BasicOptimizer.scala:89 executed in 127.69 seconds (0.981 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: 568515734810600
Reset training subject: 568518124185900
Adding measurement 3aa15d04 to history. Total: 0
LBFGS Accumulation History: 1 points
Constructing line search parameters: GD+Trust
th(0)=-24.832228868024167;dx=-2.9316507649814776E-7
Armijo: th(2.154434690031884)=-24.832228868024167; dx=-2.916745326290605E-7 evalInputDelta=0.0
Armijo: th(1.077217345015942)=-24.832228868024167; dx=-2.9209502908426855E-7 evalInputDelta=0.0
Armijo: th(0.3590724483386473)=-24.832228868024167; dx=-2.923016967693656E-7 evalInputDelta=0.0
Armijo: th(0.08976811208466183)=-24.832228868024167; dx=-2.912179132222758E-7 evalInputDelta=0.0
Armijo: th(0.017953622416932366)=-24.832228868024167; dx=-2.919017690679417E-7 evalInputDelta=0.0
WOLFE (weak): th(0.002992270402822061)=-24.832228868024167; dx=-2.917186160406991E-7 evalInputDelta=0.0
Armijo: th(0.010472946409877214)=-24.832228868024167; dx=-2.9229307646193457E-7 evalInputDelta=0.0
Armijo: th(0.006732608406349637)=-24.832228868024167; dx=-2.903078933451806E-7 evalInputDelta=0.0
WOLFE (weak): th(0.004862439404585849)=-24.832228868024167; dx=-2.9205957023790965E-7 evalInputDelta=0.0
WOLFE (weak): th(0.005797523905467743)=-24.832228868024167; dx=-2.92076555400084E-7 evalInputDelta=0.0
Armijo: th(0.00626506615590869)=-24.832228868024167; dx=-2.9086224924808875E-7 evalInputDelta=0.0
WOLFE (weak): th(0.006031295030688217)=-24.832228868024167; dx=-2.9205431958803777E-7 evalInputDelta=0.0
Armijo: th(0.006148180593298453)=-24.832228868024167; dx=-2.922248461347911E-7 evalInputDelta=0.0
Armijo: th(0.006089737811993335)=-24.832228868024167; dx=-2.9222499592819413E-7 evalInputDelta=0.0
Armijo: th(0.006060516421340776)=-24.832228868024167; dx=-2.9230338998437924E-7 evalInputDelta=0.0
WOLFE (weak): th(0.006045905726014496)=-24.832228868024167; dx=-2.9206442549569823E-7 evalInputDelta=0.0
WOLFE (weak): th(0.006053211073677636)=-24.832228868024167; dx=-2.91901744758878E-7 evalInputDelta=0.0
WOLFE (weak): th(0.0060568637475092064)=-24.832228868024167; dx=-2.9229073640878054E-7 evalInputDelta=0.0
WOLFE (weak): th(0.0060586900844249916)=-24.832228868024167; dx=-2.9229387290966346E-7 evalInputDelta=0.0
Armijo: th(0.006059603252882883)=-24.832228868024167; dx=-2.922648035047078E-7 evalInputDelta=0.0
WOLFE (weak): th(0.006059146668653937)=-24.832228868024167; dx=-2.922167004376492E-7 evalInputDelta=0.0
Armijo: th(0.00605937496076841)=-24.832228868024167; dx=-2.900884554663675E-7 evalInputDelta=0.0
Armijo: th(0.006059260814711174)=-24.832228868024167; dx=-2.921747518635764E-7 evalInputDelta=0.0
WOLFE (weak): th(0.006059203741682556)=-24.832228868024167; dx=-2.9216277305571263E-7 evalInputDelta=0.0
mu ~= nu (0.006059203741682556): th(0.0)=-24.832228868024167
Fitness changed from -24.832228868024167 to -24.832228868024167
Static Iteration Total: 67.7502; Orientation: 0.0328; Line Search: 60.1761
Iteration 1 failed. Error: -24.832228868024167
Previous Error: 0.0 -> -24.832228868024167
Retrying iteration 1
Reset training subject: 568583485289400
Adding measurement 2d136dad to history. Total: 0
LBFGS Accumulation History: 1 points
th(0)=-24.832228868024167;dx=-2.9174084349886866E-7
WOLFE (weak): th(0.013054220215108247)=-24.832228868024167; dx=-2.90782023266481E-7 evalInputDelta=0.0
WOLFE (weak): th(0.026108440430216494)=-24.832228868024167; dx=-2.9047849534833657E-7 evalInputDelta=0.0
Armijo: th(0.07832532129064948)=-24.832228868024167; dx=-2.908744537501424E-7 evalInputDelta=0.0
Armijo: th(0.05221688086043299)=-24.832228868024167; dx=-2.908520925090851E-7 evalInputDelta=0.0
Armijo: th(0.03916266064532474)=-24.832228868024167; dx=-2.9069735202627047E-7 evalInputDelta=0.0
Armijo: th(0.03263555053777062)=-24.832228868024167; dx=-2.909114411533163E-7 evalInputDelta=0.0
WOLFE (weak): th(0.02937199548399356)=-24.832228868024167; dx=-2.909066963736801E-7 evalInputDelta=0.0
Armijo: th(0.03100377301088209)=-24.832228868024167; dx=-2.9090166303304867E-7 evalInputDelta=0.0
WOLFE (weak): th(0.030187884247437825)=-24.832228868024167; dx=-2.907743203060863E-7 evalInputDelta=0.0
Armijo: th(0.03059582862915996)=-24.832228868024167; dx=-2.904877051850415E-7 evalInputDelta=0.0
WOLFE (weak): th(0.030391856438298892)=-24.832228868024167; dx=-2.908743191708602E-7 evalInputDelta=0.0
Armijo: th(0.030493842533729425)=-24.832228868024167; dx=-2.907736528584096E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03044284948601416)=-24.832228868024167; dx=-2.9088989753922184E-7 evalInputDelta=0.0
Armijo: th(0.03046834600987179)=-24.832228868024167; dx=-2.909017169282698E-7 evalInputDelta=0.0
Armijo: th(0.030455597747942976)=-24.832228868024167; dx=-2.897793721672473E-7 evalInputDelta=0.0
Armijo: th(0.03044922361697857)=-24.832228868024167; dx=-2.9086953384861124E-7 evalInputDelta=0.0
Armijo: th(0.030446036551496365)=-24.832228868024167; dx=-2.909095887414407E-7 evalInputDelta=0.0
Armijo: th(0.030444443018755263)=-24.832228868024167; dx=-2.9094184607168314E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03044364625238471)=-24.832228868024167; dx=-2.9060504723784093E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03044404463556999)=-24.832228868024167; dx=-2.8979906664474635E-7 evalInputDelta=0.0
Armijo: th(0.030444243827162626)=-24.832228868024167; dx=-2.90870971839982E-7 evalInputDelta=0.0
mu ~= nu (0.03044404463556999): th(0.0)=-24.832228868024167
Fitness changed from -24.832228868024167 to -24.832228868024167
Static Iteration Total: 59.9403; Orientation: 0.0306; Line Search: 55.1297
Iteration 2 failed. Error: -24.832228868024167
Previous Error: 0.0 -> -24.832228868024167
Optimization terminated 2
Final threshold in iteration 2: -24.832228868024167 (> -Infinity) after 127.690s (< 5400.000s)

Returns

    -24.832228868024167