GilesStrong/lumin: v0.5 The Gadient Must Flow
| Main Author: | GilesStrong |
|---|---|
| Format: | info software Journal |
| Terbitan: |
, 2020
|
| Online Access: |
https://zenodo.org/record/3660969 |
Daftar Isi:
- V0.5 The Gadient Must Flow Important changes Added support for processing and embedding of matrix data MultiHead to allow the use of multiple head blocks to handle input data containing flat and matrix inputs AbsMatrixHead abstract class for head blocks designed to process matrix data InteractionNet a new head block to apply interaction graph-nets to objects in matrix form RecurrentHead a new head block to apply recurrent layers (RNN, LSTM, GRU) to series objects in matrix form AbsConv1dHead a new abstract class for building convolutional networks from basic blocks to apply to object in matrix form. Meta data: FoldYielder now checks its foldfile for a meta_data group which contains information about the features and inputs in the data cont_feats and cat_feats now no longer need to be passed to FoldYielder during initialisation of the foldfile contains meta data add_meta_data function added to write meta data to foldfiles and is automatically called by df2foldfile Improved usage with large datasets: AddedModel.evaluate_from_by to allow batch-wise evaluation of loss bulk_move in fold_train_ensemble now also affects the validation fold, i.e. bulk_move=False no longer preloads the validation fold, and validation loss is evaluated using Model.evaluate_from_by bulk_move arguments added to fold_lr_find Added batch-size argument to Model predict methods to run predictions in batches Potentially Breaking FoldYielder.get_df() now returns any NaNs present in data rather than zeros unless nan_to_num is set to True Zero bias init for bottlenecks in MultiBlock body Additions __repr__ of Model now detail information about input variables Added support for processing and embedding of matrix data MultiHead to allow the use of multiple head blocks to handle input data containing flat and matrix inputs AbsMatrixHead abstract class for head blocks designed to process matrix data InteractionNet a new head block to apply interaction graph-nets to objects in matrix form RecurrentHead a new head block to apply recurrent layers (RNN, LSTM, GRU) to series objects in matrix form AbsConv1dHead a new abstract class for building convolutional networks from basic blocks to apply to object in matrix form. Meta data: FoldYielder now checks its foldfile for a meta_data group which contains information about the features and inputs in the data cont_feats and cat_feats now no longer need to be passed to FoldYielder during initialisation of the foldfile contains meta data add_meta_data function added to write meta data to foldfiles and is automatically called by df2foldfile get_inputs method to BatchYielder to return the inputs, optionally on device Added LSUV initialisation, implemented by LsuvInit callback Removals Fixes FoldYielder.get_df() now returns any NaNs present in data rather than zeros unless nan_to_num is set to True Various typing fixes` Body and tail modules not correctly freezing Made Swish to not be inplace - seemed to cause problems sometimes Enforced fastprogress version; latest version renamed a parameter Added support to df2foldfile for missing strat_key Added support to fold2foldfile for missing features Zero bias init for bottlenecks in MultiBlock body Changes Slight optimisation in FullyConnected when not using dense or residual networks FoldYielder.set_foldfile is now a private function FoldYielder._set_foldfile Improved usage with large datasets: AddedModel.evaluate_from_by to allow batch-wise evaluation of loss bulk_move in fold_train_ensemble now also affects the validation fold, i.e. bulk_move=False no longer preloads the validation fold, and validation loss is evaluated using Model.evaluate_from_by bulk_move arguments added to fold_lr_find Added batch-size argument to Model predict methods to run predictions in batches Depreciations Comments