Preactivations

Preactivations refer to the input values passed to an activation function within a neural network. Before an activation function is applied to a neuron's weighted sum of inputs and bias, the result of this calculation is the preactivation. This value is crucial as it directly influences the output of the activation function and, consequently, the neuron's contribution to the network's overall processing.

Different activation functions, such as ReLU, sigmoid, or tanh, operate on these preactivation values. For instance, the Rectified Linear Unit (ReLU) function simply outputs the preactivation if it's positive, and zero otherwise. The sigmoid function squashes the preactivation into a range between 0 and 1, while the tanh function maps it to a range between -1 and 1. The choice of activation function and its behavior on preactivations significantly impacts the learning dynamics and representational capacity of the neural network. Understanding preactivations helps in debugging, analyzing network behavior, and selecting appropriate activation functions for specific tasks. In essence, preactivations are the raw material that activation functions transform into the final output of a neuron.