Code from BasicOptimizer.scala:75 executed in 123.13 seconds (1.165 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: 908092219777200
    Reset training subject: 908095049271700
    Adding measurement 3f324b5a to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=0.18756204843521118;dx=-7.4002376460428066E-9
    Armijo: th(2.154434690031884)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    Armijo: th(0.002992270402822061)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    WOLFE (weak): th(4.2746720040315154E-4)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.0017098688016126062)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    Armijo: th(0.0023510696022173336)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    Armijo: th(0.00203046920191497)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.001870169001763788)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    Armijo: th(0.001950319101839379)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    Armijo: th(0.0019102440518015836)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    Armijo: th(0.0018902065267826858)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    Armijo: th(0.0018801877642732369)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.0018751783830185124)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    Armijo: th(0.0018776830736458748)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    Armijo: th(0.0018764307283321937)=0.18756204843521118; dx=-7.359717693596

...skipping 1749 bytes...

    
    Armijo: th(0.010100637505204351)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.009090573754683917)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    Armijo: th(0.009595605629944135)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.009343089692314026)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    Armijo: th(0.009469347661129082)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    Armijo: th(0.009406218676721555)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.00937465418451779)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    Armijo: th(0.009390436430619672)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    Armijo: th(0.00938254530756873)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    Armijo: th(0.00937859974604326)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    Armijo: th(0.009376626965280525)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.009375640574899157)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.009376133770089841)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.009376380367685183)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.009376503666482853)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.009376565315881689)=0.18756204843521118; dx=-7.359717693596838E-9 evalInputDelta=0.0
    mu ~= nu (0.009376565315881689): th(0.0)=0.18756204843521118
    Fitness changed from 0.18756204843521118 to 0.18756204843521118
    Static Iteration Total: 55.9035; Orientation: 0.0284; Line Search: 51.4844
    Iteration 2 failed. Error: 0.18756204843521118
    Previous Error: 0.0 -> 0.18756204843521118
    Optimization terminated 2
    Final threshold in iteration 2: 0.18756204843521118 (> -Infinity) after 123.129s (< 720.000s)
    

Returns:

    0.18756204843521118