* bug-fix for 'RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation' caused by in-place operation in model (thanks to Mohammad Alahmad) * new methods for scalable models from a underlying computational theme structure * more explicit methods to store and re-load a cached layered graph * some documentation on base module *MaskedLinearLayer* * dependency updates
0.9
* re-introduced saliency as an optional additional property on MaskedLinearLayers for communicating saliency measures on weight-level to decide on further pruning * fixed some of the simpler pruning functions such as prune_network_by_saliency() and prune_layer_by_saliency() from deepstruct.pruning * masks up to now do not consider bias vectors which might be unexpected behaviour
0.8
* deprecation of learning utilities * integrated additional normalization layers * masks on maskable layers are parameterizable to investigate on structural regularization ideas * functional dataset can now be easily stored in a pickle file
0.7
* new minimal version requirement is python 3.7 * introduced interface for "functors" which transform a nn.Module into a directed acyclic graph * created a first functor for Linear and MaskedLinear layers * a graph transform class passes a random input through a generic module and can transform it into a graph given that it consists of linear or conv2d layers (first tests added) * added mkdocs to provide an initial documentation skeleton
* new feature: concept of scalable families which is a first notion of *graph themes* analysis * file restructuring for better semantics * pypaddle will be renamed to deepstruct