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>>> train()

Epoch #    10 | TSS Error: 8.3566 | Correct: 0.1458 | RMS Error: 0.4172

   Epoch #    10, Layer = 'output'     | Units: 0.1458 | Patterns: 0.0000

Epoch #    20 | TSS Error: 6.4829 | Correct: 0.4167 | RMS Error: 0.3675

   Epoch #    20, Layer = 'output'     | Units: 0.4167 | Patterns: 0.3333

Epoch #    30 | TSS Error: 4.8166 | Correct: 0.4375 | RMS Error: 0.3168

   Epoch #    30, Layer = 'output'     | Units: 0.4375 | Patterns: 0.1667

Epoch #    40 | TSS Error: 4.8478 | Correct: 0.4583 | RMS Error: 0.3178

   Epoch #    40, Layer = 'output'     | Units: 0.4583 | Patterns: 0.0833

Epoch #    50 | TSS Error: 5.7972 | Correct: 0.4583 | RMS Error: 0.3475

   Epoch #    50, Layer = 'output'     | Units: 0.4583 | Patterns: 0.0000

Epoch #    60 | TSS Error: 7.3393 | Correct: 0.5625 | RMS Error: 0.3910

   Epoch #    60, Layer = 'output'     | Units: 0.5625 | Patterns: 0.0000

Epoch #    70 | TSS Error: 4.6184 | Correct: 0.6042 | RMS Error: 0.3102

   Epoch #    70, Layer = 'output'     | Units: 0.6042 | Patterns: 0.0833

Epoch #    80 | TSS Error: 3.3136 | Correct: 0.5625 | RMS Error: 0.2627

   Epoch #    80, Layer = 'output'     | Units: 0.5625 | Patterns: 0.0000

Epoch #    90 | TSS Error: 6.5261 | Correct: 0.6250 | RMS Error: 0.3687

   Epoch #    90, Layer = 'output'     | Units: 0.6250 | Patterns: 0.2500

Epoch #   100 | TSS Error: 3.5050 | Correct: 0.6667 | RMS Error: 0.2702

   Epoch #   100, Layer = 'output'     | Units: 0.6667 | Patterns: 0.3333

Epoch #   110 | TSS Error: 4.6672 | Correct: 0.7083 | RMS Error: 0.3118

   Epoch #   110, Layer = 'output'     | Units: 0.7083 | Patterns: 0.3333

Epoch #   120 | TSS Error: 4.4930 | Correct: 0.7500 | RMS Error: 0.3059

   Epoch #   120, Layer = 'output'     | Units: 0.7500 | Patterns: 0.5000

Epoch #   130 | TSS Error: 0.4796 | Correct: 0.7917 | RMS Error: 0.1000

   Epoch #   130, Layer = 'output'     | Units: 0.7917 | Patterns: 0.5000

Epoch #   140 | TSS Error: 1.7687 | Correct: 0.7292 | RMS Error: 0.1920

   Epoch #   140, Layer = 'output'     | Units: 0.7292 | Patterns: 0.3333

Epoch #   150 | TSS Error: 4.0810 | Correct: 0.7083 | RMS Error: 0.2916

   Epoch #   150, Layer = 'output'     | Units: 0.7083 | Patterns: 0.3333

Epoch #   160 | TSS Error: 0.2887 | Correct: 0.7500 | RMS Error: 0.0776

   Epoch #   160, Layer = 'output'     | Units: 0.7500 | Patterns: 0.5000

Epoch #   170 | TSS Error: 0.3464 | Correct: 0.8125 | RMS Error: 0.0849

   Epoch #   170, Layer = 'output'     | Units: 0.8125 | Patterns: 0.5833

Epoch #   180 | TSS Error: 0.1056 | Correct: 0.9375 | RMS Error: 0.0469

   Epoch #   180, Layer = 'output'     | Units: 0.9375 | Patterns: 0.7500

----------------------------------------------------

Final #   182 | TSS Error: 0.0644 | Correct: 1.0000 | RMS Error: 0.0366

   Final #   182, Layer = 'output'     | Units: 1.0000 | Patterns: 1.0000

----------------------------------------------------

>>> test()

Variations	1	2	3	4	5	6	7	8	

		j = 0	result = 1	

		j = 0	result = 1	

		j = 0	result = 1	

		j = 0	result = 1	

		j = 0	result = 1	

		j = 0	result = 1	

		j = 0	result = 1	

		j = 0	result = 1	

		

		j = 1	result = 2	

		j = 1	result = 2	

		j = 1	result = 2	

		j = 1	result = 2	

		j = 1	result = 2	

		j = 1	result = 2	

		j = 1	result = 2	

		j = 1	result = 2	

		

		j = 2	result = 3	

		j = 2	result = 3	

		j = 2	result = 3	

		j = 2	result = 3	

		j = 2	result = 3	

		j = 2	result = 3	

		j = 2	result = 3	

		j = 2	result = 3	

		

		j = 3	result = 4	

		j = 3	result = 4	

		j = 3	result = 4	

		j = 3	result = 4	

		j = 3	result = 4	

		j = 3	result = 4	

		j = 3	result = 4	

		j = 3	result = 4	

		

>>> 

>>> train()

Epoch #    10 | TSS Error: 8.7795 | Correct: 0.2500 | RMS Error: 0.4277

   Epoch #    10, Layer = 'output'     | Units: 0.2500 | Patterns: 0.0000

Epoch #    20 | TSS Error: 9.0301 | Correct: 0.5208 | RMS Error: 0.4337

   Epoch #    20, Layer = 'output'     | Units: 0.5208 | Patterns: 0.1667

Epoch #    30 | TSS Error: 8.8221 | Correct: 0.6042 | RMS Error: 0.4287

   Epoch #    30, Layer = 'output'     | Units: 0.6042 | Patterns: 0.2500

Epoch #    40 | TSS Error: 5.0652 | Correct: 0.5417 | RMS Error: 0.3248

   Epoch #    40, Layer = 'output'     | Units: 0.5417 | Patterns: 0.1667

Epoch #    50 | TSS Error: 8.1240 | Correct: 0.5625 | RMS Error: 0.4114

   Epoch #    50, Layer = 'output'     | Units: 0.5625 | Patterns: 0.2500

Epoch #    60 | TSS Error: 4.9242 | Correct: 0.5208 | RMS Error: 0.3203

   Epoch #    60, Layer = 'output'     | Units: 0.5208 | Patterns: 0.1667

Epoch #    70 | TSS Error: 3.8475 | Correct: 0.6458 | RMS Error: 0.2831

   Epoch #    70, Layer = 'output'     | Units: 0.6458 | Patterns: 0.3333

Epoch #    80 | TSS Error: 4.7748 | Correct: 0.6875 | RMS Error: 0.3154

   Epoch #    80, Layer = 'output'     | Units: 0.6875 | Patterns: 0.3333

Epoch #    90 | TSS Error: 4.1281 | Correct: 0.7083 | RMS Error: 0.2933

   Epoch #    90, Layer = 'output'     | Units: 0.7083 | Patterns: 0.4167

Epoch #   100 | TSS Error: 2.0875 | Correct: 0.8125 | RMS Error: 0.2085

   Epoch #   100, Layer = 'output'     | Units: 0.8125 | Patterns: 0.5000

Epoch #   110 | TSS Error: 1.8829 | Correct: 0.7500 | RMS Error: 0.1981

   Epoch #   110, Layer = 'output'     | Units: 0.7500 | Patterns: 0.5000

Epoch #   120 | TSS Error: 3.4107 | Correct: 0.8125 | RMS Error: 0.2666

   Epoch #   120, Layer = 'output'     | Units: 0.8125 | Patterns: 0.5000

Epoch #   130 | TSS Error: 0.1550 | Correct: 0.8750 | RMS Error: 0.0568

   Epoch #   130, Layer = 'output'     | Units: 0.8750 | Patterns: 0.7500

Epoch #   140 | TSS Error: 0.0925 | Correct: 1.0000 | RMS Error: 0.0439

   Epoch #   140, Layer = 'output'     | Units: 1.0000 | Patterns: 1.0000

----------------------------------------------------

Final #   140 | TSS Error: 0.0925 | Correct: 1.0000 | RMS Error: 0.0439

   Final #   140, Layer = 'output'     | Units: 1.0000 | Patterns: 1.0000

----------------------------------------------------

>>> test()

Variations	1	2	3	4	5	6	7	8	

		j = 0	result = 1	

		j = 0	result = 1	

		j = 0	result = 1	

		j = 0	result = 1	

		j = 0	result = 1	

		j = 0	result = 1	

		j = 0	result = 1	

		j = 0	result = 1	

		

		j = 1	result = 2	

		j = 1	result = 2	

		j = 1	result = 4	X 

		j = 1	result = 4	X 

		j = 1	result = 4	X 

		j = 1	result = 3	X 

		j = 1	result = 2	

		j = 1	result = 2	

		

		j = 2	result = 3	

		j = 2	result = 3	

		j = 2	result = 3	

		j = 2	result = 4	X 

		j = 2	result = 4	X 

		j = 2	result = 3	

		j = 2	result = 4	X 

		j = 2	result = 4	X 

		

		j = 3	result = 4	

		j = 3	result = 4	

		j = 3	result = 4	

		j = 3	result = 4	

		j = 3	result = 4	

		j = 3	result = 4	

		j = 3	result = 4	

		j = 3	result = 4

####################################################################################################################

>>> train()

Epoch #    10 | TSS Error: 9.4577 | Correct: 0.2917 | RMS Error: 0.4439

   Epoch #    10, Layer = 'output'     | Units: 0.2917 | Patterns: 0.0833

Epoch #    20 | TSS Error: 5.7488 | Correct: 0.2083 | RMS Error: 0.3461

   Epoch #    20, Layer = 'output'     | Units: 0.2083 | Patterns: 0.0000

Epoch #    30 | TSS Error: 8.4584 | Correct: 0.3958 | RMS Error: 0.4198

   Epoch #    30, Layer = 'output'     | Units: 0.3958 | Patterns: 0.0000

Epoch #    40 | TSS Error: 7.1423 | Correct: 0.5625 | RMS Error: 0.3857

   Epoch #    40, Layer = 'output'     | Units: 0.5625 | Patterns: 0.1667

Epoch #    50 | TSS Error: 5.6601 | Correct: 0.5000 | RMS Error: 0.3434

   Epoch #    50, Layer = 'output'     | Units: 0.5000 | Patterns: 0.0000

Epoch #    60 | TSS Error: 6.5893 | Correct: 0.6667 | RMS Error: 0.3705

   Epoch #    60, Layer = 'output'     | Units: 0.6667 | Patterns: 0.4167

Epoch #    70 | TSS Error: 7.9724 | Correct: 0.6250 | RMS Error: 0.4075

   Epoch #    70, Layer = 'output'     | Units: 0.6250 | Patterns: 0.2500

Epoch #    80 | TSS Error: 9.8965 | Correct: 0.5833 | RMS Error: 0.4541

   Epoch #    80, Layer = 'output'     | Units: 0.5833 | Patterns: 0.2500

Epoch #    90 | TSS Error: 7.7567 | Correct: 0.5833 | RMS Error: 0.4020

   Epoch #    90, Layer = 'output'     | Units: 0.5833 | Patterns: 0.2500

Epoch #   100 | TSS Error: 3.3880 | Correct: 0.8125 | RMS Error: 0.2657

   Epoch #   100, Layer = 'output'     | Units: 0.8125 | Patterns: 0.5000

Epoch #   110 | TSS Error: 4.2753 | Correct: 0.6875 | RMS Error: 0.2984

   Epoch #   110, Layer = 'output'     | Units: 0.6875 | Patterns: 0.5000

Epoch #   120 | TSS Error: 5.1072 | Correct: 0.7917 | RMS Error: 0.3262

   Epoch #   120, Layer = 'output'     | Units: 0.7917 | Patterns: 0.4167

Epoch #   130 | TSS Error: 3.8045 | Correct: 0.7917 | RMS Error: 0.2815

   Epoch #   130, Layer = 'output'     | Units: 0.7917 | Patterns: 0.5000

----------------------------------------------------

Final #   136 | TSS Error: 0.0875 | Correct: 1.0000 | RMS Error: 0.0427

   Final #   136, Layer = 'output'     | Units: 1.0000 | Patterns: 1.0000

----------------------------------------------------

>>> test()

Variations	1	2	3	4	5	6	7	8	

		j = 0	result = 1	

		j = 0	result = 1	

		j = 0	result = 1	

		j = 0	result = 1	

		j = 0	result = 1	

		j = 0	result = 1	

		j = 0	result = 1	

		j = 0	result = 1	

		

		j = 1	result = 2	

		j = 1	result = 2	

		j = 1	result = 2	

		j = 1	result = 4	X 

		j = 1	result = 4	X 

		j = 1	result = 3	X 

		j = 1	result = 2	

		j = 1	result = 2	

		

		j = 2	result = 3	

		j = 2	result = 3	

		j = 2	result = 3	

		j = 2	result = 4	X 

		j = 2	result = 4	X 

		j = 2	result = 3	

		j = 2	result = 4	X 

		j = 2	result = 4	X 

		

		j = 3	result = 4	

		j = 3	result = 4	

		j = 3	result = 4	

		j = 3	result = 4	

		j = 3	result = 4	

		j = 3	result = 4	

		j = 3	result = 4	

		j = 3	result = 4

Read a little more about networks as well.

5 June 2009 Expanded on yesterday's network, messing with various variables in order to try to get a network that could recognize no shape, circles, triangles, squares, trapezoids, and parallelograms ("shapes" group). Also tried getting the network to recognize shapes regardless of their position in the image ("blah" group"). Both could recognize the shapes each was trained on, but had trouble with the others (if small shapes were moved in the picture, for example, the network would guess that there wasn't a shape in the image):

>>> train()
shape set = shapes
# shapes = 4
tolerance = 0.3
hidden = 50
epsilon = 0.5
Epoch #    10 | TSS Error: 8.7614 | Correct: 0.6458 | RMS Error: 0.4272
   Epoch #    10, Layer = 'output'     | Units: 0.6458 | Patterns: 0.0000
Epoch #    20 | TSS Error: 7.7744 | Correct: 0.6458 | RMS Error: 0.4024
   Epoch #    20, Layer = 'output'     | Units: 0.6458 | Patterns: 0.1667
Epoch #    30 | TSS Error: 5.3608 | Correct: 0.7917 | RMS Error: 0.3342
   Epoch #    30, Layer = 'output'     | Units: 0.7917 | Patterns: 0.4167
Epoch #    40 | TSS Error: 5.5421 | Correct: 0.7083 | RMS Error: 0.3398
   Epoch #    40, Layer = 'output'     | Units: 0.7083 | Patterns: 0.3333
Epoch #    50 | TSS Error: 6.0068 | Correct: 0.8125 | RMS Error: 0.3538
   Epoch #    50, Layer = 'output'     | Units: 0.8125 | Patterns: 0.5000
Epoch #    60 | TSS Error: 4.4176 | Correct: 0.7292 | RMS Error: 0.3034
   Epoch #    60, Layer = 'output'     | Units: 0.7292 | Patterns: 0.4167
Epoch #    70 | TSS Error: 8.2014 | Correct: 0.6250 | RMS Error: 0.4134
   Epoch #    70, Layer = 'output'     | Units: 0.6250 | Patterns: 0.3333
Epoch #    80 | TSS Error: 7.5126 | Correct: 0.7083 | RMS Error: 0.3956
   Epoch #    80, Layer = 'output'     | Units: 0.7083 | Patterns: 0.3333
Epoch #    90 | TSS Error: 5.0271 | Correct: 0.6875 | RMS Error: 0.3236
   Epoch #    90, Layer = 'output'     | Units: 0.6875 | Patterns: 0.4167
Epoch #   100 | TSS Error: 6.3066 | Correct: 0.6875 | RMS Error: 0.3625
   Epoch #   100, Layer = 'output'     | Units: 0.6875 | Patterns: 0.2500
Epoch #   110 | TSS Error: 4.3986 | Correct: 0.7917 | RMS Error: 0.3027
   Epoch #   110, Layer = 'output'     | Units: 0.7917 | Patterns: 0.4167
Epoch #   120 | TSS Error: 4.1323 | Correct: 0.8125 | RMS Error: 0.2934
   Epoch #   120, Layer = 'output'     | Units: 0.8125 | Patterns: 0.4167
Epoch #   130 | TSS Error: 4.2382 | Correct: 0.8542 | RMS Error: 0.2971
   Epoch #   130, Layer = 'output'     | Units: 0.8542 | Patterns: 0.6667
Epoch #   140 | TSS Error: 2.6472 | Correct: 0.9167 | RMS Error: 0.2348
   Epoch #   140, Layer = 'output'     | Units: 0.9167 | Patterns: 0.7500
Epoch #   150 | TSS Error: 2.0734 | Correct: 0.8750 | RMS Error: 0.2078
   Epoch #   150, Layer = 'output'     | Units: 0.8750 | Patterns: 0.7500
Final #   157 | TSS Error: 0.5877 | Correct: 1.0000 | RMS Error: 0.1107
   Final #   157, Layer = 'output'     | Units: 1.0000 | Patterns: 1.0000
>>> test()
Variations		0	1	2	3	4	5	6	7	8	
Shape 0		0 	0 	0 	0 	0 	0 	0 	0 	0 	
Shape 1		1 	1 	1 	3 X 	3 X 	2 X 	3 X 	1 	2 X 	
Shape 2		2 	2 	2 	3 X 	3 X 	2 	3 X 	3 X 	2 	
Shape 3		3 	3 	3 	3 	3 	3 	3 	3 	2 X 	
>>> ================================ RESTART ================================
>>> 
Conx, version 2484 (psyco enabled)
Conx using seed: 1244269222.52
>>> train()
shape set = shapes
# shapes = 5
tolerance = 0.3
hidden = 50
epsilon = 0.5
Epoch #    10 | TSS Error: 11.8153 | Correct: 0.7333 | RMS Error: 0.3969
   Epoch #    10, Layer = 'output'     | Units: 0.7333 | Patterns: 0.0667
Epoch #    20 | TSS Error: 8.0311 | Correct: 0.7867 | RMS Error: 0.3272
   Epoch #    20, Layer = 'output'     | Units: 0.7867 | Patterns: 0.3333
Epoch #    30 | TSS Error: 9.6049 | Correct: 0.7867 | RMS Error: 0.3579
   Epoch #    30, Layer = 'output'     | Units: 0.7867 | Patterns: 0.4667
Epoch #    40 | TSS Error: 7.5708 | Correct: 0.8400 | RMS Error: 0.3177
   Epoch #    40, Layer = 'output'     | Units: 0.8400 | Patterns: 0.5333
Epoch #    50 | TSS Error: 9.0984 | Correct: 0.7600 | RMS Error: 0.3483
   Epoch #    50, Layer = 'output'     | Units: 0.7600 | Patterns: 0.4000
Epoch #    60 | TSS Error: 7.3489 | Correct: 0.8133 | RMS Error: 0.3130
   Epoch #    60, Layer = 'output'     | Units: 0.8133 | Patterns: 0.4000
Epoch #    70 | TSS Error: 8.4983 | Correct: 0.8400 | RMS Error: 0.3366
   Epoch #    70, Layer = 'output'     | Units: 0.8400 | Patterns: 0.5333
Epoch #    80 | TSS Error: 4.7782 | Correct: 0.8533 | RMS Error: 0.2524
   Epoch #    80, Layer = 'output'     | Units: 0.8533 | Patterns: 0.6000
Epoch #    90 | TSS Error: 7.1936 | Correct: 0.8267 | RMS Error: 0.3097
   Epoch #    90, Layer = 'output'     | Units: 0.8267 | Patterns: 0.4667
Epoch #   100 | TSS Error: 9.2659 | Correct: 0.8533 | RMS Error: 0.3515
   Epoch #   100, Layer = 'output'     | Units: 0.8533 | Patterns: 0.6000
Epoch #   110 | TSS Error: 6.6857 | Correct: 0.8667 | RMS Error: 0.2986
   Epoch #   110, Layer = 'output'     | Units: 0.8667 | Patterns: 0.5333
Epoch #   120 | TSS Error: 4.1355 | Correct: 0.8267 | RMS Error: 0.2348
   Epoch #   120, Layer = 'output'     | Units: 0.8267 | Patterns: 0.6000
Epoch #   130 | TSS Error: 6.0246 | Correct: 0.8533 | RMS Error: 0.2834
   Epoch #   130, Layer = 'output'     | Units: 0.8533 | Patterns: 0.5333
Epoch #   140 | TSS Error: 0.8918 | Correct: 0.9333 | RMS Error: 0.1090
   Epoch #   140, Layer = 'output'     | Units: 0.9333 | Patterns: 0.6667
Epoch #   150 | TSS Error: 2.2225 | Correct: 0.9333 | RMS Error: 0.1721
   Epoch #   150, Layer = 'output'     | Units: 0.9333 | Patterns: 0.7333
Final #   153 | TSS Error: 0.4030 | Correct: 1.0000 | RMS Error: 0.0733
   Final #   153, Layer = 'output'     | Units: 1.0000 | Patterns: 1.0000
>>> test()
Variations		0	1	2	3	4	5	6	7	8	
Shape 0		0 	0 	0 	0 	0 	0 	0 	0 	0 	
Shape 1		1 	1 	1 	3 X 	3 X 	2 X 	1 	1 	2 X 	
Shape 2		2 	2 	2 	3 X 	3 X 	2 	3 X 	3 X 	2 	
Shape 3		3 	3 	3 	3 	3 	3 	3 	3 	2 X 	
Shape 4		4 	4 	2 X 	3 X 	3 X 	2 X 	4 	4 	2 X 	
>>> ================================ RESTART ================================
>>> 
Conx, version 2484 (psyco enabled)
Conx using seed: 1244321466.86
>>> train()
shape set = blah
# shapes = 5
tolerance = 0.3
hidden = 50
epsilon = 0.5
Epoch #    10 | TSS Error: 11.8399 | Correct: 0.6933 | RMS Error: 0.3973
   Epoch #    10, Layer = 'output'     | Units: 0.6933 | Patterns: 0.0000
Epoch #    20 | TSS Error: 9.3797 | Correct: 0.7067 | RMS Error: 0.3536
   Epoch #    20, Layer = 'output'     | Units: 0.7067 | Patterns: 0.2000
Epoch #    30 | TSS Error: 8.4218 | Correct: 0.8533 | RMS Error: 0.3351
   Epoch #    30, Layer = 'output'     | Units: 0.8533 | Patterns: 0.4000
Epoch #    40 | TSS Error: 5.2952 | Correct: 0.8800 | RMS Error: 0.2657
   Epoch #    40, Layer = 'output'     | Units: 0.8800 | Patterns: 0.4667
Epoch #    50 | TSS Error: 7.5040 | Correct: 0.8267 | RMS Error: 0.3163
   Epoch #    50, Layer = 'output'     | Units: 0.8267 | Patterns: 0.4000
Epoch #    60 | TSS Error: 4.3835 | Correct: 0.9067 | RMS Error: 0.2418
   Epoch #    60, Layer = 'output'     | Units: 0.9067 | Patterns: 0.6667
Epoch #    70 | TSS Error: 6.3656 | Correct: 0.8267 | RMS Error: 0.2913
   Epoch #    70, Layer = 'output'     | Units: 0.8267 | Patterns: 0.4667
Epoch #    80 | TSS Error: 4.8228 | Correct: 0.9067 | RMS Error: 0.2536
   Epoch #    80, Layer = 'output'     | Units: 0.9067 | Patterns: 0.7333
Epoch #    90 | TSS Error: 4.7728 | Correct: 0.8800 | RMS Error: 0.2523
   Epoch #    90, Layer = 'output'     | Units: 0.8800 | Patterns: 0.6667
Epoch #   100 | TSS Error: 5.2392 | Correct: 0.8533 | RMS Error: 0.2643
   Epoch #   100, Layer = 'output'     | Units: 0.8533 | Patterns: 0.6667
Epoch #   110 | TSS Error: 5.0018 | Correct: 0.8933 | RMS Error: 0.2582
   Epoch #   110, Layer = 'output'     | Units: 0.8933 | Patterns: 0.6667
Epoch #   120 | TSS Error: 2.7895 | Correct: 0.9333 | RMS Error: 0.1929
   Epoch #   120, Layer = 'output'     | Units: 0.9333 | Patterns: 0.8000
Epoch #   130 | TSS Error: 1.3352 | Correct: 0.9600 | RMS Error: 0.1334
   Epoch #   130, Layer = 'output'     | Units: 0.9600 | Patterns: 0.8667
Epoch #   140 | TSS Error: 5.2413 | Correct: 0.8667 | RMS Error: 0.2644
   Epoch #   140, Layer = 'output'     | Units: 0.8667 | Patterns: 0.5333
Epoch #   150 | TSS Error: 3.1829 | Correct: 0.9067 | RMS Error: 0.2060
   Epoch #   150, Layer = 'output'     | Units: 0.9067 | Patterns: 0.7333
Epoch #   160 | TSS Error: 4.2475 | Correct: 0.8533 | RMS Error: 0.2380
   Epoch #   160, Layer = 'output'     | Units: 0.8533 | Patterns: 0.6667
Epoch #   170 | TSS Error: 1.3375 | Correct: 0.9467 | RMS Error: 0.1335
   Epoch #   170, Layer = 'output'     | Units: 0.9467 | Patterns: 0.8667
Epoch #   180 | TSS Error: 3.5071 | Correct: 0.9067 | RMS Error: 0.2162
   Epoch #   180, Layer = 'output'     | Units: 0.9067 | Patterns: 0.7333
Epoch #   190 | TSS Error: 2.5185 | Correct: 0.9067 | RMS Error: 0.1832
   Epoch #   190, Layer = 'output'     | Units: 0.9067 | Patterns: 0.8000
Epoch #   200 | TSS Error: 2.3102 | Correct: 0.9067 | RMS Error: 0.1755
   Epoch #   200, Layer = 'output'     | Units: 0.9067 | Patterns: 0.8000
Epoch #   210 | TSS Error: 2.0388 | Correct: 0.9333 | RMS Error: 0.1649
   Epoch #   210, Layer = 'output'     | Units: 0.9333 | Patterns: 0.8667
Epoch #   220 | TSS Error: 1.2211 | Correct: 0.9600 | RMS Error: 0.1276
   Epoch #   220, Layer = 'output'     | Units: 0.9600 | Patterns: 0.8667
Epoch #   230 | TSS Error: 0.8683 | Correct: 0.9600 | RMS Error: 0.1076
   Epoch #   230, Layer = 'output'     | Units: 0.9600 | Patterns: 0.8667
Epoch #   240 | TSS Error: 1.4609 | Correct: 0.9733 | RMS Error: 0.1396
   Epoch #   240, Layer = 'output'     | Units: 0.9733 | Patterns: 0.9333
Final #   247 | TSS Error: 0.0179 | Correct: 1.0000 | RMS Error: 0.0155
   Final #   247, Layer = 'output'     | Units: 1.0000 | Patterns: 1.0000
>>> test()
Variations		0	1	2	3	4	5	6	7	8	
Shape 0		0 	0 	0 	0 	0 	0 	0 	0 	0 	
Shape 1		1 	1 	1 	3 X 	1 	0 X 	3 X 	0 X 	0 X 	
Shape 2		2 	2 	2 	3 X 	3 X 	0 X 	3 X 	0 X 	0 X 	
Shape 3		3 	3 	3 	3 	1 X 	0 X 	3 	0 X 	0 X 	
Shape 4		4 	4 	4 	1 X 	0 X 	0 X 	2 X 	0 X 	0 X 	


 
>>> ================================ RESTART ================================
>>> 
Conx, version 2484 (psyco enabled)
Conx using seed: 1244317391.99
>>> train()
shape set = blah
# shapes = 5
tolerance = 0.3
hidden = 50
epsilon = 0.5
Epoch #    10 | TSS Error: 12.6897 | Correct: 0.6933 | RMS Error: 0.4113
   Epoch #    10, Layer = 'output'     | Units: 0.6933 | Patterns: 0.0000
Epoch #    20 | TSS Error: 7.0607 | Correct: 0.8667 | RMS Error: 0.3068
   Epoch #    20, Layer = 'output'     | Units: 0.8667 | Patterns: 0.5333
Epoch #    30 | TSS Error: 8.8573 | Correct: 0.7600 | RMS Error: 0.3437
   Epoch #    30, Layer = 'output'     | Units: 0.7600 | Patterns: 0.3333
Epoch #    40 | TSS Error: 4.2339 | Correct: 0.8400 | RMS Error: 0.2376
   Epoch #    40, Layer = 'output'     | Units: 0.8400 | Patterns: 0.6667
Epoch #    50 | TSS Error: 7.4077 | Correct: 0.8400 | RMS Error: 0.3143
   Epoch #    50, Layer = 'output'     | Units: 0.8400 | Patterns: 0.5333
Epoch #    60 | TSS Error: 4.1317 | Correct: 0.8933 | RMS Error: 0.2347
   Epoch #    60, Layer = 'output'     | Units: 0.8933 | Patterns: 0.6667
Epoch #    70 | TSS Error: 2.6337 | Correct: 0.9467 | RMS Error: 0.1874
   Epoch #    70, Layer = 'output'     | Units: 0.9467 | Patterns: 0.8000
Epoch #    80 | TSS Error: 4.8804 | Correct: 0.9200 | RMS Error: 0.2551
   Epoch #    80, Layer = 'output'     | Units: 0.9200 | Patterns: 0.7333
Epoch #    90 | TSS Error: 3.4393 | Correct: 0.9067 | RMS Error: 0.2141
   Epoch #    90, Layer = 'output'     | Units: 0.9067 | Patterns: 0.7333
Epoch #   100 | TSS Error: 3.4462 | Correct: 0.9067 | RMS Error: 0.2144
   Epoch #   100, Layer = 'output'     | Units: 0.9067 | Patterns: 0.7333
Epoch #   110 | TSS Error: 3.8030 | Correct: 0.8667 | RMS Error: 0.2252
   Epoch #   110, Layer = 'output'     | Units: 0.8667 | Patterns: 0.6000
Epoch #   120 | TSS Error: 1.4750 | Correct: 0.9600 | RMS Error: 0.1402
   Epoch #   120, Layer = 'output'     | Units: 0.9600 | Patterns: 0.8667
Epoch #   130 | TSS Error: 2.7820 | Correct: 0.9333 | RMS Error: 0.1926
   Epoch #   130, Layer = 'output'     | Units: 0.9333 | Patterns: 0.8000
Final #   138 | TSS Error: 0.3038 | Correct: 1.0000 | RMS Error: 0.0636
   Final #   138, Layer = 'output'     | Units: 1.0000 | Patterns: 1.0000
>>> test()
Variations		0	1	2	3	4	5	6	7	8	
Shape 0		2 X 	2 X 	2 X 	2 X 	2 X 	2 X 	2 X 	2 X 	2 X 	
Shape 1		0 X 	0 X 	2 X 	2 X 	2 X 	2 X 	2 X 	2 X 	2 X 	
Shape 2		1 X 	1 X 	1 X 	2 	2 	2 	2 	2 	2 	
Shape 3		2 X 	2 X 	2 X 	2 X 	2 X 	2 X 	2 X 	3 	2 X 	
Shape 4		3 X 	3 X 	3 X 	0 X 	4 	2 X 	1 X 	3 X 	2 X 
	
>>> ================================ RESTART ================================
>>> 
Conx, version 2484 (psyco enabled)
Conx using seed: 1244229005.5
>>> train()
shape set = blah
# shapes = 5
tolerance = 0.3
hidden = 50
epsilon = 0.5
Epoch #    10 | TSS Error: 12.4417 | Correct: 0.7333 | RMS Error: 0.4073
   Epoch #    10, Layer = 'output'     | Units: 0.7333 | Patterns: 0.0667
Epoch #    20 | TSS Error: 10.7405 | Correct: 0.7467 | RMS Error: 0.3784
   Epoch #    20, Layer = 'output'     | Units: 0.7467 | Patterns: 0.0000
Epoch #    30 | TSS Error: 5.0452 | Correct: 0.8933 | RMS Error: 0.2594
   Epoch #    30, Layer = 'output'     | Units: 0.8933 | Patterns: 0.5333
Epoch #    40 | TSS Error: 8.5339 | Correct: 0.8667 | RMS Error: 0.3373
   Epoch #    40, Layer = 'output'     | Units: 0.8667 | Patterns: 0.3333
Epoch #    50 | TSS Error: 3.3184 | Correct: 0.9467 | RMS Error: 0.2103
   Epoch #    50, Layer = 'output'     | Units: 0.9467 | Patterns: 0.7333
Epoch #    60 | TSS Error: 4.9180 | Correct: 0.9200 | RMS Error: 0.2561
   Epoch #    60, Layer = 'output'     | Units: 0.9200 | Patterns: 0.6000
Epoch #    70 | TSS Error: 7.1104 | Correct: 0.8667 | RMS Error: 0.3079
   Epoch #    70, Layer = 'output'     | Units: 0.8667 | Patterns: 0.5333
Epoch #    80 | TSS Error: 3.2825 | Correct: 0.9467 | RMS Error: 0.2092
   Epoch #    80, Layer = 'output'     | Units: 0.9467 | Patterns: 0.7333
Epoch #    90 | TSS Error: 2.1907 | Correct: 0.9600 | RMS Error: 0.1709
   Epoch #    90, Layer = 'output'     | Units: 0.9600 | Patterns: 0.8000
Epoch #   100 | TSS Error: 2.5514 | Correct: 0.9333 | RMS Error: 0.1844
   Epoch #   100, Layer = 'output'     | Units: 0.9333 | Patterns: 0.8000
Epoch #   110 | TSS Error: 2.5748 | Correct: 0.9333 | RMS Error: 0.1853
   Epoch #   110, Layer = 'output'     | Units: 0.9333 | Patterns: 0.8000
Epoch #   120 | TSS Error: 2.5978 | Correct: 0.9467 | RMS Error: 0.1861
   Epoch #   120, Layer = 'output'     | Units: 0.9467 | Patterns: 0.8000
Epoch #   130 | TSS Error: 2.6229 | Correct: 0.9333 | RMS Error: 0.1870
   Epoch #   130, Layer = 'output'     | Units: 0.9333 | Patterns: 0.8000
Epoch #   140 | TSS Error: 3.7036 | Correct: 0.9200 | RMS Error: 0.2222
   Epoch #   140, Layer = 'output'     | Units: 0.9200 | Patterns: 0.7333
Epoch #   150 | TSS Error: 2.3653 | Correct: 0.9333 | RMS Error: 0.1776
   Epoch #   150, Layer = 'output'     | Units: 0.9333 | Patterns: 0.8000
Epoch #   160 | TSS Error: 2.7573 | Correct: 0.9467 | RMS Error: 0.1917
   Epoch #   160, Layer = 'output'     | Units: 0.9467 | Patterns: 0.8667
Final #   166 | TSS Error: 0.0244 | Correct: 1.0000 | RMS Error: 0.0180
   Final #   166, Layer = 'output'     | Units: 1.0000 | Patterns: 1.0000
>>> test()
Variations		0	1	2	3	4	5	6	7	8	
Shape 0		0 	0 	0 	0 	0 	0 	0 	0 	0 	
Shape 1		1 	1 	1 	1 	1 	0 X 	1 	0 X 	0 X 	
Shape 2		2 	2 	2 	1 X 	2 	0 X 	1 X 	0 X 	0 X 	
Shape 3		3 	3 	3 	1 X 	0 X 	0 X 	1 X 	0 X 	0 X 	
Shape 4		4 	4 	4 	1 X 	0 X 	0 X 	2 X 	2 X 	0 X