Code from BasicOptimizer.scala:75 executed in 105.74 seconds (1.568 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: 915436430991700
    Reset training subject: 915438324540700
    Adding measurement 16d79a7d to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=0.9994028955698013;dx=-8.594730047640898E-8
    Armijo: th(2.154434690031884)=0.9994028955698013; dx=-8.594714737504861E-8 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=0.9994028955698013; dx=-8.594714865449729E-8 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    Armijo: th(0.002992270402822061)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    WOLFE (weak): th(4.2746720040315154E-4)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    Armijo: th(0.0017098688016126062)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    Armijo: th(0.001068668001007879)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    Armijo: th(7.480676007055152E-4)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    WOLFE (weak): th(5.877674005543334E-4)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    Armijo: th(6.679175006299243E-4)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    WOLFE (weak): th(6.278424505921289E-4)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    Armijo: th(6.478799756110266E-4)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    WOLFE (weak): th(6.378612131015778E-4)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    WOLFE (weak): th(6.428705943563023E-4)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    WOLFE (weak): th(6.453752849836644E-4)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    Armijo: th(6.466276302973454E-4)=0.9994028955698013; dx=-8.594714887583695E-8 evalInp

...skipping 1805 bytes...

    Delta=0.0
    Armijo: th(0.0034787479861923013)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.003130873187573071)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    Armijo: th(0.003304810586882686)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0032178418872278788)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    Armijo: th(0.0032613262370552827)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    Armijo: th(0.0032395840621415807)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0032287129746847295)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    Armijo: th(0.0032341485184131553)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    Armijo: th(0.0032314307465489424)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    Armijo: th(0.0032300718606168358)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    Armijo: th(0.0032293924176507826)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.003229052696167756)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.003229222556909269)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.003229307487280026)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0032293499524654046)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.003229371185058094)=0.9994028955698013; dx=-8.594714887583695E-8 evalInputDelta=0.0
    mu ~= nu (0.003229371185058094): th(0.0)=0.9994028955698013
    Fitness changed from 0.9994028955698013 to 0.9994028955698013
    Static Iteration Total: 46.8721; Orientation: 0.0303; Line Search: 43.3144
    Iteration 2 failed. Error: 0.9994028955698013
    Previous Error: 0.0 -> 0.9994028955698013
    Optimization terminated 2
    Final threshold in iteration 2: 0.9994028955698013 (> -Infinity) after 105.743s (< 720.000s)
    

Returns:

    0.9994028955698013