nnet.Hess
Evaluates Hessian for a Neural Network
Description
Evaluates the Hessian (matrix of second derivatives) of the specified neural network. Normally called via argument Hess=TRUE
to nnet
or via vcov.multinom
.
Usage
nnetHess(net, x, y, weights)
Arguments
net | object of class |
x | training data. |
y | classes for training data. |
weights | the (case) weights used in the |
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
# 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
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Licensed under the GNU General Public License.