Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase
Using Seed 1379151295582479360
In this apply, we use a network to learn this target input, given it's pre-evaluated output:
TrainingTester.java:332 executed in 0.01 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 ], [ 0.496 ] ],
[ [ 0.08 ], [ -0.128 ] ]
]
[
[ [ -0.608 ], [ 1.764 ] ]
]
[
[ [ -1.72 ], [ 1.524 ] ],
[ [ 1.208 ], [ 0.048 ] ]
]
[
[ [ -1.028 ], [ -0.384 ] ]
]
[
[ [ 1.912 ] ],
[ [ -0.852 ] ]
]
[
[ [ -1.688 ] ]
]
[
[ [ -0.128 ], [ 0.08 ] ],
[ [ 0.496 ], [ 0.7 ] ]
]
[
[ [ -0.608 ], [ 1.764 ] ]
]
[
[ [ 0.048 ], [ 1.524 ] ],
[ [ -1.72 ], [ 1.208 ] ]
]
[
[ [ -0.384 ], [ -1.028 ] ]
]
[
[ [ -0.852 ] ],
[ [ 1.912 ] ]
]
[
[ [ -1.688 ] ]
]
[
[ [ 0.7 ], [ 0.496 ] ],
[ [ -0.128 ], [ 0.08 ] ]
]
[
[ [ -0.608 ], [ 1.764 ] ]
]
[
[ [ 1.524 ], [ 1.208 ] ],
[ [ 0.048 ], [ -1.72 ] ]
]
[
[ [ -0.384 ], [ -1.028 ] ]
]
[
[ [ 1.912 ] ],
[ [ -0.852 ] ]
]
[
[ [ -1.688 ] ]
]
[
[ [ 0.08 ], [ 0.7 ] ],
[ [ -0.128 ], [ 0.496 ] ]
]
[
[ [ 1.764 ], [ -0.608 ] ]
]
[
[ [ -1.72 ], [ 1.208 ] ],
[ [ 1.524 ], [ 0.048 ] ]
]
[
[ [ -0.384 ], [ -1.028 ] ]
]
[
[ [ -0.852 ] ],
[ [ 1.912 ] ]
]
[
[ [ -1.688 ] ]
]
[
[ [ 0.08 ], [ 0.7 ] ],
[ [ 0.496 ], [ -0.128 ] ]
]
[
[ [ 1.764 ], [ -0.608 ] ]
]
[
[ [ -1.72 ], [ 1.524 ] ],
[ [ 0.048 ], [ 1.208 ] ]
]
[
[ [ -1.028 ], [ -0.384 ] ]
]
[
[ [ 1.912 ] ],
[ [ -0.852 ] ]
]
[
[ [ -1.688 ] ]
]
First, we train using basic gradient descent method apply weak line search conditions.
TrainingTester.java:480 executed in 0.76 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: 1047076547880
BACKPROP_AGG_SIZE = 3
THREADS = 64
SINGLE_THREADED = false
Initialized CoreSettings = {
"backpropAggregationSize" : 3,
"jvmThreads" : 64,
"singleThreaded" : false
}
Reset training subject: 1047130631180
Constructing line search parameters: GD
th(0)=85.06204825621464;dx=-9.1489152E23
New Minimum: 85.06204825621464 > 0.06857184749869756
Armijo: th(2.154434690031884)=0.06857184749869756; dx=-9.14891520008454E11 evalInputDelta=84.99347640871594
Armijo: th(1.077217345015942)=0.23454673420484293; dx=-9.148915200085021E11 evalInputDelta=84.8275015220098
Armijo: th(0.3590724483386473)=0.524243935242346; dx=-9.148915200087909E11 evalInputDelta=84.5378043209723
Armijo: th(0.08976811208466183)=1.4707041491681383; dx=-9.148915200147517E11 evalInputDelta=83.5913441070465
Armijo: th(0.017953622416932366)=3.199123712472845; dx=-9.148915200493032E11 evalInputDelta=81.8629245437418
Armijo: th(0.002992270402822061)=4.2593208204531114; dx=-9.148915201115673E11 evalInputDelta=80.80272743576153
Armijo: th(4.2746720040315154E-4)=4.581708928444404; dx=-9.148915201445807E11 evalInputDelta=80.48033932777024
Armijo: th(5.343340005039394E-5)=4.636667462831083; dx=-9.148915201511858E11 evalInputDelta=80.42538079338355
Armijo: th(5.9370444500437714E-6)=4.643826554685187; dx=-9.148915201520697E11 evalInputDelta=80.41822170152945
Armijo: th(5.937044450043771E-7)=4.644634586117414; dx=-9.148915201521698E11 evalInputDelta=80.41741367009722
Armijo: th(5.397313136403428E-8)=4.644716235206841; dx=-9.148915201521799E11 evalInputDelta=80.4173320210078
Armijo: th(4.4977609470028565E-9)=4.644723719979995; dx=-9.148915201521808E11 evalInputDelta=80.41732453623464
Armijo: th(3.4598161130791205E-10)=4.644724348074935; dx=-9.148915201521809E11 evalInputDelta=80.4173239081397
Armijo: th(2.4712972236279432E-11)=5.175240002103361; dx=-1.843200009147072E20 evalInputDelta=79.88680825411127
Armijo: th(1.6475314824186289E-12)=77.96275075919678; dx=-8.756915200003818E23 evalInputDelta=7.099297497017858
Armijo: th(1.029707176511643E-13)=85.06204825619906; dx=-9.1489152E23 evalInputDelta=1.5575096767861396E-11
Armijo: th(6.057101038303783E-15)=85.06204825621373; dx=-9.1489152E23 evalInputDelta=9.094947017729282E-13
MIN ALPHA (3.3650561323909904E-16): th(2.154434690031884)=0.06857184749869756
Fitness changed from 85.06204825621464 to 0.06857184749869756
Iteration 1 complete. Error: 0.06857184749869756 Total: 0.3595; Orientation: 0.0049; Line Search: 0.2729
th(0)=0.06857184749869756;dx=-2.2665601077936652
New Minimum: 0.06857184749869756 > 0.06857184749869746
WOLFE (weak): th(2.154434690031884E-15)=0.06857184749869746; dx=-2.2665601077936652 evalInputDelta=9.71445146547012E-17
New Minimum: 0.06857184749869746 > 0.06857184749869741
WOLFE (weak): th(4.308869380063768E-15)=0.06857184749869741; dx=-2.2665601077936652 evalInputDelta=1.5265566588595902E-16
New Minimum: 0.06857184749869741 > 0.06857184749869707
WOLFE (weak): th(1.2926608140191303E-14)=0.06857184749869707; dx=-2.2665601077936652 evalInputDelta=4.85722573273506E-16
New Minimum: 0.06857184749869707 > 0.06857184749869556
WOLFE (weak): th(5.1706432560765214E-14)=0.06857184749869556; dx=-2.266560107793665 evalInputDelta=1.9984014443252818E-15
New Minimum: 0.06857184749869556 > 0.0685718474986876
WOLFE (weak): th(2.5853216280382605E-13)=0.0685718474986876; dx=-2.266560107793664 evalInputDelta=9.96425164601078E-15
New Minimum: 0.0685718474986876 > 0.06857184749863775
WOLFE (weak): th(1.5511929768229563E-12)=0.06857184749863775; dx=-2.2665601077936586 evalInputDelta=5.981326545168031E-14
New Minimum: 0.06857184749863775 > 0.06857184749827885
WOLFE (weak): th(1.0858350837760695E-11)=0.06857184749827885; dx=-2.2665601077936195 evalInputDelta=4.1870673594957E-13
New Minimum: 0.06857184749827885 > 0.06857184749534781
WOLFE (weak): th(8.686680670208556E-11)=0.06857184749534781; dx=-2.2665601077933 evalInputDelta=3.3497510321112145E-12
New Minimum: 0.06857184749534781 > 0.06857184746854973
WOLFE (weak): th(7.8180126031877E-10)=0.06857184746854973; dx=-2.2665601077903808 evalInputDelta=3.014782867793997E-11
New Minimum: 0.06857184746854973 > 0.06857184719721915
WOLFE (weak): th(7.818012603187701E-9)=0.06857184719721915; dx=-2.26656010776082 evalInputDelta=3.0147841167948997E-10
New Minimum: 0.06857184719721915 > 0.06857184418243498
WOLFE (weak): th(8.599813863506471E-8)=0.06857184418243498; dx=-2.2665601074323676 evalInputDelta=3.316262583985541E-9
New Minimum: 0.06857184418243498 > 0.06857180770354845
WOLFE (weak): th(1.0319776636207765E-6)=0.06857180770354845; dx=-2.2665601034580973 evalInputDelta=3.979514910656956E-8
New Minimum: 0.06857180770354845 > 0.06857133016210812
WOLFE (weak): th(1.3415709627070094E-5)=0.06857133016210812; dx=-2.2665600514313606 evalInputDelta=5.173365894423076E-7
New Minimum: 0.06857133016210812 > 0.06856460485525324
WOLFE (weak): th(1.878199347789813E-4)=0.06856460485525324; dx=-2.2665593187368502 evalInputDelta=7.2426434443162435E-6
New Minimum: 0.06856460485525324 > 0.06846322340492503
WOLFE (weak): th(0.0028172990216847197)=0.06846322340492503; dx=-2.266548275433734 evalInputDelta=1.0862409377253301E-4
New Minimum: 0.06846322340492503 > 0.06683784921256264
WOLFE (weak): th(0.045076784346955515)=0.06683784921256264; dx=-2.266371683487485 evalInputDelta=0.0017339982861349218
New Minimum: 0.06683784921256264 > 0.04282580937719751
WOLFE (weak): th(0.7663053338982437)=0.04282580937719751; dx=-2.2638763944555333 evalInputDelta=0.025746038121500052
New Minimum: 0.04282580937719751 > 0.0035474171205387836
WOLFE (weak): th(13.793496010168386)=0.0035474171205387836; dx=-2.262258372126106 evalInputDelta=0.06502443037815878
New Minimum: 0.0035474171205387836 > 0.0
WOLFE (weak): th(262.07642419319933)=0.0; dx=-2.2621463401539668 evalInputDelta=0.06857184749869756
WOLFE (weak): th(5241.528483863986)=0.0; dx=-2.2621463401539668 evalInputDelta=0.06857184749869756
Armijo: th(110072.09816114372)=0.0; dx=-2.2621463401539668 evalInputDelta=0.06857184749869756
Armijo: th(57656.813322503855)=0.0; dx=-2.2621463401539668 evalInputDelta=0.06857184749869756
Armijo: th(31449.17090318392)=0.0; dx=-2.2621463401539668 evalInputDelta=0.06857184749869756
WOLFE (weak): th(18345.349693523953)=0.0; dx=-2.2621463401539668 evalInputDelta=0.06857184749869756
WOLFE (weak): th(24897.260298353936)=0.0; dx=-2.2621463401539668 evalInputDelta=0.06857184749869756
WOLFE (weak): th(28173.215600768926)=0.0; dx=-2.2621463401539668 evalInputDelta=0.06857184749869756
WOLFE (weak): th(29811.193251976423)=0.0; dx=-2.2621463401539668 evalInputDelta=0.06857184749869756
Armijo: th(30630.18207758017)=0.0; dx=-2.2621463401539668 evalInputDelta=0.06857184749869756
WOLFE (weak): th(30220.6876647783)=0.0; dx=-2.2621463401539668 evalInputDelta=0.06857184749869756
Armijo: th(30425.434871179234)=0.0; dx=-2.2621463401539668 evalInputDelta=0.06857184749869756
mu ~= nu (30220.6876647783): th(262.07642419319933)=0.0
Fitness changed from 0.06857184749869756 to 0.0
Iteration 2 complete. Error: 0.0 Total: 0.2441; Orientation: 0.0016; Line Search: 0.2351
th(0)=0.0;dx=-2.2588
Armijo: th(65329.05510369565)=0.0; dx=-2.2588 evalInputDelta=0.0
Armijo: th(32664.527551847827)=0.0; dx=-2.2588 evalInputDelta=0.0
Armijo: th(10888.175850615942)=0.0; dx=-2.2588 evalInputDelta=0.0
Armijo: th(2722.0439626539855)=0.0; dx=-2.2588 evalInputDelta=0.0
Armijo: th(544.4087925307971)=0.0; dx=-2.2588 evalInputDelta=0.0
Armijo: th(90.73479875513284)=0.0; dx=-2.2588 evalInputDelta=0.0
Armijo: th(12.96211410787612)=0.0; dx=-2.2588 evalInputDelta=0.0
Armijo: th(1.620264263484515)=0.0; dx=-2.2588 evalInputDelta=0.0
Armijo: th(0.18002936260939056)=0.0; dx=-2.2588 evalInputDelta=0.0
Armijo: th(0.018002936260939056)=0.0; dx=-2.2588 evalInputDelta=0.0
Armijo: th(0.0016366305691762778)=0.0; dx=-2.2588 evalInputDelta=0.0
Armijo: th(1.3638588076468982E-4)=0.0; dx=-2.2588 evalInputDelta=0.0
Armijo: th(1.0491221597283832E-5)=0.0; dx=-2.2588 evalInputDelta=0.0
Armijo: th(7.493729712345595E-7)=0.0; dx=-2.2588 evalInputDelta=0.0
Armijo: th(4.995819808230397E-8)=0.0; dx=-2.2588 evalInputDelta=0.0
Armijo: th(3.122387380143998E-9)=0.0; dx=-2.2588 evalInputDelta=0.0
Armijo: th(1.836698458908234E-10)=0.0; dx=-2.2588 evalInputDelta=0.0
Armijo: th(1.0203880327267967E-11)=0.0; dx=-2.2588 evalInputDelta=0.0
Armijo: th(5.370463330141035E-13)=0.0; dx=-2.2588 evalInputDelta=0.0
Armijo: th(2.6852316650705177E-14)=0.0; dx=-2.2588 evalInputDelta=0.0
Armijo: th(1.278681745271675E-15)=0.0; dx=-2.2588 evalInputDelta=0.0
MIN ALPHA (5.812189751234886E-17): th(0.0)=0.0
Fitness changed from 0.0 to 0.0
Static Iteration Total: 0.1471; Orientation: 0.0015; Line Search: 0.1405
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 0.751s (< 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 0.39 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: 1047835214148
Reset training subject: 1047840124553
Constructing line search parameters: GD
F(0.0) = LineSearchPoint{point=PointSample{avg=85.06204825621464}, derivative=-9.1489152E23}
New Minimum: 85.06204825621464 > 5.175239990713687
F(1.0E-10) = LineSearchPoint{point=PointSample{avg=5.175239990713687}, derivative=-1.843200009147072E20}, evalInputDelta = -79.88680826550095
New Minimum: 5.175239990713687 > 4.644724294517846
F(7.000000000000001E-10) = LineSearchPoint{point=PointSample{avg=4.644724294517846}, derivative=-9.148915201521809E11}, evalInputDelta = -80.4173239616968
New Minimum: 4.644724294517846 > 4.644723659127955
F(4.900000000000001E-9) = LineSearchPoint{point=PointSample{avg=4.644723659127955}, derivative=-9.148915201521808E11}, evalInputDelta = -80.41732459708669
New Minimum: 4.644723659127955 > 4.644719211407976
F(3.430000000000001E-8) = LineSearchPoint{point=PointSample{avg=4.644719211407976}, derivative=-9.148915201521803E11}, evalInputDelta = -80.41732904480666
New Minimum: 4.644719211407976 > 4.644688077821914
F(2.4010000000000004E-7) = LineSearchPoint{point=PointSample{avg=4.644688077821914}, derivative=-9.148915201521764E11}, evalInputDelta = -80.41736017839273
New Minimum: 4.644688077821914 > 4.644470164952418
F(1.6807000000000003E-6) = LineSearchPoint{point=PointSample{avg=4.644470164952418}, derivative=-9.148915201521494E11}, evalInputDelta = -80.41757809126221
New Minimum: 4.644470164952418 > 4.642945863191633
F(1.1764900000000001E-5) = LineSearchPoint{point=PointSample{avg=4.642945863191633}, derivative=-9.148915201519607E11}, evalInputDelta = -80.41910239302301
New Minimum: 4.642945863191633 > 4.6323287085691645
F(8.235430000000001E-5) = LineSearchPoint{point=PointSample{avg=4.6323287085691645}, derivative=-9.148915201506528E11}, evalInputDelta = -80.42971954764548
New Minimum: 4.6323287085691645 > 4.560483234313071
F(5.764801000000001E-4) = LineSearchPoint{point=PointSample{avg=4.560483234313071}, derivative=-9.14891520142113E11}, evalInputDelta = -80.50156502190157
New Minimum: 4.560483234313071 > 4.1489134624192845
F(0.004035360700000001) = LineSearchPoint{point=PointSample{avg=4.1489134624192845}, derivative=-9.148915201022056E11}, evalInputDelta = -80.91313479379535
New Minimum: 4.1489134624192845 > 2.775856873592897
F(0.028247524900000005) = LineSearchPoint{point=PointSample{avg=2.775856873592897}, derivative=-9.148915200361616E11}, evalInputDelta = -82.28619138262174
New Minimum: 2.775856873592897 > 0.692269998839836
F(0.19773267430000002) = LineSearchPoint{point=PointSample{avg=0.692269998839836}, derivative=-9.148915200091952E11}, evalInputDelta = -84.3697782573748
New Minimum: 0.692269998839836 > 0.17199291190380378
F(1.3841287201) = LineSearchPoint{point=PointSample{avg=0.17199291190380378}, derivative=-9.148915200084823E11}, evalInputDelta = -84.89005534431084
New Minimum: 0.17199291190380378 > 0.004709023459807467
F(9.688901040700001) = LineSearchPoint{point=PointSample{avg=0.004709023459807467}, derivative=-9.148915200084397E11}, evalInputDelta = -85.05733923275483
New Minimum: 0.004709023459807467 > 0.0
F(67.8223072849) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-9.148915200084396E11}, evalInputDelta = -85.06204825621464
F(474.7561509943) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-9.148915200084396E11}, evalInputDelta = -85.06204825621464
F(3323.2930569601003) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-9.148915200084396E11}, evalInputDelta = -85.06204825621464
F(23263.0513987207) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-9.148915200084396E11}, evalInputDelta = -85.06204825621464
F(162841.3597910449) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-9.148915200084396E11}, evalInputDelta = -85.06204825621464
F(1139889.5185373144) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-9.148915200084396E11}, evalInputDelta = -85.06204825621464
F(7979226.6297612) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-9.148915200084396E11}, evalInputDelta = -85.06204825621464
F(5.58545864083284E7) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-9.148915200084396E11}, evalInputDelta = -85.06204825621464
F(3.909821048582988E8) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-9.148915200084397E11}, evalInputDelta = -85.06204825621464
F(2.7368747340080914E9) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-9.148915200084397E11}, evalInputDelta = -85.06204825621464
F(1.915812313805664E10) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-9.148915200084396E11}, evalInputDelta = -85.06204825621464
0.0 <= 85.06204825621464
F(1.0E10) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-9.148915200084396E11}, evalInputDelta = -85.06204825621464
Right bracket at 1.0E10
Converged to right
Fitness changed from 85.06204825621464 to 0.0
Iteration 1 complete. Error: 0.0 Total: 0.2615; Orientation: 0.0014; Line Search: 0.2486
F(0.0) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-2.2588}
F(1.0E10) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-2.2588}, evalInputDelta = 0.0
0.0 <= 0.0
F(5.0E9) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-2.2588}, evalInputDelta = 0.0
Right bracket at 5.0E9
F(2.5E9) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-2.2588}, evalInputDelta = 0.0
Right bracket at 2.5E9
F(1.25E9) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-2.2588}, evalInputDelta = 0.0
Right bracket at 1.25E9
F(6.25E8) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-2.2588}, evalInputDelta = 0.0
Right bracket at 6.25E8
F(3.125E8) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-2.2588}, evalInputDelta = 0.0
Right bracket at 3.125E8
F(1.5625E8) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-2.2588}, evalInputDelta = 0.0
Right bracket at 1.5625E8
F(7.8125E7) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-2.2588}, evalInputDelta = 0.0
Right bracket at 7.8125E7
F(3.90625E7) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-2.2588}, evalInputDelta = 0.0
Right bracket at 3.90625E7
F(1.953125E7) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-2.2588}, evalInputDelta = 0.0
Right bracket at 1.953125E7
F(9765625.0) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-2.2588}, evalInputDelta = 0.0
Right bracket at 9765625.0
F(4882812.5) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-2.2588}, evalInputDelta = 0.0
Right bracket at 4882812.5
F(2441406.25) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-2.2588}, evalInputDelta = 0.0
Loops = 12
Fitness changed from 0.0 to 0.0
Static Iteration Total: 0.1240; Orientation: 0.0038; Line Search: 0.1162
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 0.387s (< 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 2.48 seconds (0.137 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: 1048229429216
Reset training subject: 1048233591947
Adding measurement 63820f17 to history. Total: 0
LBFGS Accumulation History: 1 points
Constructing line search parameters: GD
Non-optimal measurement 85.06204825621464 < 85.06204825621464. Total: 1
th(0)=85.06204825621464;dx=-9.1489152E23
Adding measurement 4ddfadc0 to history. Total: 1
New Minimum: 85.06204825621464 > 0.06857184749869756
Armijo: th(2.154434690031884)=0.06857184749869756; dx=-9.14891520008454E11 evalInputDelta=84.99347640871594
Non-optimal measurement 0.23454673420484293 < 0.06857184749869756. Total: 2
Armijo: th(1.077217345015942)=0.23454673420484293; dx=-9.148915200085021E11 evalInputDelta=84.8275015220098
Non-optimal measurement 0.524243935242346 < 0.06857184749869756. Total: 2
Armijo: th(0.3590724483386473)=0.524243935242346; dx=-9.148915200087909E11 evalInputDelta=84.5378043209723
Non-optimal measurement 1.4707041491681383 < 0.06857184749869756. Total: 2
Armijo: th(0.08976811208466183)=1.4707041491681383; dx=-9.148915200147517E11 evalInputDelta=83.5913441070465
Non-optimal measurement 3.199123712472845 < 0.06857184749869756. Total: 2
Armijo: th(0.017953622416932366)=3.199123712472845; dx=-9.148915200493032E11 evalInputDelta=81.8629245437418
Non-optimal measurement 4.2593208204531114 < 0.06857184749869756. Total: 2
Armijo: th(0.002992270402822061)=4.2593208204531114; dx=-9.148915201115673E11 evalInputDelta=80.80272743576153
Non-optimal measurement 4.581708928444404 < 0.06857184749869756. Total: 2
Armijo: th(4.2746720040315154E-4)=4.581708928444404; dx=-9.148915201445807E11 evalInputDelta=80.48033932777024
Non-optimal measurement 4.636667462831083 < 0.06857184749869756. Total: 2
Armijo: th(5.343340005039394E-5)=4.636667462831083; dx=-9.148915201511858E11 evalInputDelta=80.42538079338355
Non-optimal measurement 4.643826554685187 < 0.06857184749869756. Total: 2
Armijo: th(5.9370444500437714E-6)=4.643826554685187; dx=-9.148915201520696E11 evalInputDelta=80.41822170152945
Non-optimal measurement 4.644634586117414 < 0.06857184749869756. Total: 2
Armijo: th(5.937044450043771E-7)=4.644634586117414; dx=-9.148915201521698E11 evalInputDelta=80.41741367009722
Non-optimal measurement 4.644716235206841 < 0.06857184749869756. Total: 2
Armijo: th(5.397313136403428E-8)=4.644716235206841; dx=-9.148915201521799E11 evalInputDelta=80.4173320210078
Non-optimal measurement 4.644723719979995 < 0.06857184749869756. Total: 2
Armijo: th(4.4977609470028565E-9)=4.644723719979995; dx=-9.148915201521808E11 evalInputDelta=80.41732453623464
Non-optimal measurement 4.644724348074935 < 0.06857184749869756. Total: 2
Armijo: th(3.4598161130791205E-10)=4.644724348074935; dx=-9.148915201521809E11 evalInputDelta=80.4173239081397
Non-optimal measurement 5.175240002103361 < 0.06857184749869756. Total: 2
Armijo: th(2.4712972236279432E-11)=5.175240002103361; dx=-1.8432000091470722E20 evalInputDelta=79.88680825411127
Non-optimal measurement 77.96275075919678 < 0.06857184749869756. Total: 2
Armijo: th(1.6475314824186289E-12)=77.96275075919678; dx=-8.756915200003818E23 evalInputDelta=7.099297497017858
Non-optimal measurement 85.06204825619906 < 0.06857184749869756. Total: 2
Armijo: th(1.029707176511643E-13)=85.06204825619906; dx=-9.1489152E23 evalInputDelta=1.5575096767861396E-11
Non-optimal measurement 85.06204825621373 < 0.06857184749869756. Total: 2
Armijo: th(6.057101038303783E-15)=85.06204825621373; dx=-9.1489152E23 evalInputDelta=9.094947017729282E-13
Non-optimal measurement 0.06857184749869756 < 0.06857184749869756. Total: 2
MIN ALPHA (3.3650561323909904E-16): th(2.154434690031884)=0.06857184749869756
Fitness changed from 85.06204825621464 to 0.06857184749869756
Iteration 1 complete. Error: 0.06857184749869756 Total: 0.1190; Orientation: 0.0055; Line Search: 0.1019
Non-optimal measurement 0.06857184749869756 < 0.06857184749869756. Total: 2
LBFGS Accumulation History: 2 points
Non-optimal measurement 0.06857184749869756 < 0.06857184749869756. Total: 2
th(0)=0.06857184749869756;dx=-2.2665601077936652
Adding measurement 1f5b06fe to history. Total: 2
New Minimum: 0.06857184749869756 > 0.06857184749869746
WOLFE (weak): th(2.154434690031884E-15)=0.06857184749869746; dx=-2.2665601077936652 evalInputDelta=9.71445146547012E-17
Adding measurement 5c6d0c7e to history. Total: 3
New Minimum: 0.06857184749869746 > 0.06857184749869741
WOLFE (weak): th(4.308869380063768E-15)=0.06857184749869741; dx=-2.2665601077936652 evalInputDelta=1.5265566588595902E-16
Adding measurement 6ad636d1 to history. Total: 4
New Minimum: 0.06857184749869741 > 0.06857184749869707
WOLFE (weak): th(1.2926608140191303E-14)=0.06857184749869707; dx=-2.2665601077936652 evalInputDelta=4.85722573273506E-16
Adding measurement 2629ca56 to history. Total: 5
New Minimum: 0.06857184749869707 > 0.06857184749869556
WOLFE (weak): th(5.1706432560765214E-14)=0.06857184749869556; dx=-2.266560107793665 evalInputDelta=1.9984014443252818E-15
Adding measurement 5f57fe1f to history. Total: 6
New Minimum: 0.06857184749869556 > 0.0685718474986876
WOLFE (weak): th(2.5853216280382605E-13)=0.0685718474986876; dx=-2.266560107793664 evalInputDelta=9.96425164601078E-15
Adding measurement 72ceb0f8 to history. Total: 7
New Minimum: 0.0685718474986876 > 0.06857184749863775
WOLFE (weak): th(1.5511929768229563E-12)=0.06857184749863775; dx=-2.2665601077936586 evalInputDelta=5.981326545168031E-14
Adding measurement 20403722 to history. Total: 8
New Minimum: 0.06857184749863775 > 0.06857184749827885
WOLFE (weak): th(1.0858350837760695E-11)=0.06857184749827885; dx=-2.2665601077936195 evalInputDelta=4.1870673594957E-13
Adding measurement c0aa10 to history. Total: 9
New Minimum: 0.06857184749827885 > 0.06857184749534781
WOLFE (weak): th(8.686680670208556E-11)=0.06857184749534781; dx=-2.2665601077933 evalInputDelta=3.3497510321112145E-12
Adding measurement 6355a0bb to history. Total: 10
New Minimum: 0.06857184749534781 > 0.06857184746854973
WOLFE (weak): th(7.8180126031877E-10)=0.06857184746854973; dx=-2.2665601077903808 evalInputDelta=3.014782867793997E-11
Adding measurement 6c13eb51 to history. Total: 11
New Minimum: 0.06857184746854973 > 0.06857184719721915
WOLFE (weak): th(7.818012603187701E-9)=0.06857184719721915; dx=-2.26656010776082 evalInputDelta=3.0147841167948997E-10
Adding measurement 66a9a094 to history. Total: 12
New Minimum: 0.06857184719721915 > 0.06857184418243498
WOLFE (weak): th(8.599813863506471E-8)=0.06857184418243498; dx=-2.2665601074323676 evalInputDelta=3.316262583985541E-9
Adding measurement 40e75770 to history. Total: 13
New Minimum: 0.06857184418243498 > 0.06857180770354845
WOLFE (weak): th(1.0319776636207765E-6)=0.06857180770354845; dx=-2.2665601034580973 evalInputDelta=3.979514910656956E-8
Adding measurement 76bc870a to history. Total: 14
New Minimum: 0.06857180770354845 > 0.06857133016210812
WOLFE (weak): th(1.3415709627070094E-5)=0.06857133016210812; dx=-2.2665600514313606 evalInputDelta=5.173365894423076E-7
Adding measurement 6471ae82 to history. Total: 15
New Minimum: 0.06857133016210812 > 0.06856460485525324
WOLFE (weak): th(1.878199347789813E-4)=0.06856460485525324; dx=-2.2665593187368502 evalInputDelta=7.2426434443162435E-6
Adding measurement 5f4b5ad1 to history. Total: 16
New Minimum: 0.06856460485525324 > 0.06846322340492503
WOLFE (weak): th(0.0028172990216847197)=0.06846322340492503; dx=-2.266548275433734 evalInputDelta=1.0862409377253301E-4
Adding measurement 2dfa5b64 to history. Total: 17
New Minimum: 0.06846322340492503 > 0.06683784921256264
WOLFE (weak): th(0.045076784346955515)=0.06683784921256264; dx=-2.266371683487485 evalInputDelta=0.0017339982861349218
Adding measurement 63cb0415 to history. Total: 18
New Minimum: 0.06683784921256264 > 0.04282580937719751
WOLFE (weak): th(0.7663053338982437)=0.04282580937719751; dx=-2.2638763944555333 evalInputDelta=0.025746038121500052
Adding measurement db5af3a to history. Total: 19
New Minimum: 0.04282580937719751 > 0.0035474171205387836
WOLFE (weak): th(13.793496010168386)=0.0035474171205387836; dx=-2.262258372126106 evalInputDelta=0.06502443037815878
Adding measurement 49a88b9 to history. Total: 20
New Minimum: 0.0035474171205387836 > 0.0
WOLFE (weak): th(262.07642419319933)=0.
...skipping 33433 bytes...
1.000/1.000e+00, ab8360a0-10d3-4875-96a4-2412f22b12e1 = 0.000e+00, 59ed5d7e-2c39-4df8-9f0a-d03e50631c50 = 1.000/1.000e+00, 4a0716ee-d597-4da5-bf73-35abd5728a15 = 1.000/1.000e+00, 5ea43f64-9acd-46c6-92ac-c5a737cedcfb = 1.000/1.000e+00, 0ca168bd-513e-4bb9-a503-4522b1f5c09c = 0.000e+00, a2cc95dd-4b43-4baa-a565-a221850b36ce = 1.000/1.000e+00, cbee08d4-ea3d-4a05-bdfa-8385d85338a1 = 0.000e+00, 1e3e2308-a982-4044-9764-e2667c1b6fa0 = 1.000/1.000e+00, 9c7d92f2-2e09-455a-88ba-d6029fc39f45 = 0.000e+00, eda694d1-f7e2-4a6e-8afe-756eb9aedff3 = 0.000e+00, 92a8129d-5a38-4c25-9125-f2d994a8704c = 1.000/1.000e+00, 716bfbfb-d27f-4a01-a642-f54a4699eff2 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.0035474171205387836, 0.04282580937719751, 0.06683784921256264, 0.06846322340492503, 0.06856460485525324
Rejected: LBFGS Orientation magnitude: 1.620e+03, gradient 1.503e+00, dot -0.998; [5ff03191-bf1c-4ad0-aa66-aaaa52fd570c = 1.000/1.000e+00, 01b02028-2556-4a79-80b5-dbac2b244a25 = 1.000/1.000e+00, a00e3a06-ee9b-4a29-b94c-6cbc2be25468 = 0.000e+00, cbee08d4-ea3d-4a05-bdfa-8385d85338a1 = 0.000e+00, 1759dbbf-a29f-455d-96f5-ff2d50336cc1 = 1.000/1.000e+00, 77f38649-9753-4a1f-8b30-0910e7546ee7 = 1.000/1.000e+00, 986ea3ce-a995-42b5-acb4-367982368f55 = 1.000/1.000e+00, ac7f6ec4-96fe-492e-8bf7-a68531a80b5e = 0.000e+00, b96bf164-0e8e-4691-a1d5-aed5ea9664ad = 1.000/1.000e+00, a2cc95dd-4b43-4baa-a565-a221850b36ce = 1.000/1.000e+00, 0ca168bd-513e-4bb9-a503-4522b1f5c09c = 0.000e+00, f13a9586-5d1e-49c6-b3be-ce068f997f04 = 1.000/1.000e+00, bf7c151c-2e23-41d2-87a8-5787eeae7fd4 = 1.000/1.000e+00, f6372ab7-fd4c-4e9b-9bf4-ef195bdda0c2 = 0.000e+00, ca12f295-22e3-4b4c-bd37-4a9ccce19eb2 = 1.000/1.000e+00, 4a0716ee-d597-4da5-bf73-35abd5728a15 = 1.000/1.000e+00, 43974c08-9ced-49ae-94f7-4c72e6513080 = 1.000/1.000e+00, 9c7d92f2-2e09-455a-88ba-d6029fc39f45 = 0.000e+00, 59ed5d7e-2c39-4df8-9f0a-d03e50631c50 = 1.000/1.000e+00, 92a8129d-5a38-4c25-9125-f2d994a8704c = 1.000/1.000e+00, ab8360a0-10d3-4875-96a4-2412f22b12e1 = 0.000e+00, fb526164-4ac3-403a-b436-c356321f0372 = 1.000/1.000e+00, 79cb8d0a-bdc7-4452-911c-d95fe7be7df9 = 1.000/1.000e+00, 9155423c-9729-4a8b-b0ba-5893f5152790 = 0.000e+00, eda694d1-f7e2-4a6e-8afe-756eb9aedff3 = 0.000e+00, 99ef5174-16af-470b-b583-58d1578b8855 = 0.000e+00, 1db06a8b-c7e9-472c-b9b7-db4af242b164 = 1.000/1.000e+00, 5ea43f64-9acd-46c6-92ac-c5a737cedcfb = 1.000/1.000e+00, 1e3e2308-a982-4044-9764-e2667c1b6fa0 = 1.000/1.000e+00, 716bfbfb-d27f-4a01-a642-f54a4699eff2 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.0035474171205387836, 0.04282580937719751, 0.06683784921256264, 0.06846322340492503
Rejected: LBFGS Orientation magnitude: 1.958e+03, gradient 1.503e+00, dot -0.999; [79cb8d0a-bdc7-4452-911c-d95fe7be7df9 = 1.000/1.000e+00, 986ea3ce-a995-42b5-acb4-367982368f55 = 1.000/1.000e+00, ca12f295-22e3-4b4c-bd37-4a9ccce19eb2 = 1.000/1.000e+00, bf7c151c-2e23-41d2-87a8-5787eeae7fd4 = 1.000/1.000e+00, 1e3e2308-a982-4044-9764-e2667c1b6fa0 = 1.000/1.000e+00, 1db06a8b-c7e9-472c-b9b7-db4af242b164 = 1.000/1.000e+00, b96bf164-0e8e-4691-a1d5-aed5ea9664ad = 1.000/1.000e+00, 5ff03191-bf1c-4ad0-aa66-aaaa52fd570c = 1.000/1.000e+00, eda694d1-f7e2-4a6e-8afe-756eb9aedff3 = 0.000e+00, 59ed5d7e-2c39-4df8-9f0a-d03e50631c50 = 1.000/1.000e+00, f6372ab7-fd4c-4e9b-9bf4-ef195bdda0c2 = 0.000e+00, 716bfbfb-d27f-4a01-a642-f54a4699eff2 = 1.000/1.000e+00, ac7f6ec4-96fe-492e-8bf7-a68531a80b5e = 0.000e+00, 77f38649-9753-4a1f-8b30-0910e7546ee7 = 1.000/1.000e+00, 1759dbbf-a29f-455d-96f5-ff2d50336cc1 = 1.000/1.000e+00, a2cc95dd-4b43-4baa-a565-a221850b36ce = 1.000/1.000e+00, 43974c08-9ced-49ae-94f7-4c72e6513080 = 1.000/1.000e+00, 4a0716ee-d597-4da5-bf73-35abd5728a15 = 1.000/1.000e+00, ab8360a0-10d3-4875-96a4-2412f22b12e1 = 0.000e+00, 01b02028-2556-4a79-80b5-dbac2b244a25 = 1.000/1.000e+00, 99ef5174-16af-470b-b583-58d1578b8855 = 0.000e+00, 0ca168bd-513e-4bb9-a503-4522b1f5c09c = 0.000e+00, a00e3a06-ee9b-4a29-b94c-6cbc2be25468 = 0.000e+00, 92a8129d-5a38-4c25-9125-f2d994a8704c = 1.000/1.000e+00, f13a9586-5d1e-49c6-b3be-ce068f997f04 = 1.000/1.000e+00, fb526164-4ac3-403a-b436-c356321f0372 = 1.000/1.000e+00, 9155423c-9729-4a8b-b0ba-5893f5152790 = 0.000e+00, 5ea43f64-9acd-46c6-92ac-c5a737cedcfb = 1.000/1.000e+00, cbee08d4-ea3d-4a05-bdfa-8385d85338a1 = 0.000e+00, 9c7d92f2-2e09-455a-88ba-d6029fc39f45 = 0.000e+00]
Orientation rejected. Popping history element from 0.0, 0.0035474171205387836, 0.04282580937719751, 0.06683784921256264
LBFGS Accumulation History: 3 points
Removed measurement 49a88b9 to history. Total: 20
Removed measurement db5af3a to history. Total: 19
Removed measurement 63cb0415 to history. Total: 18
Removed measurement 2dfa5b64 to history. Total: 17
Removed measurement 5f4b5ad1 to history. Total: 16
Removed measurement 6471ae82 to history. Total: 15
Removed measurement 76bc870a to history. Total: 14
Removed measurement 40e75770 to history. Total: 13
Removed measurement 66a9a094 to history. Total: 12
Removed measurement 6c13eb51 to history. Total: 11
Removed measurement 6355a0bb to history. Total: 10
Removed measurement c0aa10 to history. Total: 9
Removed measurement 20403722 to history. Total: 8
Removed measurement 72ceb0f8 to history. Total: 7
Removed measurement 5f57fe1f to history. Total: 6
Removed measurement 2629ca56 to history. Total: 5
Removed measurement 6ad636d1 to history. Total: 4
Removed measurement 5c6d0c7e to history. Total: 3
Adding measurement 6fec340d to history. Total: 3
th(0)=0.0;dx=-2.2588
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(65329.05510369565)=0.0; dx=-2.2588 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(32664.527551847827)=0.0; dx=-2.2588 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(10888.175850615942)=0.0; dx=-2.2588 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(2722.0439626539855)=0.0; dx=-2.2588 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(544.4087925307971)=0.0; dx=-2.2588 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(90.73479875513284)=0.0; dx=-2.2588 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(12.96211410787612)=0.0; dx=-2.2588 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(1.620264263484515)=0.0; dx=-2.2588 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(0.18002936260939056)=0.0; dx=-2.2588 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(0.018002936260939056)=0.0; dx=-2.2588 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(0.0016366305691762778)=0.0; dx=-2.2588 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(1.3638588076468982E-4)=0.0; dx=-2.2588 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(1.0491221597283832E-5)=0.0; dx=-2.2588 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(7.493729712345595E-7)=0.0; dx=-2.2588 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(4.995819808230397E-8)=0.0; dx=-2.2588 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(3.122387380143998E-9)=0.0; dx=-2.2588 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(1.836698458908234E-10)=0.0; dx=-2.2588 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(1.0203880327267967E-11)=0.0; dx=-2.2588 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(5.370463330141035E-13)=0.0; dx=-2.2588 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(2.6852316650705177E-14)=0.0; dx=-2.2588 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(1.278681745271675E-15)=0.0; dx=-2.2588 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
MIN ALPHA (5.812189751234886E-17): th(0.0)=0.0
Fitness changed from 0.0 to 0.0
Static Iteration Total: 2.1876; Orientation: 2.0881; Line Search: 0.0971
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 2.476s (< 30.000s)
Returns
0.0
TrainingTester.java:432 executed in 0.12 seconds (0.000 gc):
return TestUtil.compare(title + " vs Iteration", runs);
Plotting range=[1.0, -2.163854149387173], [2.0, -0.16385414938717302]; valueStats=DoubleSummaryStatistics{count=2, sum=0.137144, min=0.068572, average=0.068572, max=0.068572}
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, -2.163854149387173], [0.243, -0.16385414938717302]; valueStats=DoubleSummaryStatistics{count=2, sum=0.137144, min=0.068572, average=0.068572, max=0.068572}
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": "4.666",
"gc_time": "0.732"
},
"created_on": 1586735634867,
"file_name": "trainingTest",
"report": {
"simpleName": "Basic",
"canonicalName": "com.simiacryptus.mindseye.layers.java.ImgTileAssemblyLayerTest.Basic",
"link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/ImgTileAssemblyLayerTest.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/java/ImgTileAssemblyLayer/Basic/trainingTest/202004125354",
"id": "88bd7708-01c0-4bf9-af1c-b1cc0d99b706",
"report_type": "Components",
"display_name": "Comparative Training",
"target": {
"simpleName": "ImgTileAssemblyLayer",
"canonicalName": "com.simiacryptus.mindseye.layers.java.ImgTileAssemblyLayer",
"link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/ImgTileAssemblyLayer.java",
"javaDoc": ""
}
}