Converter and converted modelR6 class for converting a passed model into a torch-based model with all methods pre-implemented in each layer |
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Converter of an artificial neural network |
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Converted torch-based model |
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Feature attribution methodsAll implemented feature attribution methods as R6 classes applying the corresponding method to the converted model during initialization |
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Vanilla Gradient and Gradient\(\times\)Input |
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SmoothGrad and SmoothGrad\(\times\)Input |
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Integrated Gradients |
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Expected Gradients |
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Layer-wise relevance propagation (LRP) |
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Deep learning important features (DeepLift) |
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Deep Shapley additive explanations (DeepSHAP) |
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Connection weights method |
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Local interpretable model-agnostic explanations (LIME) |
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Shapley values |
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Syntactic sugar for object construction |
Visualization and getting resultsFunctions and S4 classes for getting and visualizing the results |
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Get the result of an interpretation method |
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Get the result of an interpretation method |
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S4 class for ggplot2-based plots |
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S4 class for plotly-based plots |
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Generic print, plot and show for |
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Indexing plots of |
Generic add function for |
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Generic print, plot and show for |
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Indexing plots of |
Superclasses and package informationSuper classes intended only for the user’s information |
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Get the insight of your neural network |
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Super class for model-agnostic interpretability methods |
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Super class for gradient-based interpretation methods |
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Super class for interpreting methods |