Interface common blocks

The user interface common blocks are /JNDAT1/ and /JNDAT2/. /JNDAT1/ is the main common block while /JNDAT2/ is intended for the "advanced" user.

• COMMON /JNDAT1/ MSTJN(40),PARJN(40),MSTJM(20),PARJM(20),OIN(1000),OUT(1000),
  MXNDJM

    MSTJN is a vector of switches used to define the feed-forward network 
  used:
  
   MSTJN(1) 		 (D=3)  		 number of layers in the net
  
   MSTJN(2) 		 (D=10) 		 number of patterns per update in  JNTRAL
  
   MSTJN(3) 		 (D=1)  		 overall activation function used in the net
  
  		 		 1 
  
  		 		 2 
  
  		 		 3  (only used internally for Potts-nodes)
  
  		 		 4 
  
  		 		 5  (only used internally for
  entropy error)
  
   MSTJN(4) 		 (D=0) 		 error measure
  
  		 		 -1  log-squared error: 
  
  		 		 0  summed square error: 
  
  		 		 1  entropy error: 
  
  		 		 2  Kullback error with Potts nodes of dimension  MSTJN(4)
  
  		 		 
  
   MSTJN(5) 		 (D=0) 		 updating procedure
  
  		 		 0  normal updating
  
  		 		 1  Manhattan updating
  
  		 		 2  Langevin updating
  
  		 		 3  Quickprop updating
  
  		 		 4  Conj. Grad. updating -- Polak-Ribiere
  
  		 		 5  Conj. Grad. updating -- Hestenes-Stiefel
  
  		 		 6  Conj. Grad. updating -- Fletcher-Reeves
  
  		 		 7  Conj. Grad. updating -- Shanno
  
  		 		 8  terminate Conj. Grad. updating
  
  		 		 9  no updating
  
  		 		 10  Scaled Conj. Grad. updating -- Polak-Ribiere
  
  		 		 11  Scaled Conj. Grad. updating -- Hestenes-Stiefel
  
  		 		 12  Scaled Conj. Grad. updating -- Fletcher-Reeves
  
  		 		 13  Scaled Conj. Grad. updating -- Shanno
  
  		 		 14  terminate Scaled Conj. Grad. updating
  
  		 		 15  Rprop updating
  
   MSTJN(6) 		 (D=6) 		 file number for output statistics
  
   MSTJN(7) 		 (R) 		 number of calls to  JNTRAL
  
   MSTJN(8) 		 (I) 		 initialization done
  
   MSTJN(9) 		 (D=100) 		 number of updates per epoch
  
   MSTJN(10+I) 				 number of nodes in layer I (I=0  input 
  layer)
  
   MSTJN(10) 		 (D=16)
  
   MSTJN(11) 		 (D=8)
  
   MSTJN(12) 		 (D=1)
  
   MSTJN(13-20) 		 (D=0)
  
   MSTJN(21) 		 (D=0) 		 pruning ( on)
  
   MSTJN(22) 		 (D=0) 		 saturation measure s ( on)
  
   MSTJN(23,24) 		 (D=0) 		 (x,y)-geometry of input field when using receptive 
  fields
  
  		 		  periodic boundary conditions
  
  		 		 see  COMMON /JNINT3/ for further explanations
  
   MSTJN(25,26) 		 (D=0) 		 (x,y)-geometry of receptive fields
  
   MSTJN(27) 		 (D=1) 		 number of hidden nodes per receptive field
  
   MSTJN(28-30) 		 (D=0) 		 bit-precision ( machine precision)
  for sigmoid
  
  		 		 functions (28), thresholds (29) and weights (30)
  
   MSTJN(31) 		 (D=1) 		 procedure for handling warnings
  
  		 		  warnings are ignored
  
  		 		  execution is terminated after  MSTJN(32) warnings
  
  		 		 in any case, only  MSTJN(32) warning messages are issued
  
   MSTJN(32) 		 (D=10) 		 maximum number of warning messages to be issued
  
  		 		 (see description above)
  
   MSTJN(33) 		 (I) 		 code for latest warning issued by the program
  
   MSTJN(34) 		 (I) 		 number of warnings issued by the program so far
  
   MSTJN(35) 		 (D=10) 		 maximum number of iterations allowed in line search
  
   MSTJN(36) 		 (D=10) 		 maximum number of allowed restarts for the line search
  
   MSTJN(37) 		 (I) 		 current status of the line search
  
  		 		  minimum found
  
  		 		  searching for minimum
  
   MSTJN(38) 		 (I) 		 number of restarts in QP/CG/SCG
  
   MSTJN(39) 		 (I) 		 number of calls to  JNHESS
  
   MSTJN(40) 		  		 not used.
  
  Switches 2, 5, 6, 21, 22, 28, 29, 30, 31, 32, 35 and 36 can be changed at 
  any time by the user.
  
   PARJN is a vector of parameters determining the performance of the 
  feed-forward net:
  
   PARJN(1) 		 (D=0.001) 		 learning rate 
  
   PARJN(2) 		 (D=0.5) 		 momentum parameter 
  
   PARJN(3) 		 (D=1.0) 		 overall inverse network temperature 
  
   PARJN(4) 		 (D=0.1) 		 width  of initial weights
  
  		 		  [- PARJN(4), PARJN(4)]
  
  		 		  [0,- PARJN(4)]
  
   PARJN(5) 		 (D=0.0) 		 weight decay parameter 
  
   PARJN(6) 		 (D=0.0) 		 width  of Gaussian noise in Langevin updating
  
   PARJN(7) 		 (R) 		 last error per node
  
   PARJN(8) 		 (R) 		 mean error in last update
  
   PARJN(9) 		 (R) 		 mean error last epoch (equal to  MSTJN(9) 
  updates)
  
   PARJN(10) 		 (R) 		 weighted average error  used when pruning
  
   PARJN(11) 		 (D=1.0) 		 decrease in  (scale factor per epoch)
  
  		 		  ``bold driver'' dynamics
  
  		 		  geometric decrease
  
   PARJN(12) 		 		 decrease in momentum alpha (scale factor per epoch).
  
   PARJN(13) 		 (D=1.0) 		 decrease in temperature T (scale factor per 
  epoch)
  
   PARJN(14) 		 (D=0.0) 		 pruning parameter 
  
   PARJN(15) 		 (D=) 		 change  of 
  
   PARJN(16) 		 (D=0.9) 		 parameter  used for calculation of 
   PARJN(10)
  
   PARJN(17) 		 (D=0.9) 		 pruning rescaling factor c
  
   PARJN(18) 		 (D=1.0) 		 scale parameter  used in pruning
  
   PARJN(19) 		 (D=0.0) 		 target error D used in pruning
  
   PARJN(20) 		 (D=1.0) 		 decrease in Langevin noise (scale factor per epoch)
  
   PARJN(21) 		 (D=1.75) 		 maximum scale in QP updating
  
   PARJN(22) 		 (D=1000.0) 		 maximum allowed size of weights in QP
  
   PARJN(23) 		 (D=0.0) 		 constant added to  to avoid ``flat spot'' in 
  QP
  
   PARJN(24) 		 (D=0.1) 		 line search convergence parameter 
  
   PARJN(25) 		 (D=0.05) 		 tolerance  of minimum in line search
  
   PARJN(26) 		 (D=0.001) 		  minimum allowed change in error in line search
  
   PARJN(27) 		 (D=2.0) 		 maximum allowed step size in line search
  
   PARJN(28) 		 (D=) 		 constant  used in SCG for computing 
   s
  
   PARJN(29) 		 (D=) 		 initial value for  in SCG
  
   PARJN(30) 		 (D=1.2) 		 scale-up factor  used in Rprop
  
   PARJN(31) 		 (D=0.5) 		  scale-down factor  used in Rprop
  
   PARJN(32) 		 (D=50.0) 		 maximum scale-up factor in Rprop
  
   PARJN(33) 		 (D=) 		 minimum scale-down factor in Rprop
  
   PARJN(34-40) 		 		 not used
  
  All parameters can be changed at any time by the user.
  
  

  (See [1] for descriptions on switches MSTJM and parameters PARJM.)

  OIN is the vector used to pass the values of the input nodes to the program.
  

  OUT is a vector used both to pass the desired value of the output nodes to the program during supervised training and to pass the output produced by the network given an input pattern in OIN.
  

• COMMON /JNDAT2/ TINV(10),IGFN(10),ETAL(10),WIDL(10),SATM(10)

   
   TINV(I) 		 (D=0.0) 		 if greater than 0.0, this value is used as
  inverse temperature 
  
  		 		 in the sigmoid function for layer  I, otherwise the overall inverse
  
  		 		 temperature  PARJN(3) is used. Can be changed at any time by
  
  		 		 the user.
  
   IGFN(I) 		 (D=0) 		 if greater than 0, these switches determine the 
  sigmoid function
  
  		 		 to be used in layer  I, otherwise the overall function 
  determined
  
  		 		by  MSTJN(3) is used. These switches are only active before the
  
  		 		 network is initialized with subroutine  JNINIT.
  
   ETAL(I) 		 (D=0.0) 		 if greater than 0.0, this value is used for the
  learning rate  for
  
  		 		 weights in weight layer  I. The weights between 
  input and first
  
  		 		 hidden layer is considered to be weight layer number one. Can
  
  		 		 be 
  changed at any time by the user.
  
   WIDL(I) 		 (D=0.0) 		 if greater than 0.0, this value is used for the
  width  for initial
  
  		 		 weight values in weight layer  I.
  
   SATM(I) 		 (R) 		 if  MSTJN(22)  0 this vector contains the
  average saturation
  
  		 		 of nodes in layer  I.