Different Activation function Neural Networks
There are a number of common activation functions in use with neural networks. This is not an exhaustive list.
A step function is a function like that used by the original Perceptron. The output is a certain value, A1, if the input sum is above a certain threshold and A0 if the input sum is below a certain threshold. The values used by the Perceptron were A1 = 1 and A0 = 0.
These kinds of step activation functions are useful for binary classification schemes. In other words, when we want to classify an input pattern into one of two groups, we can use a binary classifier with a step activation function. Another use for this would be to create a set of small feature identifiers. Each identifier would be a small network that would output a 1 if a particular input feature is present, and a 0 otherwise. Combining multiple feature detectors into a single network would allow a very complicated clustering or classification problem to be solved.
A linear combination is where the weighted sum input of the neuron plus a linearly dependant bias becomes the system output. Specifically:
y = ζ + b
In these cases, the sign of the output is considered to be equivalent to the 1 or 0 of the step function systems, which
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