nnetHess
From nnet v7.312
by Brian Ripley
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 bynnet
.  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
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=5e4, maxit=200)
eigen(nnetHess(ir1, ir[samp,], targets[samp,]), TRUE)$values
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