Code from BasicOptimizer.scala:75 executed in 2913.40 seconds (25.229 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: 1051897726286600
    Reset training subject: 1051949064201300
    Adding measurement 710a7188 to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=-0.5594395399093628;dx=-7.00296123045621E-8
    Armijo: th(2.154434690031884)=-0.5594395399093628; dx=-6.968530054611682E-8 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=-0.5594395101070404; dx=-6.969595699440441E-8 evalInputDelta=-2.9802322387695312E-8
    Armijo: th(0.3590724483386473)=-0.5594395399093628; dx=-6.96942215881728E-8 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=-0.5594395399093628; dx=-6.96956003336926E-8 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=-0.5594395399093628; dx=-6.96888157533053E-8 evalInputDelta=0.0
    Armijo: th(0.002992270402822061)=-0.5594395399093628; dx=-6.970125033915038E-8 evalInputDelta=0.0
    WOLFE (weak): th(4.2746720040315154E-4)=-0.5594395399093628; dx=-6.968563823458929E-8 evalInputDelta=0.0
    Armijo: th(0.0017098688016126062)=-0.5594395399093628; dx=-6.970046422829626E-8 evalInputDelta=0.0
    Armijo: th(0.001068668001007879)=-0.5594395399093628; dx=-6.969103316862533E-8 evalInputDelta=0.0
    WOLFE (weak): th(7.480676007055152E-4)=-0.5594395399093628; dx=-6.969521262819875E-8 evalInputDelta=0.0
    Armijo: th(9.083678008566971E-4)=-0.5594395399093628; dx=-6.969326252370853E-8 evalInputDelta=0.0
    Armijo: th(8.282177007811061E-4)=-0.5594395399093628; dx=-6.969306666182557E-8 evalInputDelta=0.0
    WOLFE (weak): th(7.881426507433107E-4)=-0.5594395399093628; dx=-6.969664393728334E-8 evalInputDelta=0.0
    Armijo: th(8.081801757622084E-4)=-0.5594395399093628; dx=-6.970272630103036E-8 evalInputDelta=0.0
    Armijo: th(7.981614132527596E-4)=-0.5594395399093628; dx=-6.96975321434981E-8 evalInputDelta=0.0
    Armijo: th(7.931520319980352E-4)=-0.5594395399093628; dx=-6.968778074333482E-8 evalInputDelta=0.0
    WOLFE (weak): th(7.90647341370673E-4)=-0.5594395399093628; dx=-6.97065535702297E-8 evalInputDelta=0.0
    WOLFE (weak): th(7.918996866843541E-4)=-0.5594395399093628; dx=-6

...skipping 1870 bytes...

    a=0.0
    Armijo: th(0.0042694429910408605)=-0.5594395399093628; dx=-6.969757088430046E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.003842498691936774)=-0.5594395399093628; dx=-6.9702323425038E-8 evalInputDelta=0.0
    Armijo: th(0.004055970841488817)=-0.5594395399093628; dx=-6.969215881724192E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.003949234766712795)=-0.5594395399093628; dx=-6.969540888010409E-8 evalInputDelta=0.0
    Armijo: th(0.004002602804100806)=-0.5594395399093628; dx=-6.970762951813215E-8 evalInputDelta=0.0
    Armijo: th(0.0039759187854068005)=-0.5594395399093628; dx=-6.969989248660129E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.003962576776059798)=-0.5594395399093628; dx=-6.969727159939467E-8 evalInputDelta=0.0
    Armijo: th(0.0039692477807333)=-0.5594395399093628; dx=-6.968883609014557E-8 evalInputDelta=0.0
    Armijo: th(0.003965912278396549)=-0.5594395399093628; dx=-6.969605161084152E-8 evalInputDelta=0.0
    Armijo: th(0.003964244527228173)=-0.5594395399093628; dx=-6.968900551531975E-8 evalInputDelta=0.0
    Armijo: th(0.003963410651643986)=-0.5594395399093628; dx=-6.969467751180938E-8 evalInputDelta=0.0
    Armijo: th(0.003962993713851892)=-0.5594395399093628; dx=-6.96926896646371E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0039627852449558455)=-0.5594395399093628; dx=-6.970146010646115E-8 evalInputDelta=0.0
    Armijo: th(0.003962889479403869)=-0.5594395399093628; dx=-6.970504566836627E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.003962837362179858)=-0.5594395399093628; dx=-6.970417645577558E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.003962863420791864)=-0.5594395399093628; dx=-6.969492253200904E-8 evalInputDelta=0.0
    mu ~= nu (0.003962863420791864): th(0.0)=-0.5594395399093628
    Fitness changed from -0.5594395399093628 to -0.5594395399093628
    Static Iteration Total: 1302.9593; Orientation: 0.0795; Line Search: 1200.7770
    Iteration 2 failed. Error: -0.5594395399093628
    Previous Error: 0.0 -> -0.5594395399093628
    Optimization terminated 2
    Final threshold in iteration 2: -0.5594395399093628 (> -Infinity) after 2913.395s (< 3600.000s)
    

Returns:

    -0.5594395399093628