Do you mean a graph that contains all neurons from the network in one structure? Similar to these: https://gfycat.com/BonyTotalArthropods https://gfycat.com/Bon

Do you mean a graph that contains all neurons from the network in one structure? Similar to these: https://gfycat.com/BonyTotalArthropods https://gfycat.com/BonyTotalArthropods https://gfycat.com/BitesizedWeeBlacklemur https://gfycat.com/BitesizedWeeBlacklemur That would be wonderful and I have been trying to do this. However, unfortunately some 'assumptions'/shortcuts have to be made. For example, the attention matrix is not known without input, so if just the structure of the network (weighted by the weights) is wanted, you have to put in some value 'p' ('1', '-1', w/e) to these edges. Also skip connections have to be dealt with explicitly instead of just adding them to a block diagonal matrix as one would with an MLP. I am very interested if someone has a good solution that already has done these things though.