Evaluates the Hessian (matrix of second derivatives) of the specified
neural network. Normally called via argument Hess=TRUE
to nnet
or via
vcov.multinom
.
nnetHess(net, x, y, weights)
object of class nnet
as returned by nnet
.
training data.
classes for training data.
the (case) weights used in the nnet
fit.
square symmetric matrix of the Hessian evaluated at the weights stored in the net.
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.
# NOT RUN { # 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 # }
Run the code above in your browser using DataCamp Workspace