Code from BasicOptimizer.scala:75 executed in 2930.86 seconds (53.214 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: 1085997437550199
    Reset training subject: 1086049590493400
    Adding measurement 24ed4582 to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=1.6659832645496944;dx=-1.147355510746241E-7
    Armijo: th(2.154434690031884)=1.6659832645496944; dx=-1.1355029189096332E-7 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=1.6659832645496944; dx=-1.135502909643921E-7 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=1.6659832645496944; dx=-1.1355029097293321E-7 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=1.6659832645496944; dx=-1.1355029097293321E-7 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=1.6659832645496944; dx=-1.1355029097293321E-7 evalInputDelta=0.0
    Armijo: th(0.002992270402822061)=1.6659832645496944; dx=-1.1355029097293321E-7 evalInputDelta=0.0
    WOLFE (weak): th(4.2746720040315154E-4)=1.6659832645496944; dx=-1.1355029097293321E-7 evalInputDelta=0.0
    Armijo: th(0.0017098688016126062)=1.6659832645496944; dx=-1.1355029097293321E-7 evalInputDelta=0.0
    Armijo: th(0.001068668001007879)=1.6659832645496944; dx=-1.1355029097293321E-7 evalInputDelta=0.0
    WOLFE (weak): th(7.480676007055152E-4)=1.6659832645496944; dx=-1.1355029097293321E-7 evalInputDelta=0.0
    WOLFE (weak): th(9.083678008566971E-4)=1.6659832645496944; dx=-1.1355029097293321E-7 evalInputDelta=0.0
    Armijo: th(9.88517900932288E-4)=1.6659832645496944; dx=-1.1355029097293321E-7 evalInputDelta=0.0
    WOLFE (weak): th(9.484428508944925E-4)=1.6659832645496944; dx=-1.1355029097293321E-7 evalInputDelta=0.0
    Armijo: th(9.684803759133902E-4)=1.6659832645496944; dx=-1.1355029097293321E-7 evalInputDelta=0.0
    WOLFE (weak): th(9.584616134039414E-4)=1.6659832645496944; dx=-1.1355029097293321E-7 evalInputDelta=0.0
    WOLFE (weak): th(9.634709946586659E-4)=1.6659832645496944; dx=-1.1355029097293321E-7 evalInputDelta=0.0
    WOLFE (weak): th(9.65975685286028E-4)=1.6659832645496944; dx=-1.1355029097293321E-7 evalInputDelta=0.0
    WOLFE (weak): th(9.672280305997091E-4)=1.6659832645496944; dx=-

...skipping 1865 bytes...

    mijo: th(0.00521177416346926)=1.6659832645496944; dx=-1.1355029097111308E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.004690596747122334)=1.6659832645496944; dx=-1.1355029097111308E-7 evalInputDelta=0.0
    Armijo: th(0.004951185455295796)=1.6659832645496944; dx=-1.1355029097111308E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.0048208911012090645)=1.6659832645496944; dx=-1.1355029097111308E-7 evalInputDelta=0.0
    Armijo: th(0.00488603827825243)=1.6659832645496944; dx=-1.1355029097111308E-7 evalInputDelta=0.0
    Armijo: th(0.004853464689730747)=1.6659832645496944; dx=-1.1355029097111308E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.004837177895469906)=1.6659832645496944; dx=-1.1355029097111308E-7 evalInputDelta=0.0
    Armijo: th(0.004845321292600327)=1.6659832645496944; dx=-1.1355029097111308E-7 evalInputDelta=0.0
    Armijo: th(0.004841249594035117)=1.6659832645496944; dx=-1.1355029097111308E-7 evalInputDelta=0.0
    Armijo: th(0.004839213744752512)=1.6659832645496944; dx=-1.1355029097111308E-7 evalInputDelta=0.0
    Armijo: th(0.004838195820111209)=1.6659832645496944; dx=-1.1355029097111308E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.004837686857790558)=1.6659832645496944; dx=-1.1355029097111308E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.004837941338950883)=1.6659832645496944; dx=-1.1355029097111308E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.004838068579531046)=1.6659832645496944; dx=-1.1355029097111308E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.004838132199821127)=1.6659832645496944; dx=-1.1355029097111308E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.0048381640099661675)=1.6659832645496944; dx=-1.1355029097111308E-7 evalInputDelta=0.0
    mu ~= nu (0.0048381640099661675): th(0.0)=1.6659832645496944
    Fitness changed from 1.6659832645496944 to 1.6659832645496944
    Static Iteration Total: 1293.9111; Orientation: 0.0917; Line Search: 1189.4150
    Iteration 2 failed. Error: 1.6659832645496944
    Previous Error: 0.0 -> 1.6659832645496944
    Optimization terminated 2
    Final threshold in iteration 2: 1.6659832645496944 (> -Infinity) after 2930.856s (< 3600.000s)
    

Returns:

    1.6659832645496944