gov.sandia.cognition.learning.function.scalar

## Class SoftPlusFunction

• All Implemented Interfaces:
Evaluator<java.lang.Double,java.lang.Double>, DifferentiableEvaluator<java.lang.Double,java.lang.Double,java.lang.Double>, DifferentiableUnivariateScalarFunction, ScalarFunction<java.lang.Double>, UnivariateScalarFunction, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

```@PublicationReference(author="Wikipedia",
title="Rectifier",
type=WebPage,
year=2014,
url="http://en.wikipedia.org/wiki/Rectifier_(neural_networks)")
public class SoftPlusFunction
extends AbstractDifferentiableUnivariateScalarFunction```
A smoothed approximation for rectified linear unit. It is typically used as an activation function in a neural network. Its value is computed as f(x) = log(1 + e^x). Thus, its derivative is the logistic sigmoid, f'(x) = 1 / (1 + e^-x). As such, it can help avoid the vanishing gradient problem. Its output is always positive, it is roughly linear for positive values and it approaches zero for negative values.
Since:
3.4.0
Author:
Justin Basilico
Serialized Form
• ### Constructor Summary

Constructors
Constructor and Description
`SoftPlusFunction()`
• ### Method Summary

All Methods
Modifier and Type Method and Description
`SoftPlusFunction` `clone()`
This makes public the clone method on the `Object` class and removes the exception that it throws.
`double` `differentiate(double input)`
Differentiates the output of the function about the given input
`double` `evaluate(double input)`
Produces a double output for the given double input
• ### Methods inherited from class java.lang.Object

`equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`
• ### Methods inherited from interface gov.sandia.cognition.math.DifferentiableUnivariateScalarFunction

`differentiate`
• ### Methods inherited from interface gov.sandia.cognition.math.UnivariateScalarFunction

`evaluate, evaluateAsDouble`

• ### Method Detail

• #### clone

`public SoftPlusFunction clone()`
Description copied from class: `AbstractCloneableSerializable`
This makes public the clone method on the `Object` class and removes the exception that it throws. Its default behavior is to automatically create a clone of the exact type of object that the clone is called on and to copy all primitives but to keep all references, which means it is a shallow copy. Extensions of this class may want to override this method (but call `super.clone()` to implement a "smart copy". That is, to target the most common use case for creating a copy of the object. Because of the default behavior being a shallow copy, extending classes only need to handle fields that need to have a deeper copy (or those that need to be reset). Some of the methods in `ObjectUtil` may be helpful in implementing a custom clone method. Note: The contract of this method is that you must use `super.clone()` as the basis for your implementation.
Specified by:
`clone` in interface `CloneableSerializable`
Overrides:
`clone` in class `AbstractCloneableSerializable`
Returns:
A clone of this object.
• #### evaluate

`public double evaluate(double input)`
Description copied from interface: `UnivariateScalarFunction`
Produces a double output for the given double input
Parameters:
`input` - Input to the Evaluator
Returns:
output at the given input
• #### differentiate

`public double differentiate(double input)`
Description copied from interface: `DifferentiableUnivariateScalarFunction`
Differentiates the output of the function about the given input
Parameters:
`input` - Input about which to compute the derivative of the function output
Returns:
Derivative of the output with respect to the input