The Function hierarchy is potentially overly flat

[realmarcin]

Using Claude Opus for initial suggestions for parent classes for Function entities.

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In the context of an AI ontology, these deep neural network (DNN) activation functions can be grouped into several categories based on their properties and behaviors. Here's a possible categorization:

Sigmoid-like Functions:

Sigmoid Function
Hard Sigmoid Function
Softmax Function (a generalization of the sigmoid function for multi-class classification)
Rectified Functions:

ReLU (Rectified Linear Unit) Function
ELU (Exponential Linear Unit) Function
SELU (Scaled Exponential Linear Unit) Function
Smooth Approximations to Rectified Functions:

Softplus Function
Swish Function
GELU (Gaussian Error Linear Unit) Function
Hyperbolic Functions:

Tanh (Hyperbolic Tangent) Function
Linear Functions:

Linear Function
Other Functions:

Softsign Function
Exponential Function