Code from BasicOptimizer.scala:75 executed in 3485.46 seconds (209.196 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: 1097018841566500
    Reset training subject: 1097088920314200
    Adding measurement 4df207e1 to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=1.3212233836551128;dx=-1.0703680031489985E-7
    Armijo: th(2.154434690031884)=1.3212233836551128; dx=-1.0609316569591355E-7 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=1.3212234432597576; dx=-1.0609312286124398E-7 evalInputDelta=-5.9604644775390625E-8
    Armijo: th(0.3590724483386473)=1.3212233836551128; dx=-1.0609312278705316E-7 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=1.3212233836551128; dx=-1.0609312278705316E-7 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=1.3212233836551128; dx=-1.0609312278705316E-7 evalInputDelta=0.0
    Armijo: th(0.002992270402822061)=1.3212233836551128; dx=-1.0609312278705316E-7 evalInputDelta=0.0
    WOLFE (weak): th(4.2746720040315154E-4)=1.3212233836551128; dx=-1.0609312278705316E-7 evalInputDelta=0.0
    Armijo: th(0.0017098688016126062)=1.3212233836551128; dx=-1.0609312278651653E-7 evalInputDelta=0.0
    Armijo: th(0.001068668001007879)=1.3212233836551128; dx=-1.0609312278705316E-7 evalInputDelta=0.0
    WOLFE (weak): th(7.480676007055152E-4)=1.3212233836551128; dx=-1.0609312278705316E-7 evalInputDelta=0.0
    WOLFE (weak): th(9.083678008566971E-4)=1.3212233836551128; dx=-1.0609312278705316E-7 evalInputDelta=0.0
    WOLFE (weak): th(9.88517900932288E-4)=1.3212233836551128; dx=-1.0609312278705316E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.0010285929509700835)=1.3212233836551128; dx=-1.0609312278705316E-7 evalInputDelta=0.0
    Armijo: th(0.0010486304759889814)=1.3212233836551128; dx=-1.0609312278705316E-7 evalInputDelta=0.0
    Armijo: th(0.0010386117134795324)=1.3212233836551128; dx=-1.0609312278705316E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.001033602332224808)=1.3212233836551128; dx=-1.0609312278705316E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.0010361070228521703)=1.3212233836551128; dx=-1.0609312278705316E-7 evalInputDelta=0.0
    Armijo: th(0.0010373593681658514)=1.3

...skipping 1801 bytes...

    mijo: th(0.0055866488071255925)=1.3212233836551128; dx=-1.0609312278597994E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.005027983926413033)=1.3212233836551128; dx=-1.0609312278651653E-7 evalInputDelta=0.0
    Armijo: th(0.005307316366769312)=1.3212233836551128; dx=-1.0609312278651653E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.0051676501465911726)=1.3212233836551128; dx=-1.0609312278651653E-7 evalInputDelta=0.0
    Armijo: th(0.005237483256680242)=1.3212233836551128; dx=-1.0609312278597994E-7 evalInputDelta=0.0
    Armijo: th(0.0052025667016357075)=1.3212233836551128; dx=-1.0609312278651653E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.00518510842411344)=1.3212233836551128; dx=-1.0609312278597994E-7 evalInputDelta=0.0
    Armijo: th(0.005193837562874573)=1.3212233836551128; dx=-1.0609312278597994E-7 evalInputDelta=0.0
    Armijo: th(0.005189472993494006)=1.3212233836551128; dx=-1.0609312278597994E-7 evalInputDelta=0.0
    Armijo: th(0.005187290708803723)=1.3212233836551128; dx=-1.0609312278597994E-7 evalInputDelta=0.0
    Armijo: th(0.005186199566458581)=1.3212233836551128; dx=-1.0609312278597994E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.00518565399528601)=1.3212233836551128; dx=-1.0609312278597994E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.005185926780872296)=1.3212233836551128; dx=-1.0609312278597994E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.005186063173665439)=1.3212233836551128; dx=-1.0609312278651653E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.005186131370062011)=1.3212233836551128; dx=-1.0609312278597994E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.005186165468260296)=1.3212233836551128; dx=-1.0609312278651653E-7 evalInputDelta=0.0
    mu ~= nu (0.005186165468260296): th(0.0)=1.3212233836551128
    Fitness changed from 1.3212233836551128 to 1.3212233836551128
    Static Iteration Total: 1714.1679; Orientation: 0.0996; Line Search: 1575.2705
    Iteration 2 failed. Error: 1.3212233836551128
    Previous Error: 0.0 -> 1.3212233836551128
    Optimization terminated 2
    Final threshold in iteration 2: 1.3212233836551128 (> -Infinity) after 3485.457s (< 3600.000s)
    

Returns:

    1.3212233836551128