BasicOptimizer.scala:89 executed in 118.78 seconds (0.877 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: 637062264379000
Reset training subject: 637064581599600
Adding measurement 118d7f7 to history. Total: 0
LBFGS Accumulation History: 1 points
Constructing line search parameters: GD+Trust
th(0)=-22.828135913426745;dx=-2.3049385579438008E-7
Armijo: th(2.154434690031884)=-22.828135913426745; dx=-2.3057345194107907E-7 evalInputDelta=0.0
Armijo: th(1.077217345015942)=-22.828135913426745; dx=-2.3076390561926E-7 evalInputDelta=0.0
Armijo: th(0.3590724483386473)=-22.828135913426745; dx=-2.2946196043713958E-7 evalInputDelta=0.0
Armijo: th(0.08976811208466183)=-22.828135913426745; dx=-2.298163353056326E-7 evalInputDelta=0.0
Armijo: th(0.017953622416932366)=-22.828135913426745; dx=-2.3075734330285455E-7 evalInputDelta=0.0
WOLFE (weak): th(0.002992270402822061)=-22.828135913426745; dx=-2.3052914389987478E-7 evalInputDelta=0.0
Armijo: th(0.010472946409877214)=-22.828135913426745; dx=-2.3047015065025617E-7 evalInputDelta=0.0
WOLFE (weak): th(0.006732608406349637)=-22.828135913426745; dx=-2.3059216265997362E-7 evalInputDelta=0.0
Armijo: th(0.008602777408113426)=-22.828135913426745; dx=-2.3070976023952637E-7 evalInputDelta=0.0
WOLFE (weak): th(0.0076676929072315315)=-22.828135913426745; dx=-2.3069426499606293E-7 evalInputDelta=0.0
Armijo: th(0.008135235157672479)=-22.828135913426745; dx=-2.2942467816580392E-7 evalInputDelta=0.0
Armijo: th(0.007901464032452005)=-22.828135913426745; dx=-2.3039251977655413E-7 evalInputDelta=0.0
Armijo: th(0.007784578469841768)=-22.828135913426745; dx=-2.3061866722714853E-7 evalInputDelta=0.0
Armijo: th(0.00772613568853665)=-22.828135913426745; dx=-2.3013807696394682E-7 evalInputDelta=0.0
WOLFE (weak): th(0.007696914297884091)=-22.828135913426745; dx=-2.3046143184438262E-7 evalInputDelta=0.0
Armijo: th(0.007711524993210371)=-22.828135913426745; dx=-2.30651420913796E-7 evalInputDelta=0.0
WOLFE (weak): th(0.00770421964554723)=-22.828135913426745; dx=-2.3059830859429607E-7 evalInputDelta=0.0
Armijo: th(0.0077078723193788005)=-22.828135913426745; dx=-2.3068327062142165E-7 evalInputDelta=0.0
WOLFE (weak): th(0.007706045982463015)=-22.828135913426745; dx=-2.307395665211783E-7 evalInputDelta=0.0
Armijo: th(0.007706959150920908)=-22.828135913426745; dx=-2.3031464576506768E-7 evalInputDelta=0.0
WOLFE (weak): th(0.007706502566691961)=-22.828135913426745; dx=-2.3073176257050257E-7 evalInputDelta=0.0
WOLFE (weak): th(0.007706730858806434)=-22.828135913426745; dx=-2.3062408805129985E-7 evalInputDelta=0.0
Armijo: th(0.007706845004863671)=-22.828135913426745; dx=-2.3079609975501056E-7 evalInputDelta=0.0
Armijo: th(0.007706787931835052)=-22.828135913426745; dx=-2.2956579934527566E-7 evalInputDelta=0.0
mu ~= nu (0.007706730858806434): th(0.0)=-22.828135913426745
Fitness changed from -22.828135913426745 to -22.828135913426745
Static Iteration Total: 63.8075; Orientation: 0.0434; Line Search: 57.0357
Iteration 1 failed. Error: -22.828135913426745
Previous Error: 0.0 -> -22.828135913426745
Retrying iteration 1
Reset training subject: 637126072061500
Adding measurement 5782ba71 to history. Total: 0
LBFGS Accumulation History: 1 points
th(0)=-22.828135913426745;dx=-2.3029800563028718E-7
WOLFE (weak): th(0.016603709789008155)=-22.828135913426745; dx=-2.2944507828183136E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03320741957801631)=-22.828135913426745; dx=-2.296111324436669E-7 evalInputDelta=0.0
Armijo: th(0.09962225873404892)=-22.828135913426745; dx=-2.292918579890529E-7 evalInputDelta=0.0
Armijo: th(0.06641483915603262)=-22.828135913426745; dx=-2.2928284126007367E-7 evalInputDelta=0.0
Armijo: th(0.04981112936702446)=-22.828135913426745; dx=-2.297236923034043E-7 evalInputDelta=0.0
Armijo: th(0.04150927447252038)=-22.828135913426745; dx=-2.293361537097208E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03735834702526834)=-22.828135913426745; dx=-2.2957983501869434E-7 evalInputDelta=0.0
Armijo: th(0.03943381074889436)=-22.828135913426745; dx=-2.2954348499150168E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03839607888708135)=-22.828135913426745; dx=-2.2969201305865998E-7 evalInputDelta=0.0
Armijo: th(0.03891494481798786)=-22.828135913426745; dx=-2.2895166892020162E-7 evalInputDelta=0.0
Armijo: th(0.0386555118525346)=-22.828135913426745; dx=-2.2966024063782555E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03852579536980798)=-22.828135913426745; dx=-2.294342765540576E-7 evalInputDelta=0.0
Armijo: th(0.03859065361117129)=-22.828135913426745; dx=-2.2969211044806862E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03855822449048964)=-22.828135913426745; dx=-2.2966732982571677E-7 evalInputDelta=0.0
Armijo: th(0.03857443905083047)=-22.828135913426745; dx=-2.2931783809358844E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03856633177066005)=-22.828135913426745; dx=-2.2968483882282242E-7 evalInputDelta=0.0
Armijo: th(0.038570385410745256)=-22.828135913426745; dx=-2.29413255829176E-7 evalInputDelta=0.0
Armijo: th(0.03856835859070265)=-22.828135913426745; dx=-2.2956873179781632E-7 evalInputDelta=0.0
Armijo: th(0.03856734518068135)=-22.828135913426745; dx=-2.295810271776555E-7 evalInputDelta=0.0
Armijo: th(0.0385668384756707)=-22.828135913426745; dx=-2.2945590514236418E-7 evalInputDelta=0.0
Armijo: th(0.03856658512316538)=-22.828135913426745; dx=-2.2973929402574173E-7 evalInputDelta=0.0
mu ~= nu (0.03856633177066005): th(0.0)=-22.828135913426745
Fitness changed from -22.828135913426745 to -22.828135913426745
Static Iteration Total: 54.9727; Orientation: 0.0349; Line Search: 50.6633
Iteration 2 failed. Error: -22.828135913426745
Previous Error: 0.0 -> -22.828135913426745
Optimization terminated 2
Final threshold in iteration 2: -22.828135913426745 (> -Infinity) after 118.780s (< 5400.000s)

Returns

    -22.828135913426745