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5 Commits (7efcfaae0ca205a375c658eee8d9c65bd6a58810)

Author	SHA1	Message	Date
mckay	2f83f42e5b	feat: Enhance training pipeline and configuration management - Updated Docker run command with improved GPU and user settings - Modified learning rate scheduler to support epoch-based adjustments - Refactored loss computation to return detailed loss components - Added TensorBoard logging for individual loss components - Implemented checkpoint saving mechanism with configurable frequency - Updated training script to use dynamic configuration and improved error handling	3 months ago
mckay	d42b3b46dd	NOTE it's base	4 months ago
mckay	ee7bf687b2	feat: Add advanced loss computation methods to LossManager - Implemented `eikonal_loss` to compute gradient regularization for non-manifold points - Added `offsurface_loss` to penalize points far from the surface with near-zero predictions - Introduced `consistency_loss` to enforce prediction consistency between manifold and non-manifold points	4 months ago
mckay	a874ea78fb	feat: Enhance LossManager with advanced loss computation methods - Added `position_loss` method to calculate manifold loss using mean absolute value - Implemented `normals_loss` method to compute normal vector loss with gradient calculation - Updated `train.py` to log input and output tensor shapes during training - Modified network architecture in `train.py` by increasing hidden layer dimensions from [64, 64, 64] to [256, 256, 256]	4 months ago
mckay	6be600a52f	feat: Add LossManager for manifold loss computation - Created `loss.py` with a `LossManager` class to handle loss calculation - Integrated `LossManager` into the training pipeline in `train.py` - Implemented a basic manifold loss computation using mean absolute value	4 months ago