nnetHess

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Evaluates Hessian for a Neural Network

Evaluates the Hessian (matrix of second derivatives) of the specified neural network. Normally called via argument Hess=TRUE to nnet or via vcov.multinom.

Keywords
neural
Usage
nnetHess(net, x, y, weights)
Arguments
net
object of class nnet as returned by nnet.
x
training data.
y
classes for training data.
weights
the (case) weights used in the nnet fit.
Value

square symmetric matrix of the Hessian evaluated at the weights stored in the net.

References

Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

nnet, predict.nnet

Aliases
  • nnetHess
Examples
library(nnet) # use half the iris data ir <- rbind(iris3[,,1], iris3[,,2], iris3[,,3]) targets <- matrix(c(rep(c(1,0,0),50), rep(c(0,1,0),50), rep(c(0,0,1),50)), 150, 3, byrow=TRUE) samp <- c(sample(1:50,25), sample(51:100,25), sample(101:150,25)) ir1 <- nnet(ir[samp,], targets[samp,], size=2, rang=0.1, decay=5e-4, maxit=200) eigen(nnetHess(ir1, ir[samp,], targets[samp,]), TRUE)$values
Documentation reproduced from package nnet, version 7.3-12, License: GPL-2 | GPL-3

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