BasicOptimizer.scala:89 executed in 117.29 seconds (0.741 gc):

    val lineSearchInstance: LineSearchStrategy = lineSearchFactory
    val trainer = new IterativeTrainer(trainable)
    trainer.setOrientation(orientation())
    trainer.setMonitor(new TrainingMonitor() {
      override def clear(): Unit = trainingMonitor.clear()
  
      override def log(msg: String): Unit = {
        trainingMonitor.log(msg)
        BasicOptimizer.this.log(msg)
      }
  
      override def onStepFail(currentPoint: Step): Boolean = {
        BasicOptimizer.this.onStepFail(trainable.addRef().asInstanceOf[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.addRef().asInstanceOf[Trainable], currentPoint)
        trainingMonitor.onStepComplete(currentPoint)
        super.onStepComplete(currentPoint)
      }
    })
    trainer.setTimeout(trainingMinutes, TimeUnit.MINUTES)
    trainer.setMaxIterations(trainingIterations)
    trainer.setLineSearchFactory((_: CharSequence) => lineSearchInstance)
    trainer.setTerminateThreshold(java.lang.Double.NEGATIVE_INFINITY)
    val result = trainer.run.asInstanceOf[lang.Double]
    trainer.freeRef()
    result
Logging 
Reset training subject: 639083752885000
Reset training subject: 639086097953800
Adding measurement 64c1907e to history. Total: 0
LBFGS Accumulation History: 1 points
Constructing line search parameters: GD+Trust
th(0)=-22.685613411892543;dx=-2.307868480331764E-7
Armijo: th(2.154434690031884)=-22.685613411892543; dx=-2.296580389525347E-7 evalInputDelta=0.0
Armijo: th(1.077217345015942)=-22.685613411892543; dx=-2.2969235907199735E-7 evalInputDelta=0.0
Armijo: th(0.3590724483386473)=-22.685613411892543; dx=-2.2949498775895344E-7 evalInputDelta=0.0
Armijo: th(0.08976811208466183)=-22.685613411892543; dx=-2.2983381094805514E-7 evalInputDelta=0.0
Armijo: th(0.017953622416932366)=-22.685613411892543; dx=-2.29767825853099E-7 evalInputDelta=0.0
WOLFE (weak): th(0.002992270402822061)=-22.685613411892543; dx=-2.2997497094767813E-7 evalInputDelta=0.0
Armijo: th(0.010472946409877214)=-22.685613411892543; dx=-2.296538922285241E-7 evalInputDelta=0.0
WOLFE (weak): th(0.006732608406349637)=-22.685613411892543; dx=-2.2995900661645837E-7 evalInputDelta=0.0
Armijo: th(0.008602777408113426)=-22.685613411892543; dx=-2.2978912184463773E-7 evalInputDelta=0.0
WOLFE (weak): th(0.0076676929072315315)=-22.685613411892543; dx=-2.2991580772123312E-7 evalInputDelta=0.0
Armijo: th(0.008135235157672479)=-22.685613411892543; dx=-2.2996080118904547E-7 evalInputDelta=0.0
Armijo: th(0.007901464032452005)=-22.685613411892543; dx=-2.289764883877768E-7 evalInputDelta=0.0
Armijo: th(0.007784578469841768)=-22.685613411892543; dx=-2.2989197036015656E-7 evalInputDelta=0.0
Armijo: th(0.00772613568853665)=-22.685613411892543; dx=-2.2995957231533915E-7 evalInputDelta=0.0
WOLFE (weak): th(0.007696914297884091)=-22.685613411892543; dx=-2.2997367138935343E-7 evalInputDelta=0.0
Armijo: th(0.007711524993210371)=-22.685613411892543; dx=-2.298827875603566E-7 evalInputDelta=0.0
Armijo: th(0.00770421964554723)=-22.685613411892543; dx=-2.2970475779622767E-7 evalInputDelta=0.0
Armijo: th(0.00770056697171566)=-22.685613411892543; dx=-2.297407150246298E-7 evalInputDelta=0.0
Armijo: th(0.007698740634799875)=-22.685613411892543; dx=-2.300226988940435E-7 evalInputDelta=0.0
Armijo: th(0.007697827466341983)=-22.685613411892543; dx=-2.2984563816746306E-7 evalInputDelta=0.0
Armijo: th(0.007697370882113037)=-22.685613411892543; dx=-2.295791276565241E-7 evalInputDelta=0.0
Armijo: th(0.0076971425899985645)=-22.685613411892543; dx=-2.2992150411353496E-7 evalInputDelta=0.0
Armijo: th(0.007697028443941328)=-22.685613411892543; dx=-2.2970871531618348E-7 evalInputDelta=0.0
Armijo: th(0.007696971370912709)=-22.685613411892543; dx=-2.299610731158262E-7 evalInputDelta=0.0
mu ~= nu (0.007696914297884091): th(0.0)=-22.685613411892543
Fitness changed from -22.685613411892543 to -22.685613411892543
Static Iteration Total: 63.1528; Orientation: 0.0347; Line Search: 56.4923
Iteration 1 failed. Error: -22.685613411892543
Previous Error: 0.0 -> -22.685613411892543
Retrying iteration 1
Reset training subject: 639146905824700
Adding measurement 380412ac to history. Total: 0
LBFGS Accumulation History: 1 points
th(0)=-22.685613411892543;dx=-2.3093116685173018E-7
WOLFE (weak): th(0.016582560649620246)=-22.685613411892543; dx=-2.3032585890069024E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03316512129924049)=-22.685613411892543; dx=-2.30291005013829E-7 evalInputDelta=0.0
Armijo: th(0.09949536389772148)=-22.685613411892543; dx=-2.301293719773178E-7 evalInputDelta=0.0
Armijo: th(0.06633024259848098)=-22.685613411892543; dx=-2.3024152422070785E-7 evalInputDelta=0.0
Armijo: th(0.04974768194886074)=-22.685613411892543; dx=-2.2999856241773287E-7 evalInputDelta=0.0
Armijo: th(0.04145640162405062)=-22.685613411892543; dx=-2.3001912498502646E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03731076146164555)=-22.685613411892543; dx=-2.3009723846094447E-7 evalInputDelta=0.0
Armijo: th(0.03938358154284809)=-22.685613411892543; dx=-2.3031562722970054E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03834717150224682)=-22.685613411892543; dx=-2.3004113815144003E-7 evalInputDelta=0.0
Armijo: th(0.03886537652254746)=-22.685613411892543; dx=-2.3026909341395263E-7 evalInputDelta=0.0
Armijo: th(0.038606274012397135)=-22.685613411892543; dx=-2.3027044482624774E-7 evalInputDelta=0.0
Armijo: th(0.038476722757321974)=-22.685613411892543; dx=-2.3021995725852622E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03841194712978439)=-22.685613411892543; dx=-2.2922065439454456E-7 evalInputDelta=0.0
WOLFE (weak): th(0.038444334943553184)=-22.685613411892543; dx=-2.3036134382423797E-7 evalInputDelta=0.0
WOLFE (weak): th(0.038460528850437575)=-22.685613411892543; dx=-2.291598104052145E-7 evalInputDelta=0.0
Armijo: th(0.038468625803879775)=-22.685613411892543; dx=-2.3039160216288685E-7 evalInputDelta=0.0
Armijo: th(0.038464577327158675)=-22.685613411892543; dx=-2.3030383342432017E-7 evalInputDelta=0.0
Armijo: th(0.038462553088798125)=-22.685613411892543; dx=-2.299924059206145E-7 evalInputDelta=0.0
Armijo: th(0.03846154096961785)=-22.685613411892543; dx=-2.3005327213662732E-7 evalInputDelta=0.0
Armijo: th(0.03846103491002771)=-22.685613411892543; dx=-2.303096815663082E-7 evalInputDelta=0.0
Armijo: th(0.03846078188023264)=-22.685613411892543; dx=-2.303133300770656E-7 evalInputDelta=0.0
mu ~= nu (0.038460528850437575): th(0.0)=-22.685613411892543
Fitness changed from -22.685613411892543 to -22.685613411892543
Static Iteration Total: 54.1324; Orientation: 0.0317; Line Search: 49.8415
Iteration 2 failed. Error: -22.685613411892543
Previous Error: 0.0 -> -22.685613411892543
Optimization terminated 2
Final threshold in iteration 2: -22.685613411892543 (> -Infinity) after 117.286s (< 5400.000s)

Returns

    -22.685613411892543