Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase
Using Seed 6036976966780438528
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
[
[ [ 1.032, 1.208, 1.048, 1.556, -0.852, -0.384, -1.72, 1.512, ... ] ]
]
[
[ [ 1.524, 1.108, 1.556, -0.128, 1.764, 0.636, 0.08, -1.028, ... ] ]
]
[
[ [ 0.028, -0.804, 1.208, -1.028, 1.764, -1.616, 1.556, 1.512, ... ] ]
]
[
[ [ 0.7, -0.768, 1.512, 1.032, -1.72, 0.788, 1.556, -1.028, ... ] ]
]
[
[ [ 1.512, 1.556, -0.608, -1.688, -0.384, -0.804, 0.028, -0.128, ... ] ]
]
First, we train using basic gradient descent method apply weak line search conditions.
TrainingTester.java:480 executed in 0.62 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: 1286290633588
BACKPROP_AGG_SIZE = 3
THREADS = 64
SINGLE_THREADED = false
Initialized CoreSettings = {
"backpropAggregationSize" : 3,
"jvmThreads" : 64,
"singleThreaded" : false
}
Reset training subject: 1286323711264
Constructing line search parameters: GD
th(0)=202.9915928682288;dx=-1.9341644799999998E24
New Minimum: 202.9915928682288 > 0.043110496929857064
Armijo: th(2.154434690031884)=0.043110496929857064; dx=-1.9341644800130122E12 evalInputDelta=202.94848237129892
Armijo: th(1.077217345015942)=0.0591050918853719; dx=-1.9341644800130156E12 evalInputDelta=202.9324877763434
Armijo: th(0.3590724483386473)=0.6396985484673673; dx=-1.934164480013625E12 evalInputDelta=202.3518943197614
Armijo: th(0.08976811208466183)=2.6488236737892255; dx=-1.9341644800235193E12 evalInputDelta=200.34276919443957
Armijo: th(0.017953622416932366)=5.177269994861233; dx=-1.9341644800680894E12 evalInputDelta=197.81432287336756
Armijo: th(0.002992270402822061)=6.592759456883958; dx=-1.934164480151676E12 evalInputDelta=196.39883341134484
Armijo: th(4.2746720040315154E-4)=7.04702516570215; dx=-1.9341644802167144E12 evalInputDelta=195.94456770252665
Armijo: th(5.343340005039394E-5)=7.130450999726091; dx=-1.9341644802338367E12 evalInputDelta=195.8611418685027
Armijo: th(5.9370444500437714E-6)=7.141522992263906; dx=-1.93416448023628E12 evalInputDelta=195.8500698759649
Armijo: th(5.937044450043771E-7)=7.142775894971912; dx=-1.934164480236559E12 evalInputDelta=195.84881697325687
Armijo: th(5.397313136403428E-8)=7.142902533805264; dx=-1.9341644802365874E12 evalInputDelta=195.84869033442354
Armijo: th(4.4977609470028565E-9)=7.1429141431279195; dx=-1.93416448023659E12 evalInputDelta=195.84867872510088
Armijo: th(3.4598161130791205E-10)=7.142915117342641; dx=-1.9341644802365903E12 evalInputDelta=195.84867775088614
Armijo: th(2.4712972236279432E-11)=11.665191484422754; dx=-7.671680001932404E21 evalInputDelta=191.32640138380603
Armijo: th(1.6475314824186289E-12)=176.9460960458213; dx=-1.6772364800013497E24 evalInputDelta=26.0454968224075
Armijo: th(1.029707176511643E-13)=202.99159286820463; dx=-1.9341644799999998E24 evalInputDelta=2.4158453015843406E-11
Armijo: th(6.057101038303783E-15)=202.99159286822737; dx=-1.9341644799999998E24 evalInputDelta=1.4210854715202004E-12
MIN ALPHA (3.3650561323909904E-16): th(2.154434690031884)=0.043110496929857064
Fitness changed from 202.9915928682288 to 0.043110496929857064
Iteration 1 complete. Error: 0.043110496929857064 Total: 0.2259; Orientation: 0.0044; Line Search: 0.1775
th(0)=0.043110496929857064;dx=-4.95563425371292
New Minimum: 0.043110496929857064 > 0.04311049692985706
WOLFE (weak): th(2.154434690031884E-15)=0.04311049692985706; dx=-4.95563425371292 evalInputDelta=6.938893903907228E-18
New Minimum: 0.04311049692985706 > 0.04311049692985705
WOLFE (weak): th(4.308869380063768E-15)=0.04311049692985705; dx=-4.95563425371292 evalInputDelta=1.3877787807814457E-17
New Minimum: 0.04311049692985705 > 0.04311049692985703
WOLFE (weak): th(1.2926608140191303E-14)=0.04311049692985703; dx=-4.95563425371292 evalInputDelta=3.469446951953614E-17
New Minimum: 0.04311049692985703 > 0.04311049692985692
WOLFE (weak): th(5.1706432560765214E-14)=0.04311049692985692; dx=-4.95563425371292 evalInputDelta=1.457167719820518E-16
New Minimum: 0.04311049692985692 > 0.04311049692985634
WOLFE (weak): th(2.5853216280382605E-13)=0.04311049692985634; dx=-4.95563425371292 evalInputDelta=7.216449660063518E-16
New Minimum: 0.04311049692985634 > 0.04311049692985275
WOLFE (weak): th(1.5511929768229563E-12)=0.04311049692985275; dx=-4.95563425371292 evalInputDelta=4.315992008230296E-15
New Minimum: 0.04311049692985275 > 0.04311049692982684
WOLFE (weak): th(1.0858350837760695E-11)=0.04311049692982684; dx=-4.955634253712919 evalInputDelta=3.0225821845419887E-14
New Minimum: 0.04311049692982684 > 0.04311049692961536
WOLFE (weak): th(8.686680670208556E-11)=0.04311049692961536; dx=-4.955634253712909 evalInputDelta=2.417024913548005E-13
New Minimum: 0.04311049692961536 > 0.04311049692768177
WOLFE (weak): th(7.8180126031877E-10)=0.04311049692768177; dx=-4.955634253712817 evalInputDelta=2.1752946666175887E-12
New Minimum: 0.04311049692768177 > 0.04311049690810411
WOLFE (weak): th(7.818012603187701E-9)=0.04311049690810411; dx=-4.955634253711884 evalInputDelta=2.175295360506979E-11
New Minimum: 0.04311049690810411 > 0.04311049669057466
WOLFE (weak): th(8.599813863506471E-8)=0.04311049669057466; dx=-4.955634253701521 evalInputDelta=2.3928240638904086E-10
New Minimum: 0.04311049669057466 > 0.04311049405846833
WOLFE (weak): th(1.0319776636207765E-6)=0.04311049405846833; dx=-4.955634253576125 evalInputDelta=2.8713887378906122E-9
New Minimum: 0.04311049405846833 > 0.04311045960181455
WOLFE (weak): th(1.3415709627070094E-5)=0.04311045960181455; dx=-4.955634251934584 evalInputDelta=3.7328042511164394E-8
New Minimum: 0.04311045960181455 > 0.043109974339433005
WOLFE (weak): th(1.878199347789813E-4)=0.043109974339433005; dx=-4.955634228816457 evalInputDelta=5.225904240596657E-7
New Minimum: 0.043109974339433005 > 0.04310265856443256
WOLFE (weak): th(0.0028172990216847197)=0.04310265856443256; dx=-4.955633880320118 evalInputDelta=7.838365424506222E-6
New Minimum: 0.04310265856443256 > 0.042985209115911624
WOLFE (weak): th(0.045076784346955515)=0.042985209115911624; dx=-4.955628293313877 evalInputDelta=1.2528781394544053E-4
New Minimum: 0.042985209115911624 > 0.041016178402771296
WOLFE (weak): th(0.7663053338982437)=0.041016178402771296; dx=-4.95553677987355 evalInputDelta=0.0020943185270857687
New Minimum: 0.041016178402771296 > 0.014004641982487498
WOLFE (weak): th(13.793496010168386)=0.014004641982487498; dx=-4.954590631084917 evalInputDelta=0.029105854947369565
New Minimum: 0.014004641982487498 > 0.0
WOLFE (weak): th(262.07642419319933)=0.0; dx=-4.954302892004928 evalInputDelta=0.043110496929857064
WOLFE (weak): th(5241.528483863986)=0.0; dx=-4.954302892004928 evalInputDelta=0.043110496929857064
Armijo: th(110072.09816114372)=0.0; dx=-4.954302892004928 evalInputDelta=0.043110496929857064
Armijo: th(57656.813322503855)=0.0; dx=-4.954302892004928 evalInputDelta=0.043110496929857064
Armijo: th(31449.17090318392)=0.0; dx=-4.954302892004928 evalInputDelta=0.043110496929857064
Armijo: th(18345.349693523953)=0.0; dx=-4.954302892004928 evalInputDelta=0.043110496929857064
Armijo: th(11793.43908869397)=0.0; dx=-4.954302892004928 evalInputDelta=0.043110496929857064
WOLFE (weak): th(8517.483786278977)=0.0; dx=-4.954302892004928 evalInputDelta=0.043110496929857064
Armijo: th(10155.461437486472)=0.0; dx=-4.954302892004928 evalInputDelta=0.043110496929857064
Armijo: th(9336.472611882724)=0.0; dx=-4.954302892004928 evalInputDelta=0.043110496929857064
Armijo: th(8926.978199080851)=0.0; dx=-4.954302892004928 evalInputDelta=0.043110496929857064
Armijo: th(8722.230992679913)=0.0; dx=-4.954302892004928 evalInputDelta=0.043110496929857064
WOLFE (weak): th(8619.857389479446)=0.0; dx=-4.954302892004928 evalInputDelta=0.043110496929857064
WOLFE (weak): th(8671.04419107968)=0.0; dx=-4.954302892004928 evalInputDelta=0.043110496929857064
mu ~= nu (8671.04419107968): th(262.07642419319933)=0.0
Fitness changed from 0.043110496929857064 to 0.0
Iteration 2 complete. Error: 0.0 Total: 0.3166; Orientation: 0.0014; Line Search: 0.3073
th(0)=0.0;dx=-4.9536736
Armijo: th(18736.33771458118)=0.0; dx=-4.9536736 evalInputDelta=0.0
Armijo: th(9368.16885729059)=0.0; dx=-4.9536736 evalInputDelta=0.0
Armijo: th(3122.7229524301965)=0.0; dx=-4.9536736 evalInputDelta=0.0
Armijo: th(780.6807381075491)=0.0; dx=-4.9536736 evalInputDelta=0.0
Armijo: th(156.13614762150982)=0.0; dx=-4.9536736 evalInputDelta=0.0
Armijo: th(26.022691270251638)=0.0; dx=-4.9536736 evalInputDelta=0.0
Armijo: th(3.7175273243216624)=0.0; dx=-4.9536736 evalInputDelta=0.0
Armijo: th(0.4646909155402078)=0.0; dx=-4.9536736 evalInputDelta=0.0
Armijo: th(0.05163232394891198)=0.0; dx=-4.9536736 evalInputDelta=0.0
Armijo: th(0.005163232394891198)=0.0; dx=-4.9536736 evalInputDelta=0.0
Armijo: th(4.693847631719271E-4)=0.0; dx=-4.9536736 evalInputDelta=0.0
Armijo: th(3.911539693099393E-5)=0.0; dx=-4.9536736 evalInputDelta=0.0
Armijo: th(3.008876686999533E-6)=0.0; dx=-4.9536736 evalInputDelta=0.0
Armijo: th(2.149197633571095E-7)=0.0; dx=-4.9536736 evalInputDelta=0.0
Armijo: th(1.43279842238073E-8)=0.0; dx=-4.9536736 evalInputDelta=0.0
Armijo: th(8.954990139879562E-10)=0.0; dx=-4.9536736 evalInputDelta=0.0
Armijo: th(5.2676412587526837E-11)=0.0; dx=-4.9536736 evalInputDelta=0.0
Armijo: th(2.9264673659737133E-12)=0.0; dx=-4.9536736 evalInputDelta=0.0
Armijo: th(1.540245982091428E-13)=0.0; dx=-4.9536736 evalInputDelta=0.0
Armijo: th(7.701229910457141E-15)=0.0; dx=-4.9536736 evalInputDelta=0.0
MIN ALPHA (3.6672523383129243E-16): th(0.0)=0.0
Fitness changed from 0.0 to 0.0
Static Iteration Total: 0.0684; Orientation: 0.0007; Line Search: 0.0661
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 0.611s (< 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 0.27 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: 1286907666807
Reset training subject: 1286909099196
Constructing line search parameters: GD
F(0.0) = LineSearchPoint{point=PointSample{avg=202.9915928682288}, derivative=-1.9341644799999998E24}
New Minimum: 202.9915928682288 > 7.184075187094467
F(1.0E-10) = LineSearchPoint{point=PointSample{avg=7.184075187094467}, derivative=-1.9352140698809243E12}, evalInputDelta = -195.80751768113433
New Minimum: 7.184075187094467 > 7.1429150342722165
F(7.000000000000001E-10) = LineSearchPoint{point=PointSample{avg=7.1429150342722165}, derivative=-1.9341644802365903E12}, evalInputDelta = -195.84867783395657
New Minimum: 7.1429150342722165 > 7.142914048742602
F(4.900000000000001E-9) = LineSearchPoint{point=PointSample{avg=7.142914048742602}, derivative=-1.93416448023659E12}, evalInputDelta = -195.8486788194862
New Minimum: 7.142914048742602 > 7.14290715006116
F(3.430000000000001E-8) = LineSearchPoint{point=PointSample{avg=7.14290715006116}, derivative=-1.9341644802365884E12}, evalInputDelta = -195.84868571816764
New Minimum: 7.14290715006116 > 7.142858860557965
F(2.4010000000000004E-7) = LineSearchPoint{point=PointSample{avg=7.142858860557965}, derivative=-1.9341644802365776E12}, evalInputDelta = -195.84873400767083
New Minimum: 7.142858860557965 > 7.142520896093265
F(1.6807000000000003E-6) = LineSearchPoint{point=PointSample{avg=7.142520896093265}, derivative=-1.9341644802365024E12}, evalInputDelta = -195.84907197213553
New Minimum: 7.142520896093265 > 7.140158178618383
F(1.1764900000000001E-5) = LineSearchPoint{point=PointSample{avg=7.140158178618383}, derivative=-1.9341644802359766E12}, evalInputDelta = -195.85143468961041
New Minimum: 7.140158178618383 > 7.123765440885508
F(8.235430000000001E-5) = LineSearchPoint{point=PointSample{avg=7.123765440885508}, derivative=-1.9341644802323818E12}, evalInputDelta = -195.8678274273433
New Minimum: 7.123765440885508 > 7.015468699300719
F(5.764801000000001E-4) = LineSearchPoint{point=PointSample{avg=7.015468699300719}, derivative=-1.93416448021078E12}, evalInputDelta = -195.97612416892807
New Minimum: 7.015468699300719 > 6.44447762008636
F(0.004035360700000001) = LineSearchPoint{point=PointSample{avg=6.44447762008636}, derivative=-1.9341644801371558E12}, evalInputDelta = -196.54711524814243
New Minimum: 6.44447762008636 > 4.589082683630305
F(0.028247524900000005) = LineSearchPoint{point=PointSample{avg=4.589082683630305}, derivative=-1.9341644800517825E12}, evalInputDelta = -198.4025101845985
New Minimum: 4.589082683630305 > 1.2539182624460785
F(0.19773267430000002) = LineSearchPoint{point=PointSample{avg=1.2539182624460785}, derivative=-1.934164480015216E12}, evalInputDelta = -201.7376746057827
New Minimum: 1.2539182624460785 > 0.047278410468632445
F(1.3841287201) = LineSearchPoint{point=PointSample{avg=0.047278410468632445}, derivative=-1.9341644800130132E12}, evalInputDelta = -202.94431445776016
New Minimum: 0.047278410468632445 > 0.018841517293046933
F(9.688901040700001) = LineSearchPoint{point=PointSample{avg=0.018841517293046933}, derivative=-1.93416448001301E12}, evalInputDelta = -202.97275135093574
New Minimum: 0.018841517293046933 > 0.0
F(67.8223072849) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-1.9341644800130095E12}, evalInputDelta = -202.9915928682288
F(474.7561509943) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-1.9341644800130095E12}, evalInputDelta = -202.9915928682288
F(3323.2930569601003) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-1.9341644800130095E12}, evalInputDelta = -202.9915928682288
F(23263.0513987207) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-1.9341644800130095E12}, evalInputDelta = -202.9915928682288
F(162841.3597910449) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-1.9341644800130095E12}, evalInputDelta = -202.9915928682288
F(1139889.5185373144) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-1.9341644800130095E12}, evalInputDelta = -202.9915928682288
F(7979226.6297612) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-1.9341644800130095E12}, evalInputDelta = -202.9915928682288
F(5.58545864083284E7) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-1.9341644800130095E12}, evalInputDelta = -202.9915928682288
F(3.909821048582988E8) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-1.9341644800130095E12}, evalInputDelta = -202.9915928682288
F(2.7368747340080914E9) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-1.9341644800130095E12}, evalInputDelta = -202.9915928682288
F(1.915812313805664E10) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-1.9341644800130095E12}, evalInputDelta = -202.9915928682288
0.0 <= 202.9915928682288
F(1.0E10) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-1.9341644800130095E12}, evalInputDelta = -202.9915928682288
Right bracket at 1.0E10
Converged to right
Fitness changed from 202.9915928682288 to 0.0
Iteration 1 complete. Error: 0.0 Total: 0.1760; Orientation: 0.0009; Line Search: 0.1709
F(0.0) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-4.9536736}
F(1.0E10) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-4.9536736}, evalInputDelta = 0.0
0.0 <= 0.0
F(5.0E9) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-4.9536736}, evalInputDelta = 0.0
Right bracket at 5.0E9
F(2.5E9) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-4.9536736}, evalInputDelta = 0.0
Right bracket at 2.5E9
F(1.25E9) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-4.9536736}, evalInputDelta = 0.0
Right bracket at 1.25E9
F(6.25E8) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-4.9536736}, evalInputDelta = 0.0
Right bracket at 6.25E8
F(3.125E8) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-4.9536736}, evalInputDelta = 0.0
Right bracket at 3.125E8
F(1.5625E8) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-4.9536736}, evalInputDelta = 0.0
Right bracket at 1.5625E8
F(7.8125E7) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-4.9536736}, evalInputDelta = 0.0
Right bracket at 7.8125E7
F(3.90625E7) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-4.9536736}, evalInputDelta = 0.0
Right bracket at 3.90625E7
F(1.953125E7) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-4.9536736}, evalInputDelta = 0.0
Right bracket at 1.953125E7
F(9765625.0) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-4.9536736}, evalInputDelta = 0.0
Right bracket at 9765625.0
F(4882812.5) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-4.9536736}, evalInputDelta = 0.0
Right bracket at 4882812.5
F(2441406.25) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=-4.9536736}, evalInputDelta = 0.0
Loops = 12
Fitness changed from 0.0 to 0.0
Static Iteration Total: 0.0935; Orientation: 0.0008; Line Search: 0.0911
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 0.270s (< 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 5.10 seconds (0.067 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: 1287182167605
Reset training subject: 1287183794632
Adding measurement 7045d086 to history. Total: 0
LBFGS Accumulation History: 1 points
Constructing line search parameters: GD
Non-optimal measurement 202.9915928682288 < 202.9915928682288. Total: 1
th(0)=202.9915928682288;dx=-1.9341644799999998E24
Adding measurement 24383041 to history. Total: 1
New Minimum: 202.9915928682288 > 0.043110496929857064
Armijo: th(2.154434690031884)=0.043110496929857064; dx=-1.9341644800130122E12 evalInputDelta=202.94848237129892
Non-optimal measurement 0.0591050918853719 < 0.043110496929857064. Total: 2
Armijo: th(1.077217345015942)=0.0591050918853719; dx=-1.9341644800130156E12 evalInputDelta=202.9324877763434
Non-optimal measurement 0.6396985484673673 < 0.043110496929857064. Total: 2
Armijo: th(0.3590724483386473)=0.6396985484673673; dx=-1.934164480013625E12 evalInputDelta=202.3518943197614
Non-optimal measurement 2.6488236737892255 < 0.043110496929857064. Total: 2
Armijo: th(0.08976811208466183)=2.6488236737892255; dx=-1.9341644800235193E12 evalInputDelta=200.34276919443957
Non-optimal measurement 5.177269994861233 < 0.043110496929857064. Total: 2
Armijo: th(0.017953622416932366)=5.177269994861233; dx=-1.9341644800680894E12 evalInputDelta=197.81432287336756
Non-optimal measurement 6.592759456883958 < 0.043110496929857064. Total: 2
Armijo: th(0.002992270402822061)=6.592759456883958; dx=-1.934164480151676E12 evalInputDelta=196.39883341134484
Non-optimal measurement 7.04702516570215 < 0.043110496929857064. Total: 2
Armijo: th(4.2746720040315154E-4)=7.04702516570215; dx=-1.9341644802167144E12 evalInputDelta=195.94456770252665
Non-optimal measurement 7.130450999726091 < 0.043110496929857064. Total: 2
Armijo: th(5.343340005039394E-5)=7.130450999726091; dx=-1.9341644802338367E12 evalInputDelta=195.8611418685027
Non-optimal measurement 7.141522992263906 < 0.043110496929857064. Total: 2
Armijo: th(5.9370444500437714E-6)=7.141522992263906; dx=-1.93416448023628E12 evalInputDelta=195.8500698759649
Non-optimal measurement 7.142775894971912 < 0.043110496929857064. Total: 2
Armijo: th(5.937044450043771E-7)=7.142775894971912; dx=-1.9341644802365593E12 evalInputDelta=195.84881697325687
Non-optimal measurement 7.142902533805264 < 0.043110496929857064. Total: 2
Armijo: th(5.397313136403428E-8)=7.142902533805264; dx=-1.9341644802365874E12 evalInputDelta=195.84869033442354
Non-optimal measurement 7.1429141431279195 < 0.043110496929857064. Total: 2
Armijo: th(4.4977609470028565E-9)=7.1429141431279195; dx=-1.93416448023659E12 evalInputDelta=195.84867872510088
Non-optimal measurement 7.142915117342641 < 0.043110496929857064. Total: 2
Armijo: th(3.4598161130791205E-10)=7.142915117342641; dx=-1.9341644802365903E12 evalInputDelta=195.84867775088614
Non-optimal measurement 11.665191484422754 < 0.043110496929857064. Total: 2
Armijo: th(2.4712972236279432E-11)=11.665191484422754; dx=-7.671680001932403E21 evalInputDelta=191.32640138380603
Non-optimal measurement 176.9460960458213 < 0.043110496929857064. Total: 2
Armijo: th(1.6475314824186289E-12)=176.9460960458213; dx=-1.6772364800013497E24 evalInputDelta=26.0454968224075
Non-optimal measurement 202.99159286820463 < 0.043110496929857064. Total: 2
Armijo: th(1.029707176511643E-13)=202.99159286820463; dx=-1.9341644799999998E24 evalInputDelta=2.4158453015843406E-11
Non-optimal measurement 202.99159286822737 < 0.043110496929857064. Total: 2
Armijo: th(6.057101038303783E-15)=202.99159286822737; dx=-1.9341644799999998E24 evalInputDelta=1.4210854715202004E-12
Non-optimal measurement 0.043110496929857064 < 0.043110496929857064. Total: 2
MIN ALPHA (3.3650561323909904E-16): th(2.154434690031884)=0.043110496929857064
Fitness changed from 202.9915928682288 to 0.043110496929857064
Iteration 1 complete. Error: 0.043110496929857064 Total: 0.0767; Orientation: 0.0042; Line Search: 0.0675
Non-optimal measurement 0.043110496929857064 < 0.043110496929857064. Total: 2
LBFGS Accumulation History: 2 points
Non-optimal measurement 0.043110496929857064 < 0.043110496929857064. Total: 2
th(0)=0.043110496929857064;dx=-4.95563425371292
Adding measurement 4023b2e0 to history. Total: 2
New Minimum: 0.043110496929857064 > 0.04311049692985706
WOLFE (weak): th(2.154434690031884E-15)=0.04311049692985706; dx=-4.95563425371292 evalInputDelta=6.938893903907228E-18
Adding measurement 502b7966 to history. Total: 3
New Minimum: 0.04311049692985706 > 0.04311049692985705
WOLFE (weak): th(4.308869380063768E-15)=0.04311049692985705; dx=-4.95563425371292 evalInputDelta=1.3877787807814457E-17
Adding measurement 5a7ddfc9 to history. Total: 4
New Minimum: 0.04311049692985705 > 0.04311049692985703
WOLFE (weak): th(1.2926608140191303E-14)=0.04311049692985703; dx=-4.95563425371292 evalInputDelta=3.469446951953614E-17
Adding measurement 7c220693 to history. Total: 5
New Minimum: 0.04311049692985703 > 0.04311049692985692
WOLFE (weak): th(5.1706432560765214E-14)=0.04311049692985692; dx=-4.95563425371292 evalInputDelta=1.457167719820518E-16
Adding measurement 384c9a22 to history. Total: 6
New Minimum: 0.04311049692985692 > 0.04311049692985634
WOLFE (weak): th(2.5853216280382605E-13)=0.04311049692985634; dx=-4.95563425371292 evalInputDelta=7.216449660063518E-16
Adding measurement 7a8fde77 to history. Total: 7
New Minimum: 0.04311049692985634 > 0.04311049692985275
WOLFE (weak): th(1.5511929768229563E-12)=0.04311049692985275; dx=-4.95563425371292 evalInputDelta=4.315992008230296E-15
Adding measurement 7d0f5a12 to history. Total: 8
New Minimum: 0.04311049692985275 > 0.04311049692982684
WOLFE (weak): th(1.0858350837760695E-11)=0.04311049692982684; dx=-4.955634253712919 evalInputDelta=3.0225821845419887E-14
Adding measurement 37563ff9 to history. Total: 9
New Minimum: 0.04311049692982684 > 0.04311049692961536
WOLFE (weak): th(8.686680670208556E-11)=0.04311049692961536; dx=-4.955634253712909 evalInputDelta=2.417024913548005E-13
Adding measurement 331d2d64 to history. Total: 10
New Minimum: 0.04311049692961536 > 0.04311049692768177
WOLFE (weak): th(7.8180126031877E-10)=0.04311049692768177; dx=-4.955634253712817 evalInputDelta=2.1752946666175887E-12
Adding measurement 62b97939 to history. Total: 11
New Minimum: 0.04311049692768177 > 0.04311049690810411
WOLFE (weak): th(7.818012603187701E-9)=0.04311049690810411; dx=-4.955634253711884 evalInputDelta=2.175295360506979E-11
Adding measurement 4914bc39 to history. Total: 12
New Minimum: 0.04311049690810411 > 0.04311049669057466
WOLFE (weak): th(8.599813863506471E-8)=0.04311049669057466; dx=-4.955634253701521 evalInputDelta=2.3928240638904086E-10
Adding measurement 4208b999 to history. Total: 13
New Minimum: 0.04311049669057466 > 0.04311049405846833
WOLFE (weak): th(1.0319776636207765E-6)=0.04311049405846833; dx=-4.955634253576125 evalInputDelta=2.8713887378906122E-9
Adding measurement 50c1924a to history. Total: 14
New Minimum: 0.04311049405846833 > 0.04311045960181455
WOLFE (weak): th(1.3415709627070094E-5)=0.04311045960181455; dx=-4.955634251934584 evalInputDelta=3.7328042511164394E-8
Adding measurement 45ae196 to history. Total: 15
New Minimum: 0.04311045960181455 > 0.043109974339433005
WOLFE (weak): th(1.878199347789813E-4)=0.043109974339433005; dx=-4.955634228816457 evalInputDelta=5.225904240596657E-7
Adding measurement 32133585 to history. Total: 16
New Minimum: 0.043109974339433005 > 0.04310265856443256
WOLFE (weak): th(0.0028172990216847197)=0.04310265856443256; dx=-4.955633880320118 evalInputDelta=7.838365424506222E-6
Adding measurement 1487ad3 to history. Total: 17
New Minimum: 0.04310265856443256 > 0.042985209115911624
WOLFE (weak): th(0.045076784346955515)=0.042985209115911624; dx=-4.955628293313877 evalInputDelta=1.2528781394544053E-4
Adding measurement 60872916 to history. Total: 18
New Minimum: 0.042985209115911624 > 0.041016178402771296
WOLFE (weak): th(0.7663053338982437)=0.041016178402771296; dx=-4.95553677987355 evalInputDelta=0.0020943185270857687
Adding measurement 7e21a9f7 to history. Total: 19
New Minimum: 0.041016178402771296 > 0.014004641982487498
WOLFE (weak): th(13.793496010168386)=0.014004641982487498; dx=-4.954590631084917 evalInputDelta=0.029105854947369565
Adding measurement 69d868c to history. Tota
...skipping 9653 bytes...
669057466, 0.04311049690810411, 0.04311049692768177, 0.04311049692961536
Rejected: LBFGS Orientation magnitude: 1.229e+05, gradient 2.226e+00, dot -0.991; [966080af-1380-47e4-9d4f-3ee8684db250 = 1.000/1.000e+00, dba23f46-dc88-4f0d-92d5-8ce76ad97189 = 1.000/1.000e+00, ee0a8e39-a282-44cd-8e6c-40f240006e3c = 1.000/1.000e+00, 2aac5bee-b120-43bb-a6b6-3f4863c2bd5a = 1.000/1.000e+00, d62077ad-4331-48d0-b8c1-9a1f3368f2b4 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.014004641982487498, 0.041016178402771296, 0.042985209115911624, 0.04310265856443256, 0.043109974339433005, 0.04311045960181455, 0.04311049405846833, 0.04311049669057466, 0.04311049690810411, 0.04311049692768177
Rejected: LBFGS Orientation magnitude: 1.229e+05, gradient 2.226e+00, dot -0.991; [2aac5bee-b120-43bb-a6b6-3f4863c2bd5a = 1.000/1.000e+00, ee0a8e39-a282-44cd-8e6c-40f240006e3c = 1.000/1.000e+00, d62077ad-4331-48d0-b8c1-9a1f3368f2b4 = 1.000/1.000e+00, dba23f46-dc88-4f0d-92d5-8ce76ad97189 = 1.000/1.000e+00, 966080af-1380-47e4-9d4f-3ee8684db250 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.014004641982487498, 0.041016178402771296, 0.042985209115911624, 0.04310265856443256, 0.043109974339433005, 0.04311045960181455, 0.04311049405846833, 0.04311049669057466, 0.04311049690810411
Rejected: LBFGS Orientation magnitude: 1.229e+05, gradient 2.226e+00, dot -0.991; [dba23f46-dc88-4f0d-92d5-8ce76ad97189 = 1.000/1.000e+00, d62077ad-4331-48d0-b8c1-9a1f3368f2b4 = 1.000/1.000e+00, ee0a8e39-a282-44cd-8e6c-40f240006e3c = 1.000/1.000e+00, 2aac5bee-b120-43bb-a6b6-3f4863c2bd5a = 1.000/1.000e+00, 966080af-1380-47e4-9d4f-3ee8684db250 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.014004641982487498, 0.041016178402771296, 0.042985209115911624, 0.04310265856443256, 0.043109974339433005, 0.04311045960181455, 0.04311049405846833, 0.04311049669057466
Rejected: LBFGS Orientation magnitude: 1.229e+05, gradient 2.226e+00, dot -0.991; [d62077ad-4331-48d0-b8c1-9a1f3368f2b4 = 1.000/1.000e+00, ee0a8e39-a282-44cd-8e6c-40f240006e3c = 1.000/1.000e+00, 2aac5bee-b120-43bb-a6b6-3f4863c2bd5a = 1.000/1.000e+00, 966080af-1380-47e4-9d4f-3ee8684db250 = 1.000/1.000e+00, dba23f46-dc88-4f0d-92d5-8ce76ad97189 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.014004641982487498, 0.041016178402771296, 0.042985209115911624, 0.04310265856443256, 0.043109974339433005, 0.04311045960181455, 0.04311049405846833
Rejected: LBFGS Orientation magnitude: 1.542e+05, gradient 2.226e+00, dot -0.973; [2aac5bee-b120-43bb-a6b6-3f4863c2bd5a = 1.000/1.000e+00, dba23f46-dc88-4f0d-92d5-8ce76ad97189 = 1.000/1.000e+00, d62077ad-4331-48d0-b8c1-9a1f3368f2b4 = 1.000/1.000e+00, 966080af-1380-47e4-9d4f-3ee8684db250 = 1.000/1.000e+00, ee0a8e39-a282-44cd-8e6c-40f240006e3c = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.014004641982487498, 0.041016178402771296, 0.042985209115911624, 0.04310265856443256, 0.043109974339433005, 0.04311045960181455
Rejected: LBFGS Orientation magnitude: 1.674e+05, gradient 2.226e+00, dot -1.000; [966080af-1380-47e4-9d4f-3ee8684db250 = 1.000/1.000e+00, 2aac5bee-b120-43bb-a6b6-3f4863c2bd5a = 1.000/1.000e+00, ee0a8e39-a282-44cd-8e6c-40f240006e3c = 1.000/1.000e+00, dba23f46-dc88-4f0d-92d5-8ce76ad97189 = 1.000/1.000e+00, d62077ad-4331-48d0-b8c1-9a1f3368f2b4 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.014004641982487498, 0.041016178402771296, 0.042985209115911624, 0.04310265856443256, 0.043109974339433005
Rejected: LBFGS Orientation magnitude: 1.669e+05, gradient 2.226e+00, dot -1.000; [966080af-1380-47e4-9d4f-3ee8684db250 = 1.000/1.000e+00, ee0a8e39-a282-44cd-8e6c-40f240006e3c = 1.000/1.000e+00, 2aac5bee-b120-43bb-a6b6-3f4863c2bd5a = 1.000/1.000e+00, dba23f46-dc88-4f0d-92d5-8ce76ad97189 = 1.000/1.000e+00, d62077ad-4331-48d0-b8c1-9a1f3368f2b4 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.014004641982487498, 0.041016178402771296, 0.042985209115911624, 0.04310265856443256
Rejected: LBFGS Orientation magnitude: 1.738e+05, gradient 2.226e+00, dot -1.000; [dba23f46-dc88-4f0d-92d5-8ce76ad97189 = 1.000/1.000e+00, 2aac5bee-b120-43bb-a6b6-3f4863c2bd5a = 1.000/1.000e+00, ee0a8e39-a282-44cd-8e6c-40f240006e3c = 1.000/1.000e+00, d62077ad-4331-48d0-b8c1-9a1f3368f2b4 = 1.000/1.000e+00, 966080af-1380-47e4-9d4f-3ee8684db250 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.0, 0.014004641982487498, 0.041016178402771296, 0.042985209115911624
LBFGS Accumulation History: 3 points
Removed measurement 69d868c to history. Total: 20
Removed measurement 7e21a9f7 to history. Total: 19
Removed measurement 60872916 to history. Total: 18
Removed measurement 1487ad3 to history. Total: 17
Removed measurement 32133585 to history. Total: 16
Removed measurement 45ae196 to history. Total: 15
Removed measurement 50c1924a to history. Total: 14
Removed measurement 4208b999 to history. Total: 13
Removed measurement 4914bc39 to history. Total: 12
Removed measurement 62b97939 to history. Total: 11
Removed measurement 331d2d64 to history. Total: 10
Removed measurement 37563ff9 to history. Total: 9
Removed measurement 7d0f5a12 to history. Total: 8
Removed measurement 7a8fde77 to history. Total: 7
Removed measurement 384c9a22 to history. Total: 6
Removed measurement 7c220693 to history. Total: 5
Removed measurement 5a7ddfc9 to history. Total: 4
Removed measurement 502b7966 to history. Total: 3
Adding measurement 45a08a32 to history. Total: 3
th(0)=0.0;dx=-4.9536736
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(18736.33771458118)=0.0; dx=-4.9536736 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(9368.16885729059)=0.0; dx=-4.9536736 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(3122.7229524301965)=0.0; dx=-4.9536736 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(780.6807381075491)=0.0; dx=-4.9536736 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(156.13614762150982)=0.0; dx=-4.9536736 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(26.022691270251638)=0.0; dx=-4.9536736 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(3.7175273243216624)=0.0; dx=-4.9536736 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(0.4646909155402078)=0.0; dx=-4.9536736 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(0.05163232394891198)=0.0; dx=-4.9536736 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(0.005163232394891198)=0.0; dx=-4.9536736 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(4.693847631719271E-4)=0.0; dx=-4.9536736 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(3.911539693099393E-5)=0.0; dx=-4.9536736 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(3.008876686999533E-6)=0.0; dx=-4.9536736 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(2.149197633571095E-7)=0.0; dx=-4.9536736 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(1.43279842238073E-8)=0.0; dx=-4.9536736 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(8.954990139879562E-10)=0.0; dx=-4.9536736 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(5.2676412587526837E-11)=0.0; dx=-4.9536736 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(2.9264673659737133E-12)=0.0; dx=-4.9536736 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(1.540245982091428E-13)=0.0; dx=-4.9536736 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
Armijo: th(7.701229910457141E-15)=0.0; dx=-4.9536736 evalInputDelta=0.0
Non-optimal measurement 0.0 < 0.0. Total: 4
MIN ALPHA (3.6672523383129243E-16): th(0.0)=0.0
Fitness changed from 0.0 to 0.0
Static Iteration Total: 4.8850; Orientation: 4.7393; Line Search: 0.1441
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 5.100s (< 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, -2.365416971053535], [2.0, -0.3654169710535353]; valueStats=DoubleSummaryStatistics{count=2, sum=0.086221, min=0.043110, average=0.043110, max=0.043110}
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, -2.365416971053535], [0.316, -0.3654169710535353]; valueStats=DoubleSummaryStatistics{count=2, sum=0.086221, min=0.043110, average=0.043110, max=0.043110}
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": "6.680",
"gc_time": "0.288"
},
"created_on": 1586735874094,
"file_name": "trainingTest",
"report": {
"simpleName": "Basic1",
"canonicalName": "com.simiacryptus.mindseye.layers.java.ReshapeLayerTest.Basic1",
"link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/ReshapeLayerTest.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/ReshapeLayer/Basic1/trainingTest/202004125754",
"id": "fdda7a7f-94d5-408b-93af-6a6444a9bd8b",
"report_type": "Components",
"display_name": "Comparative Training",
"target": {
"simpleName": "ReshapeLayer",
"canonicalName": "com.simiacryptus.mindseye.layers.java.ReshapeLayer",
"link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/ReshapeLayer.java",
"javaDoc": ""
}
}