Code from BasicOptimizer.scala:75 executed in 88.92 seconds (1.183 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: 899668853257100
    Reset training subject: 899670525967200
    Adding measurement 491cafec to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=1.045686662197113;dx=-3.8817282205190065E-8
    Armijo: th(2.154434690031884)=1.045686662197113; dx=-3.881728044239933E-8 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=1.045686662197113; dx=-3.881728104669473E-8 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=1.045686662197113; dx=-3.881728148086275E-8 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=1.045686662197113; dx=-3.881728245211455E-8 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=1.045686662197113; dx=-3.881728268771463E-8 evalInputDelta=0.0
    Armijo: th(0.002992270402822061)=1.045686662197113; dx=-3.881728220037412E-8 evalInputDelta=0.0
    WOLFE (weak): th(4.2746720040315154E-4)=1.045686662197113; dx=-3.8817282156682394E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0017098688016126062)=1.045686662197113; dx=-3.881728219203388E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0023510696022173336)=1.045686662197113; dx=-3.881728220340086E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0026716700025196972)=1.045686662197113; dx=-3.8817282210386626E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.002831970202670879)=1.045686662197113; dx=-3.8817282210386626E-8 evalInputDelta=0.0
    Armijo: th(0.00291212030274647)=1.045686662197113; dx=-3.881728220037412E-8 evalInputDelta=0.0
    Armijo: th(0.0028720452527086745)=1.045686662197113; dx=-3.8817282210386626E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0028520077276897766)=1.045686662197113; dx=-3.8817282210386626E-8 evalInputDelta=0.0
    Armijo: th(0.0028620264901992255)=1.045686662197113; dx=-3.8817282210386626E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.002857017108944501)=1.045686662197113; dx=-3.8817282210386626E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.002859521799571863)=1.045686662197113; dx=-3.8817282210386626E-8 evalInputDelta=0.0
    Armijo: th(0.0028607741448855445)=1.045686662197113; dx=-3.8817282210386626E-8

...skipping 1587 bytes...

    271329418E-8 evalInputDelta=0.0
    Armijo: th(0.015404846933411171)=1.045686662197113; dx=-3.881728268997628E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.013864362240070054)=1.045686662197113; dx=-3.8817282720667894E-8 evalInputDelta=0.0
    Armijo: th(0.014634604586740613)=1.045686662197113; dx=-3.881728275101118E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.014249483413405335)=1.045686662197113; dx=-3.8817282717489555E-8 evalInputDelta=0.0
    Armijo: th(0.014442044000072974)=1.045686662197113; dx=-3.881728274911634E-8 evalInputDelta=0.0
    Armijo: th(0.014345763706739154)=1.045686662197113; dx=-3.8817282717489555E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.014297623560072244)=1.045686662197113; dx=-3.8817282717489555E-8 evalInputDelta=0.0
    Armijo: th(0.0143216936334057)=1.045686662197113; dx=-3.8817282717489555E-8 evalInputDelta=0.0
    Armijo: th(0.014309658596738972)=1.045686662197113; dx=-3.8817282717489555E-8 evalInputDelta=0.0
    Armijo: th(0.014303641078405607)=1.045686662197113; dx=-3.8817282717489555E-8 evalInputDelta=0.0
    Armijo: th(0.014300632319238926)=1.045686662197113; dx=-3.8817282717489555E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.014299127939655584)=1.045686662197113; dx=-3.8817282717489555E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.014299880129447256)=1.045686662197113; dx=-3.8817282717489555E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.01430025622434309)=1.045686662197113; dx=-3.8817282717489555E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.014300444271791009)=1.045686662197113; dx=-3.8817282717489555E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.014300538295514968)=1.045686662197113; dx=-3.8817282717489555E-8 evalInputDelta=0.0
    mu ~= nu (0.014300538295514968): th(0.0)=1.045686662197113
    Fitness changed from 1.045686662197113 to 1.045686662197113
    Static Iteration Total: 40.4735; Orientation: 0.0150; Line Search: 37.2253
    Iteration 2 failed. Error: 1.045686662197113
    Previous Error: 0.0 -> 1.045686662197113
    Optimization terminated 2
    Final threshold in iteration 2: 1.045686662197113 (> -Infinity) after 88.924s (< 3600.000s)
    

Returns:

    1.045686662197113