  We introduce the new "Goldilocks" class of activation functions, which
non-linearly deform the input signal only locally when the input signal is in
the appropriate range. The small local deformation of the signal enables better
understanding of how and why the signal is transformed through the layers.
Numerical results on CIFAR-10 and CIFAR-100 data sets show that Goldilocks
networks perform better than, or comparably to SELU and RELU, while introducing
tractability of data deformation through the layers.
