MetaNNvis
MetaNNvis is a tool for accessing introspection methods for neural networks regardless of the framework in which the neural network has been built. It is easily extendable and currently supports models from TensorFlow 2.0 and PyTorch in combination with methods from Captum and tf-keras-vis. For more details, see the project report.
Installation
The latest version of metaNNvis can be installed via pip:
pip install metaNNvis
Additionally, you need the following dependencies:
torch, seaborn, numpy, tensorflow, onnx2torch, onnx, captum, tf-keras-vis, torchvision, matplotlib
and onnx2keras. All dependencies can be installed via pip except for onnx2keras, which can be downloaded from the
project's GitHub page
Usage
For instructions on how to use cross-framework introspection and how to extend it by new methods, see the user guide.
Available methods
Cross-Framework Introspection currently supports most methods from Captum and all methods from tf-keras-vis. The supported methods are:
| Method | Category |
|---|---|
| Captum | |
| Integrated Gradients | primary, layer, neuron |
| Saliency | primary |
| DeepLift | primary, layer, neuron |
| GradientShap | primary, layer, neuron |
| Input X Gradient | primary |
| Gradient X Activation | layer |
| Deconvolution | primary, neuron |
| Feature Ablation | primary, layer, neuron |
| Feature Permutation | primary |
| Conductance | layer, neuron |
| Layer Activation | layer |
| GradCAM | layer |
| Neuron Gradient | neuron |
| tf-keras-vis | |
| Activation Maximization | feature visualization |
| Vanilla Saliency / SmoothGrad | attribution |
| GradCAM | attribution |
| GradCAM++ | attribution |
| ScoreCAM | attribution |
| LayerCAM | attribution |
Currently not supported are:
| Method | Category |
|---|---|
| Captum | |
| DeepLiftShap | primary, layer, neuron |
| Guided Backpropagation | primary, neuron |
| Guided GradCAM | primary |
| Occlusion | primary |
| Shapley Value Sampling | primary |
| Lime | primary |
| KernelShap | primary |
| Layer Relevance Propagation | primary, layer |
| Internal Influence | layer |
