Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase
Using Seed 5496271331333262336
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
[ [ -0.128, 0.08 ], [ 0.496, 0.7 ] ]
[ [ 0.08, -0.128 ], [ 0.496, 0.7 ] ]
[ [ 0.7, 0.08 ], [ 0.496, -0.128 ] ]
[ [ 0.7, 0.08 ], [ -0.128, 0.496 ] ]
[ [ -0.128, 0.496 ], [ 0.08, 0.7 ] ]
First, we train using basic gradient descent method apply weak line search conditions.
TrainingTester.java:480 executed in 0.32 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: 4601633019803
BACKPROP_AGG_SIZE = 3
THREADS = 64
SINGLE_THREADED = false
Initialized CoreSettings = {
"backpropAggregationSize" : 3,
"jvmThreads" : 64,
"singleThreaded" : false
}
Reset training subject: 4601662195059
Constructing line search parameters: GD
th(0)=10.55592927933435;dx=-3.9537279999999996E22
New Minimum: 10.55592927933435 > 0.035231246933823346
Armijo: th(2.154434690031884)=0.035231246933823346; dx=-3.95372800003116E10 evalInputDelta=10.520698032400526
Armijo: th(1.077217345015942)=0.19199140676609097; dx=-3.953728000035789E10 evalInputDelta=10.36393787256826
Armijo: th(0.3590724483386473)=0.45007451993611786; dx=-3.953728000055006E10 evalInputDelta=10.105854759398232
Armijo: th(0.08976811208466183)=0.6423418078696101; dx=-3.95372800010157E10 evalInputDelta=9.91358747146474
Armijo: th(0.017953622416932366)=0.7320017115627426; dx=-3.95372800015974E10 evalInputDelta=9.823927567771607
Armijo: th(0.002992270402822061)=0.757800459476007; dx=-3.9537280001869125E10 evalInputDelta=9.798128819858343
Armijo: th(4.2746720040315154E-4)=0.762669020406957; dx=-3.953728000192807E10 evalInputDelta=9.793260258927393
Armijo: th(5.343340005039394E-5)=0.7633918583843899; dx=-3.953728000193705E10 evalInputDelta=9.79253742094996
Armijo: th(5.9370444500437714E-6)=0.7634838882781448; dx=-3.9537280001938194E10 evalInputDelta=9.792445391056205
Armijo: th(5.937044450043771E-7)=0.7634942450537956; dx=-3.9537280001938324E10 evalInputDelta=9.792435034280555
Armijo: th(5.397313136403428E-8)=0.7634952912311502; dx=-3.953728000193834E10 evalInputDelta=9.7924339881032
Armijo: th(4.4977609470028565E-9)=0.7634953871310937; dx=-3.953728000193834E10 evalInputDelta=9.792433892203256
Armijo: th(3.4598161130791205E-10)=0.763495395178644; dx=-3.953728000193834E10 evalInputDelta=9.792433884155706
Armijo: th(2.4712972236279432E-11)=0.7845990975332436; dx=-4.023862019097235E10 evalInputDelta=9.771330181801106
Armijo: th(1.6475314824186289E-12)=2.1869522135088704; dx=-2.5600000074634917E20 evalInputDelta=8.36897706582548
Armijo: th(1.029707176511643E-13)=10.55592927933415; dx=-3.9537279999999996E22 evalInputDelta=2.007283228522283E-13
Armijo: th(6.057101038303783E-15)=10.55592927933434; dx=-3.9537280000000004E22 evalInputDelta=1.0658141036401503E-14
MIN ALPHA (3.3650561323909904E-16): th(2.154434690031884)=0.035231246933823346
Fitness changed from 10.55592927933435 to 0.035231246933823346
Iteration 1 complete. Error: 0.035231246933823346 Total: 0.1352; Orientation: 0.0033; Line Search: 0.0952
th(0)=0.035231246933823346;dx=-0.15067740517961764
New Minimum: 0.035231246933823346 > 0.03523124693382333
WOLFE (weak): th(2.154434690031884E-15)=0.03523124693382333; dx=-0.15067740517961764 evalInputDelta=1.3877787807814457E-17
WOLFE (weak): th(4.308869380063768E-15)=0.03523124693382333; dx=-0.15067740517961764 evalInputDelta=1.3877787807814457E-17
New Minimum: 0.03523124693382333 > 0.035231246933823304
WOLFE (weak): th(1.2926608140191303E-14)=0.035231246933823304; dx=-0.15067740517961764 evalInputDelta=4.163336342344337E-17
New Minimum: 0.035231246933823304 > 0.035231246933823165
WOLFE (weak): th(5.1706432560765214E-14)=0.035231246933823165; dx=-0.1506774051796176 evalInputDelta=1.8041124150158794E-16
New Minimum: 0.035231246933823165 > 0.03523124693382251
WOLFE (weak): th(2.5853216280382605E-13)=0.03523124693382251; dx=-0.15067740517961759 evalInputDelta=8.326672684688674E-16
New Minimum: 0.03523124693382251 > 0.03523124693381834
WOLFE (weak): th(1.5511929768229563E-12)=0.03523124693381834; dx=-0.15067740517961736 evalInputDelta=5.002942504717112E-15
New Minimum: 0.03523124693381834 > 0.035231246933788395
WOLFE (weak): th(1.0858350837760695E-11)=0.035231246933788395; dx=-0.15067740517961575 evalInputDelta=3.495120859398071E-14
New Minimum: 0.035231246933788395 > 0.035231246933543785
WOLFE (weak): th(8.686680670208556E-11)=0.035231246933543785; dx=-0.15067740517960257 evalInputDelta=2.795610964945183E-13
New Minimum: 0.035231246933543785 > 0.035231246931307345
WOLFE (weak): th(7.8180126031877E-10)=0.035231246931307345; dx=-0.15067740517948214 evalInputDelta=2.5160012961933376E-12
New Minimum: 0.035231246931307345 > 0.035231246908663374
WOLFE (weak): th(7.818012603187701E-9)=0.035231246908663374; dx=-0.15067740517826275 evalInputDelta=2.5159971328569952E-11
New Minimum: 0.035231246908663374 > 0.03523124665706369
WOLFE (weak): th(8.599813863506471E-8)=0.03523124665706369; dx=-0.15067740516471395 evalInputDelta=2.7675965685869386E-10
New Minimum: 0.03523124665706369 > 0.035231243612707575
WOLFE (weak): th(1.0319776636207765E-6)=0.035231243612707575; dx=-0.15067740500077348 evalInputDelta=3.321115771282024E-9
New Minimum: 0.035231243612707575 > 0.035231203759332724
WOLFE (weak): th(1.3415709627070094E-5)=0.035231203759332724; dx=-0.1506774028546454 evalInputDelta=4.3174490621522565E-8
New Minimum: 0.035231203759332724 > 0.03523064249379311
WOLFE (weak): th(1.878199347789813E-4)=0.03523064249379311; dx=-0.1506773726303256 evalInputDelta=6.044400302357422E-7
New Minimum: 0.03523064249379311 > 0.03522218097521202
WOLFE (weak): th(0.0028172990216847197)=0.03522218097521202; dx=-0.15067691701242356 evalInputDelta=9.06595861132481E-6
New Minimum: 0.03522218097521202 > 0.03508635636393318
WOLFE (weak): th(0.045076784346955515)=0.03508635636393318; dx=-0.1506696130187345 evalInputDelta=1.4489056989016452E-4
New Minimum: 0.03508635636393318 > 0.03281458772436788
WOLFE (weak): th(0.7663053338982437)=0.03281458772436788; dx=-0.150550083441627 evalInputDelta=0.0024166592094554684
New Minimum: 0.03281458772436788 > 0.002606234876939925
WOLFE (weak): th(13.793496010168386)=0.002606234876939925; dx=-0.14933742649254747 evalInputDelta=0.03262501205688342
New Minimum: 0.002606234876939925 > 0.0
WOLFE (weak): th(262.07642419319933)=0.0; dx=-0.14925575474414246 evalInputDelta=0.035231246933823346
WOLFE (weak): th(5241.528483863986)=0.0; dx=-0.14925575474414246 evalInputDelta=0.035231246933823346
WOLFE (weak): th(110072.09816114372)=0.0; dx=-0.14925575474414246 evalInputDelta=0.035231246933823346
Armijo: th(2421586.1595451618)=0.0; dx=-0.14925575474414246 evalInputDelta=0.035231246933823346
Armijo: th(1265829.1288531527)=0.0; dx=-0.14925575474414246 evalInputDelta=0.035231246933823346
Armijo: th(687950.6135071482)=0.0; dx=-0.14925575474414246 evalInputDelta=0.035231246933823346
Armijo: th(399011.355834146)=0.0; dx=-0.14925575474414246 evalInputDelta=0.035231246933823346
Armijo: th(254541.72699764484)=0.0; dx=-0.14925575474414246 evalInputDelta=0.035231246933823346
WOLFE (weak): th(182306.9125793943)=0.0; dx=-0.14925575474414246 evalInputDelta=0.035231246933823346
WOLFE (weak): th(218424.31978851958)=0.0; dx=-0.14925575474414246 evalInputDelta=0.035231246933823346
Armijo: th(236483.0233930822)=0.0; dx=-0.14925575474414246 evalInputDelta=0.035231246933823346
WOLFE (weak): th(227453.6715908009)=0.0; dx=-0.14925575474414246 evalInputDelta=0.035231246933823346
WOLFE (weak): th(231968.34749194153)=0.0; dx=-0.14925575474414246 evalInputDelta=0.035231246933823346
Armijo: th(234225.68544251187)=0.0; dx=-0.14925575474414246 evalInputDelta=0.035231246933823346
mu ~= nu (231968.34749194153): th(262.07642419319933)=0.0
Fitness changed from 0.035231246933823346 to 0.0
Iteration 2 complete. Error: 0.0 Total: 0.1254; Orientation: 0.0026; Line Search: 0.1183
th(0)=0.0;dx=-0.14848319999999998
Armijo: th(502192.2984199265)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Armijo: th(251096.14920996325)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Armijo: th(83698.71640332109)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Armijo: th(20924.67910083027)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Armijo: th(4184.935820166054)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Armijo: th(697.4893033610091)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Armijo: th(99.64132905157273)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Armijo: th(12.45516613144659)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Armijo: th(1.38390734793851)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Armijo: th(0.138390734793851)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Armijo: th(0.012580975890350092)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Armijo: th(0.0010484146575291742)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Armijo: th(8.064728134839802E-5)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Armijo: th(5.760520096314144E-6)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Armijo: th(3.840346730876096E-7)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Armijo: th(2.40021670679756E-8)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Armijo: th(1.411892180469153E-9)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Armijo: th(7.84384544705085E-11)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Armijo: th(4.128339708974132E-12)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Armijo: th(2.0641698544870658E-13)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Armijo: th(9.829380259462218E-15)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
MIN ALPHA (4.467900117937372E-16): th(0.0)=0.0
Fitness changed from 0.0 to 0.0
Static Iteration Total: 0.0551; Orientation: 0.0007; Line Search: 0.0528
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 0.317s (< 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 0.12 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: 4601956076305
Reset training subject: 4601957757264
Constructing line search parameters: GD
F(0.0) = LineSearchPoint{point=PointSample{avg=10.55592927933435}, derivative=-3.9537279999999996E22}
New Minimum: 10.55592927933435 > 0.7634953956554396
F(1.0E-10) = LineSearchPoint{point=PointSample{avg=0.7634953956554396}, derivative=-3.953728000193834E10}, evalInputDelta = -9.79243388367891
New Minimum: 0.7634953956554396 > 0.7634953944924369
F(7.000000000000001E-10) = LineSearchPoint{point=PointSample{avg=0.7634953944924369}, derivative=-3.953728000193834E10}, evalInputDelta = -9.792433884841913
New Minimum: 0.7634953944924369 > 0.7634953863514186
F(4.900000000000001E-9) = LineSearchPoint{point=PointSample{avg=0.7634953863514186}, derivative=-3.953728000193834E10}, evalInputDelta = -9.792433892982931
New Minimum: 0.7634953863514186 > 0.7634953293643025
F(3.430000000000001E-8) = LineSearchPoint{point=PointSample{avg=0.7634953293643025}, derivative=-3.953728000193834E10}, evalInputDelta = -9.792433949970048
New Minimum: 0.7634953293643025 > 0.7634949304550757
F(2.4010000000000004E-7) = LineSearchPoint{point=PointSample{avg=0.7634949304550757}, derivative=-3.953728000193833E10}, evalInputDelta = -9.792434348879274
New Minimum: 0.7634949304550757 > 0.7634921381191723
F(1.6807000000000003E-6) = LineSearchPoint{point=PointSample{avg=0.7634921381191723}, derivative=-3.953728000193829E10}, evalInputDelta = -9.792437141215178
New Minimum: 0.7634921381191723 > 0.7634725931732248
F(1.1764900000000001E-5) = LineSearchPoint{point=PointSample{avg=0.7634725931732248}, derivative=-3.953728000193806E10}, evalInputDelta = -9.792456686161126
New Minimum: 0.7634725931732248 > 0.7633358473573911
F(8.235430000000001E-5) = LineSearchPoint{point=PointSample{avg=0.7633358473573911}, derivative=-3.953728000193635E10}, evalInputDelta = -9.79259343197696
New Minimum: 0.7633358473573911 > 0.7623819785027509
F(5.764801000000001E-4) = LineSearchPoint{point=PointSample{avg=0.7623819785027509}, derivative=-3.9537280001924515E10}, evalInputDelta = -9.7935473008316
New Minimum: 0.7623819785027509 > 0.7558627052102217
F(0.004035360700000001) = LineSearchPoint{point=PointSample{avg=0.7558627052102217}, derivative=-3.9537280001846405E10}, evalInputDelta = -9.800066574124129
New Minimum: 0.7558627052102217 > 0.7162898070601061
F(0.028247524900000005) = LineSearchPoint{point=PointSample{avg=0.7162898070601061}, derivative=-3.953728000146051E10}, evalInputDelta = -9.839639472274245
New Minimum: 0.7162898070601061 > 0.5509521337838003
F(0.19773267430000002) = LineSearchPoint{point=PointSample{avg=0.5509521337838003}, derivative=-3.953728000072466E10}, evalInputDelta = -10.00497714555055
New Minimum: 0.5509521337838003 > 0.12336515036840687
F(1.3841287201) = LineSearchPoint{point=PointSample{avg=0.12336515036840687}, derivative=-3.9537280000336136E10}, evalInputDelta = -10.432564128965943
New Minimum: 0.12336515036840687 > 0.004709023459807467
F(9.688901040700001) = LineSearchPoint{point=PointSample{avg=0.004709023459807467}, derivative=-3.953728000030511E10}, evalInputDelta = -10.551220255874544
New Minimum: 0.004709023459807467 > 0.0
F(67.8223072849) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-3.9537280000304955E10}, evalInputDelta = -10.55592927933435
F(474.7561509943) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-3.9537280000304955E10}, evalInputDelta = -10.55592927933435
F(3323.2930569601003) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-3.9537280000304955E10}, evalInputDelta = -10.55592927933435
F(23263.0513987207) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-3.9537280000304955E10}, evalInputDelta = -10.55592927933435
F(162841.3597910449) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-3.9537280000304955E10}, evalInputDelta = -10.55592927933435
F(1139889.5185373144) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-3.953728000030496E10}, evalInputDelta = -10.55592927933435
F(7979226.6297612) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-3.9537280000304955E10}, evalInputDelta = -10.55592927933435
F(5.58545864083284E7) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-3.9537280000304955E10}, evalInputDelta = -10.55592927933435
F(3.909821048582988E8) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-3.953728000030496E10}, evalInputDelta = -10.55592927933435
F(2.7368747340080914E9) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-3.9537280000304955E10}, evalInputDelta = -10.55592927933435
F(1.915812313805664E10) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-3.953728000030496E10}, evalInputDelta = -10.55592927933435
0.0 <= 10.55592927933435
F(1.0E10) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-3.9537280000304955E10}, evalInputDelta = -10.55592927933435
Right bracket at 1.0E10
Converged to right
Fitness changed from 10.55592927933435 to 0.0
Iteration 1 complete. Error: 0.0 Total: 0.0713; Orientation: 0.0007; Line Search: 0.0663
F(0.0) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.14848319999999998}
F(1.0E10) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.14848319999999998}, evalInputDelta = 0.0
0.0 <= 0.0
F(5.0E9) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.14848319999999998}, evalInputDelta = 0.0
Right bracket at 5.0E9
F(2.5E9) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.14848319999999998}, evalInputDelta = 0.0
Right bracket at 2.5E9
F(1.25E9) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.14848319999999998}, evalInputDelta = 0.0
Right bracket at 1.25E9
F(6.25E8) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.14848319999999998}, evalInputDelta = 0.0
Right bracket at 6.25E8
F(3.125E8) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.14848319999999998}, evalInputDelta = 0.0
Right bracket at 3.125E8
F(1.5625E8) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.14848319999999998}, evalInputDelta = 0.0
Right bracket at 1.5625E8
F(7.8125E7) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.14848319999999998}, evalInputDelta = 0.0
Right bracket at 7.8125E7
F(3.90625E7) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.14848319999999998}, evalInputDelta = 0.0
Right bracket at 3.90625E7
F(1.953125E7) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.14848319999999998}, evalInputDelta = 0.0
Right bracket at 1.953125E7
F(9765625.0) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.14848319999999998}, evalInputDelta = 0.0
Right bracket at 9765625.0
F(4882812.5) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.14848319999999998}, evalInputDelta = 0.0
Right bracket at 4882812.5
F(2441406.25) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-0.14848319999999998}, evalInputDelta = 0.0
Loops = 12
Fitness changed from 0.0 to 0.0
Static Iteration Total: 0.0479; Orientation: 0.0006; Line Search: 0.0460
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 0.120s (< 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 0.89 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: 4602079252643
Reset training subject: 4602080137197
Adding measurement 7b8fd83d to history. Total: 0
LBFGS Accumulation History: 1 points
Constructing line search parameters: GD
Non-optimal measurement 10.55592927933435 < 10.55592927933435. Total: 1
th(0)=10.55592927933435;dx=-3.9537279999999996E22
Adding measurement 15063dfd to history. Total: 1
New Minimum: 10.55592927933435 > 0.035231246933823346
Armijo: th(2.154434690031884)=0.035231246933823346; dx=-3.95372800003116E10 evalInputDelta=10.520698032400526
Non-optimal measurement 0.19199140676609097 < 0.035231246933823346. Total: 2
Armijo: th(1.077217345015942)=0.19199140676609097; dx=-3.953728000035789E10 evalInputDelta=10.36393787256826
Non-optimal measurement 0.45007451993611786 < 0.035231246933823346. Total: 2
Armijo: th(0.3590724483386473)=0.45007451993611786; dx=-3.953728000055006E10 evalInputDelta=10.105854759398232
Non-optimal measurement 0.6423418078696101 < 0.035231246933823346. Total: 2
Armijo: th(0.08976811208466183)=0.6423418078696101; dx=-3.95372800010157E10 evalInputDelta=9.91358747146474
Non-optimal measurement 0.7320017115627426 < 0.035231246933823346. Total: 2
Armijo: th(0.017953622416932366)=0.7320017115627426; dx=-3.95372800015974E10 evalInputDelta=9.823927567771607
Non-optimal measurement 0.757800459476007 < 0.035231246933823346. Total: 2
Armijo: th(0.002992270402822061)=0.757800459476007; dx=-3.9537280001869125E10 evalInputDelta=9.798128819858343
Non-optimal measurement 0.762669020406957 < 0.035231246933823346. Total: 2
Armijo: th(4.2746720040315154E-4)=0.762669020406957; dx=-3.953728000192807E10 evalInputDelta=9.793260258927393
Non-optimal measurement 0.7633918583843899 < 0.035231246933823346. Total: 2
Armijo: th(5.343340005039394E-5)=0.7633918583843899; dx=-3.953728000193705E10 evalInputDelta=9.79253742094996
Non-optimal measurement 0.7634838882781448 < 0.035231246933823346. Total: 2
Armijo: th(5.9370444500437714E-6)=0.7634838882781448; dx=-3.95372800019382E10 evalInputDelta=9.792445391056205
Non-optimal measurement 0.7634942450537956 < 0.035231246933823346. Total: 2
Armijo: th(5.937044450043771E-7)=0.7634942450537956; dx=-3.9537280001938324E10 evalInputDelta=9.792435034280555
Non-optimal measurement 0.7634952912311502 < 0.035231246933823346. Total: 2
Armijo: th(5.397313136403428E-8)=0.7634952912311502; dx=-3.953728000193834E10 evalInputDelta=9.7924339881032
Non-optimal measurement 0.7634953871310937 < 0.035231246933823346. Total: 2
Armijo: th(4.4977609470028565E-9)=0.7634953871310937; dx=-3.953728000193834E10 evalInputDelta=9.792433892203256
Non-optimal measurement 0.763495395178644 < 0.035231246933823346. Total: 2
Armijo: th(3.4598161130791205E-10)=0.763495395178644; dx=-3.953728000193834E10 evalInputDelta=9.792433884155706
Non-optimal measurement 0.7845990975332436 < 0.035231246933823346. Total: 2
Armijo: th(2.4712972236279432E-11)=0.7845990975332436; dx=-4.023862019097236E10 evalInputDelta=9.771330181801106
Non-optimal measurement 2.1869522135088704 < 0.035231246933823346. Total: 2
Armijo: th(1.6475314824186289E-12)=2.1869522135088704; dx=-2.5600000074634917E20 evalInputDelta=8.36897706582548
Non-optimal measurement 10.55592927933415 < 0.035231246933823346. Total: 2
Armijo: th(1.029707176511643E-13)=10.55592927933415; dx=-3.9537279999999996E22 evalInputDelta=2.007283228522283E-13
Non-optimal measurement 10.55592927933434 < 0.035231246933823346. Total: 2
Armijo: th(6.057101038303783E-15)=10.55592927933434; dx=-3.9537279999999996E22 evalInputDelta=1.0658141036401503E-14
Non-optimal measurement 0.035231246933823346 < 0.035231246933823346. Total: 2
MIN ALPHA (3.3650561323909904E-16): th(2.154434690031884)=0.035231246933823346
Fitness changed from 10.55592927933435 to 0.035231246933823346
Iteration 1 complete. Error: 0.035231246933823346 Total: 0.0512; Orientation: 0.0030; Line Search: 0.0455
Non-optimal measurement 0.035231246933823346 < 0.035231246933823346. Total: 2
LBFGS Accumulation History: 2 points
Non-optimal measurement 0.035231246933823346 < 0.035231246933823346. Total: 2
th(0)=0.035231246933823346;dx=-0.15067740517961764
Adding measurement 6c033d3d to history. Total: 2
New Minimum: 0.035231246933823346 > 0.03523124693382333
WOLFE (weak): th(2.154434690031884E-15)=0.03523124693382333; dx=-0.15067740517961764 evalInputDelta=1.3877787807814457E-17
Non-optimal measurement 0.03523124693382333 < 0.03523124693382333. Total: 3
WOLFE (weak): th(4.308869380063768E-15)=0.03523124693382333; dx=-0.15067740517961764 evalInputDelta=1.3877787807814457E-17
Adding measurement 54124014 to history. Total: 3
New Minimum: 0.03523124693382333 > 0.035231246933823304
WOLFE (weak): th(1.2926608140191303E-14)=0.035231246933823304; dx=-0.15067740517961764 evalInputDelta=4.163336342344337E-17
Adding measurement 645a41db to history. Total: 4
New Minimum: 0.035231246933823304 > 0.035231246933823165
WOLFE (weak): th(5.1706432560765214E-14)=0.035231246933823165; dx=-0.1506774051796176 evalInputDelta=1.8041124150158794E-16
Adding measurement 54ac3231 to history. Total: 5
New Minimum: 0.035231246933823165 > 0.03523124693382251
WOLFE (weak): th(2.5853216280382605E-13)=0.03523124693382251; dx=-0.15067740517961759 evalInputDelta=8.326672684688674E-16
Adding measurement 1065fd2d to history. Total: 6
New Minimum: 0.03523124693382251 > 0.03523124693381834
WOLFE (weak): th(1.5511929768229563E-12)=0.03523124693381834; dx=-0.15067740517961736 evalInputDelta=5.002942504717112E-15
Adding measurement 30e72490 to history. Total: 7
New Minimum: 0.03523124693381834 > 0.035231246933788395
WOLFE (weak): th(1.0858350837760695E-11)=0.035231246933788395; dx=-0.15067740517961575 evalInputDelta=3.495120859398071E-14
Adding measurement 6476c800 to history. Total: 8
New Minimum: 0.035231246933788395 > 0.035231246933543785
WOLFE (weak): th(8.686680670208556E-11)=0.035231246933543785; dx=-0.15067740517960257 evalInputDelta=2.795610964945183E-13
Adding measurement 138150ff to history. Total: 9
New Minimum: 0.035231246933543785 > 0.035231246931307345
WOLFE (weak): th(7.8180126031877E-10)=0.035231246931307345; dx=-0.15067740517948214 evalInputDelta=2.5160012961933376E-12
Adding measurement 2b84b636 to history. Total: 10
New Minimum: 0.035231246931307345 > 0.035231246908663374
WOLFE (weak): th(7.818012603187701E-9)=0.035231246908663374; dx=-0.15067740517826275 evalInputDelta=2.5159971328569952E-11
Adding measurement 1d66c56b to history. Total: 11
New Minimum: 0.035231246908663374 > 0.03523124665706369
WOLFE (weak): th(8.599813863506471E-8)=0.03523124665706369; dx=-0.15067740516471395 evalInputDelta=2.7675965685869386E-10
Adding measurement 3b9c8be0 to history. Total: 12
New Minimum: 0.03523124665706369 > 0.035231243612707575
WOLFE (weak): th(1.0319776636207765E-6)=0.035231243612707575; dx=-0.15067740500077348 evalInputDelta=3.321115771282024E-9
Adding measurement 7d2d6bad to history. Total: 13
New Minimum: 0.035231243612707575 > 0.035231203759332724
WOLFE (weak): th(1.3415709627070094E-5)=0.035231203759332724; dx=-0.1506774028546454 evalInputDelta=4.3174490621522565E-8
Adding measurement 46ef8fe4 to history. Total: 14
New Minimum: 0.035231203759332724 > 0.03523064249379311
WOLFE (weak): th(1.878199347789813E-4)=0.03523064249379311; dx=-0.1506773726303256 evalInputDelta=6.044400302357422E-7
Adding measurement 6ede73bd to history. Total: 15
New Minimum: 0.03523064249379311 > 0.03522218097521202
WOLFE (weak): th(0.0028172990216847197)=0.03522218097521202; dx=-0.15067691701242356 evalInputDelta=9.06595861132481E-6
Adding measurement 566ef8d4 to history. Total: 16
New Minimum: 0.03522218097521202 > 0.03508635636393318
WOLFE (weak): th(0.045076784346955515)=0.03508635636393318; dx=-0.1506696130187345 evalInputDelta=1.4489056989016452E-4
Adding measurement 48a90aa to history. Total: 17
New Minimum: 0.03508635636393318 > 0.03281458772436788
WOLFE (weak): th(0.7663053338982437)=0.03281458772436788; dx=-0.150550083441627 evalInputDelta=0.0024166592094554684
Adding measurement 5f212eb1 to history. Total: 18
New Minimum: 0.03281458772436788 > 0.002606234876939925
WOLFE (weak): th(13.793496010168386)=0.002606234876939925; dx=-0.14933742649254747 evalInputDelta=0.03262501205688342
Adding measurement 1e163e
...skipping 9174 bytes...
00, 3b309031-3051-40d2-88c9-1ee565c889eb = 1.000/1.000e+00, 871d37cd-d15d-4f81-b332-0c6ed5ca6be0 = 1.000/1.000e+00, 5a05df1e-d496-4cab-acb6-0759746829a0 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.002606234876939925, 0.03281458772436788, 0.03508635636393318, 0.03522218097521202, 0.03523064249379311, 0.035231203759332724, 0.035231243612707575, 0.03523124665706369, 0.035231246908663374, 0.035231246931307345
Rejected: LBFGS Orientation magnitude: 5.381e+02, gradient 3.853e-01, dot -0.988; [d8bcbfa8-8583-4422-b94a-455a0fa4dc62 = 1.000/1.000e+00, 5a05df1e-d496-4cab-acb6-0759746829a0 = 1.000/1.000e+00, c361c2f2-da5b-4b67-a34a-69005b033a26 = 1.000/1.000e+00, 3b309031-3051-40d2-88c9-1ee565c889eb = 1.000/1.000e+00, 871d37cd-d15d-4f81-b332-0c6ed5ca6be0 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.002606234876939925, 0.03281458772436788, 0.03508635636393318, 0.03522218097521202, 0.03523064249379311, 0.035231203759332724, 0.035231243612707575, 0.03523124665706369, 0.035231246908663374
Rejected: LBFGS Orientation magnitude: 5.381e+02, gradient 3.853e-01, dot -0.988; [d8bcbfa8-8583-4422-b94a-455a0fa4dc62 = 1.000/1.000e+00, 871d37cd-d15d-4f81-b332-0c6ed5ca6be0 = 1.000/1.000e+00, 3b309031-3051-40d2-88c9-1ee565c889eb = 1.000/1.000e+00, c361c2f2-da5b-4b67-a34a-69005b033a26 = 1.000/1.000e+00, 5a05df1e-d496-4cab-acb6-0759746829a0 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.002606234876939925, 0.03281458772436788, 0.03508635636393318, 0.03522218097521202, 0.03523064249379311, 0.035231203759332724, 0.035231243612707575, 0.03523124665706369
Rejected: LBFGS Orientation magnitude: 5.381e+02, gradient 3.853e-01, dot -0.988; [c361c2f2-da5b-4b67-a34a-69005b033a26 = 1.000/1.000e+00, 5a05df1e-d496-4cab-acb6-0759746829a0 = 1.000/1.000e+00, d8bcbfa8-8583-4422-b94a-455a0fa4dc62 = 1.000/1.000e+00, 871d37cd-d15d-4f81-b332-0c6ed5ca6be0 = 1.000/1.000e+00, 3b309031-3051-40d2-88c9-1ee565c889eb = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.002606234876939925, 0.03281458772436788, 0.03508635636393318, 0.03522218097521202, 0.03523064249379311, 0.035231203759332724, 0.035231243612707575
Rejected: LBFGS Orientation magnitude: 6.614e+02, gradient 3.853e-01, dot -0.976; [3b309031-3051-40d2-88c9-1ee565c889eb = 1.000/1.000e+00, c361c2f2-da5b-4b67-a34a-69005b033a26 = 1.000/1.000e+00, 5a05df1e-d496-4cab-acb6-0759746829a0 = 1.000/1.000e+00, 871d37cd-d15d-4f81-b332-0c6ed5ca6be0 = 1.000/1.000e+00, d8bcbfa8-8583-4422-b94a-455a0fa4dc62 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.002606234876939925, 0.03281458772436788, 0.03508635636393318, 0.03522218097521202, 0.03523064249379311, 0.035231203759332724
Rejected: LBFGS Orientation magnitude: 6.608e+02, gradient 3.853e-01, dot -1.000; [3b309031-3051-40d2-88c9-1ee565c889eb = 1.000/1.000e+00, 5a05df1e-d496-4cab-acb6-0759746829a0 = 1.000/1.000e+00, 871d37cd-d15d-4f81-b332-0c6ed5ca6be0 = 1.000/1.000e+00, c361c2f2-da5b-4b67-a34a-69005b033a26 = 1.000/1.000e+00, d8bcbfa8-8583-4422-b94a-455a0fa4dc62 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.002606234876939925, 0.03281458772436788, 0.03508635636393318, 0.03522218097521202, 0.03523064249379311
Rejected: LBFGS Orientation magnitude: 6.689e+02, gradient 3.853e-01, dot -1.000; [871d37cd-d15d-4f81-b332-0c6ed5ca6be0 = 1.000/1.000e+00, c361c2f2-da5b-4b67-a34a-69005b033a26 = 1.000/1.000e+00, d8bcbfa8-8583-4422-b94a-455a0fa4dc62 = 1.000/1.000e+00, 5a05df1e-d496-4cab-acb6-0759746829a0 = 1.000/1.000e+00, 3b309031-3051-40d2-88c9-1ee565c889eb = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.002606234876939925, 0.03281458772436788, 0.03508635636393318, 0.03522218097521202
Rejected: LBFGS Orientation magnitude: 6.971e+02, gradient 3.853e-01, dot -1.000; [5a05df1e-d496-4cab-acb6-0759746829a0 = 1.000/1.000e+00, c361c2f2-da5b-4b67-a34a-69005b033a26 = 1.000/1.000e+00, d8bcbfa8-8583-4422-b94a-455a0fa4dc62 = 1.000/1.000e+00, 3b309031-3051-40d2-88c9-1ee565c889eb = 1.000/1.000e+00, 871d37cd-d15d-4f81-b332-0c6ed5ca6be0 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.002606234876939925, 0.03281458772436788, 0.03508635636393318
LBFGS Accumulation History: 3 points
Removed measurement 1e163e60 to history. Total: 19
Removed measurement 5f212eb1 to history. Total: 18
Removed measurement 48a90aa to history. Total: 17
Removed measurement 566ef8d4 to history. Total: 16
Removed measurement 6ede73bd to history. Total: 15
Removed measurement 46ef8fe4 to history. Total: 14
Removed measurement 7d2d6bad to history. Total: 13
Removed measurement 3b9c8be0 to history. Total: 12
Removed measurement 1d66c56b to history. Total: 11
Removed measurement 2b84b636 to history. Total: 10
Removed measurement 138150ff to history. Total: 9
Removed measurement 6476c800 to history. Total: 8
Removed measurement 30e72490 to history. Total: 7
Removed measurement 1065fd2d to history. Total: 6
Removed measurement 54ac3231 to history. Total: 5
Removed measurement 645a41db to history. Total: 4
Removed measurement 54124014 to history. Total: 3
Adding measurement 517ef490 to history. Total: 3
th(0)=0.0;dx=-0.14848319999999998
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(502192.2984199265)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(251096.14920996325)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(83698.71640332109)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(20924.67910083027)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(4184.935820166054)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(697.4893033610091)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(99.64132905157273)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(12.45516613144659)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(1.38390734793851)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(0.138390734793851)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(0.012580975890350092)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(0.0010484146575291742)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(8.064728134839802E-5)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(5.760520096314144E-6)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(3.840346730876096E-7)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(2.40021670679756E-8)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(1.411892180469153E-9)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(7.84384544705085E-11)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(4.128339708974132E-12)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(2.0641698544870658E-13)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(9.829380259462218E-15)=0.0; dx=-0.14848319999999998 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
MIN ALPHA (4.467900117937372E-16): th(0.0)=0.0
Fitness changed from 0.0 to 0.0
Static Iteration Total: 0.7764; Orientation: 0.7442; Line Search: 0.0311
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 0.892s (< 30.000s)
Returns
0.0
TrainingTester.java:432 executed in 0.11 seconds (0.000 gc):
return TestUtil.compare(title + " vs Iteration", runs);
Plotting range=[1.0, -2.4530719856323993], [2.0, -0.45307198563239903]; valueStats=DoubleSummaryStatistics{count=2, sum=0.070462, min=0.035231, average=0.035231, max=0.035231}
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, -2.4530719856323993], [0.125, -0.45307198563239903]; valueStats=DoubleSummaryStatistics{count=2, sum=0.070462, min=0.035231, average=0.035231, max=0.035231}
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": "2.096",
"gc_time": "0.346"
},
"created_on": 1586739189439,
"file_name": "trainingTest",
"report": {
"simpleName": "Basic",
"canonicalName": "com.simiacryptus.mindseye.layers.java.AssertDimensionsLayerTest.Basic",
"link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/AssertDimensionsLayerTest.java",
"javaDoc": ""
},
"training_analysis": {
"input": {
"LBFGS": {
"type": "Converged",
"value": 0.0
},
"CjGD": {
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