An ontology modeling classes and relationships describing deep learning networks, their component layers and activation functions, machine learning methods, as well as AI/ML potential biases.
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:
Using Claude Opus for initial suggestions for parent classes for Function entities.
####### 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