1. Test Modules
  2. Training Characteristics
    1. Input Learning
      1. Gradient Descent
      2. Conjugate Gradient Descent
      3. Limited-Memory BFGS
    2. Results
  3. Results

Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase

Test Modules

Using Seed 3475823376444303360

Training Characteristics

Input Learning

In this apply, we use a network to learn this target input, given it's pre-evaluated output:

TrainingTester.java:332 executed in 0.02 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

    [
    	[ [ 1.356 ], [ -1.492 ], [ 1.912 ], [ -0.408 ], [ -1.688 ], [ -0.608 ] ],
    	[ [ 0.496 ], [ -1.616 ], [ 1.048 ], [ 0.3 ], [ -1.572 ], [ 0.092 ] ],
    	[ [ 0.08 ], [ 0.7 ], [ -0.068 ], [ 0.392 ], [ -1.228 ], [ -0.804 ] ],
    	[ [ 0.788 ], [ -0.176 ], [ -0.852 ], [ 1.64 ], [ -0.636 ], [ -0.384 ] ],
    	[ [ -1.028 ], [ -0.712 ], [ 1.556 ], [ 1.512 ], [ -1.516 ], [ 1.524 ] ],
    	[ [ 0.048 ], [ -0.768 ], [ 1.764 ], [ 1.552 ], [ -0.384 ], [ 1.108 ] ],
    	[ [ -0.128 ], [ 0.636 ], [ 1.876 ], [ 1.556 ], [ 1.704 ], [ 0.028 ] ],
    	[ [ 1.208 ], [ 1.032 ], [ -1.476 ], [ 1.612 ], [ -0.556 ], [ -1.72 ] ]
    ]
    [
    	[ [ 0.636 ], [ 1.556 ], [ -0.712 ], [ -1.228 ], [ 0.048 ], [ -0.636 ] ],
    	[ [ 0.7 ], [ 1.108 ], [ -1.028 ], [ -0.556 ], [ 0.092 ], [ 1.912 ] ],
    	[ [ -1.516 ], [ 1.048 ], [ -1.616 ], [ 0.3 ], [ 0.028 ], [ -0.608 ] ],
    	[ [ -0.408 ], [ -0.128 ], [ -1.72 ], [ 1.764 ], [ -0.804 ], [ 0.392 ] ],
    	[ [ 1.704 ], [ 0.496 ], [ 1.356 ], [ 0.788 ], [ -1.476 ], [ 1.556 ] ],
    	[ [ 1.208 ], [ 1.512 ], [ -1.688 ], [ 1.032 ], [ 1.552 ], [ -0.768 ] ],
    	[ [ 1.524 ], [ 1.64 ], [ 1.612 ], [ -0.176 ], [ -0.852 ], [ -1.572 ] ],
    	[ [ -1.492 ], [ -0.384 ], [ -0.068 ], [ -0.384 ], [ 0.08 ], [ 1.876 ] ]
    ]
    [
    	[ [ -0.128 ], [ -1.616 ], [ -0.608 ], [ 1.704 ], [ 1.556 ], [ 1.048 ] ],
    	[ [ 1.524 ], [ -1.516 ], [ -0.768 ], [ 1.512 ], [ -1.028 ], [ -0.408 ] ],
    	[ [ -0.852 ], [ -1.476 ], [ -1.72 ], [ 1.612 ], [ 1.764 ], [ 1.556 ] ],
    	[ [ -1.572 ], [ -0.176 ], [ 0.636 ], [ 1.108 ], [ 0.3 ], [ -0.384 ] ],
    	[ [ 0.392 ], [ 0.496 ], [ 0.08 ], [ 0.092 ], [ 1.912 ], [ -0.712 ] ],
    	[ [ 0.028 ], [ 1.876 ], [ -1.688 ], [ 0.7 ], [ 1.552 ], [ -1.492 ] ],
    	[ [ 1.356 ], [ 0.788 ], [ 0.048 ], [ -0.556 ], [ 1.64 ], [ -0.804 ] ],
    	[ [ -0.636 ], [ -1.228 ], [ 1.032 ], [ -0.068 ], [ 1.208 ], [ -0.384 ] ]
    ]
    [
    	[ [ -0.176 ], [ 1.612 ], [ 1.356 ], [ -0.712 ], [ 1.764 ], [ -0.384 ] ],
    	[ [ -1.616 ], [ -0.128 ], [ 1.552 ], [ 1.556 ], [ 1.912 ], [ -1.688 ] ],
    	[ [ -1.028 ], [ 1.704 ], [ 1.512 ], [ 0.3 ], [ -1.492 ], [ -1.572 ] ],
    	[ [ 0.496 ], [ -1.72 ], [ -1.516 ], [ -0.608 ], [ -0.384 ], [ 1.048 ] ],
    	[ [ 0.028 ], [ 0.7 ], [ 1.556 ], [ 0.08 ], [ 0.048 ], [ -0.556 ] ],
    	[ [ 1.208 ], [ 0.788 ], [ -0.768 ], [ -0.068 ], [ 1.108 ], [ 0.092 ] ],
    	[ [ 0.392 ], [ -0.852 ], [ 0.636 ], [ 1.032 ], [ -0.636 ], [ 1.64 ] ],
    	[ [ -0.804 ], [ -1.228 ], [ -0.408 ], [ 1.876 ], [ -1.476 ], [ 1.524 ] ]
    ]
    [
    	[ [ -0.768 ], [ -1.028 ], [ 1.612 ], [ 1.108 ], [ 0.08 ], [ -1.688 ] ],
    	[ [ 1.556 ], [ -0.852 ], [ -0.556 ], [ 1.64 ], [ 0.7 ], [ 1.552 ] ],
    	[ [ 0.092 ], [ 1.912 ], [ 0.028 ], [ -1.492 ], [ 1.556 ], [ 0.048 ] ],
    	[ [ -0.128 ], [ 1.356 ], [ 0.392 ], [ 0.3 ], [ -1.572 ], [ -1.476 ] ],
    	[ [ -0.176 ], [ -1.516 ], [ -0.804 ], [ 1.524 ], [ 1.704 ], [ -0.408 ] ],
    	[ [ 0.636 ], [ -0.068 ], [ 1.032 ], [ 1.208 ], [ -0.384 ], [ 1.048 ] ],
    	[ [ 0.788 ], [ -0.636 ], [ -1.72 ], [ 1.876 ], [ -1.228 ], [ 1.764 ] ],
    	[ [ -1.616 ], [ 1.512 ], [ -0.608 ], [ -0.712 ], [ 0.496 ], [ -0.384 ] ]
    ]

Gradient Descent

First, we train using basic gradient descent method apply weak line search conditions.

TrainingTester.java:480 executed in 1.28 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();
    }
Logging
Reset training subject: 1960038977217
Reset training subject: 1960093327924
Constructing line search parameters: GD
th(0)=3.6756010666666667;dx=-0.24504007111111112
New Minimum: 3.6756010666666667 > 3.166634491314626
WOLFE (weak): th(2.154434690031884)=3.166634491314626; dx=-0.22744264345612253 evalInputDelta=0.5089665753520407
New Minimum: 3.166634491314626 > 2.695580424557819
END: th(4.308869380063768)=2.695580424557819; dx=-0.20984521580113397 evalInputDelta=0.9800206421088475
Fitness changed from 3.6756010666666667 to 2.695580424557819
Iteration 1 complete. Error: 2.695580424557819 Total: 0.2963; Orientation: 0.0082; Line Search: 0.0993
th(0)=2.695580424557819;dx=-0.17970536163718798
New Minimum: 2.695580424557819 > 1.285452601457372
END: th(9.283177667225559)=1.285452601457372; dx=-0.12409746830948677 evalInputDelta=1.4101278231004473
Fitness changed from 2.695580424557819 to 1.285452601457372
Iteration 2 complete. Error: 1.285452601457372 Total: 0.0609; Orientation: 0.0018; Line Search: 0.0397
th(0)=1.285452601457372;dx=-0.08569684009715813
New Minimum: 1.285452601457372 > 0.14282806682859678
END: th(20.000000000000004)=0.14282806682859678; dx=-0.028565613365719368 evalInputDelta=1.1426245346287751
Fitness changed from 1.285452601457372 to 0.14282806682859678
Iteration 3 complete. Error: 0.14282806682859678 Total: 0.0522; Orientation: 0.0049; Line Search: 0.0315
th(0)=0.14282806682859678;dx=-0.009521871121906453
New Minimum: 0.14282806682859678 > 0.027187148811241867
WOLF (strong): th(43.088693800637685)=0.027187148811241867; dx=0.0041542951841256 evalInputDelta=0.1156409180173549
New Minimum: 0.027187148811241867 > 0.011346590029017667
END: th(21.544346900318843)=0.011346590029017667; dx=-0.002683787968890427 evalInputDelta=0.1314814767995791
Fitness changed from 0.14282806682859678 to 0.011346590029017667
Iteration 4 complete. Error: 0.011346590029017667 Total: 0.0631; Orientation: 0.0025; Line Search: 0.0478
th(0)=0.011346590029017667;dx=-7.564393352678444E-4
New Minimum: 0.011346590029017667 > 0.0033974387235990488
WOLF (strong): th(46.4158883361278)=0.0033974387235990488; dx=4.1392078869372244E-4 evalInputDelta=0.007949151305418619
New Minimum: 0.0033974387235990488 > 5.816012729512604E-4
END: th(23.2079441680639)=5.816012729512604E-4; dx=-1.71259273287061E-4 evalInputDelta=0.010764988756066407
Fitness changed from 0.011346590029017667 to 5.816012729512604E-4
Iteration 5 complete. Error: 5.816012729512604E-4 Total: 0.0587; Orientation: 0.0015; Line Search: 0.0457
th(0)=5.816012729512604E-4;dx=-3.8773418196750696E-5
New Minimum: 5.816012729512604E-4 > 2.584894546450047E-4
WOLF (strong): th(50.000000000000014)=2.584894546450047E-4; dx=2.5848945464500467E-5 evalInputDelta=3.231118183062557E-4
New Minimum: 2.584894546450047E-4 > 1.615559091531271E-5
END: th(25.000000000000007)=1.615559091531271E-5; dx=-6.462236366125101E-6 evalInputDelta=5.654456820359477E-4
Fitness changed from 5.816012729512604E-4 to 1.615559091531271E-5
Iteration 6 complete. Error: 1.615559091531271E-5 Total: 0.0539; Orientation: 0.0019; Line Search: 0.0420
th(0)=1.615559091531271E-5;dx=-1.077039394354181E-6
New Minimum: 1.615559091531271E-5 > 1.0220044511659011E-5
WOLF (strong): th(53.860867250797114)=1.0220044511659011E-5; dx=8.566364670854645E-7 evalInputDelta=5.9355464036537E-6
New Minimum: 1.0220044511659011E-5 > 1.691353536019409E-7
END: th(26.930433625398557)=1.691353536019409E-7; dx=-1.1020146363435611E-7 evalInputDelta=1.598645556171077E-5
Fitness changed from 1.615559091531271E-5 to 1.691353536019409E-7
Iteration 7 complete. Error: 1.691353536019409E-7 Total: 0.0590; Orientation: 0.0013; Line Search: 0.0459
th(0)=1.691353536019409E-7;dx=-1.1275690240129395E-8
New Minimum: 1.691353536019409E-7 > 1.4754477054609567E-7
WOLF (strong): th(58.01986042015976)=1.4754477054609567E-7; dx=1.053144222231296E-8 evalInputDelta=2.159058305584522E-8
New Minimum: 1.4754477054609567E-7 > 1.8421436966168228E-10
END: th(29.00993021007988)=1.8421436966168228E-10; dx=-3.721240089079463E-10 evalInputDelta=1.6895113923227921E-7
Fitness changed from 1.691353536019409E-7 to 1.8421436966168228E-10
Iteration 8 complete. Error: 1.8421436966168228E-10 Total: 0.0512; Orientation: 0.0015; Line Search: 0.0382
Low gradient: 3.5044197775730986E-6
th(0)=1.8421436966168228E-10;dx=-1.2280957977445485E-11
Armijo: th(62.50000000000003)=2.16196031061607E-10; dx=1.3304371142242674E-11 evalInputDelta=-3.198166139992471E-11
New Minimum: 1.8421436966168228E-10 > 3.198166139886146E-13
WOLF (strong): th(31.250000000000014)=3.198166139886146E-13; dx=5.117065823876726E-13 evalInputDelta=1.8389455304769367E-10
END: th(10.416666666666671)=7.849721114650484E-11; dx=-8.016736457507262E-12 evalInputDelta=1.0571715851517744E-10
Fitness changed from 1.8421436966168228E-10 to 3.198166139886146E-13
Iteration 9 complete. Error: 3.198166139886146E-13 Total: 0.0519; Orientation: 0.0010; Line Search: 0.0433
Low gradient: 1.4601749073053194E-7
th(0)=3.198166139886146E-13;dx=-2.1321107599240977E-14
New Minimum: 3.198166139886146E-13 > 2.029873932340406E-14
END: th(22.442028021165466)=2.029873932340406E-14; dx=-5.371477793203915E-15 evalInputDelta=2.9951787466521057E-13
Fitness changed from 3.198166139886146E-13 to 2.029873932340406E-14
Iteration 10 complete. Error: 2.029873932340406E-14 Total: 0.0329; Orientation: 0.0010; Line Search: 0.0229
Low gradient: 3.6786536779465084E-8
th(0)=2.029873932340406E-14;dx=-1.3532492882269375E-15
New Minimum: 2.029873932340406E-14 > 7.59439511523515E-15
WOLF (strong): th(48.34988368346647)=7.59439511523515E-15; dx=8.277322346510414E-16 evalInputDelta=1.270434420816891E-14
New Minimum: 7.59439511523515E-15 > 7.652918500426129E-16
END: th(24.174941841733236)=7.652918500426129E-16; dx=-2.627585268335476E-16 evalInputDelta=1.9533447473361446E-14
Fitness changed from 2.029873932340406E-14 to 7.652918500426129E-16
Iteration 11 complete. Error: 7.652918500426129E-16 Total: 0.0552; Orientation: 0.0014; Line Search: 0.0464
Low gradient: 7.142790537983564E-9
th(0)=7.652918500426129E-16;dx=-5.101945666950753E-17
New Minimum: 7.652918500426129E-16 > 4.146807110989511E-16
WOLF (strong): th(52.083333333333364)=4.146807110989511E-16; dx=3.755598893349635E-17 evalInputDelta=3.506111389436618E-16
New Minimum: 4.146807110989511E-16 > 1.3323223347141845E-17
END: th(26.041666666666682)=1.3323223347141845E-17; dx=-6.731733883914675E-18 evalInputDelta=7.519686266954711E-16
Fitness changed from 7.652918500426129E-16 to 1.3323223347141845E-17
Iteration 12 complete. Error: 1.3323223347141845E-17 Total: 0.0606; Orientation: 0.0013; Line Search: 0.0456
Low gradient: 9.424515318091728E-10
th(0)=1.3323223347141845E-17;dx=-8.882148898094563E-19
New Minimum: 1.3323223347141845E-17 > 1.0088266079387763E-17
WOLF (strong): th(56.105070052913675)=1.0088266079387763E-17; dx=7.728970670609759E-19 evalInputDelta=3.2349572677540818E-18
New Minimum: 1.0088266079387763E-17 > 5.614435676432093E-20
END: th(28.052535026456837)=5.614435676432093E-20; dx=-5.765891296052557E-20 evalInputDelta=1.3267078990377524E-17
Fitness changed from 1.3323223347141845E-17 to 5.614435676432093E-20
Iteration 13 complete. Error: 5.614435676432093E-20 Total: 0.0536; Orientation: 0.0012; Line Search: 0.0425
Zero gradient: 6.117971165036164E-11
th(0)=5.614435676432093E-20;dx=-3.7429571176213954E-21
Armijo: th(60.4373546043331)=5.779328916336852E-20; dx=3.7975237811392614E-21 evalInputDelta=-1.6489323990475831E-21
New Minimum: 5.614435676432093E-20 > 2.9831394645188913E-24
WOLF (strong): th(30.21867730216655)=2.9831394645188913E-24; dx=2.728340531815924E-23 evalInputDelta=5.614137362485642E-20
END: th(10.072892434055516)=2.4771489791556083E-20; dx=-2.4862102891668093E-21 evalInputDelta=3.1372866972764854E-20
Fitness changed from 5.614435676432093E-20 to 2.9831394645188913E-24
Iteration 14 complete. Error: 2.9831394645188913E-24 Total: 0.0523; Orientation: 0.0009; Line Search: 0.0429
Zero gradient: 4.4595511467103887E-13
th(0)=2.9831394645188913E-24;dx=-1.9887596430125944E-25
New Minimum: 2.9831394645188913E-24 > 2.2826122998653377E-25
END: th(21.701388888888903)=2.2826122998653377E-25; dx=-5.501252695118117E-26 evalInputDelta=2.7548782345323576E-24
Fitness changed from 2.9831394645188913E-24 to 2.2826122998653377E-25
Iteration 15 complete. Error: 2.2826122998653377E-25 Total: 0.0278; Orientation: 0.0011; Line Search: 0.0194
Zero gradient: 1.233588883398176E-13
th(0)=2.2826122998653377E-25;dx=-1.5217415332435584E-26
New Minimum: 2.2826122998653377E-25 > 7.119005403362357E-26
WOLF (strong): th(46.75422504409473)=7.119005403362357E-26; dx=8.498347247735047E-27 evalInputDelta=1.570711759529102E-25
New Minimum: 7.119005403362357E-26 > 1.1122216958977501E-26
END: th(23.377112522047366)=1.1122216958977501E-26; dx=-3.35908083872153E-27 evalInputDelta=2.171390130275563E-25
Fitness changed from 2.2826122998653377E-25 to 1.1122216958977501E-26
Iteration 16 complete. Error: 1.1122216958977501E-26 Total: 0.0487; Orientation: 0.0010; Line Search: 0.0396
Zero gradient: 2.723015113065846E-14
th(0)=1.1122216958977501E-26;dx=-7.414811305985001E-28
New Minimum: 1.1122216958977501E-26 > 5.124755235370662E-27
WOLF (strong): th(50.364462170277584)=5.124755235370662E-27; dx=5.033161957712531E-28 evalInputDelta=5.997461723606839E-27
New Minimum: 5.124755235370662E-27 > 2.868134934530492E-28
END: th(25.182231085138792)=2.868134934530492E-28; dx=-1.1906981223495506E-28 evalInputDelta=1.0835403465524451E-26
Fitness changed from 1.1122216958977501E-26 to 2.868134934530492E-28
Iteration 17 complete. Error: 2.868134934530492E-28 Total: 0.0389; Orientation: 0.0009; Line Search: 0.0308
Zero gradient: 4.372745083301405E-15
th(0)=2.868134934530492E-28;dx=-1.9120899563536614E-29
New Minimum: 2.868134934530492E-28 > 1.8728385076857934E-28
WOLF (strong): th(54.253472222222264)=1.8728385076857934E-28; dx=1.5450984617148265E-29 evalInputDelta=9.952964268446985E-29
New Minimum: 1.8728385076857934E-28 > 2.663073210834969E-30
END: th(27.126736111111132)=2.663073210834969E-30; dx=-1.841892900730817E-30 evalInputDelta=2.8415042024221424E-28
Fitness changed from 2.868134934530492E-28 to 2.663073210834969E-30
Iteration 18 complete. Error: 2.663073210834969E-30 Total: 0.0362; Orientation: 0.0009; Line Search: 0.0279
Zero gradient: 4.213528379584794E-16
th(0)=2.663073210834969E-30;dx=-1.775382140556646E-31
New Minimum: 2.663073210834969E-30 > 2.3181810868950462E-30
WOLF (strong): th(58.44278130511842)=2.3181810868950462E-30; dx=1.6554881866936798E-31 evalInputDelta=3.448921239399226E-31
New Minimum: 2.3181810868950462E-30 > 1.3341077218932414E-34
END: th(29.22139065255921)=1.3341077218932414E-34; dx=-3.504657518244662E-34 evalInputDelta=2.6629398000627794E-30
Fitness changed from 2.663073210834969E-30 to 1.3341077218932414E-34
Iteration 19 complete. Error: 1.3341077218932414E-34 Total: 0.0397; Orientation: 0.0011; Line Search: 0.0318
Zero gradient: 2.9822896370554415E-18
th(0)=1.3341077218932414E-34;dx=-8.894051479288277E-36
Armijo: th(62.955577712846996)=1.3341077218932414E-34; dx=8.894051479288277E-36 evalInputDelta=0.0
New Minimum: 1.3341077218932414E-34 > 0.0
END: th(31.477788856423498)=0.0; dx=0.0 evalInputDelta=1.3341077218932414E-34
Fitness changed from 1.3341077218932414E-34 to 0.0
Iteration 20 complete. Error: 0.0 Total: 0.0469; Orientation: 0.0018; Line Search: 0.0321
Zero gradient: 0.0
th(0)=0.0;dx=0.0 (ERROR: Starting derivative negative)
Fitness changed from 0.0 to 0.0
Static Iteration Total: 0.0292; Orientation: 0.0011; Line Search: 0.0196
Iteration 21 failed. Error: 0.0
Previous Error: 0.0 -> 0.0
Optimization terminated 21
Final threshold in iteration 21: 0.0 (> 0.0) after 1.271s (< 30.000s)

Returns

    0.0

Training Converged

Conjugate Gradient Descent

First, we use a conjugate gradient descent method, which converges the fastest for purely linear functions.

TrainingTester.java:452 executed in 0.33 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();
    }
Logging
Reset training subject: 1961317158974
Reset training subject: 1961324896016
Constructing line search parameters: GD
F(0.0) = LineSearchPoint{point=PointSample{avg=3.6756010666666667}, derivative=-0.24504007111111112}
New Minimum: 3.6756010666666667 > 3.6756010666421624
F(1.0E-10) = LineSearchPoint{point=PointSample{avg=3.6756010666421624}, derivative=-0.24504007111029433}, evalInputDelta = -2.4504398510316605E-11
New Minimum: 3.6756010666421624 > 3.675601066495138
F(7.000000000000001E-10) = LineSearchPoint{point=PointSample{avg=3.675601066495138}, derivative=-0.2450400711053935}, evalInputDelta = -1.7152856912616699E-10
New Minimum: 3.675601066495138 > 3.6756010654659703
F(4.900000000000001E-9) = LineSearchPoint{point=PointSample{avg=3.6756010654659703}, derivative=-0.2450400710710879}, evalInputDelta = -1.20069643116949E-9
New Minimum: 3.6756010654659703 > 3.6756010582617926
F(3.430000000000001E-8) = LineSearchPoint{point=PointSample{avg=3.6756010582617926}, derivative=-0.24504007083094864}, evalInputDelta = -8.404874130008011E-9
New Minimum: 3.6756010582617926 > 3.675601007832546
F(2.4010000000000004E-7) = LineSearchPoint{point=PointSample{avg=3.675601007832546}, derivative=-0.24504006914997375}, evalInputDelta = -5.8834120686412916E-8
New Minimum: 3.675601007832546 > 3.675600654827831
F(1.6807000000000003E-6) = LineSearchPoint{point=PointSample{avg=3.675600654827831}, derivative=-0.24504005738314955}, evalInputDelta = -4.118388359231062E-7
New Minimum: 3.675600654827831 > 3.6755981837952993
F(1.1764900000000001E-5) = LineSearchPoint{point=PointSample{avg=3.6755981837952993}, derivative=-0.24503997501538005}, evalInputDelta = -2.8828713674045048E-6
New Minimum: 3.6755981837952993 > 3.6755808865908364
F(8.235430000000001E-5) = LineSearchPoint{point=PointSample{avg=3.6755808865908364}, derivative=-0.2450393984409935}, evalInputDelta = -2.0180075830378286E-5
New Minimum: 3.6755808865908364 > 3.6754598072992017
F(5.764801000000001E-4) = LineSearchPoint{point=PointSample{avg=3.6754598072992017}, derivative=-0.24503536242028784}, evalInputDelta = -1.4125936746500756E-4
New Minimum: 3.6754598072992017 > 3.6746123080982103
F(0.004035360700000001) = LineSearchPoint{point=PointSample{avg=3.6746123080982103}, derivative=-0.24500711027534822}, evalInputDelta = -9.887585684564826E-4
New Minimum: 3.6746123080982103 > 3.6686825498735587
F(0.028247524900000005) = LineSearchPoint{point=PointSample{avg=3.6686825498735587}, derivative=-0.24480934526077083}, evalInputDelta = -0.006918516793108065
New Minimum: 3.6686825498735587 > 3.6273083152331673
F(0.19773267430000002) = LineSearchPoint{point=PointSample{avg=3.6273083152331673}, derivative=-0.24342499015872904}, evalInputDelta = -0.048292751433499426
New Minimum: 3.6273083152331673 > 3.344258246426606
F(1.3841287201) = LineSearchPoint{point=PointSample{avg=3.344258246426606}, derivative=-0.2337345044444366}, evalInputDelta = -0.33134282024006056
New Minimum: 3.344258246426606 > 1.6848168749135806
F(9.688901040700001) = LineSearchPoint{point=PointSample{avg=1.6848168749135806}, derivative=-0.16590110444438955}, evalInputDelta = -1.9907841917530862
F(67.8223072849) = LineSearchPoint{point=PointSample{avg=5.842273670834609}, derivative=0.30893269555593983}, evalInputDelta = 2.166672604167942
F(5.217100560376924) = LineSearchPoint{point=PointSample{avg=2.508361283258558}, derivative=-0.20242678136749184}, evalInputDelta = -1.167239783408109
New Minimum: 1.6848168749135806 > 0.17359675662965532
F(36.51970392263847) = LineSearchPoint{point=PointSample{avg=0.17359675662965532}, derivative=0.053252957094224035}, evalInputDelta = -3.5020043100370115
0.17359675662965532 <= 3.6756010666666667
New Minimum: 0.17359675662965532 > 2.5615068878052774E-32
F(30.0) = LineSearchPoint{point=PointSample{avg=2.5615068878052774E-32}, derivative=2.1391067929322905E-18}, evalInputDelta = -3.6756010666666667
Right bracket at 30.0
Converged to right
Fitness changed from 3.6756010666666667 to 2.5615068878052774E-32
Iteration 1 complete. Error: 2.5615068878052774E-32 Total: 0.2587; Orientation: 0.0010; Line Search: 0.2319
Zero gradient: 4.1323979219538526E-17
F(0.0) = LineSearchPoint{point=PointSample{avg=2.5615068878052774E-32}, derivative=-1.7076712585368518E-33}
New Minimum: 2.5615068878052774E-32 > 0.0
F(30.0) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=0.0}, evalInputDelta = -2.5615068878052774E-32
0.0 <= 2.5615068878052774E-32
F(15.0) = LineSearchPoint{point=PointSample{avg=6.975076281922438E-33}, derivative=-7.711744485650556E-34}, evalInputDelta = -1.8639992596130335E-32
Left bracket at 15.0
Converged to right
Fitness changed from 2.5615068878052774E-32 to 0.0
Iteration 2 complete. Error: 0.0 Total: 0.0541; Orientation: 0.0012; Line Search: 0.0435
Zero gradient: 0.0
F(0.0) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=0.0}
Fitness changed from 0.0 to 0.0
Static Iteration Total: 0.0141; Orientation: 0.0009; Line Search: 0.0069
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 0.327s (< 30.000s)

Returns

    0.0

Training Converged

Limited-Memory BFGS

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 6.92 seconds (0.115 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();
    }
Logging
Reset training subject: 1961649165146
Reset training subject: 1961655439654
Adding measurement 74408fb0 to history. Total: 0
LBFGS Accumulation History: 1 points
Constructing line search parameters: GD
Non-optimal measurement 3.6756010666666667 < 3.6756010666666667. Total: 1
th(0)=3.6756010666666667;dx=-0.24504007111111112
Adding measurement 5075f86b to history. Total: 1
New Minimum: 3.6756010666666667 > 3.166634491314626
WOLFE (weak): th(2.154434690031884)=3.166634491314626; dx=-0.22744264345612253 evalInputDelta=0.5089665753520407
Adding measurement 1719675 to history. Total: 2
New Minimum: 3.166634491314626 > 2.695580424557819
END: th(4.308869380063768)=2.695580424557819; dx=-0.20984521580113397 evalInputDelta=0.9800206421088475
Fitness changed from 3.6756010666666667 to 2.695580424557819
Iteration 1 complete. Error: 2.695580424557819 Total: 0.0653; Orientation: 0.0068; Line Search: 0.0336
Non-optimal measurement 2.695580424557819 < 2.695580424557819. Total: 3
LBFGS Accumulation History: 3 points
Non-optimal measurement 2.695580424557819 < 2.695580424557819. Total: 3
th(0)=2.695580424557819;dx=-0.17970536163718798
Adding measurement 50524dbf to history. Total: 3
New Minimum: 2.695580424557819 > 1.285452601457372
END: th(9.283177667225559)=1.285452601457372; dx=-0.12409746830948677 evalInputDelta=1.4101278231004473
Fitness changed from 2.695580424557819 to 1.285452601457372
Iteration 2 complete. Error: 1.285452601457372 Total: 0.0338; Orientation: 0.0023; Line Search: 0.0224
Non-optimal measurement 1.285452601457372 < 1.285452601457372. Total: 4
Rejected: LBFGS Orientation magnitude: 8.782e+00, gradient 2.927e-01, dot -1.000; [d14413ac-42b5-49ec-9d6d-75afdfa0c061 = 1.000/1.000e+00, 00e4efeb-7387-4a65-951c-930046b05a94 = 1.000/1.000e+00, d085fcbd-e5f6-4463-b223-478e30bada36 = 1.000/1.000e+00, 2d762929-6171-4d03-8781-1a08a2fe2e6a = 1.000/1.000e+00, 0adf1c53-1569-43dc-ad5d-8e05f928e878 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 1.285452601457372, 2.695580424557819, 3.166634491314626, 3.6756010666666667
LBFGS Accumulation History: 3 points
Removed measurement 50524dbf to history. Total: 3
Adding measurement 1d07209b to history. Total: 3
th(0)=1.285452601457372;dx=-0.08569684009715813
Adding measurement 7007d510 to history. Total: 4
New Minimum: 1.285452601457372 > 0.14282806682859678
END: th(20.000000000000004)=0.14282806682859678; dx=-0.028565613365719368 evalInputDelta=1.1426245346287751
Fitness changed from 1.285452601457372 to 0.14282806682859678
Iteration 3 complete. Error: 0.14282806682859678 Total: 0.1200; Orientation: 0.0914; Line Search: 0.0220
Non-optimal measurement 0.14282806682859678 < 0.14282806682859678. Total: 5
Rejected: LBFGS Orientation magnitude: 2.927e+00, gradient 9.758e-02, dot -1.000; [00e4efeb-7387-4a65-951c-930046b05a94 = 1.000/1.000e+00, 2d762929-6171-4d03-8781-1a08a2fe2e6a = 1.000/1.000e+00, 0adf1c53-1569-43dc-ad5d-8e05f928e878 = 1.000/1.000e+00, d14413ac-42b5-49ec-9d6d-75afdfa0c061 = 1.000/1.000e+00, d085fcbd-e5f6-4463-b223-478e30bada36 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.14282806682859678, 1.285452601457372, 2.695580424557819, 3.166634491314626, 3.6756010666666667
Rejected: LBFGS Orientation magnitude: 2.927e+00, gradient 9.758e-02, dot -1.000; [2d762929-6171-4d03-8781-1a08a2fe2e6a = 1.000/1.000e+00, d14413ac-42b5-49ec-9d6d-75afdfa0c061 = 1.000/1.000e+00, d085fcbd-e5f6-4463-b223-478e30bada36 = 1.000/1.000e+00, 00e4efeb-7387-4a65-951c-930046b05a94 = 1.000/1.000e+00, 0adf1c53-1569-43dc-ad5d-8e05f928e878 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.14282806682859678, 1.285452601457372, 2.695580424557819, 3.166634491314626
LBFGS Accumulation History: 3 points
Removed measurement 7007d510 to history. Total: 4
Removed measurement 1d07209b to history. Total: 3
Adding measurement 5e31330f to history. Total: 3
th(0)=0.14282806682859678;dx=-0.009521871121906453
Adding measurement 35d6658e to history. Total: 4
New Minimum: 0.14282806682859678 > 0.027187148811241867
WOLF (strong): th(43.088693800637685)=0.027187148811241867; dx=0.0041542951841256 evalInputDelta=0.1156409180173549
Adding measurement 2ab71019 to history. Total: 5
New Minimum: 0.027187148811241867 > 0.011346590029017667
END: th(21.544346900318843)=0.011346590029017667; dx=-0.002683787968890427 evalInputDelta=0.1314814767995791
Fitness changed from 0.14282806682859678 to 0.011346590029017667
Iteration 4 complete. Error: 0.011346590029017667 Total: 0.2556; Orientation: 0.2089; Line Search: 0.0388
Non-optimal measurement 0.011346590029017667 < 0.011346590029017667. Total: 6
Rejected: LBFGS Orientation magnitude: 8.251e-01, gradient 2.750e-02, dot -1.000; [2d762929-6171-4d03-8781-1a08a2fe2e6a = 1.000/1.000e+00, 0adf1c53-1569-43dc-ad5d-8e05f928e878 = 1.000/1.000e+00, d085fcbd-e5f6-4463-b223-478e30bada36 = 1.000/1.000e+00, d14413ac-42b5-49ec-9d6d-75afdfa0c061 = 1.000/1.000e+00, 00e4efeb-7387-4a65-951c-930046b05a94 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.011346590029017667, 0.027187148811241867, 0.14282806682859678, 2.695580424557819, 3.166634491314626, 3.6756010666666667
Rejected: LBFGS Orientation magnitude: 8.251e-01, gradient 2.750e-02, dot -1.000; [0adf1c53-1569-43dc-ad5d-8e05f928e878 = 1.000/1.000e+00, d14413ac-42b5-49ec-9d6d-75afdfa0c061 = 1.000/1.000e+00, 2d762929-6171-4d03-8781-1a08a2fe2e6a = 1.000/1.000e+00, 00e4efeb-7387-4a65-951c-930046b05a94 = 1.000/1.000e+00, d085fcbd-e5f6-4463-b223-478e30bada36 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.011346590029017667, 0.027187148811241867, 0.14282806682859678, 2.695580424557819, 3.166634491314626
Rejected: LBFGS Orientation magnitude: 8.251e-01, gradient 2.750e-02, dot -1.000; [2d762929-6171-4d03-8781-1a08a2fe2e6a = 1.000/1.000e+00, d085fcbd-e5f6-4463-b223-478e30bada36 = 1.000/1.000e+00, 00e4efeb-7387-4a65-951c-930046b05a94 = 1.000/1.000e+00, 0adf1c53-1569-43dc-ad5d-8e05f928e878 = 1.000/1.000e+00, d14413ac-42b5-49ec-9d6d-75afdfa0c061 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 0.011346590029017667, 0.027187148811241867, 0.14282806682859678, 2.695580424557819
LBFGS Accumulation History: 3 points
Removed measurement 2ab71019 to history. Total: 5
Removed measurement 35d6658e to history. Total: 4
Removed measurement 5e31330f to history. Total: 3
Adding measurement 69718dd4 to history. Total: 3
th(0)=0.011346590029017667;dx=-7.564393352678444E-4
Adding measurement 6c41aa58 to history. Total: 4
New Minimum: 0.011346590029017667 > 0.0033974387235990488
WOLF (strong): th(46.4158883361278)=0.0033974387235990488; dx=4.1392078869372244E-4 evalInputDelta=0.007949151305418619
Adding measurement 53f3eef2 to history. Total: 5
New Minimum: 0.0033974387235990488 > 5.816012729512604E-4
END: th(23.2079441680639)=5.816012729512604E-4; dx=-1.71259273287061E-4 evalInputDelta=0.010764988756066407
Fitness changed from 0.011346590029017667 to 5.816012729512604E-4
Iteration 5 complete. Error: 5.816012729512604E-4 Total: 0.4056; Orientation: 0.3619; Line Search: 0.0363
Non-optimal measurement 5.816012729512604E-4 < 5.816012729512604E-4. Total: 6
Rejected: LBFGS Orientation magnitude: 1.868e-01, gradient 6.227e-03, dot -1.000; [d085fcbd-e5f6-4463-b223-478e30bada36 = 1.000/1.000e+00, 0adf1c53-1569-43dc-ad5d-8e05f928e878 = 1.000/1.000e+00, 2d762929-6171-4d03-8781-1a08a2fe2e6a = 1.000/1.000e+00, d14413ac-42b5-49ec-9d6d-75afdfa0c061 = 1.000/1.000e+00, 00e4efeb-7387-4a65-951c-930046b05a94 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 5.816012729512604E-4, 0.0033974387235990488, 0.011346590029017667, 2.695580424557819, 3.166634491314626, 3.6756010666666667
Rejected: LBFGS Orientation magnitude: 1.868e-01, gradient 6.227e-03, dot -1.000; [0adf1c53-1569-43dc-ad5d-8e05f928e878 = 1.000/1.000e+00, d14413ac-42b5-49ec-9d6d-75afdfa0c061 = 1.000/1.000e+00, 00e4efeb-7387-4a65-951c-930046b05a94 = 1.000/1.000e+00, d085fcbd-e5f6-4463-b223-478e30bada36 = 1.000/1.000e+00, 2d762929-6171-4d03-8781-1a08a2fe2e6a = 1.000/1.000e+00]
Orientation rejected. Popping history element from 5.816012729512604E-4, 0.0033974387235990488, 0.011346590029017667, 2.695580424557819, 

...skipping 27942 bytes...

05a94 = 1.000/1.000e+00, 0adf1c53-1569-43dc-ad5d-8e05f928e878 = 1.000/1.000e+00, 2d762929-6171-4d03-8781-1a08a2fe2e6a = 1.000/1.000e+00]
Orientation rejected. Popping history element from 2.868134934530492E-28, 5.124755235370662E-27, 1.1122216958977501E-26, 2.695580424557819, 3.166634491314626
Rejected: LBFGS Orientation magnitude: 1.312e-13, gradient 4.373e-15, dot -1.000; [2d762929-6171-4d03-8781-1a08a2fe2e6a = 1.000/1.000e+00, d085fcbd-e5f6-4463-b223-478e30bada36 = 1.000/1.000e+00, 00e4efeb-7387-4a65-951c-930046b05a94 = 1.000/1.000e+00, 0adf1c53-1569-43dc-ad5d-8e05f928e878 = 1.000/1.000e+00, d14413ac-42b5-49ec-9d6d-75afdfa0c061 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 2.868134934530492E-28, 5.124755235370662E-27, 1.1122216958977501E-26, 2.695580424557819
LBFGS Accumulation History: 3 points
Removed measurement 618dbfa9 to history. Total: 5
Removed measurement 3e6f8e24 to history. Total: 4
Removed measurement 5cb512f1 to history. Total: 3
Adding measurement 1640e968 to history. Total: 3
th(0)=2.868134934530492E-28;dx=-1.9120899563536614E-29
Adding measurement 29d464ef to history. Total: 4
New Minimum: 2.868134934530492E-28 > 1.8728385076857934E-28
WOLF (strong): th(54.253472222222264)=1.8728385076857934E-28; dx=1.5450984617148265E-29 evalInputDelta=9.952964268446985E-29
Adding measurement 573f4c51 to history. Total: 5
New Minimum: 1.8728385076857934E-28 > 2.663073210834969E-30
END: th(27.126736111111132)=2.663073210834969E-30; dx=-1.841892900730817E-30 evalInputDelta=2.8415042024221424E-28
Fitness changed from 2.868134934530492E-28 to 2.663073210834969E-30
Iteration 18 complete. Error: 2.663073210834969E-30 Total: 0.3396; Orientation: 0.2990; Line Search: 0.0318
Non-optimal measurement 2.663073210834969E-30 < 2.663073210834969E-30. Total: 6
Rejected: LBFGS Orientation magnitude: 1.264e-14, gradient 4.214e-16, dot -1.000; [2d762929-6171-4d03-8781-1a08a2fe2e6a = 1.000/1.000e+00, d14413ac-42b5-49ec-9d6d-75afdfa0c061 = 1.000/1.000e+00, d085fcbd-e5f6-4463-b223-478e30bada36 = 1.000/1.000e+00, 00e4efeb-7387-4a65-951c-930046b05a94 = 1.000/1.000e+00, 0adf1c53-1569-43dc-ad5d-8e05f928e878 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 2.663073210834969E-30, 1.8728385076857934E-28, 2.868134934530492E-28, 2.695580424557819, 3.166634491314626, 3.6756010666666667
Rejected: LBFGS Orientation magnitude: 1.264e-14, gradient 4.214e-16, dot -1.000; [0adf1c53-1569-43dc-ad5d-8e05f928e878 = 1.000/1.000e+00, d14413ac-42b5-49ec-9d6d-75afdfa0c061 = 1.000/1.000e+00, d085fcbd-e5f6-4463-b223-478e30bada36 = 1.000/1.000e+00, 2d762929-6171-4d03-8781-1a08a2fe2e6a = 1.000/1.000e+00, 00e4efeb-7387-4a65-951c-930046b05a94 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 2.663073210834969E-30, 1.8728385076857934E-28, 2.868134934530492E-28, 2.695580424557819, 3.166634491314626
Rejected: LBFGS Orientation magnitude: 1.264e-14, gradient 4.214e-16, dot -1.000; [2d762929-6171-4d03-8781-1a08a2fe2e6a = 1.000/1.000e+00, 0adf1c53-1569-43dc-ad5d-8e05f928e878 = 1.000/1.000e+00, d14413ac-42b5-49ec-9d6d-75afdfa0c061 = 1.000/1.000e+00, 00e4efeb-7387-4a65-951c-930046b05a94 = 1.000/1.000e+00, d085fcbd-e5f6-4463-b223-478e30bada36 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 2.663073210834969E-30, 1.8728385076857934E-28, 2.868134934530492E-28, 2.695580424557819
LBFGS Accumulation History: 3 points
Removed measurement 573f4c51 to history. Total: 5
Removed measurement 29d464ef to history. Total: 4
Removed measurement 1640e968 to history. Total: 3
Adding measurement 2f731f80 to history. Total: 3
th(0)=2.663073210834969E-30;dx=-1.775382140556646E-31
Adding measurement 5b97260e to history. Total: 4
New Minimum: 2.663073210834969E-30 > 2.3181810868950462E-30
WOLF (strong): th(58.44278130511842)=2.3181810868950462E-30; dx=1.6554881866936798E-31 evalInputDelta=3.448921239399226E-31
Adding measurement 4bcee3fb to history. Total: 5
New Minimum: 2.3181810868950462E-30 > 1.3341077218932414E-34
END: th(29.22139065255921)=1.3341077218932414E-34; dx=-3.5046575182446617E-34 evalInputDelta=2.6629398000627794E-30
Fitness changed from 2.663073210834969E-30 to 1.3341077218932414E-34
Iteration 19 complete. Error: 1.3341077218932414E-34 Total: 0.5207; Orientation: 0.4838; Line Search: 0.0286
Non-optimal measurement 1.3341077218932414E-34 < 1.3341077218932414E-34. Total: 6
Rejected: LBFGS Orientation magnitude: 8.947e-17, gradient 2.982e-18, dot -1.000; [d14413ac-42b5-49ec-9d6d-75afdfa0c061 = 1.000/1.000e+00, 2d762929-6171-4d03-8781-1a08a2fe2e6a = 0.000e+00, 00e4efeb-7387-4a65-951c-930046b05a94 = 0.000e+00, d085fcbd-e5f6-4463-b223-478e30bada36 = 1.000/1.000e+00, 0adf1c53-1569-43dc-ad5d-8e05f928e878 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 1.3341077218932414E-34, 2.3181810868950462E-30, 2.663073210834969E-30, 2.695580424557819, 3.166634491314626, 3.6756010666666667
Rejected: LBFGS Orientation magnitude: 8.947e-17, gradient 2.982e-18, dot -1.000; [0adf1c53-1569-43dc-ad5d-8e05f928e878 = 1.000/1.000e+00, 00e4efeb-7387-4a65-951c-930046b05a94 = 0.000e+00, d14413ac-42b5-49ec-9d6d-75afdfa0c061 = 1.000/1.000e+00, d085fcbd-e5f6-4463-b223-478e30bada36 = 1.000/1.000e+00, 2d762929-6171-4d03-8781-1a08a2fe2e6a = 0.000e+00]
Orientation rejected. Popping history element from 1.3341077218932414E-34, 2.3181810868950462E-30, 2.663073210834969E-30, 2.695580424557819, 3.166634491314626
Rejected: LBFGS Orientation magnitude: 8.947e-17, gradient 2.982e-18, dot -1.000; [2d762929-6171-4d03-8781-1a08a2fe2e6a = 0.000e+00, 0adf1c53-1569-43dc-ad5d-8e05f928e878 = 1.000/1.000e+00, d14413ac-42b5-49ec-9d6d-75afdfa0c061 = 1.000/1.000e+00, 00e4efeb-7387-4a65-951c-930046b05a94 = 0.000e+00, d085fcbd-e5f6-4463-b223-478e30bada36 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 1.3341077218932414E-34, 2.3181810868950462E-30, 2.663073210834969E-30, 2.695580424557819
LBFGS Accumulation History: 3 points
Removed measurement 4bcee3fb to history. Total: 5
Removed measurement 5b97260e to history. Total: 4
Removed measurement 2f731f80 to history. Total: 3
Adding measurement 7c38228a to history. Total: 3
th(0)=1.3341077218932414E-34;dx=-8.894051479288277E-36
Non-optimal measurement 1.3341077218932414E-34 < 1.3341077218932414E-34. Total: 4
Armijo: th(62.955577712846996)=1.3341077218932414E-34; dx=8.894051479288277E-36 evalInputDelta=0.0
Adding measurement 4125b7a8 to history. Total: 4
New Minimum: 1.3341077218932414E-34 > 0.0
END: th(31.477788856423498)=0.0; dx=0.0 evalInputDelta=1.3341077218932414E-34
Fitness changed from 1.3341077218932414E-34 to 0.0
Iteration 20 complete. Error: 0.0 Total: 0.3726; Orientation: 0.3202; Line Search: 0.0454
Non-optimal measurement 0.0 < 0.0. Total: 5
Rejected: LBFGS Orientation magnitude: 0.000e+00, gradient 0.000e+00, dot NaN; [0adf1c53-1569-43dc-ad5d-8e05f928e878 = 0.000e+00, d085fcbd-e5f6-4463-b223-478e30bada36 = 0.000e+00, d14413ac-42b5-49ec-9d6d-75afdfa0c061 = 0.000e+00, 2d762929-6171-4d03-8781-1a08a2fe2e6a = 0.000e+00, 00e4efeb-7387-4a65-951c-930046b05a94 = 0.000e+00]
Orientation rejected. Popping history element from 0.0, 1.3341077218932414E-34, 2.695580424557819, 3.166634491314626, 3.6756010666666667
Rejected: LBFGS Orientation magnitude: 0.000e+00, gradient 0.000e+00, dot NaN; [d085fcbd-e5f6-4463-b223-478e30bada36 = 0.000e+00, 2d762929-6171-4d03-8781-1a08a2fe2e6a = 0.000e+00, d14413ac-42b5-49ec-9d6d-75afdfa0c061 = 0.000e+00, 00e4efeb-7387-4a65-951c-930046b05a94 = 0.000e+00, 0adf1c53-1569-43dc-ad5d-8e05f928e878 = 0.000e+00]
Orientation rejected. Popping history element from 0.0, 1.3341077218932414E-34, 2.695580424557819, 3.166634491314626
LBFGS Accumulation History: 3 points
Removed measurement 4125b7a8 to history. Total: 4
Removed measurement 7c38228a to history. Total: 3
Adding measurement 77bf5aac to history. Total: 3
th(0)=0.0;dx=0.0 (ERROR: Starting derivative negative)
Non-optimal measurement 0.0 < 0.0. Total: 4
Fitness changed from 0.0 to 0.0
Static Iteration Total: 0.2192; Orientation: 0.1916; Line Search: 0.0201
Iteration 21 failed. Error: 0.0
Previous Error: 0.0 -> 0.0
Optimization terminated 21
Final threshold in iteration 21: 0.0 (> 0.0) after 6.916s (< 30.000s)

Returns

    0.0

Training Converged

TrainingTester.java:432 executed in 0.15 seconds (0.000 gc):

    return TestUtil.compare(title + " vs Iteration", runs);
Logging
Plotting range=[1.0, -33.87480910210236], [20.0, 0.43065229384249415]; valueStats=DoubleSummaryStatistics{count=39, sum=8.271611, min=0.000000, average=0.212093, max=2.695580}
Plotting 20 points for GD
Plotting 2 points for CjGD
Plotting 20 points for LBFGS

Returns

Result

TrainingTester.java:435 executed in 0.02 seconds (0.000 gc):

    return TestUtil.compareTime(title + " vs Time", runs);
Logging
Plotting range=[0.0, -33.87480910210236], [6.631, 0.43065229384249415]; valueStats=DoubleSummaryStatistics{count=39, sum=8.271611, min=0.000000, average=0.212093, max=2.695580}
Plotting 20 points for GD
Plotting 2 points for CjGD
Plotting 20 points for LBFGS

Returns

Result

Results

TrainingTester.java:255 executed in 0.00 seconds (0.000 gc):

    return grid(inputLearning, modelLearning, completeLearning);

Returns

Result

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());

Results

detailsresult
{"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": "9.570",
      "gc_time": "0.461"
    },
    "created_on": 1586736591640,
    "file_name": "trainingTest",
    "report": {
      "simpleName": "UL",
      "canonicalName": "com.simiacryptus.mindseye.layers.cudnn.ImgTileSelectLayerTest.UL",
      "link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/test/java/com/simiacryptus/mindseye/layers/cudnn/ImgTileSelectLayerTest.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/cudnn/ImgTileSelectLayer/UL/trainingTest/202004130951",
    "id": "a1024ce7-dc7f-4c6b-bf66-24c1c82e408d",
    "report_type": "Components",
    "display_name": "Comparative Training",
    "target": {
      "simpleName": "ImgTileSelectLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.cudnn.ImgTileSelectLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/main/java/com/simiacryptus/mindseye/layers/cudnn/ImgTileSelectLayer.java",
      "javaDoc": ""
    }
  }