SWAP-Score, a novel training-free metric based on sample-wise activation patterns for measuring network expressivity, can be extended to other activation functions beyond ReLU by adapting the calculation of activation patterns accordingly. For instance, for activation functions like Sigmoid or Tanh, which are non-linear and have different characteristics compared to ReLU, the binarization process in SWAP-Score would need to consider these unique properties. By modifying the computation of post-activation values and their binarization according to the specific behavior of each activation function, SWAP-Score can be applied effectively across a wider range of neural network architectures using various activation functions.