BasicOptimizer.scala:89 executed in 141.71 seconds (1.557 gc):

    val lineSearchInstance: LineSearchStrategy = lineSearchFactory
    val trainer = new IterativeTrainer(trainable)
    trainer.setOrientation(orientation())
    trainer.setMonitor(new TrainingMonitor() {
      override def clear(): Unit = trainingMonitor.clear()
  
      override def log(msg: String): Unit = {
        trainingMonitor.log(msg)
        BasicOptimizer.this.log(msg)
      }
  
      override def onStepFail(currentPoint: Step): Boolean = {
        BasicOptimizer.this.onStepFail(trainable.addRef().asInstanceOf[Trainable], currentPoint)
      }
  
      override def onStepComplete(currentPoint: Step): Unit = {
        if (0 < logEvery && (0 == currentPoint.iteration % logEvery || currentPoint.iteration < logEvery)) {
          val image = currentImage()
          timelineAnimation += image
          val caption = "Iteration " + currentPoint.iteration
          out.p(caption + "\n" + out.jpg(image, caption))
        }
        BasicOptimizer.this.onStepComplete(trainable.addRef().asInstanceOf[Trainable], currentPoint)
        trainingMonitor.onStepComplete(currentPoint)
        super.onStepComplete(currentPoint)
      }
    })
    trainer.setTimeout(trainingMinutes, TimeUnit.MINUTES)
    trainer.setMaxIterations(trainingIterations)
    trainer.setLineSearchFactory((_: CharSequence) => lineSearchInstance)
    trainer.setTerminateThreshold(java.lang.Double.NEGATIVE_INFINITY)
    val result = trainer.run.asInstanceOf[lang.Double]
    trainer.freeRef()
    result
Logging 
Reset training subject: 71277412579800
Reset training subject: 71280395377400
Adding measurement 2f1148f3 to history. Total: 0
LBFGS Accumulation History: 1 points
Constructing line search parameters: GD+Trust
th(0)=-20.736070005550136;dx=-6.784344756405551E-7
Armijo: th(2.154434690031884)=-20.736070005550136; dx=-6.590732227757816E-7 evalInputDelta=0.0
Armijo: th(1.077217345015942)=-20.736070005550136; dx=-6.590942643633941E-7 evalInputDelta=0.0
Armijo: th(0.3590724483386473)=-20.736070005550136; dx=-6.592944212417939E-7 evalInputDelta=0.0
Armijo: th(0.08976811208466183)=-20.736070005550136; dx=-6.59111400133021E-7 evalInputDelta=0.0
Armijo: th(0.017953622416932366)=-20.736070005550136; dx=-6.590308703726972E-7 evalInputDelta=0.0
Armijo: th(0.002992270402822061)=-20.736070005550136; dx=-6.586593482231818E-7 evalInputDelta=0.0
WOLFE (weak): th(4.2746720040315154E-4)=-20.736070005550136; dx=-6.590978874345272E-7 evalInputDelta=0.0
WOLFE (weak): th(0.0017098688016126062)=-20.736070005550136; dx=-6.590825406685919E-7 evalInputDelta=0.0
WOLFE (weak): th(0.0023510696022173336)=-20.736070005550136; dx=-6.59007311361203E-7 evalInputDelta=0.0
Armijo: th(0.0026716700025196972)=-20.736070005550136; dx=-6.590893371593556E-7 evalInputDelta=0.0
WOLFE (weak): th(0.0025113698023685157)=-20.736070005550136; dx=-6.590889339379389E-7 evalInputDelta=0.0
WOLFE (weak): th(0.0025915199024441064)=-20.736070005550136; dx=-6.590896551778561E-7 evalInputDelta=0.0
Armijo: th(0.0026315949524819016)=-20.736070005550136; dx=-6.589769148894938E-7 evalInputDelta=0.0
WOLFE (weak): th(0.002611557427463004)=-20.736070005550136; dx=-6.590951626687015E-7 evalInputDelta=0.0
Armijo: th(0.0026215761899724527)=-20.736070005550136; dx=-6.5880887970767E-7 evalInputDelta=0.0
WOLFE (weak): th(0.002616566808717728)=-20.736070005550136; dx=-6.590471432483563E-7 evalInputDelta=0.0
Armijo: th(0.00261907149934509)=-20.736070005550136; dx=-6.587910275760503E-7 evalInputDelta=0.0
WOLFE (weak): th(0.002617819154031409)=-20.736070005550136; dx=-6.591869966418062E-7 evalInputDelta=0.0
Armijo: th(0.00261844532668825)=-20.736070005550136; dx=-6.590757902836601E-7 evalInputDelta=0.0
WOLFE (weak): th(0.0026181322403598297)=-20.736070005550136; dx=-6.590047283678208E-7 evalInputDelta=0.0
WOLFE (weak): th(0.00261828878352404)=-20.736070005550136; dx=-6.589949846503057E-7 evalInputDelta=0.0
Armijo: th(0.002618367055106145)=-20.736070005550136; dx=-6.59072295208683E-7 evalInputDelta=0.0
Armijo: th(0.002618327919315092)=-20.736070005550136; dx=-6.590883726800037E-7 evalInputDelta=0.0
WOLFE (weak): th(0.002618308351419566)=-20.736070005550136; dx=-6.591596048761337E-7 evalInputDelta=0.0
mu ~= nu (0.002618308351419566): th(0.0)=-20.736070005550136
Fitness changed from -20.736070005550136 to -20.736070005550136
Static Iteration Total: 76.2650; Orientation: 0.0620; Line Search: 68.2147
Iteration 1 failed. Error: -20.736070005550136
Previous Error: 0.0 -> -20.736070005550136
Retrying iteration 1
Reset training subject: 71353677797800
Adding measurement 11015447 to history. Total: 0
LBFGS Accumulation History: 1 points
th(0)=-20.736070005550136;dx=-6.78856033819919E-7
WOLFE (weak): th(0.005640995420374971)=-20.736070005550136; dx=-6.589522781526303E-7 evalInputDelta=0.0
WOLFE (weak): th(0.011281990840749942)=-20.736070005550136; dx=-6.589136244635649E-7 evalInputDelta=0.0
Armijo: th(0.03384597252224983)=-20.736070005550136; dx=-6.586665763848941E-7 evalInputDelta=0.0
Armijo: th(0.022563981681499885)=-20.736070005550136; dx=-6.589257522239985E-7 evalInputDelta=0.0
Armijo: th(0.016922986261124914)=-20.736070005550136; dx=-6.58998057688576E-7 evalInputDelta=0.0
Armijo: th(0.014102488550937427)=-20.736070005550136; dx=-6.587594829754723E-7 evalInputDelta=0.0
WOLFE (weak): th(0.012692239695843686)=-20.736070005550136; dx=-6.58796889247538E-7 evalInputDelta=0.0
Armijo: th(0.013397364123390557)=-20.736070005550136; dx=-6.589138548621896E-7 evalInputDelta=0.0
WOLFE (weak): th(0.013044801909617121)=-20.736070005550136; dx=-6.589832728938354E-7 evalInputDelta=0.0
Armijo: th(0.013221083016503838)=-20.736070005550136; dx=-6.587860552029044E-7 evalInputDelta=0.0
Armijo: th(0.01313294246306048)=-20.736070005550136; dx=-6.589146772579079E-7 evalInputDelta=0.0
Armijo: th(0.0130888721863388)=-20.736070005550136; dx=-6.587865605965674E-7 evalInputDelta=0.0
WOLFE (weak): th(0.01306683704797796)=-20.736070005550136; dx=-6.589940246782124E-7 evalInputDelta=0.0
WOLFE (weak): th(0.01307785461715838)=-20.736070005550136; dx=-6.58405509262382E-7 evalInputDelta=0.0
WOLFE (weak): th(0.01308336340174859)=-20.736070005550136; dx=-6.589164229416105E-7 evalInputDelta=0.0
Armijo: th(0.013086117794043695)=-20.736070005550136; dx=-6.589211729053589E-7 evalInputDelta=0.0
Armijo: th(0.013084740597896143)=-20.736070005550136; dx=-6.59081443882707E-7 evalInputDelta=0.0
Armijo: th(0.013084051999822367)=-20.736070005550136; dx=-6.589095406512341E-7 evalInputDelta=0.0
Armijo: th(0.013083707700785478)=-20.736070005550136; dx=-6.587838177695777E-7 evalInputDelta=0.0
Armijo: th(0.013083535551267034)=-20.736070005550136; dx=-6.586731830111738E-7 evalInputDelta=0.0
WOLFE (weak): th(0.013083449476507813)=-20.736070005550136; dx=-6.589202950492126E-7 evalInputDelta=0.0
mu ~= nu (0.013083449476507813): th(0.0)=-20.736070005550136
Fitness changed from -20.736070005550136 to -20.736070005550136
Static Iteration Total: 65.4446; Orientation: 0.0590; Line Search: 60.2866
Iteration 2 failed. Error: -20.736070005550136
Previous Error: 0.0 -> -20.736070005550136
Optimization terminated 2
Final threshold in iteration 2: -20.736070005550136 (> -Infinity) after 141.710s (< 5400.000s)

Returns

    -20.736070005550136