AI News, BOOK REVIEW: Artificial Neural Networks/Activation Functions

Artificial Neural Networks/Activation Functions

There are a number of common activation functions in use with neural networks.

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.

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.

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.

linear combination is where the weighted sum input of the neuron plus a linearly dependant bias becomes the system output.

In these cases, the sign of the output is considered to be equivalent to the 1 or 0 of the step function systems, which enables the two methods be to equivalent if

This is called the log-sigmoid because a sigmoid can also be constructed using the hyperbolic tangent function instead of this relation, in which case it would be called a tan-sigmoid.

Sigmoid functions in this respect are very similar to the input-output relationships of biological neurons, although not exactly the same.

Sigmoid functions are also prized because their derivatives are easy to calculate, which is helpful for calculating the weight updates in certain training algorithms.

The softmax activation function is useful predominantly in the output layer of a clustering system.

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