Code from BasicOptimizer.scala:75 executed in 8649.21 seconds (166.575 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: 1526401480965500
    Reset training subject: 1526686690654500
    Adding measurement 615c9e9b to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=46.46043307469324;dx=-7.743558056122506E-7
    Armijo: th(2.154434690031884)=46.46043307469324; dx=-7.743189524819088E-7 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=46.46043307469324; dx=-7.743189524882542E-7 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=46.46043307469324; dx=-7.743189524877485E-7 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=46.46043307469324; dx=-7.743189524877485E-7 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=46.46043307469324; dx=-7.743189524877485E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.002992270402822061)=46.46043307469324; dx=-7.743189524877485E-7 evalInputDelta=0.0
    Armijo: th(0.010472946409877214)=46.46043307469324; dx=-7.743189524877485E-7 evalInputDelta=0.0
    Armijo: th(0.006732608406349637)=46.46043307469324; dx=-7.743189524877485E-7 evalInputDelta=0.0
    Armijo: th(0.004862439404585849)=46.46043307469324; dx=-7.743189524877485E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.003927354903703955)=46.46043307469324; dx=-7.743189524877485E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.004394897154144902)=46.46043307469324; dx=-7.743189524877485E-7 evalInputDelta=0.0
    Armijo: th(0.004628668279365375)=46.46043307469324; dx=-7.743189524877485E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.0045117827167551385)=46.46043307469324; dx=-7.743189524877485E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.004570225498060257)=46.46043307469324; dx=-7.743189524877485E-7 evalInputDelta=0.0
    Armijo: th(0.004599446888712816)=46.46043307469324; dx=-7.743189524877485E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.004584836193386536)=46.46043307469324; dx=-7.743189524877485E-7 evalInputDelta=0.0
    Armijo: th(0.0045921415410496765)=46.46043307469324; dx=-7.743189524877485E-7 evalInputDelta=0.0
    Armijo: th(0.004588488867218106)=46.46043307469324; dx=-7.743189524877485E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.004586662530302321)=46.46043307469324; dx=-7.743189524877485E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.004587575698760214)=46.46043307469324; dx=-7.743189524877485E-7 evalInputDelta=0.0
    Armijo: th(0.0045880322829891605)=46.46043307469324; dx=-7.743189524877485E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.0045878039908746875)=46.46043307469324; dx=-7.743189524877485E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.004587918136931924)=46.46043307469324; dx=-7.743189524877485E-7 evalInputDelta=0.0
    Armijo: th(0.004587975209960542)=46.46043307469324; dx=-7.743189524877485E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.0045879466734462335)=46.46043307469324; dx=-7.743189524877485E-7 evalInputDelta=0.0
    mu ~= nu (0.0045879466734462335): th(0.0)=46.46043307469324
    Fitness changed from 46.46043307469324 to 46.46043307469324
    Static Iteration Total: 8649.1995; Orientation: 1.2543; Line Search: 7792.2051
    Iteration 1 failed. Error: 46.46043307469324
    Previous Error: 0.0 -> 46.46043307469324
    Retrying iteration 1
    Final threshold in iteration 1: 46.46043307469324 (> -Infinity) after 8649.199s (< 7200.000s)
    

Returns:

    46.46043307469324