Code from BasicOptimizer.scala:75 executed in 12086.50 seconds (169.754 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: 1463266432075000
    Reset training subject: 1463664990291600
    Adding measurement 2e2b1310 to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=44.82561066746712;dx=-1.0439174938967276E-6
    Armijo: th(2.154434690031884)=44.82561066746712; dx=-1.0439038774172013E-6 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=44.82561066746712; dx=-1.0439038772791468E-6 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=44.82561066746712; dx=-1.0439038772315382E-6 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=44.82561066746712; dx=-1.0439038772315382E-6 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=44.82561066746712; dx=-1.0439038772315382E-6 evalInputDelta=0.0
    WOLFE (weak): th(0.002992270402822061)=44.82561066746712; dx=-1.0439038772315382E-6 evalInputDelta=0.0
    Armijo: th(0.010472946409877214)=44.82561066746712; dx=-1.0439038772315382E-6 evalInputDelta=0.0
    Armijo: th(0.006732608406349637)=44.82561066746712; dx=-1.0439038772315382E-6 evalInputDelta=0.0
    Armijo: th(0.004862439404585849)=44.82561066746712; dx=-1.0439038772315382E-6 evalInputDelta=0.0
    Armijo: th(0.003927354903703955)=44.82561066746712; dx=-1.0439038772315382E-6 evalInputDelta=0.0
    Armijo: th(0.0034598126532630075)=44.82561066746712; dx=-1.0439038772315382E-6 evalInputDelta=0.0
    WOLFE (weak): th(0.003226041528042534)=44.82561066746712; dx=-1.0439038772315382E-6 evalInputDelta=0.0
    WOLFE (weak): th(0.0033429270906527708)=44.82561066746712; dx=-1.0439038772315382E-6 evalInputDelta=0.0
    WOLFE (weak): th(0.003401369871957889)=44.82561066746712; dx=-1.0439038772315382E-6 evalInputDelta=0.0
    Armijo: th(0.0034305912626104483)=44.82561066746712; dx=-1.0439038772315382E-6 evalInputDelta=0.0
    Armijo: th(0.0034159805672841687)=44.82561066746712; dx=-1.0439038772315382E-6 evalInputDelta=0.0
    Armijo: th(0.0034086752196210287)=44.82561066746712; dx=-1.0439038772315382E-6 evalInputDelta=0.0
    Armijo: th(0.003405022545789459)=44.82561066746712; dx=-1.0439038772315382E-6 evalInputDelta=0.0
    WOLFE (weak): th(0.0034031962088736743)=44.82561066746712; dx=-1.0439038772315382E-6 evalInputDelta=0.0
    Armijo: th(0.003404109377331567)=44.82561066746712; dx=-1.0439038772315382E-6 evalInputDelta=0.0
    Armijo: th(0.0034036527931026205)=44.82561066746712; dx=-1.0439038772315382E-6 evalInputDelta=0.0
    Armijo: th(0.003403424500988147)=44.82561066746712; dx=-1.0439038772315382E-6 evalInputDelta=0.0
    Armijo: th(0.0034033103549309107)=44.82561066746712; dx=-1.0439038772315382E-6 evalInputDelta=0.0
    Armijo: th(0.0034032532819022925)=44.82561066746712; dx=-1.0439038772315382E-6 evalInputDelta=0.0
    WOLFE (weak): th(0.0034032247453879834)=44.82561066746712; dx=-1.0439038772315382E-6 evalInputDelta=0.0
    mu ~= nu (0.0034032247453879834): th(0.0)=44.82561066746712
    Fitness changed from 44.82561066746712 to 44.82561066746712
    Static Iteration Total: 12086.4955; Orientation: 1.2730; Line Search: 10894.6070
    Iteration 1 failed. Error: 44.82561066746712
    Previous Error: 0.0 -> 44.82561066746712
    Retrying iteration 1
    Final threshold in iteration 1: 44.82561066746712 (> -Infinity) after 12086.496s (< 7200.000s)
    

Returns:

    44.82561066746712