Weights2-class: Weights2 objects

In TeachNet: Fits Neural Networks to Learn About Backpropagation

Description

Contains the weights for a two hidden layer neural network in TeachNet the here cold "Arguments" are the slots in the S4 class Weights2

Arguments

alpha	Intercept from output layer
alpha_1m	Intercept from hidden layer
alpha_2h	Intercept from second hidden layer
w_h	Weights from second hidden layer to output layer
q_mh	Weights from first hidden layer to second hidden layer
w_im	Weights from input layer to first hidden layer

Author(s)

Georg Steinbuss

See Also

Weights-class

Examples

M <- 3 # number of neurons in first hidden layer
H <- 3 # number of neurons in second hidden layer
I <- 6 # number of inputs

random_weights <- new("Weights2", alpha = rnorm(1), alpha_1m = rnorm(M), alpha_2h = rnorm(H), 
                      w_h = rnorm(H), q_mh = matrix(nrow=M,ncol=H, data=rnorm(M*H)),
                      w_im = matrix(nrow=I,ncol=M, data=rnorm(I*M)))

TeachNet documentation built on May 2, 2019, 7 a.m.