Sigmoid function


A sigmoid function is a mathematical function having a characteristic "S"-shaped curve or sigmoid curve. A common example of a sigmoid function is the logistic function shown in the first figure and defined by the formula:
Other standard sigmoid functions are given in the Examples section.
Special cases of the sigmoid function include the Gompertz curve and the ogee curve. Sigmoid functions have domain of all real numbers, with return value commonly monotonically increasing but could be decreasing. Sigmoid functions most often show a return value in the range 0 to 1. Another commonly used range is from −1 to 1.
A wide variety of sigmoid functions including the logistic and hyperbolic tangent functions have been used as the activation function of artificial neurons. Sigmoid curves are also common in statistics as cumulative distribution functions, such as the integrals of the logistic density, the normal density, and Student's t probability density functions. The sigmoid function is invertible, and its inverse is the logit function.

Definition

A sigmoid function is a bounded, differentiable, real function that is defined for all real input values and has a non-negative derivative at each point and exactly one inflection point. A sigmoid "function" and a sigmoid "curve" refer to the same object.

Properties

In general, a sigmoid function is monotonic, and has a first derivative which is bell shaped. Conversely, the integral of any continuous, non-negative, bell-shaped function will be sigmoidal. Thus the cumulative distribution functions for many common probability distributions are sigmoidal. One such example is the error function, which is related to the cumulative distribution function of a normal distribution.
A sigmoid function is constrained by a pair of horizontal asymptotes as.
A sigmoid function is convex for values less than 0, and it is concave for values greater than 0.

Examples

Many natural processes, such as those of complex system learning curves, exhibit a progression from small beginnings that accelerates and approaches a climax over time. When a specific mathematical model is lacking, a sigmoid function is often used.
The van Genuchten–Gupta model is based on an inverted S-curve and applied to the response of crop yield to soil salinity.
Examples of the application of the logistic S-curve to the response of crop yield to both the soil salinity and depth to water table in the soil are shown in.
In artificial neural networks, sometimes non-smooth functions are used instead for efficiency; these are known as hard sigmoids.
In audio signal processing, sigmoid functions are used as waveshaper transfer functions to emulate the sound of analog circuitry clipping.
In biochemistry and pharmacology, the Hill equation and Hill-Langmuir equation are sigmoid functions.
In computer graphics, and real-time rendering, some of the sigmoid functions are used to blend colors or geometry, between two values smoothly and without visible seams or discontinuities.