optimizations for training workflows
- More flexible training with advanced learning rate strategies
- Alternative RNN architecture (GRU) for different use cases
- Better sequence modeling with bidirectional context
- Robust regularization options for preventing overfitting
- Reliable model persistence for production deployments
- Comprehensive examples for real-world applications
- Complete Training System: Full backpropagation through time (BPTT) implementation
- Multiple Optimizers: SGD, Adam, and RMSprop with configurable parameters
- Loss Functions: MSE, MAE, and Cross-Entropy with numerically stable implementations
- Training Infrastructure:
- LSTMTrainer with configurable training loops
- Gradient clipping to prevent exploding gradients
- Validation support with metrics tracking
- Training configuration system
- Advanced Features:
- Gradient accumulation across time steps
- Parameter state management for optimizers
- Comprehensive metrics tracking and history
- Enhanced Network Architecture:
- Network-level backward propagation
- Multi-layer gradient computation
- Sequence processing with caching
- Examples and Documentation:
- Complete training example with sine wave prediction
- Optimizer comparison demonstrations
- Comprehensive API documentation
- LSTM Cell: Added backward propagation with full gradient computation
- LSTM Network: Enhanced with training capabilities and caching
- Architecture: Restructured for better separation of concerns
- Performance: Optimized gradient computation and memory usage
- Implemented mathematically correct BPTT algorithm
- Added bias correction for Adam optimizer
- Implemented numerically stable softmax function
- Enhanced multi-layer gradient flow
- Added proper parameter update mechanisms
- Core LSTM Implementation:
- Basic LSTM cell with forward propagation
- Standard LSTM equations implementation
- Multi-layer LSTM network support
- Peephole LSTM Variant:
- Peephole connections for enhanced performance
- Direct cell state to gate connections
- Basic Architecture:
- Modular design with separate layers and models
- Clean separation between cell and network levels
- Configurable network architecture (input size, hidden size, layers)
- Utility Functions:
- Sigmoid and tanh activation functions
- Random weight initialization
- Examples:
- Basic usage demonstrations
- Multi-layer network examples
- Time series prediction examples
- Text generation examples
- Peephole LSTM usage
- Forward-pass only implementation
- Xavier-uniform weight initialization
- Support for variable sequence lengths
- Proper hidden and cell state management
- Forward-pass only LSTM cells
- Basic multi-layer networks
- Simple examples and demonstrations
- Full backward propagation (BPTT)
- Multiple optimization algorithms
- Comprehensive loss functions
- Training infrastructure with validation
- Advanced gradient management
- Cleaned and optimized documentation
- Enhanced code clarity
- Comprehensive examples
- Performance optimizations
- Enhanced Architectures:
- Bidirectional LSTM
- GRU (Gated Recurrent Unit)
- Attention mechanisms
- Performance Optimizations:
- GPU acceleration support
- Batch processing capabilities
- SIMD optimizations
- Advanced Training Features:
- Learning rate scheduling
- Regularization techniques (dropout, weight decay)
- Early stopping
- Data Handling:
- Built-in data preprocessing
- Common dataset loaders
- Data augmentation
- Visualization & Monitoring:
- Training progress visualization
- Loss curve plotting
- Model analysis tools
When contributing to this project, please:
- Update the CHANGELOG.md file with your changes
- Follow the established coding style
- Add tests for new functionality
- Update documentation as needed
- v0.1.0: Initial LSTM implementation with forward pass
- v0.2.0: Complete training system with BPTT and optimizers
- v0.3.0: Learning rate scheduling, GRU implementation, BiLSTM, enhanced dropout, and model persistence
- v0.4.0: Advanced learning rate scheduling with 12 different schedulers, warmup support, cyclical rates, and visualization