Code from BasicOptimizer.scala:75 executed in 176.49 seconds (1.467 gc):

    val lineSearchInstance: LineSearchStrategy = lineSearchFactory
    IterativeTrainer.wrap(trainable)
      .setOrientation(orientation())
      .setMonitor(new TrainingMonitor() {
        override def clear(): Unit = trainingMonitor.clear()
  
        override def log(msg: String): Unit = trainingMonitor.log(msg)
  
        override def onStepFail(currentPoint: Step): Boolean = {
          BasicOptimizer.this.onStepFail(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, currentPoint)
          trainingMonitor.onStepComplete(currentPoint)
          super.onStepComplete(currentPoint)
        }
      })
      .setTimeout(trainingMinutes, TimeUnit.MINUTES)
      .setMaxIterations(trainingIterations)
      .setLineSearchFactory((_: CharSequence) => lineSearchInstance)
      .setTerminateThreshold(java.lang.Double.NEGATIVE_INFINITY)
      .runAndFree
      .asInstanceOf[lang.Double]

Logging:

    Reset training subject: 906289031682900
    Reset training subject: 906292269000900
    Adding measurement 2c78771b to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=0.02596670389175415;dx=-1.4585996630524777E-9
    Armijo: th(2.154434690031884)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    Armijo: th(0.002992270402822061)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    WOLFE (weak): th(4.2746720040315154E-4)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    Armijo: th(0.0017098688016126062)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.001068668001007879)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    Armijo: th(0.0013892684013102426)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    Armijo: th(0.0012289682011590608)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.00114881810108347)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.0011888931511212654)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    Armijo: th(0.001208930676140163)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    Armijo: th(0.001198911913630714)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    Armijo: th(0.0011939025323759898)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    Armijo: th(0.0011913978417486277)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    Armijo: th(0.0011901454964349466)=0.02596670389175415; dx

...skipping 1793 bytes...

    057212494169815)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.005765149124475283)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    Armijo: th(0.006085435186946132)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.005925292155710708)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    Armijo: th(0.00600536367132842)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    Armijo: th(0.005965327913519563)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.005945310034615136)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    Armijo: th(0.00595531897406735)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    Armijo: th(0.005950314504341242)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    Armijo: th(0.005947812269478189)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    Armijo: th(0.005946561152046663)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.0059459355933308995)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.005946248372688781)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.005946404762367722)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.005946482957207192)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.005946522054626927)=0.02596670389175415; dx=-1.4463961687749484E-9 evalInputDelta=0.0
    mu ~= nu (0.005946522054626927): th(0.0)=0.02596670389175415
    Fitness changed from 0.02596670389175415 to 0.02596670389175415
    Static Iteration Total: 80.1405; Orientation: 0.0324; Line Search: 74.6358
    Iteration 2 failed. Error: 0.02596670389175415
    Previous Error: 0.0 -> 0.02596670389175415
    Optimization terminated 2
    Final threshold in iteration 2: 0.02596670389175415 (> -Infinity) after 176.491s (< 720.000s)
    

Returns:

    0.02596670389175415