xor.chk

著名的神经网络工具箱

=============================================================================
	This file contains data to test the learning functions
=============================================================================

Network-Files:	xor_untrained.net
==============	

Pattern-Files:	xor.pat
==============	

Description:	- load network xor_untrained.net	
============	- load patterns xor.pat	
		- do NOT press init button
		- set cycles to 50
		- set shuffle off 
		- for each test set:
			- set learing function given in test set.
			- set learning parameters given in test set.
			- set update function given in test set.
			- set update parameters given in test set.
			- press the all button
			- compare the results with the given results 
			- reload network xor_untrained.net	

Test set 1)
===========

learning function  : Std_Backpropagation          0.2   0.0   0.0   0.0   0.0
update function    : Topological_Order		  0.0   0.0   0.0   0.0   0.0

  epoch:         SSE            MSE            SSE/o-units
     50:       1.48720        0.37180           1.48720
     45:       1.35070        0.33767           1.35070
     40:       1.21259        0.30315           1.21259
     35:       1.11305        0.27826           1.11305
     30:       1.06212        0.26553           1.06212
     25:       1.04107        0.26027           1.04107
     20:       1.03299        0.25825           1.03299
     15:       1.02982        0.25745           1.02982
     10:       1.02845        0.25711           1.02845
      5:       1.02776        0.25694           1.02776
      1:       1.02740        0.25685           1.02740



Test set 2)
===========

learning function  : Backpropagation with Momentum 0.8   0.6   0.1   0.0   0.0
update function    : Topological_Order		   0.0   0.0   0.0   0.0   0.0

  epoch:         SSE            MSE            SSE/o-units

     50:       1.50424        0.37606           1.50424
     45:       1.15013        0.28753           1.15013
     40:       1.14990        0.28747           1.14990
     35:       1.14882        0.28720           1.14882
     30:       1.14758        0.28690           1.14758
     25:       1.14595        0.28649           1.14595
     20:       1.14361        0.28590           1.14361
     15:       1.14019        0.28505           1.14019
     10:       1.13513        0.28378           1.13513
      5:       1.12771        0.28193           1.12771
      1:       1.11948        0.27987           1.11948


Test set 3)
===========

learning function  : Quickprop                     0.2   1.75  0.0001 0.0   0.0
update function    : Topological_Order		   0.0   0.0   0.0    0.0   0.0

  epoch:         SSE            MSE            SSE/o-units

     50:       1.48601        0.37150           1.48601
     45:       0.99689        0.24922           0.99689
     40:       0.99322        0.24831           0.99322
     35:       0.94188        0.23547           0.94188
     30:       0.75239        0.18810           0.75239
     25:       0.67531        0.16883           0.67531
     20:       0.44633        0.11158           0.44633
     15:       0.39025        0.09756           0.39025
     10:       0.31539        0.07885           0.31539
      5:       0.20783        0.05196           0.20783
      1:       0.17218        0.04305           0.17218


Test set 4)
===========

learning function  : Rprop                         0.2   0.0   0.0   0.0   0.0
update function    : Topological_Order		   0.0   0.0   0.0   0.0   0.0

  epoch:         SSE            MSE            SSE/o-units


     50:       1.48720        0.37180           1.48720
     45:       1.35069        0.33767           1.35069
     40:       1.21258        0.30315           1.21258
     35:       1.11305        0.27826           1.11305
     30:       1.06212        0.26553           1.06212
     25:       1.04107        0.26027           1.04107
     20:       1.03299        0.25825           1.03299
     15:       1.02982        0.25745           1.02982
     10:       1.02845        0.25711           1.02845
      5:       1.02776        0.25694           1.02776
      1:       1.02740        0.25685           1.02740