squash: Squash

In brain-r/brain: Neural Network

Description

Squash functions, more commonly known as activation functions.

Set activation functions for your layers.

Usage

squash_function(func)

squash_sigmoid()

squash_logistic()

squash_tanh()

squash_identity()

squash_relu()

squash_step()

squash_hlim()

squash_softsign()

squash_sinusoid()

squash_gaussian()

squash_bent_identity()

squash_bipolar()

squash_bipolar_sigmoid()

squash_hard_tanh()

squash_aboslute()

squash_inverse()

run_squash(func, x = seq(-1, 1, by = 0.1), print = TRUE, plot = TRUE)

squash_input(brain, squash, bias = NULL)

squash_output(brain, squash, bias = NULL)

squash_hidden(brain, squash, bias = NULL)

Arguments

func	A squashing functions name.
x	A vector of integer.
print	If TRUE prints the equation.
plot	if TRUE plots the activation and the derivate.
brain	An object of class brain as returned by brain.
squash	A object of class squash as returned by squash_function.
bias	A bias, numerical.

Functions

• squash_function: A squash function to use in your brain.

• run_squash: Test and expore the squash_function.

• squash_*: Convenient wrappers around squash_function.

Squashing functions

logistic also known as sigmoid

tanh

identity

relu

step or hlim (hard limit function)

softsign

sinusoid

gaussian

bent_identity

bipolar

bipolar_sigmoid

hard_tanh

absolute

inverse

See Also

squash_function for valid activation functions.

Examples

run_squash("tanh")

(x <- seq(-10, 10, by = .2))
run_squash("sigmoid", x)

brain() %>%
  perceptron(c(2,3,1)) %>%
  squash_input(
    squash_function("tanh")
  )

brain-r/brain documentation built on May 21, 2019, 4:05 a.m.