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Function Synopsis

[E, Y, VOut, WOut] = lyngby_nn_etrain(X, T, VOld, WOld, Reg, ...

Help text

 lyngby_nn_etrain     - Entropic neural network, training 

       function [E, Y, VOut, WOut] = lyngby_nn_etrain(X, T, VOld, ...
           WOld, Reg, 'PropertyName', 'PropertyValue')

       Input:    X       Neural network input
                 T       Target output 
		  VOld    Old input weights
		  WOld    Old output weights
                 Reg     Regularization (weight decay)

       Property: MaxIteration  {200} Iteration stop criterion
                 Method        Optimization type
                 MinCost       {0} Iteration stop criterion
                 MinGradient   {10^(-7)} Iteration stop criterion
                 WeightAcc     [ {0} | ~0 ] Accumulate weights
                 Info          [ {0} | ~0 ] Reporting of
                               costfunction and gradient

       Output:   E     Entropic error (cost without regularization)
                 Y     Computed Outputs
                 WOut  New trained output weights or accumulated
		        weights (depending on 'WeightAcc')
                 VOut  New trained hidden weights or accumulated
                       weights (depending on 'WeightAcc') 

       This function trains a neural network, either pruned or fully
       connected. It will continue until one of the stop criterions
       are meet: maxIteration is the number of epochs (optimization
       steps), minCost is the highest acceptable value for the cost
       function, minGradient is the hightest acceptable value for the
       norm of the gradient.

 $Id: lyngby_nn_etrain.m,v 1.12 2002/03/18 18:15:36 fnielsen Exp $

Cross-Reference Information

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Produced by mat2html on Wed Jul 29 15:43:40 2009
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