Code from BasicOptimizer.scala:75 executed in 1662.15 seconds (17.362 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: 1058869528007700
    Reset training subject: 1058899684528500
    Adding measurement 4b35feb0 to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=-0.6161392852663994;dx=-6.546717908431923E-8
    Armijo: th(2.154434690031884)=-0.6161392852663994; dx=-6.516869606077831E-8 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=-0.6161392852663994; dx=-6.516213984068728E-8 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=-0.6161392852663994; dx=-6.516331905029149E-8 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=-0.6161392852663994; dx=-6.516796825717164E-8 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=-0.6161392852663994; dx=-6.517134781027534E-8 evalInputDelta=0.0
    Armijo: th(0.002992270402822061)=-0.6161392852663994; dx=-6.517029163911467E-8 evalInputDelta=0.0
    WOLFE (weak): th(4.2746720040315154E-4)=-0.6161392852663994; dx=-6.516522193964939E-8 evalInputDelta=0.0
    Armijo: th(0.0017098688016126062)=-0.6161392852663994; dx=-6.517018181421786E-8 evalInputDelta=0.0
    Armijo: th(0.001068668001007879)=-0.6161392852663994; dx=-6.51629451835907E-8 evalInputDelta=0.0
    WOLFE (weak): th(7.480676007055152E-4)=-0.6161392852663994; dx=-6.516095221476275E-8 evalInputDelta=0.0
    Armijo: th(9.083678008566971E-4)=-0.6161392852663994; dx=-6.517173248751818E-8 evalInputDelta=0.0
    WOLFE (weak): th(8.282177007811061E-4)=-0.6161392852663994; dx=-6.516833155584519E-8 evalInputDelta=0.0
    Armijo: th(8.682927508189016E-4)=-0.6161392852663994; dx=-6.515761860786672E-8 evalInputDelta=0.0
    Armijo: th(8.482552258000039E-4)=-0.6161392852663994; dx=-6.516329959235587E-8 evalInputDelta=0.0
    WOLFE (weak): th(8.38236463290555E-4)=-0.6161392852663994; dx=-6.515970465344393E-8 evalInputDelta=0.0
    WOLFE (weak): th(8.432458445452795E-4)=-0.6161392852663994; dx=-6.516567435517981E-8 evalInputDelta=0.0
    WOLFE (weak): th(8.457505351726417E-4)=-0.6161392852663994; dx=-6.516824423009199E-8 evalInputDelta=0.0
    WOLFE (weak): th(8.470028804863229E-4)=-0.6161392852663994; dx=-6.5

...skipping 1859 bytes...

    a=0.0
    Armijo: th(0.004566997680959044)=-0.6161392852663994; dx=-6.515505900676687E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.00411029791286314)=-0.6161392852663994; dx=-6.515781284463553E-8 evalInputDelta=0.0
    Armijo: th(0.004338647796911092)=-0.6161392852663994; dx=-6.516270213755958E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.004224472854887116)=-0.6161392852663994; dx=-6.515123030657295E-8 evalInputDelta=0.0
    Armijo: th(0.004281560325899104)=-0.6161392852663994; dx=-6.515995058184833E-8 evalInputDelta=0.0
    Armijo: th(0.0042530165903931095)=-0.6161392852663994; dx=-6.516780578018079E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.004238744722640113)=-0.6161392852663994; dx=-6.515978092155617E-8 evalInputDelta=0.0
    Armijo: th(0.004245880656516611)=-0.6161392852663994; dx=-6.51617490929511E-8 evalInputDelta=0.0
    Armijo: th(0.004242312689578361)=-0.6161392852663994; dx=-6.516363562094286E-8 evalInputDelta=0.0
    Armijo: th(0.004240528706109237)=-0.6161392852663994; dx=-6.516713080769359E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0042396367143746755)=-0.6161392852663994; dx=-6.51616396530552E-8 evalInputDelta=0.0
    Armijo: th(0.0042400827102419564)=-0.6161392852663994; dx=-6.515845741901961E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.004239859712308316)=-0.6161392852663994; dx=-6.516875338839736E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.004239971211275137)=-0.6161392852663994; dx=-6.516234922592216E-8 evalInputDelta=0.0
    Armijo: th(0.0042400269607585465)=-0.6161392852663994; dx=-6.51692265375693E-8 evalInputDelta=0.0
    Armijo: th(0.004239999086016842)=-0.6161392852663994; dx=-6.516574927814486E-8 evalInputDelta=0.0
    mu ~= nu (0.004239971211275137): th(0.0)=-0.6161392852663994
    Fitness changed from -0.6161392852663994 to -0.6161392852663994
    Static Iteration Total: 743.9297; Orientation: 0.0547; Line Search: 685.9088
    Iteration 2 failed. Error: -0.6161392852663994
    Previous Error: 0.0 -> -0.6161392852663994
    Optimization terminated 2
    Final threshold in iteration 2: -0.6161392852663994 (> -Infinity) after 1662.152s (< 3600.000s)
    

Returns:

    -0.6161392852663994