V0.5 The Gradient 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

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

__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

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

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