Code from BasicOptimizer.scala:75 executed in 108.62 seconds (1.898 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: 913489564263200
    Reset training subject: 913491658825400
    Adding measurement 4a8406db to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=0.8929334282875061;dx=-6.247466883531011E-8
    Armijo: th(2.154434690031884)=0.8929334282875061; dx=-6.247241843480245E-8 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=0.8929334282875061; dx=-6.247241847458836E-8 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    Armijo: th(0.002992270402822061)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    WOLFE (weak): th(4.2746720040315154E-4)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    Armijo: th(0.0017098688016126062)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    Armijo: th(0.001068668001007879)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    WOLFE (weak): th(7.480676007055152E-4)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    Armijo: th(9.083678008566971E-4)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    WOLFE (weak): th(8.282177007811061E-4)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    WOLFE (weak): th(8.682927508189016E-4)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    WOLFE (weak): th(8.883302758377993E-4)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    Armijo: th(8.983490383472482E-4)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    Armijo: th(8.933396570925238E-4)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    Armijo: th(8.908349664651616E-4)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    Armijo: th(8.895826211514804E-4)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelt

...skipping 1784 bytes...

    valInputDelta=0.0
    Armijo: th(0.004785743721488642)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.004307169349339777)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    Armijo: th(0.004546456535414209)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.004426812942376993)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    Armijo: th(0.004486634738895601)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    Armijo: th(0.004456723840636297)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.004441768391506645)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    Armijo: th(0.004449246116071471)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    Armijo: th(0.004445507253789058)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    Armijo: th(0.004443637822647851)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    Armijo: th(0.004442703107077248)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.004442235749291947)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.004442469428184598)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.004442586267630923)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.004442644687354086)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0044426738972156675)=0.8929334282875061; dx=-6.247241844469377E-8 evalInputDelta=0.0
    mu ~= nu (0.0044426738972156675): th(0.0)=0.8929334282875061
    Fitness changed from 0.8929334282875061 to 0.8929334282875061
    Static Iteration Total: 46.1964; Orientation: 0.0308; Line Search: 42.6936
    Iteration 2 failed. Error: 0.8929334282875061
    Previous Error: 0.0 -> 0.8929334282875061
    Optimization terminated 2
    Final threshold in iteration 2: 0.8929334282875061 (> -Infinity) after 108.616s (< 720.000s)
    

Returns:

    0.8929334282875061