What's Changed GenNet interpret: adds the interpretation module with six interpretation methods get_weight_scores: uses the weights to calculate the importance of each feature and node DeepExplain: uses the gradient (see DeepExplain) to calculate the importance RLIPP: uses logistic regression with signals to and from the node to calculate a measure of non-linearity for all nodes NID: Finds interacting features based on the features with the strongest weights DFIM: perturbs each input (or N inputs in the order of importance), and tracks which other features change importance to find interacting features PathExplain: Uses the Expected Hessian to find interacting features bugfixes for converting plink2 files module for converting toplogy to npz matrices add one-hot encoding support adds multiple filters. LocallyDirected1D now supports multiple filters akin to channels or feature maps in CNNs