Version 1.0.2

Release Notes

• Support for tensorflow version 2.16.1
• Udate setup and CI
• Setting of random seeds
• API changes of dependent libraries: ruamel.yaml

Added Functionality:

• Added option to limit value range of direction vector to stabilize training

Commits and PRs

• Update setup and ci by @mhuen in #5
• Update to tensorflow2.16.1 by @mhuen in #6

Full Changelog: v1.0.1...v1.0.2

Version 1.0.1

Release Notes:

• Minor bug fixes and clean up

Added Functionality:

• Option to define keys to load in default label loader module

Changes:

• Supress NaturalNameWarning from tables package
• make iteration order over internal dicts deterministic
• Cleaned up old model configs

Bug Fixes:

• add __del__() method to properly terminate multiprocessing jobs in DataHandler

Version 1.0.0

Release Notes:

• DNN reco now supports TF2
• TF1 still works in compatibility mode
• Documentation also updated to python3 and TF2

Added Functionality:

• Support for learning rate schedulers
• option to only apply input data filter on specific PDG particle types

Changes:

• Progress bars via tqdm for create trafo model and counting
• more detailed output when NaNs are found in input data
• add is_neutrino and is_numu labels to event_selection_labels.upgoing_tracks

Bug Fixes:

• add dependencies tables, uncertainties, tqdm
• fix git origin url for version control
• pickle protocol fixed to 2: ensures models can be used in python 2 and 3
• Azimuth and Zenith of I3Particle are now transformed to proper range in I3Module
• fixed python3 multiprocessing bug for data input pipeline

Version 0.2.0

Changes:

• Reduced memory footprint
• DOM exclusions are now handled directly in DNN I3Module
• Additional configuration checks when applying an exported DNN model

Added Functionality:

• Define event weights in loss function
• Label smoothing for fuzzy labels
• GNN comparison architecture and config
• Option to load FilterMask in general misc data loader
• File weighting for files from different datasets
• Gradient clipping and NaN replacement in gradients
• DNN LLh classes to obtain directional error contours
• Biased sampling of training events based on misc data or NN prediction

Added Models and Configs:

• NuGen L2 event selection
• Various starting event selection models
• Muon classification
• Track reconstruction at L2 (track length, direction, ..)
• HESE Spice3.2 reconstruction (used for cascade real-time alert stream)
• Muon multiplicity
• Muon scattering event selection
• Starting event selection model based on Cascade-based input data

Bug Fixes:

• Fix naming of I3Modules: allows inclusion of multiple DNN reco segments
• Throw error when pulses are not in frame
• Stabilize loss and architectures
• Fix global time offset shift
• Fix python3 compatibility
• Automatically find packages in setup.py

Initial Release

Initial Release of DNN reco Project.