Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase
Using Seed 6873818038393424896
In this apply, we use a network to learn this target input, given it's pre-evaluated output:
TrainingTester.java:332 executed in 0.00 seconds (0.000 gc):
return RefArrays.stream(RefUtil.addRef(input_target)).flatMap(RefArrays::stream).map(x -> {
try {
return x.prettyPrint();
} finally {
x.freeRef();
}
}).reduce((a, b) -> a + "\n" + b).orElse("");
Returns
[ 0.7, 0.08, -0.128 ]
[ 0.08, -0.128, 0.7 ]
[ 0.7, 0.08, -0.128 ]
[ -0.128, 0.7, 0.08 ]
[ 0.7, -0.128, 0.08 ]
First, we train using basic gradient descent method apply weak line search conditions.
TrainingTester.java:480 executed in 0.33 seconds (0.000 gc):
IterativeTrainer iterativeTrainer = new IterativeTrainer(trainable.addRef());
try {
iterativeTrainer.setLineSearchFactory(label -> new ArmijoWolfeSearch());
iterativeTrainer.setOrientation(new GradientDescent());
iterativeTrainer.setMonitor(TrainingTester.getMonitor(history));
iterativeTrainer.setTimeout(30, TimeUnit.SECONDS);
iterativeTrainer.setMaxIterations(250);
iterativeTrainer.setTerminateThreshold(0);
return iterativeTrainer.run();
} finally {
iterativeTrainer.freeRef();
}
Reset training subject: 1967636850230
BACKPROP_AGG_SIZE = 3
THREADS = 64
SINGLE_THREADED = false
Initialized CoreSettings = {
"backpropAggregationSize" : 3,
"jvmThreads" : 64,
"singleThreaded" : false
}
Reset training subject: 1967670227278
Constructing line search parameters: GD
th(0)=12.542898623723326;dx=-5.9056000000000004E22
New Minimum: 12.542898623723326 > 0.03072951215421036
Armijo: th(2.154434690031884)=0.03072951215421036; dx=-5.905600000028649E10 evalInputDelta=12.512169111569117
Armijo: th(1.077217345015942)=0.061692781280216914; dx=-5.905600000029825E10 evalInputDelta=12.48120584244311
Armijo: th(0.3590724483386473)=0.15589428575146805; dx=-5.905600000042532E10 evalInputDelta=12.387004337971858
Armijo: th(0.08976811208466183)=0.33355502351189414; dx=-5.905600000113872E10 evalInputDelta=12.209343600211433
Armijo: th(0.017953622416932366)=0.44725559313631147; dx=-5.90560000023163E10 evalInputDelta=12.095643030587015
Armijo: th(0.002992270402822061)=0.48653576877380794; dx=-5.905600000299476E10 evalInputDelta=12.05636285494952
Armijo: th(4.2746720040315154E-4)=0.4944186582516769; dx=-5.9056000003155075E10 evalInputDelta=12.04847996547165
Armijo: th(5.343340005039394E-5)=0.4956034054134074; dx=-5.905600000317995E10 evalInputDelta=12.04729521830992
Armijo: th(5.9370444500437714E-6)=0.4957545175152256; dx=-5.9056000003183136E10 evalInputDelta=12.047144106208101
Armijo: th(5.937044450043771E-7)=0.4957715271055691; dx=-5.90560000031835E10 evalInputDelta=12.047127096617757
Armijo: th(5.397313136403428E-8)=0.4957732453526928; dx=-5.905600000318353E10 evalInputDelta=12.047125378370634
Armijo: th(4.4977609470028565E-9)=0.49577340285965865; dx=-5.905600000318353E10 evalInputDelta=12.047125220863668
Armijo: th(3.4598161130791205E-10)=0.49577341607703385; dx=-5.905600000318353E10 evalInputDelta=12.047125207646292
Armijo: th(2.4712972236279432E-11)=0.5168771188316795; dx=-5.975734019221755E10 evalInputDelta=12.026021504891647
Armijo: th(1.6475314824186289E-12)=1.893952378565147; dx=-2.5600000057279568E20 evalInputDelta=10.648946245158179
Armijo: th(1.029707176511643E-13)=12.542898623723; dx=-5.9056000000000004E22 evalInputDelta=3.268496584496461E-13
Armijo: th(6.057101038303783E-15)=12.542898623723309; dx=-5.9056000000000004E22 evalInputDelta=1.7763568394002505E-14
MIN ALPHA (3.3650561323909904E-16): th(2.154434690031884)=0.03072951215421036
Fitness changed from 12.542898623723326 to 0.03072951215421036
Iteration 1 complete. Error: 0.03072951215421036 Total: 0.1431; Orientation: 0.0032; Line Search: 0.0967
th(0)=0.03072951215421036;dx=-0.10112970423055659
New Minimum: 0.03072951215421036 > 0.030729512154210353
WOLFE (weak): th(2.154434690031884E-15)=0.030729512154210353; dx=-0.10112970423055659 evalInputDelta=6.938893903907228E-18
New Minimum: 0.030729512154210353 > 0.030729512154210346
WOLFE (weak): th(4.308869380063768E-15)=0.030729512154210346; dx=-0.10112970423055659 evalInputDelta=1.3877787807814457E-17
New Minimum: 0.030729512154210346 > 0.03072951215421032
WOLFE (weak): th(1.2926608140191303E-14)=0.03072951215421032; dx=-0.10112970423055659 evalInputDelta=4.163336342344337E-17
New Minimum: 0.03072951215421032 > 0.030729512154210204
WOLFE (weak): th(5.1706432560765214E-14)=0.030729512154210204; dx=-0.10112970423055659 evalInputDelta=1.5612511283791264E-16
New Minimum: 0.030729512154210204 > 0.030729512154209614
WOLFE (weak): th(2.5853216280382605E-13)=0.030729512154209614; dx=-0.10112970423055656 evalInputDelta=7.4593109467002705E-16
New Minimum: 0.030729512154209614 > 0.030729512154205895
WOLFE (weak): th(1.5511929768229563E-12)=0.030729512154205895; dx=-0.10112970423055637 evalInputDelta=4.4651782271643015E-15
New Minimum: 0.030729512154205895 > 0.03072951215417915
WOLFE (weak): th(1.0858350837760695E-11)=0.03072951215417915; dx=-0.101129704230555 evalInputDelta=3.1211144779774713E-14
New Minimum: 0.03072951215417915 > 0.030729512153960727
WOLFE (weak): th(8.686680670208556E-11)=0.030729512153960727; dx=-0.10112970423054389 evalInputDelta=2.4963364708696645E-13
New Minimum: 0.030729512153960727 > 0.030729512151963696
WOLFE (weak): th(7.8180126031877E-10)=0.030729512151963696; dx=-0.10112970423044226 evalInputDelta=2.2466646598662265E-12
New Minimum: 0.030729512151963696 > 0.030729512131743703
WOLFE (weak): th(7.818012603187701E-9)=0.030729512131743703; dx=-0.10112970422941324 evalInputDelta=2.246665700700312E-11
New Minimum: 0.030729512131743703 > 0.03072951190707714
WOLFE (weak): th(8.599813863506471E-8)=0.03072951190707714; dx=-0.10112970421797964 evalInputDelta=2.4713322013814043E-10
New Minimum: 0.03072951190707714 > 0.030729509188611864
WOLFE (weak): th(1.0319776636207765E-6)=0.030729509188611864; dx=-0.10112970407963301 evalInputDelta=2.965598495940913E-9
New Minimum: 0.030729509188611864 > 0.030729473601442004
WOLFE (weak): th(1.3415709627070094E-5)=0.030729473601442004; dx=-0.10112970226855134 evalInputDelta=3.8552768356209244E-8
New Minimum: 0.030729473601442004 > 0.03072897241784871
WOLFE (weak): th(1.878199347789813E-4)=0.03072897241784871; dx=-0.10112967676274873 evalInputDelta=5.397363616495288E-7
New Minimum: 0.03072897241784871 > 0.030721416650426304
WOLFE (weak): th(0.0028172990216847197)=0.030721416650426304; dx=-0.10112929227352566 evalInputDelta=8.095503784055769E-6
New Minimum: 0.030721416650426304 > 0.030600123141941094
WOLFE (weak): th(0.045076784346955515)=0.030600123141941094; dx=-0.10112312832871292 evalInputDelta=1.2938901226926605E-4
New Minimum: 0.030600123141941094 > 0.028569139606479905
WOLFE (weak): th(0.7663053338982437)=0.028569139606479905; dx=-0.1010221947415572 evalInputDelta=0.0021603725477304554
New Minimum: 0.028569139606479905 > 0.0018823644871977182
WOLFE (weak): th(13.793496010168386)=0.0018823644871977182; dx=-0.10000225442001724 evalInputDelta=0.028847147667012643
New Minimum: 0.0018823644871977182 > 0.0
WOLFE (weak): th(262.07642419319933)=0.0; dx=-0.09994153397254872 evalInputDelta=0.03072951215421036
WOLFE (weak): th(5241.528483863986)=0.0; dx=-0.09994153397254872 evalInputDelta=0.03072951215421036
WOLFE (weak): th(110072.09816114372)=0.0; dx=-0.09994153397254872 evalInputDelta=0.03072951215421036
Armijo: th(2421586.1595451618)=0.0; dx=-0.09994153397254872 evalInputDelta=0.03072951215421036
Armijo: th(1265829.1288531527)=0.0; dx=-0.09994153397254872 evalInputDelta=0.03072951215421036
Armijo: th(687950.6135071482)=0.0; dx=-0.09994153397254872 evalInputDelta=0.03072951215421036
Armijo: th(399011.355834146)=0.0; dx=-0.09994153397254872 evalInputDelta=0.03072951215421036
WOLFE (weak): th(254541.72699764484)=0.0; dx=-0.09994153397254872 evalInputDelta=0.03072951215421036
Armijo: th(326776.5414158954)=0.0; dx=-0.09994153397254872 evalInputDelta=0.03072951215421036
WOLFE (weak): th(290659.1342067701)=0.0; dx=-0.09994153397254872 evalInputDelta=0.03072951215421036
Armijo: th(308717.8378113328)=0.0; dx=-0.09994153397254872 evalInputDelta=0.03072951215421036
WOLFE (weak): th(299688.48600905144)=0.0; dx=-0.09994153397254872 evalInputDelta=0.03072951215421036
Armijo: th(304203.1619101921)=0.0; dx=-0.09994153397254872 evalInputDelta=0.03072951215421036
WOLFE (weak): th(301945.8239596218)=0.0; dx=-0.09994153397254872 evalInputDelta=0.03072951215421036
mu ~= nu (301945.8239596218): th(262.07642419319933)=0.0
Fitness changed from 0.03072951215421036 to 0.0
Iteration 2 complete. Error: 0.0 Total: 0.1241; Orientation: 0.0010; Line Search: 0.1193
th(0)=0.0;dx=-0.09927999999999998
Armijo: th(652954.2012427867)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Armijo: th(326477.10062139336)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Armijo: th(108825.70020713111)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Armijo: th(27206.42505178278)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Armijo: th(5441.285010356556)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Armijo: th(906.880835059426)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Armijo: th(129.55440500848943)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Armijo: th(16.194300626061178)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Armijo: th(1.7993667362290198)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Armijo: th(0.17993667362290197)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Armijo: th(0.016357879420263816)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Armijo: th(0.001363156618355318)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Armijo: th(1.0485820141194754E-4)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Armijo: th(7.489871529424824E-6)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Armijo: th(4.993247686283216E-7)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Armijo: th(3.12077980392701E-8)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Armijo: th(1.8357528258394178E-9)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Armijo: th(1.0198626810218987E-10)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Armijo: th(5.367698321167888E-12)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Armijo: th(2.683849160583944E-13)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Armijo: th(1.2780234098018781E-14)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
MIN ALPHA (5.809197317281265E-16): th(0.0)=0.0
Fitness changed from 0.0 to 0.0
Static Iteration Total: 0.0524; Orientation: 0.0008; Line Search: 0.0497
Iteration 3 failed. Error: 0.0
Previous Error: 0.0 -> 0.0
Optimization terminated 3
Final threshold in iteration 3: 0.0 (> 0.0) after 0.320s (< 30.000s)
Returns
0.0
First, we use a conjugate gradient descent method, which converges the fastest for purely linear functions.
TrainingTester.java:452 executed in 0.11 seconds (0.000 gc):
IterativeTrainer iterativeTrainer = new IterativeTrainer(trainable.addRef());
try {
iterativeTrainer.setLineSearchFactory(label -> new QuadraticSearch());
iterativeTrainer.setOrientation(new GradientDescent());
iterativeTrainer.setMonitor(TrainingTester.getMonitor(history));
iterativeTrainer.setTimeout(30, TimeUnit.SECONDS);
iterativeTrainer.setMaxIterations(250);
iterativeTrainer.setTerminateThreshold(0);
return iterativeTrainer.run();
} finally {
iterativeTrainer.freeRef();
}
Reset training subject: 1967963106972
Reset training subject: 1967968459186
Constructing line search parameters: GD
F(0.0) = LineSearchPoint{point=PointSample{avg=12.542898623723326}, derivative=-5.9056000000000004E22}
New Minimum: 12.542898623723326 > 0.4957734168601274
F(1.0E-10) = LineSearchPoint{point=PointSample{avg=0.4957734168601274}, derivative=-5.905600000318353E10}, evalInputDelta = -12.0471252068632
New Minimum: 0.4957734168601274 > 0.4957734149500005
F(7.000000000000001E-10) = LineSearchPoint{point=PointSample{avg=0.4957734149500005}, derivative=-5.905600000318353E10}, evalInputDelta = -12.047125208773325
New Minimum: 0.4957734149500005 > 0.4957734015791127
F(4.900000000000001E-9) = LineSearchPoint{point=PointSample{avg=0.4957734015791127}, derivative=-5.905600000318353E10}, evalInputDelta = -12.047125222144214
New Minimum: 0.4957734015791127 > 0.4957733079829314
F(3.430000000000001E-8) = LineSearchPoint{point=PointSample{avg=0.4957733079829314}, derivative=-5.905600000318353E10}, evalInputDelta = -12.047125315740395
New Minimum: 0.4957733079829314 > 0.49577265281128896
F(2.4010000000000004E-7) = LineSearchPoint{point=PointSample{avg=0.49577265281128896}, derivative=-5.905600000318352E10}, evalInputDelta = -12.047125970912038
New Minimum: 0.49577265281128896 > 0.49576806668948636
F(1.6807000000000003E-6) = LineSearchPoint{point=PointSample{avg=0.49576806668948636}, derivative=-5.9056000003183426E10}, evalInputDelta = -12.04713055703384
New Minimum: 0.49576806668948636 > 0.49573596774124146
F(1.1764900000000001E-5) = LineSearchPoint{point=PointSample{avg=0.49573596774124146}, derivative=-5.905600000318274E10}, evalInputDelta = -12.047162655982085
New Minimum: 0.49573596774124146 > 0.49551146618965597
F(8.235430000000001E-5) = LineSearchPoint{point=PointSample{avg=0.49551146618965597}, derivative=-5.905600000317801E10}, evalInputDelta = -12.04738715753367
New Minimum: 0.49551146618965597 > 0.49394924102822524
F(5.764801000000001E-4) = LineSearchPoint{point=PointSample{avg=0.49394924102822524}, derivative=-5.905600000314528E10}, evalInputDelta = -12.0489493826951
New Minimum: 0.49394924102822524 > 0.48344371681393355
F(0.004035360700000001) = LineSearchPoint{point=PointSample{avg=0.48344371681393355}, derivative=-5.905600000293428E10}, evalInputDelta = -12.059454906909393
New Minimum: 0.48344371681393355 > 0.4250756455754243
F(0.028247524900000005) = LineSearchPoint{point=PointSample{avg=0.4250756455754243}, derivative=-5.90560000020083E10}, evalInputDelta = -12.117822978147903
New Minimum: 0.4250756455754243 > 0.24087142350333685
F(0.19773267430000002) = LineSearchPoint{point=PointSample{avg=0.24087142350333685}, derivative=-5.9056000000667496E10}, evalInputDelta = -12.302027200219989
New Minimum: 0.24087142350333685 > 0.04625788275853439
F(1.3841287201) = LineSearchPoint{point=PointSample{avg=0.04625788275853439}, derivative=-5.905600000029259E10}, evalInputDelta = -12.496640740964793
New Minimum: 0.04625788275853439 > 0.002617448459314281
F(9.688901040700001) = LineSearchPoint{point=PointSample{avg=0.002617448459314281}, derivative=-5.905600000028018E10}, evalInputDelta = -12.540281175264012
New Minimum: 0.002617448459314281 > 0.0
F(67.8223072849) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-5.905600000028012E10}, evalInputDelta = -12.542898623723326
F(474.7561509943) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-5.905600000028012E10}, evalInputDelta = -12.542898623723326
F(3323.2930569601003) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-5.905600000028012E10}, evalInputDelta = -12.542898623723326
F(23263.0513987207) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-5.905600000028012E10}, evalInputDelta = -12.542898623723326
F(162841.3597910449) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-5.905600000028012E10}, evalInputDelta = -12.542898623723326
F(1139889.5185373144) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-5.905600000028012E10}, evalInputDelta = -12.542898623723326
F(7979226.6297612) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-5.905600000028012E10}, evalInputDelta = -12.542898623723326
F(5.58545864083284E7) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-5.905600000028012E10}, evalInputDelta = -12.542898623723326
F(3.909821048582988E8) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-5.905600000028012E10}, evalInputDelta = -12.542898623723326
F(2.7368747340080914E9) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-5.905600000028012E10}, evalInputDelta = -12.542898623723326
F(1.915812313805664E10) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-5.905600000028012E10}, evalInputDelta = -12.542898623723326
0.0 <= 12.542898623723326
F(1.0E10) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-5.905600000028012E10}, evalInputDelta = -12.542898623723326
Right bracket at 1.0E10
Converged to right
Fitness changed from 12.542898623723326 to 0.0
Iteration 1 complete. Error: 0.0 Total: 0.0668; Orientation: 0.0006; Line Search: 0.0582
F(0.0) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.09927999999999998}
F(1.0E10) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.09927999999999998}, evalInputDelta = 0.0
0.0 <= 0.0
F(5.0E9) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.09927999999999998}, evalInputDelta = 0.0
Right bracket at 5.0E9
F(2.5E9) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.09927999999999998}, evalInputDelta = 0.0
Right bracket at 2.5E9
F(1.25E9) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.09927999999999998}, evalInputDelta = 0.0
Right bracket at 1.25E9
F(6.25E8) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.09927999999999998}, evalInputDelta = 0.0
Right bracket at 6.25E8
F(3.125E8) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.09927999999999998}, evalInputDelta = 0.0
Right bracket at 3.125E8
F(1.5625E8) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.09927999999999998}, evalInputDelta = 0.0
Right bracket at 1.5625E8
F(7.8125E7) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.09927999999999998}, evalInputDelta = 0.0
Right bracket at 7.8125E7
F(3.90625E7) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.09927999999999998}, evalInputDelta = 0.0
Right bracket at 3.90625E7
F(1.953125E7) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.09927999999999998}, evalInputDelta = 0.0
Right bracket at 1.953125E7
F(9765625.0) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.09927999999999998}, evalInputDelta = 0.0
Right bracket at 9765625.0
F(4882812.5) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.09927999999999998}, evalInputDelta = 0.0
Right bracket at 4882812.5
F(2441406.25) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.09927999999999998}, evalInputDelta = 0.0
Loops = 12
Fitness changed from 0.0 to 0.0
Static Iteration Total: 0.0438; Orientation: 0.0005; Line Search: 0.0419
Iteration 2 failed. Error: 0.0
Previous Error: 0.0 -> 0.0
Optimization terminated 2
Final threshold in iteration 2: 0.0 (> 0.0) after 0.111s (< 30.000s)
Returns
0.0
Next, we apply the same optimization using L-BFGS, which is nearly ideal for purely second-order or quadratic functions.
TrainingTester.java:509 executed in 0.56 seconds (0.000 gc):
IterativeTrainer iterativeTrainer = new IterativeTrainer(trainable.addRef());
try {
iterativeTrainer.setLineSearchFactory(label -> new ArmijoWolfeSearch());
iterativeTrainer.setOrientation(new LBFGS());
iterativeTrainer.setMonitor(TrainingTester.getMonitor(history));
iterativeTrainer.setTimeout(30, TimeUnit.SECONDS);
iterativeTrainer.setIterationsPerSample(100);
iterativeTrainer.setMaxIterations(250);
iterativeTrainer.setTerminateThreshold(0);
return iterativeTrainer.run();
} finally {
iterativeTrainer.freeRef();
}
Reset training subject: 1968078568601
Reset training subject: 1968080339446
Adding measurement 5234ee1c to history. Total: 0
LBFGS Accumulation History: 1 points
Constructing line search parameters: GD
Non-optimal measurement 12.542898623723326 < 12.542898623723326. Total: 1
th(0)=12.542898623723326;dx=-5.9056000000000004E22
Adding measurement 2fb410a0 to history. Total: 1
New Minimum: 12.542898623723326 > 0.03072951215421036
Armijo: th(2.154434690031884)=0.03072951215421036; dx=-5.905600000028649E10 evalInputDelta=12.512169111569117
Non-optimal measurement 0.061692781280216914 < 0.03072951215421036. Total: 2
Armijo: th(1.077217345015942)=0.061692781280216914; dx=-5.905600000029825E10 evalInputDelta=12.48120584244311
Non-optimal measurement 0.15589428575146805 < 0.03072951215421036. Total: 2
Armijo: th(0.3590724483386473)=0.15589428575146805; dx=-5.905600000042532E10 evalInputDelta=12.387004337971858
Non-optimal measurement 0.33355502351189414 < 0.03072951215421036. Total: 2
Armijo: th(0.08976811208466183)=0.33355502351189414; dx=-5.905600000113872E10 evalInputDelta=12.209343600211433
Non-optimal measurement 0.44725559313631147 < 0.03072951215421036. Total: 2
Armijo: th(0.017953622416932366)=0.44725559313631147; dx=-5.90560000023163E10 evalInputDelta=12.095643030587015
Non-optimal measurement 0.48653576877380794 < 0.03072951215421036. Total: 2
Armijo: th(0.002992270402822061)=0.48653576877380794; dx=-5.905600000299477E10 evalInputDelta=12.05636285494952
Non-optimal measurement 0.4944186582516769 < 0.03072951215421036. Total: 2
Armijo: th(4.2746720040315154E-4)=0.4944186582516769; dx=-5.9056000003155075E10 evalInputDelta=12.04847996547165
Non-optimal measurement 0.4956034054134074 < 0.03072951215421036. Total: 2
Armijo: th(5.343340005039394E-5)=0.4956034054134074; dx=-5.905600000317995E10 evalInputDelta=12.04729521830992
Non-optimal measurement 0.4957545175152256 < 0.03072951215421036. Total: 2
Armijo: th(5.9370444500437714E-6)=0.4957545175152256; dx=-5.9056000003183136E10 evalInputDelta=12.047144106208101
Non-optimal measurement 0.4957715271055691 < 0.03072951215421036. Total: 2
Armijo: th(5.937044450043771E-7)=0.4957715271055691; dx=-5.9056000003183495E10 evalInputDelta=12.047127096617757
Non-optimal measurement 0.4957732453526928 < 0.03072951215421036. Total: 2
Armijo: th(5.397313136403428E-8)=0.4957732453526928; dx=-5.905600000318353E10 evalInputDelta=12.047125378370634
Non-optimal measurement 0.49577340285965865 < 0.03072951215421036. Total: 2
Armijo: th(4.4977609470028565E-9)=0.49577340285965865; dx=-5.905600000318353E10 evalInputDelta=12.047125220863668
Non-optimal measurement 0.49577341607703385 < 0.03072951215421036. Total: 2
Armijo: th(3.4598161130791205E-10)=0.49577341607703385; dx=-5.905600000318353E10 evalInputDelta=12.047125207646292
Non-optimal measurement 0.5168771188316795 < 0.03072951215421036. Total: 2
Armijo: th(2.4712972236279432E-11)=0.5168771188316795; dx=-5.975734019221755E10 evalInputDelta=12.026021504891647
Non-optimal measurement 1.893952378565147 < 0.03072951215421036. Total: 2
Armijo: th(1.6475314824186289E-12)=1.893952378565147; dx=-2.5600000057279568E20 evalInputDelta=10.648946245158179
Non-optimal measurement 12.542898623723 < 0.03072951215421036. Total: 2
Armijo: th(1.029707176511643E-13)=12.542898623723; dx=-5.9056000000000004E22 evalInputDelta=3.268496584496461E-13
Non-optimal measurement 12.542898623723309 < 0.03072951215421036. Total: 2
Armijo: th(6.057101038303783E-15)=12.542898623723309; dx=-5.9056000000000004E22 evalInputDelta=1.7763568394002505E-14
Non-optimal measurement 0.03072951215421036 < 0.03072951215421036. Total: 2
MIN ALPHA (3.3650561323909904E-16): th(2.154434690031884)=0.03072951215421036
Fitness changed from 12.542898623723326 to 0.03072951215421036
Iteration 1 complete. Error: 0.03072951215421036 Total: 0.0559; Orientation: 0.0030; Line Search: 0.0489
Non-optimal measurement 0.03072951215421036 < 0.03072951215421036. Total: 2
LBFGS Accumulation History: 2 points
Non-optimal measurement 0.03072951215421036 < 0.03072951215421036. Total: 2
th(0)=0.03072951215421036;dx=-0.10112970423055659
Adding measurement 513301a5 to history. Total: 2
New Minimum: 0.03072951215421036 > 0.030729512154210353
WOLFE (weak): th(2.154434690031884E-15)=0.030729512154210353; dx=-0.10112970423055659 evalInputDelta=6.938893903907228E-18
Adding measurement 824999e to history. Total: 3
New Minimum: 0.030729512154210353 > 0.030729512154210346
WOLFE (weak): th(4.308869380063768E-15)=0.030729512154210346; dx=-0.10112970423055659 evalInputDelta=1.3877787807814457E-17
Adding measurement 54ac3231 to history. Total: 4
New Minimum: 0.030729512154210346 > 0.03072951215421032
WOLFE (weak): th(1.2926608140191303E-14)=0.03072951215421032; dx=-0.10112970423055659 evalInputDelta=4.163336342344337E-17
Adding measurement 205e9ec5 to history. Total: 5
New Minimum: 0.03072951215421032 > 0.030729512154210204
WOLFE (weak): th(5.1706432560765214E-14)=0.030729512154210204; dx=-0.10112970423055659 evalInputDelta=1.5612511283791264E-16
Adding measurement 6321a469 to history. Total: 6
New Minimum: 0.030729512154210204 > 0.030729512154209614
WOLFE (weak): th(2.5853216280382605E-13)=0.030729512154209614; dx=-0.10112970423055656 evalInputDelta=7.4593109467002705E-16
Adding measurement 2344d991 to history. Total: 7
New Minimum: 0.030729512154209614 > 0.030729512154205895
WOLFE (weak): th(1.5511929768229563E-12)=0.030729512154205895; dx=-0.10112970423055637 evalInputDelta=4.4651782271643015E-15
Adding measurement 6035e93d to history. Total: 8
New Minimum: 0.030729512154205895 > 0.03072951215417915
WOLFE (weak): th(1.0858350837760695E-11)=0.03072951215417915; dx=-0.101129704230555 evalInputDelta=3.1211144779774713E-14
Adding measurement 450148cc to history. Total: 9
New Minimum: 0.03072951215417915 > 0.030729512153960727
WOLFE (weak): th(8.686680670208556E-11)=0.030729512153960727; dx=-0.10112970423054389 evalInputDelta=2.4963364708696645E-13
Adding measurement 356d4d6d to history. Total: 10
New Minimum: 0.030729512153960727 > 0.030729512151963696
WOLFE (weak): th(7.8180126031877E-10)=0.030729512151963696; dx=-0.10112970423044226 evalInputDelta=2.2466646598662265E-12
Adding measurement 68e0ad2f to history. Total: 11
New Minimum: 0.030729512151963696 > 0.030729512131743703
WOLFE (weak): th(7.818012603187701E-9)=0.030729512131743703; dx=-0.10112970422941324 evalInputDelta=2.246665700700312E-11
Adding measurement 323c4b4f to history. Total: 12
New Minimum: 0.030729512131743703 > 0.03072951190707714
WOLFE (weak): th(8.599813863506471E-8)=0.03072951190707714; dx=-0.10112970421797964 evalInputDelta=2.4713322013814043E-10
Adding measurement 56e95f97 to history. Total: 13
New Minimum: 0.03072951190707714 > 0.030729509188611864
WOLFE (weak): th(1.0319776636207765E-6)=0.030729509188611864; dx=-0.10112970407963301 evalInputDelta=2.965598495940913E-9
Adding measurement 7dedb436 to history. Total: 14
New Minimum: 0.030729509188611864 > 0.030729473601442004
WOLFE (weak): th(1.3415709627070094E-5)=0.030729473601442004; dx=-0.10112970226855134 evalInputDelta=3.8552768356209244E-8
Adding measurement 3b1d2367 to history. Total: 15
New Minimum: 0.030729473601442004 > 0.03072897241784871
WOLFE (weak): th(1.878199347789813E-4)=0.03072897241784871; dx=-0.10112967676274873 evalInputDelta=5.397363616495288E-7
Adding measurement 48a90aa to history. Total: 16
New Minimum: 0.03072897241784871 > 0.030721416650426304
WOLFE (weak): th(0.0028172990216847197)=0.030721416650426304; dx=-0.10112929227352566 evalInputDelta=8.095503784055769E-6
Adding measurement 66059df9 to history. Total: 17
New Minimum: 0.030721416650426304 > 0.030600123141941094
WOLFE (weak): th(0.045076784346955515)=0.030600123141941094; dx=-0.10112312832871292 evalInputDelta=1.2938901226926605E-4
Adding measurement 6f9cb950 to history. Total: 18
New Minimum: 0.030600123141941094 > 0.028569139606479905
WOLFE (weak): th(0.7663053338982437)=0.028569139606479905; dx=-0.1010221947415572 evalInputDelta=0.0021603725477304554
Adding measurement 3dba1bdd to history. Total: 19
New Minimum: 0.028569139606479905 > 0.0018823644871977182
WOLFE (weak): th(13.793496010168386)=0.0018823644871977182; dx=-0.10000225442001724 e
...skipping 9253 bytes...
769a20 = 1.000/1.000e+00, f238759f-ca8e-4f2a-bd35-d4e892f11fa4 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.0018823644871977182, 0.028569139606479905, 0.030600123141941094, 0.030721416650426304, 0.03072897241784871, 0.030729473601442004, 0.030729509188611864, 0.03072951190707714, 0.030729512131743703, 0.030729512151963696
Rejected: LBFGS Orientation magnitude: 3.282e+02, gradient 3.151e-01, dot -0.997; [f238759f-ca8e-4f2a-bd35-d4e892f11fa4 = 1.000/1.000e+00, 5da2f8d7-8f38-4387-b294-93d6c3769a20 = 1.000/1.000e+00, ff4eb3b3-e275-4d6d-a5e7-271d65444c0c = 1.000/1.000e+00, 0a73e91d-ad2e-4113-b894-a250b394debd = 1.000/1.000e+00, 358d7e6c-d671-472a-a3a6-9fd38267c66a = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.0018823644871977182, 0.028569139606479905, 0.030600123141941094, 0.030721416650426304, 0.03072897241784871, 0.030729473601442004, 0.030729509188611864, 0.03072951190707714, 0.030729512131743703
Rejected: LBFGS Orientation magnitude: 3.282e+02, gradient 3.151e-01, dot -0.997; [5da2f8d7-8f38-4387-b294-93d6c3769a20 = 1.000/1.000e+00, 358d7e6c-d671-472a-a3a6-9fd38267c66a = 1.000/1.000e+00, 0a73e91d-ad2e-4113-b894-a250b394debd = 1.000/1.000e+00, f238759f-ca8e-4f2a-bd35-d4e892f11fa4 = 1.000/1.000e+00, ff4eb3b3-e275-4d6d-a5e7-271d65444c0c = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.0018823644871977182, 0.028569139606479905, 0.030600123141941094, 0.030721416650426304, 0.03072897241784871, 0.030729473601442004, 0.030729509188611864, 0.03072951190707714
Rejected: LBFGS Orientation magnitude: 3.282e+02, gradient 3.151e-01, dot -0.997; [0a73e91d-ad2e-4113-b894-a250b394debd = 1.000/1.000e+00, f238759f-ca8e-4f2a-bd35-d4e892f11fa4 = 1.000/1.000e+00, ff4eb3b3-e275-4d6d-a5e7-271d65444c0c = 1.000/1.000e+00, 358d7e6c-d671-472a-a3a6-9fd38267c66a = 1.000/1.000e+00, 5da2f8d7-8f38-4387-b294-93d6c3769a20 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.0018823644871977182, 0.028569139606479905, 0.030600123141941094, 0.030721416650426304, 0.03072897241784871, 0.030729473601442004, 0.030729509188611864
Rejected: LBFGS Orientation magnitude: 3.282e+02, gradient 3.151e-01, dot -0.997; [0a73e91d-ad2e-4113-b894-a250b394debd = 1.000/1.000e+00, 5da2f8d7-8f38-4387-b294-93d6c3769a20 = 1.000/1.000e+00, ff4eb3b3-e275-4d6d-a5e7-271d65444c0c = 1.000/1.000e+00, 358d7e6c-d671-472a-a3a6-9fd38267c66a = 1.000/1.000e+00, f238759f-ca8e-4f2a-bd35-d4e892f11fa4 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.0018823644871977182, 0.028569139606479905, 0.030600123141941094, 0.030721416650426304, 0.03072897241784871, 0.030729473601442004
Rejected: LBFGS Orientation magnitude: 4.317e+02, gradient 3.151e-01, dot -1.000; [358d7e6c-d671-472a-a3a6-9fd38267c66a = 1.000/1.000e+00, 0a73e91d-ad2e-4113-b894-a250b394debd = 1.000/1.000e+00, 5da2f8d7-8f38-4387-b294-93d6c3769a20 = 1.000/1.000e+00, ff4eb3b3-e275-4d6d-a5e7-271d65444c0c = 1.000/1.000e+00, f238759f-ca8e-4f2a-bd35-d4e892f11fa4 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.0018823644871977182, 0.028569139606479905, 0.030600123141941094, 0.030721416650426304, 0.03072897241784871
Rejected: LBFGS Orientation magnitude: 4.342e+02, gradient 3.151e-01, dot -1.000; [5da2f8d7-8f38-4387-b294-93d6c3769a20 = 1.000/1.000e+00, f238759f-ca8e-4f2a-bd35-d4e892f11fa4 = 1.000/1.000e+00, 358d7e6c-d671-472a-a3a6-9fd38267c66a = 1.000/1.000e+00, 0a73e91d-ad2e-4113-b894-a250b394debd = 1.000/1.000e+00, ff4eb3b3-e275-4d6d-a5e7-271d65444c0c = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.0018823644871977182, 0.028569139606479905, 0.030600123141941094, 0.030721416650426304
Rejected: LBFGS Orientation magnitude: 4.525e+02, gradient 3.151e-01, dot -1.000; [ff4eb3b3-e275-4d6d-a5e7-271d65444c0c = 1.000/1.000e+00, 358d7e6c-d671-472a-a3a6-9fd38267c66a = 1.000/1.000e+00, f238759f-ca8e-4f2a-bd35-d4e892f11fa4 = 1.000/1.000e+00, 5da2f8d7-8f38-4387-b294-93d6c3769a20 = 1.000/1.000e+00, 0a73e91d-ad2e-4113-b894-a250b394debd = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.0018823644871977182, 0.028569139606479905, 0.030600123141941094
LBFGS Accumulation History: 3 points
Removed measurement 79677be0 to history. Total: 20
Removed measurement 3dba1bdd to history. Total: 19
Removed measurement 6f9cb950 to history. Total: 18
Removed measurement 66059df9 to history. Total: 17
Removed measurement 48a90aa to history. Total: 16
Removed measurement 3b1d2367 to history. Total: 15
Removed measurement 7dedb436 to history. Total: 14
Removed measurement 56e95f97 to history. Total: 13
Removed measurement 323c4b4f to history. Total: 12
Removed measurement 68e0ad2f to history. Total: 11
Removed measurement 356d4d6d to history. Total: 10
Removed measurement 450148cc to history. Total: 9
Removed measurement 6035e93d to history. Total: 8
Removed measurement 2344d991 to history. Total: 7
Removed measurement 6321a469 to history. Total: 6
Removed measurement 205e9ec5 to history. Total: 5
Removed measurement 54ac3231 to history. Total: 4
Removed measurement 824999e to history. Total: 3
Adding measurement 776f33ff to history. Total: 3
th(0)=0.0;dx=-0.09927999999999998
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(652954.2012427867)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(326477.10062139336)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(108825.70020713111)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(27206.42505178278)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(5441.285010356556)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(906.880835059426)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(129.55440500848943)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(16.194300626061178)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(1.7993667362290198)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(0.17993667362290197)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(0.016357879420263816)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(0.001363156618355318)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(1.0485820141194754E-4)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(7.489871529424824E-6)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(4.993247686283216E-7)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(3.12077980392701E-8)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(1.8357528258394178E-9)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(1.0198626810218987E-10)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(5.367698321167888E-12)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(2.683849160583944E-13)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(1.2780234098018781E-14)=0.0; dx=-0.09927999999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
MIN ALPHA (5.809197317281265E-16): th(0.0)=0.0
Fitness changed from 0.0 to 0.0
Static Iteration Total: 0.4395; Orientation: 0.4003; Line Search: 0.0380
Iteration 3 failed. Error: 0.0
Previous Error: 0.0 -> 0.0
Optimization terminated 3
Final threshold in iteration 3: 0.0 (> 0.0) after 0.564s (< 30.000s)
Returns
0.0
TrainingTester.java:432 executed in 0.10 seconds (0.000 gc):
return TestUtil.compare(title + " vs Iteration", runs);
Plotting range=[1.0, -2.512444334322737], [2.0, -0.512444334322737]; valueStats=DoubleSummaryStatistics{count=2, sum=0.061459, min=0.030730, average=0.030730, max=0.030730}
Plotting 2 points for GD
Only 1 points for CjGD
Plotting 2 points for LBFGS
Returns
TrainingTester.java:435 executed in 0.01 seconds (0.000 gc):
return TestUtil.compareTime(title + " vs Time", runs);
Plotting range=[0.0, -2.512444334322737], [0.123, -0.512444334322737]; valueStats=DoubleSummaryStatistics{count=2, sum=0.061459, min=0.030730, average=0.030730, max=0.030730}
Plotting 2 points for GD
Only 1 points for CjGD
Plotting 2 points for LBFGS
Returns
TrainingTester.java:255 executed in 0.00 seconds (0.000 gc):
return grid(inputLearning, modelLearning, completeLearning);
Returns
TrainingTester.java:258 executed in 0.00 seconds (0.000 gc):
return new ComponentResult(null == inputLearning ? null : inputLearning.value,
null == modelLearning ? null : modelLearning.value, null == completeLearning ? null : completeLearning.value);
Returns
{"input":{ "LBFGS": { "type": "Converged", "value": 0.0 }, "CjGD": { "type": "Converged", "value": 0.0 }, "GD": { "type": "Converged", "value": 0.0 } }, "model":null, "complete":null}
LayerTests.java:425 executed in 0.00 seconds (0.000 gc):
throwException(exceptions.addRef());
| details | result |
|---|---|
| {"input":{ "LBFGS": { "type": "Converged", "value": 0.0 }, "CjGD": { "type": "Converged", "value": 0.0 }, "GD": { "type": "Converged", "value": 0.0 } }, "model":null, "complete":null} | OK |
{
"result": "OK",
"performance": {
"execution_time": "1.751",
"gc_time": "0.326"
},
"created_on": 1586736555443,
"file_name": "trainingTest",
"report": {
"simpleName": "Basic",
"canonicalName": "com.simiacryptus.mindseye.layers.java.MonitoringWrapperTest.Basic",
"link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/MonitoringWrapperTest.java",
"javaDoc": ""
},
"training_analysis": {
"input": {
"LBFGS": {
"type": "Converged",
"value": 0.0
},
"CjGD": {
"type": "Converged",
"value": 0.0
},
"GD": {
"type": "Converged",
"value": 0.0
}
}
},
"archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/MonitoringWrapperLayer/Basic/trainingTest/202004130915",
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}