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. ().
