A novelty in ` JETNET 3.0` is the possibility to compute the
Hessian matrix for an MLP network by invoking the subroutine ` JNHESS`. The
computation is done much in the same way as the training; training patterns
are iteratively placed in the vectors ` OIN` and ` OUT` before ` JNHESS`
is called. When one full epoch, the size of which is controlled by ` MSTJN(2)` and
` MSTJN(9)`, has been presented, it normalizes and symmetrizes
the Hessian and places it in the internal common block ` /JNINT5/`.

If the Hessian has been symmetrized, the eigenvectors and eigenvalues of the
Hessian can be computed by invoking ` JNHEIG` (single precision).
Eigenvalues are then placed in the vector ` OUT` and the eigenvectors
replace the columns of the Hessian matrix. The Hessian can be printed out
by invoking the subroutine ` JNSTAT`. However, anticipating possible
questions, the terms of the Hessian matrix are ordered in ` JETNET 3.0`
according to (cf. fig. and eq. ())

with obvious interpretation and extension to more layers.

` JNHESS` assumes the summed square error in eq. ().

Fri Feb 24 11:28:59 MET 1995