Code from BasicOptimizer.scala:75 executed in 244.63 seconds (2.274 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: 909284926342200
    Reset training subject: 909289602739600
    Adding measurement 5e45b0 to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=0.4584881663322449;dx=-9.32459079145665E-9
    Armijo: th(2.154434690031884)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    Armijo: th(0.002992270402822061)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    WOLFE (weak): th(4.2746720040315154E-4)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.0017098688016126062)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.0023510696022173336)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.0026716700025196972)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.002831970202670879)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.00291212030274647)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.0029521953527842657)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.0029722328778031635)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    Armijo: th(0.002982251640312612)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    Armijo: th(0.0029772422590578877)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.0029747375684305256)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.0029759899137442067)=0.4584881663322449; dx=-9

...skipping 1632 bytes...

    54E-9 evalInputDelta=0.0
    Armijo: th(0.01603225969143223)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.014429033722289009)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    Armijo: th(0.015230646706860619)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.014829840214574813)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    Armijo: th(0.015030243460717716)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    Armijo: th(0.014930041837646265)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.014879941026110538)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    Armijo: th(0.0149049914318784)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    Armijo: th(0.01489246622899447)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    Armijo: th(0.014886203627552504)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    Armijo: th(0.01488307232683152)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.01488150667647103)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.014882289501651276)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.014882680914241398)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.014882876620536459)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.01488297447368399)=0.4584881663322449; dx=-9.307428148753954E-9 evalInputDelta=0.0
    mu ~= nu (0.01488297447368399): th(0.0)=0.4584881663322449
    Fitness changed from 0.4584881663322449 to 0.4584881663322449
    Static Iteration Total: 113.0943; Orientation: 0.0610; Line Search: 104.5342
    Iteration 2 failed. Error: 0.4584881663322449
    Previous Error: 0.0 -> 0.4584881663322449
    Optimization terminated 2
    Final threshold in iteration 2: 0.4584881663322449 (> -Infinity) after 244.630s (< 1800.000s)
    

Returns:

    0.4584881663322449