- Deleted keras_contrib dependecy as it was a frequent source of installation problems, and was only used peripherally.
- Deteled scikit-optimize and tqdm as dependencies, as they were only used in the
demos/hp_search.py
script. - Added
demos/atlas_specific_usecases/use_trained_model/inference.py
which provides a single function for doing inference with a trained model. - Added both the master's thesis that this package was made in conjunction with, along with a short overview of the knowledge that came out of that thesis.
- Added strides as an available hyperparameter.
- Saved images of models as .png, as .pdf was causing some trouble on some platforms.
- Made minor convenient changes to
utils
functions.
- Updated demos:
- Included a more realistic use case of training a model.
- Included a demo showing how to load and use a trained model.
- Made
hp_search.py
more memory efficient in that different processes don't have their own copy of the data.
- Added functionality for multiplying the model output with a scalar variable (see the
multiply_output_name
argument ofdeepcalo/utils/load_atlas_data
). - Added bias correction classes, which use a (1D or 2D) spline to fit the median error of a model.
- Added network_in_network model
- Changed name of time_net to gate_net, as multiple types of cell data can be processed using this - this breaks backward compatibility!
- Added possibility to scan learning rates logarithmically in
LRFinder
(#1) - Made it possible to divide target by a scalar variable (e.g. the total accordion energy, when doing ER) in load_atlas_data
- Changed naming convention of ECAL layers in load_atlas_data to fit the new data
- Bugfix when trying to load gate_net weights into TimeDistributed
- Bugfix when trying to plot the FiLM generator
- Bugfix when giving tracks to self.cnn_with_upsampling in model_container.py
- Bugfix in merge_dicts
- Added 1Cycle learning rate schedule and improved docs for learning rate schedules in general
- Bugfix in
SGDR_lr_schedule.py
(missing imports) - Bugfix in
load_atlas_data
(targets were divided by 1000) - Changed the way GPUs were counted in the demos, as the old code was wrong if more than 9 GPUs were used
- Updated
load_data
toload_atlas_data
, which now works with the newly uploaded data - Added custom model checkpoint callback that allows models to be saved as jsons
- Bugfix in get_track_net
- Bugfix in datagenerator
- Bugfix in utils.set_auto_lr
- Save models instead of just weights
- Deleted unneeded Python path insertion
- Deleted
deepcalo.utils.apply_preprocessing
, such that there is no dependency onscikit-learn
- Initial release