Code from BasicOptimizer.scala:75 executed in 345.05 seconds (2.673 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: 904850940260400
    Reset training subject: 904857251368000
    Adding measurement 49639118 to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=0.10393908619880676;dx=-1.267996100354917E-9
    Armijo: th(2.154434690031884)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.002992270402822061)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    Armijo: th(0.010472946409877214)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    Armijo: th(0.006732608406349637)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.004862439404585849)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    Armijo: th(0.005797523905467743)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.005329981655026796)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    Armijo: th(0.0055637527802472695)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.005446867217637033)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    Armijo: th(0.005505309998942151)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    Armijo: th(0.005476088608289592)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.005461477912963312)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.005468783260626452)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    Armijo: th(0.0054724359344580226)=0.103939086198806

...skipping 1799 bytes...

    0.029474476589940377)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.026527028930946342)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    Armijo: th(0.028000752760443358)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.02726389084569485)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    Armijo: th(0.027632321803069106)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    Armijo: th(0.02744810632438198)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.027355998585038413)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    Armijo: th(0.027402052454710196)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    Armijo: th(0.027379025519874305)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    Armijo: th(0.02736751205245636)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    Armijo: th(0.027361755318747384)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.0273588769518929)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.02736031613532014)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.027361035727033762)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.027361395522890575)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.02736157542081898)=0.10393908619880676; dx=-1.2497609008952974E-9 evalInputDelta=0.0
    mu ~= nu (0.02736157542081898): th(0.0)=0.10393908619880676
    Fitness changed from 0.10393908619880676 to 0.10393908619880676
    Static Iteration Total: 159.0785; Orientation: 0.0637; Line Search: 146.4251
    Iteration 2 failed. Error: 0.10393908619880676
    Previous Error: 0.0 -> 0.10393908619880676
    Optimization terminated 2
    Final threshold in iteration 2: 0.10393908619880676 (> -Infinity) after 345.044s (< 720.000s)
    

Returns:

    0.10393908619880676