Code from BasicOptimizer.scala:75 executed in 88.57 seconds (1.032 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: 899573460469600
    Reset training subject: 899575117908600
    Adding measurement 6661d8c0 to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=1.120660811662674;dx=-2.3082463923257442E-8
    Armijo: th(2.154434690031884)=1.120660811662674; dx=-2.3082462550192136E-8 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=1.120660811662674; dx=-2.3082463293794658E-8 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=1.120660811662674; dx=-2.3082463778594938E-8 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=1.120660811662674; dx=-2.3082464045380882E-8 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=1.120660811662674; dx=-2.308246396222341E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.002992270402822061)=1.120660811662674; dx=-2.3082463935953814E-8 evalInputDelta=0.0
    Armijo: th(0.010472946409877214)=1.120660811662674; dx=-2.30824639342854E-8 evalInputDelta=0.0
    Armijo: th(0.006732608406349637)=1.120660811662674; dx=-2.308246393112464E-8 evalInputDelta=0.0
    Armijo: th(0.004862439404585849)=1.120660811662674; dx=-2.308246393408203E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.003927354903703955)=1.120660811662674; dx=-2.3082463945316218E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.004394897154144902)=1.120660811662674; dx=-2.3082463939417702E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.004628668279365375)=1.120660811662674; dx=-2.308246393408203E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.004745553841975612)=1.120660811662674; dx=-2.308246393408203E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0048039966232807305)=1.120660811662674; dx=-2.308246393408203E-8 evalInputDelta=0.0
    Armijo: th(0.00483321801393329)=1.120660811662674; dx=-2.308246393408203E-8 evalInputDelta=0.0
    Armijo: th(0.00481860731860701)=1.120660811662674; dx=-2.308246393408203E-8 evalInputDelta=0.0
    Armijo: th(0.00481130197094387)=1.120660811662674; dx=-2.308246393408203E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.004807649297112301)=1.120660811662674; dx=-2.308246393408203E-8 evalInputDelta=0.

...skipping 1658 bytes...

    639986844E-8 evalInputDelta=0.0
    Armijo: th(0.025906020418653343)=1.120660811662674; dx=-2.3082463960933262E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.023315418376788008)=1.120660811662674; dx=-2.308246397440296E-8 evalInputDelta=0.0
    Armijo: th(0.024610719397720675)=1.120660811662674; dx=-2.3082463965570513E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.023963068887254343)=1.120660811662674; dx=-2.3082463965312658E-8 evalInputDelta=0.0
    Armijo: th(0.02428689414248751)=1.120660811662674; dx=-2.3082463965570513E-8 evalInputDelta=0.0
    Armijo: th(0.024124981514870927)=1.120660811662674; dx=-2.3082463965344865E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.024044025201062635)=1.120660811662674; dx=-2.3082463965312658E-8 evalInputDelta=0.0
    Armijo: th(0.02408450335796678)=1.120660811662674; dx=-2.3082463965312658E-8 evalInputDelta=0.0
    Armijo: th(0.02406426427951471)=1.120660811662674; dx=-2.3082463965312658E-8 evalInputDelta=0.0
    Armijo: th(0.02405414474028867)=1.120660811662674; dx=-2.3082463965312658E-8 evalInputDelta=0.0
    Armijo: th(0.024049084970675653)=1.120660811662674; dx=-2.3082463965312658E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.024046555085869142)=1.120660811662674; dx=-2.3082463965312658E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.024047820028272396)=1.120660811662674; dx=-2.3082463965312658E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.024048452499474023)=1.120660811662674; dx=-2.3082463965312658E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.02404876873507484)=1.120660811662674; dx=-2.3082463965312658E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.024048926852875248)=1.120660811662674; dx=-2.3082463965312658E-8 evalInputDelta=0.0
    mu ~= nu (0.024048926852875248): th(0.0)=1.120660811662674
    Fitness changed from 1.120660811662674 to 1.120660811662674
    Static Iteration Total: 39.6234; Orientation: 0.0147; Line Search: 36.5107
    Iteration 2 failed. Error: 1.120660811662674
    Previous Error: 0.0 -> 1.120660811662674
    Optimization terminated 2
    Final threshold in iteration 2: 1.120660811662674 (> -Infinity) after 88.574s (< 3600.000s)
    

Returns:

    1.120660811662674