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Implementation Status

Completed Features

1. Activation Functions

  • ELU: Exponential Linear Unit with backward pass
  • SELU: Scaled Exponential Linear Unit with backward pass
  • Swish: x * sigmoid(x) with backward pass
  • Mish: x * tanh(softplus(x)) with backward pass
  • Hard Swish: x * ReLU6(x + 3) / 6 with backward pass
  • Layer Wrappers: nn_elu(), nn_selu(), nn_swish(), nn_mish(), nn_hard_swish()

Files:

  • src/autograd/forward_ops.c - Forward implementations
  • src/autograd/backward_ops.c - Backward implementations
  • src/nn/layers/activations.c - Layer wrappers
  • include/nn/layers/activations.h - Headers

2. Tensor Operations

  • Concatenate: tensor_concat(tensors, num_tensors, dim) - Concatenate along dimension
  • Stack: tensor_stack(tensors, num_tensors, dim) - Stack along new dimension
  • Split: tensor_split(tensor, num_splits, dim, split_sizes) - Split tensor
  • Gather: tensor_gather(input, indices, dim) - Gather values using indices
  • Scatter: tensor_scatter(input, dim, index, src) - Scatter values at indices

Files:

  • src/tensor_manipulation.c - Full implementations
  • include/tensor/ops.h - Function declarations

3. Memory Management

  • Memory Pools: Pre-allocated memory blocks for faster allocation
  • Tensor Pools: Pre-allocated tensors for reuse
  • Tensor Reuse: Reuse tensor memory when shapes match

Files:

  • src/Core/memory_pools.c - Memory pool implementation
  • include/Core/memory_pools.h - Memory pool API

4. Optimizers (Previously Completed)

  • SGD with Momentum: Momentum buffer implementation
  • RMSprop: Root Mean Square Propagation optimizer
  • Adagrad: Adaptive Gradient optimizer
  • Adam: Adaptive Moment Estimation (already existed)

Files:

  • src/optim.c - Optimizer implementations
  • include/optim/optimizer.h - Optimizer API

5. Loss Functions (Previously Completed)

  • Huber Loss: Smooth L1 loss with backward pass
  • KL Divergence: Kullback-Leibler divergence loss with backward pass

Files:

  • src/autograd/loss_functions.c - Loss function implementations
  • include/autograd/loss_functions.h - Loss function API

6. Layers (Previously Completed)

  • LayerNorm: Layer normalization with learnable parameters

Files:

  • src/nn/layers/layernorm.c - LayerNorm implementation
  • include/nn/layers/layernorm.h - LayerNorm API

Recently Completed Features

1. Training Metrics

  • Status: ✅ COMPLETE - Fully automatic metrics capture
  • Features:
  • Automatic Metrics Capture: No manual tracking code needed - metrics captured by default
  • Automatic epoch timing: Detected via optimizer_zero_grad() calls
  • Training/validation/test metrics tracking: training_metrics_auto_capture_*() functions
  • Gradient norm monitoring: Automatic tracking via optimizer_step() integration
  • Learning rate tracking: Current learning rate per epoch with scheduler information
  • Loss reduction rate: Percentage reduction in loss
  • Loss stability: Standard deviation of recent losses
  • Early Stopping Support: Track actual vs expected epochs with training_metrics_mark_early_stop()
  • LR Scheduler Visualization: Display scheduler type and parameters in UI
  • Real-time JSON Export: Continuously updates training.json during training
  • Automatic Dataset Evaluation: training_metrics_evaluate_dataset() for validation/test
  • Model Architecture Export: Automatic export of model structure to JSON
  • Files:
  • src/Core/training_metrics.c - Full implementation with global state management
  • include/Core/training_metrics.h - Training metrics API
  • src/optim.c - Optimizer integration for automatic metrics capture
  • src/autograd/autograd.c - Loss capture via tensor_backward()
  • src/cml.c - Global metrics initialization/cleanup

2. Visualization UI

  • Status: ✅ COMPLETE - Enhanced with early stopping and LR scheduling
  • Features:
  • Interactive training dashboard: Real-time visualization of training metrics
  • Computational graph visualization: Visual representation of ops topology
  • Model architecture view: Interactive model structure visualization using Cytoscape
  • Bias-variance analysis: Plot training, validation, and test metrics together
  • Early Stopping Visualization: Badges and icons showing early stopping status
  • Dynamic X-Axis: Charts adapt to actual epochs when early stopping occurs
  • LR Scheduler Display: Show scheduler type and parameters in metrics panel
  • Modern web interface: React-based UI with Vite
  • FastAPI backend: Python server for serving JSON data and SSE streams
  • Files:
  • viz-ui/src/ - React frontend components
  • scripts/fastapi_server.py - Backend API server
  • scripts/viz.py - Visualization launcher

3. Broadcasting - Enhanced

  • Status: COMPLETE - Full NumPy-style implementation
  • Features:
  • Complete NumPy-style broadcasting rules
  • Scalar broadcasting support (0D tensors)
  • Multi-shape broadcasting: broadcast_multi_shapes()
  • Proper dimension alignment from right to left
  • Invalid dimension checking
  • Files:
  • src/autograd/autograd.c - Enhanced can_broadcast_shapes() and broadcast_shapes()
  • include/autograd/autograd.h - Added broadcast_multi_shapes()

4. Data Augmentation

  • Status: ✅ COMPLETE
  • Features:
  • Random crop: augment_random_crop()
  • Random horizontal flip: augment_random_horizontal_flip()
  • Random vertical flip: augment_random_vertical_flip()
  • Random rotation: augment_random_rotation()
  • Color jitter: augment_color_jitter() (brightness, contrast)
  • Normalization: augment_normalize()
  • Pipeline: augment_apply() - Apply all augmentations
  • Files:
  • src/Core/augmentation.c - Full implementation
  • include/Core/augmentation.h - Augmentation API

5. Data Loading

  • Status: ✅ COMPLETE - Basic implementation with prefetching support
  • Features:
  • DataLoader: dataloader_create(), dataloader_next_batch()
  • Batch iteration: dataloader_has_next(), dataloader_reset()
  • Shuffling support
  • Prefetching support (infrastructure ready, prefetch_factor field)
  • Batch callbacks: on_batch_start(), on_batch_end()
  • Drop last batch option
  • Note: Multi-threading infrastructure is in place (num_workers field), but actual threading implementation would require pthreads
  • Files:
  • src/Core/dataset.c - DataLoader implementation
  • include/Core/dataset.h - DataLoader API

6. Gradient Checkpointing

  • Status: ✅ COMPLETE - Basic implementation
  • Features:
  • Enable/disable checkpointing: autograd_set_checkpointing()
  • Checkpoint tensors: autograd_checkpoint()
  • Recompute tensors: autograd_recompute()
  • Saves computation graph for recomputation
  • Note: Full recomputation during backward pass requires integration with autograd engine
  • Files:
  • src/autograd/checkpointing.c - Checkpointing implementation
  • include/autograd/checkpointing.h - Checkpointing API

7. Profiling

  • Status: ✅ COMPLETE
  • Features:
  • Timer: timer_create(), timer_start(), timer_stop()
  • Profiler: profiler_create(), profiler_start(), profiler_stop()
  • Profiling report: profiler_print_report()
  • Get total time: profiler_get_total_time()
  • Enable/disable: profiler_set_enabled()
  • Files:
  • src/Core/profiling.c - Profiling implementation
  • include/Core/profiling.h - Profiling API

8. Examples Build and Training Utilities

  • Status: ✅ COMPLETE
  • Features:
  • Makefile builds example binaries under build/examples/
  • CMake option BUILD_EXAMPLES builds all examples
  • Example demonstrates automatic metrics capture, early stopping, and LR scheduling
  • Centralized Cleanup: CleanupContext for resource management
  • Dataset Splitting: dataset_split_three() for train/val/test splits
  • Files:
  • CMakeLists.txt - Example targets
  • Makefile - Example build rules
  • examples/training_loop_example.c - Training loop with automatic metrics
  • examples/test.c - Comprehensive training with train/val/test splits
  • examples/early_stopping_lr_scheduler.c - Early stopping and LR scheduling example
  • include/Core/cleanup.h - Centralized cleanup API
  • src/Core/cleanup.c - Cleanup implementation

Implementation Notes

Activation Functions

All activation functions include:

  • Forward pass with proper formula
  • Backward pass with gradient computation
  • Autograd integration
  • Layer wrappers for easy use

Tensor Operations

All operations include:

  • Shape validation
  • Bounds checking
  • Proper memory management
  • Error handling

Memory Pools

  • Pre-allocates memory blocks for faster operations
  • Reduces memory fragmentation
  • Supports tensor reuse for common shapes

Next Steps

  1. Complete Broadcasting: Enhance existing implementation with full NumPy rules
  2. Data Augmentation: Add image/data augmentation functions
  3. Multi-threading: Add worker threads for data loading
  4. Prefetching: Preload next batch while processing current
  5. Gradient Checkpointing: Implement memory-efficient backward pass
  6. Profiling: Add performance profiling tools