3 Commits

Author	SHA1	Message	Date
Vijay Janapa Reddi	3fe7111d64	Add spatial helpers and rename to Conv2d ... Stage 4 of TinyTorch API simplification: - Added flatten() and max_pool2d() helper functions - Renamed MultiChannelConv2D to Conv2d for PyTorch compatibility - Updated Conv2d to inherit from Module base class - Use Parameter() for weights and bias with automatic registration - Added backward compatibility alias: MultiChannelConv2D = Conv2d - Updated all test code to use Conv2d - Exported changes to tinytorch.core.spatial API now provides PyTorch-like spatial operations while maintaining educational value of implementing core convolution algorithms.	2025-09-23 08:07:35 -04:00
Vijay Janapa Reddi	791d1e3153	Update generated notebooks and package exports ... - Regenerate all .ipynb files from fixed .py modules - Update tinytorch package exports with corrected implementations - Sync package module index with current 16-module structure These generated files reflect all the module fixes and ensure consistent .py ↔ .ipynb conversion with the updated module implementations.	2025-09-18 16:42:57 -04:00
Vijay Janapa Reddi	f9309e8b9d	🔧 Complete module restructuring and integration fixes ... 📦 Module File Organization: - Renamed networks_dev.py → dense_dev.py in 05_dense module - Renamed cnn_dev.py → spatial_dev.py in 06_spatial module - Added new 07_attention module with attention_dev.py - Updated module.yaml files to reference correct filenames - Updated #| default_exp directives for proper package exports 🔄 Core Package Updates: - Added tinytorch.core.dense (Sequential, MLP architectures) - Added tinytorch.core.spatial (Conv2D, pooling operations) - Added tinytorch.core.attention (self-attention mechanisms) - Updated all core modules with latest implementations - Fixed tensor assignment issues in compression module 🧪 Test Integration Fixes: - Updated integration tests to use correct module imports - Fixed tensor activation tests for new module structure - Ensured compatibility with renamed components - Maintained 100% individual module test success rate Result: Complete 14-module TinyTorch framework with proper organization, working integrations, and comprehensive test coverage ready for production use.	2025-07-18 02:10:49 -04:00