Code from BasicOptimizer.scala:75 executed in 66.43 seconds (1.430 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: 907969989106600
    Reset training subject: 907971180225900
    Adding measurement 3dee7142 to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=-0.08693721890449524;dx=-7.164503541503615E-9
    Armijo: th(2.154434690031884)=-0.08693721890449524; dx=-7.072449860355652E-9 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    Armijo: th(0.002992270402822061)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    WOLFE (weak): th(4.2746720040315154E-4)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    Armijo: th(0.0017098688016126062)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    Armijo: th(0.001068668001007879)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    WOLFE (weak): th(7.480676007055152E-4)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    WOLFE (weak): th(9.083678008566971E-4)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    Armijo: th(9.88517900932288E-4)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    WOLFE (weak): th(9.484428508944925E-4)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    WOLFE (weak): th(9.684803759133902E-4)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    Armijo: th(9.78499138422839E-4)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    Armijo: th(9.734897571681146E-4)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    Armijo: th(9.709850665407525E-4)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    Armijo: th(9.697327212270714E-4)=-0.08693721890449524; dx

...skipping 1892 bytes...

    05620786075)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.004694841505870746)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    Armijo: th(0.0049556660339746764)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.004825253769922711)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    Armijo: th(0.004890459901948694)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    Armijo: th(0.004857856835935703)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.004841555302929207)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    Armijo: th(0.004849706069432455)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    Armijo: th(0.004845630686180831)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    Armijo: th(0.004843592994555019)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    Armijo: th(0.004842574148742113)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.00484206472583566)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.004842319437288887)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.0048424467930155)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.004842510470878806)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.004842542309810459)=-0.08693721890449524; dx=-7.072449831486047E-9 evalInputDelta=0.0
    mu ~= nu (0.004842542309810459): th(0.0)=-0.08693721890449524
    Fitness changed from -0.08693721890449524 to -0.08693721890449524
    Static Iteration Total: 29.4259; Orientation: 0.0144; Line Search: 27.1541
    Iteration 2 failed. Error: -0.08693721890449524
    Previous Error: 0.0 -> -0.08693721890449524
    Optimization terminated 2
    Final threshold in iteration 2: -0.08693721890449524 (> -Infinity) after 66.433s (< 720.000s)
    

Returns:

    -0.08693721890449524