# nnetHess

From nnet v7.3-0
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 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

##### 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
```

*Documentation reproduced from package nnet, version 7.3-0, License: GPL-2 | GPL-3*

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