Code from BasicOptimizer.scala:75 executed in 5153.99 seconds (57.899 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: 1447344344240900
    Reset training subject: 1447439535377500
    Adding measurement 3653bb61 to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=6.658423215150833;dx=-1.417925765076188E-7
    Armijo: th(2.154434690031884)=6.658423215150833; dx=-1.4178374853964478E-7 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.002992270402822061)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    Armijo: th(0.010472946409877214)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    Armijo: th(0.006732608406349637)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    Armijo: th(0.004862439404585849)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    Armijo: th(0.003927354903703955)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    Armijo: th(0.0034598126532630075)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    Armijo: th(0.003226041528042534)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.003109155965432297)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    Armijo: th(0.0031675987467374156)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    Armijo: th(0.0031383773560848564)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.003123766660758577)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.003131072008421717)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    Armijo: th(0.0031347246822532866)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    Armijo: th(0.

...skipping 1640 bytes...

    dx=-1.417837485734256E-7 evalInputDelta=0.0
    Armijo: th(0.016869066934267235)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.015182160240840513)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    Armijo: th(0.016025613587553875)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.015603886914197194)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    Armijo: th(0.015814750250875535)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    Armijo: th(0.015709318582536366)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.01565660274836678)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    Armijo: th(0.01568296066545157)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    Armijo: th(0.015669781706909178)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    Armijo: th(0.015663192227637977)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    Armijo: th(0.01565989748800238)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.01565825011818458)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.01565907380309348)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.01565948564554793)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.015659691566775154)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.01565979452738877)=6.658423215150833; dx=-1.417837485734256E-7 evalInputDelta=0.0
    mu ~= nu (0.01565979452738877): th(0.0)=6.658423215150833
    Fitness changed from 6.658423215150833 to 6.658423215150833
    Static Iteration Total: 2302.4461; Orientation: 0.3171; Line Search: 2105.3146
    Iteration 2 failed. Error: 6.658423215150833
    Previous Error: 0.0 -> 6.658423215150833
    Optimization terminated 2
    Final threshold in iteration 2: 6.658423215150833 (> -Infinity) after 5153.986s (< 7200.000s)
    

Returns:

    6.658423215150833