# R: Evaluates Hessian for a Neural Network

nnetHess {nnet} | R Documentation

## 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 `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`

### Examples

    data(iris3)
    # 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

[Package nnet version 7.2-36 Index]