BasicOptimizer.scala:88 executed in 840.84 seconds (6.716 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: 5180466963628
Reset training subject: 5195401084302
Adding measurement 246ad101 to history. Total: 0
LBFGS Accumulation History: 1 points
Constructing line search parameters: GD+Trust
th(0)=4.47716043012299;dx=-1.582766485340172E-8
Armijo: th(2.154434690031884)=4.47716043012299; dx=-1.5576214673889687E-8 evalInputDelta=0.0
Armijo: th(1.077217345015942)=4.47716043012299; dx=-1.5576214674401997E-8 evalInputDelta=0.0
Armijo: th(0.3590724483386473)=4.47716043012299; dx=-1.5576214674345255E-8 evalInputDelta=0.0
Armijo: th(0.08976811208466183)=4.47716043012299; dx=-1.5576214672737266E-8 evalInputDelta=0.0
WOLFE (weak): th(0.017953622416932366)=4.47716043012299; dx=-1.5576214674139092E-8 evalInputDelta=0.0
Armijo: th(0.0538608672507971)=4.47716043012299; dx=-1.557621467469896E-8 evalInputDelta=0.0
Armijo: th(0.03590724483386473)=4.47716043012299; dx=-1.5576214674144254E-8 evalInputDelta=0.0
WOLFE (weak): th(0.02693043362539855)=4.47716043012299; dx=-1.5576214672435795E-8 evalInputDelta=0.0
Armijo: th(0.03141883922963164)=4.47716043012299; dx=-1.5576214673930543E-8 evalInputDelta=0.0
Armijo: th(0.029174636427515093)=4.47716043012299; dx=-1.5576214673751363E-8 evalInputDelta=0.0
WOLFE (weak): th(0.02805253502645682)=4.47716043012299; dx=-1.5576214674083386E-8 evalInputDelta=0.0
Armijo: th(0.028613585726985954)=4.47716043012299; dx=-1.5576214673310972E-8 evalInputDelta=0.0
Armijo: th(0.028333060376721387)=4.47716043012299; dx=-1.5576214674597387E-8 evalInputDelta=0.0
Armijo: th(0.028192797701589105)=4.47716043012299; dx=-1.5576214673058794E-8 evalInputDelta=0.0
Armijo: th(0.028122666364022962)=4.47716043012299; dx=-1.5576214674235965E-8 evalInputDelta=0.0
Armijo: th(0.02808760069523989)=4.47716043012299; dx=-1.55762146746379E-8 evalInputDelta=0.0
Armijo: th(0.028070067860848355)=4.47716043012299; dx=-1.5576214673373252E-8 evalInputDelta=0.0
Armijo: th(0.028061301443652587)=4.47716043012299; dx=-1.5576214674899873E-8 evalInputDelta=0.0
WOLFE (weak): th(0.028056918235054705)=4.47716043012299; dx=-1.5576214672472628E-8 evalInputDelta=0.0
Armijo: th(0.028059109839353648)=4.47716043012299; dx=-1.5576214673934226E-8 evalInputDelta=0.0
Armijo: th(0.028058014037204176)=4.47716043012299; dx=-1.5576214674667406E-8 evalInputDelta=0.0
WOLFE (weak): th(0.02805746613612944)=4.47716043012299; dx=-1.5576214674577316E-8 evalInputDelta=0.0
WOLFE (weak): th(0.02805774008666681)=4.47716043012299; dx=-1.557621467491365E-8 evalInputDelta=0.0
mu ~= nu (0.02805774008666681): th(0.0)=4.47716043012299
Fitness changed from 4.47716043012299 to 4.47716043012299
Static Iteration Total: 444.7514; Orientation: 0.2702; Line Search: 399.5735
Iteration 1 failed. Error: 4.47716043012299
Previous Error: 0.0 -> 4.47716043012299
Retrying iteration 1
Reset training subject: 5625218735217
Adding measurement 1a107167 to history. Total: 0
LBFGS Accumulation History: 1 points
th(0)=4.47716043012299;dx=-1.582766485400573E-8
WOLFE (weak): th(0.060448863670883694)=4.47716043012299; dx=-1.531063632229458E-8 evalInputDelta=0.0
WOLFE (weak): th(0.12089772734176739)=4.47716043012299; dx=-1.5576214673626273E-8 evalInputDelta=0.0
Armijo: th(0.36269318202530215)=4.47716043012299; dx=-1.557621467347964E-8 evalInputDelta=0.0
Armijo: th(0.24179545468353478)=4.47716043012299; dx=-1.5576214674692787E-8 evalInputDelta=0.0
Armijo: th(0.18134659101265108)=4.47716043012299; dx=-1.557621467241184E-8 evalInputDelta=0.0
Armijo: th(0.15112215917720923)=4.47716043012299; dx=-1.5576214675633258E-8 evalInputDelta=0.0
WOLFE (weak): th(0.13600994325948831)=4.47716043012299; dx=-1.5576214674241014E-8 evalInputDelta=0.0
Armijo: th(0.14356605121834876)=4.47716043012299; dx=-1.55762146742905E-8 evalInputDelta=0.0
WOLFE (weak): th(0.13978799723891855)=4.47716043012299; dx=-1.5576214674470646E-8 evalInputDelta=0.0
Armijo: th(0.14167702422863365)=4.47716043012299; dx=-1.5576214673757457E-8 evalInputDelta=0.0
Armijo: th(0.1407325107337761)=4.47716043012299; dx=-1.53106363226328E-8 evalInputDelta=0.0
WOLFE (weak): th(0.14026025398634734)=4.47716043012299; dx=-1.557621467304981E-8 evalInputDelta=0.0
Armijo: th(0.14049638236006173)=4.47716043012299; dx=-1.557621467497122E-8 evalInputDelta=0.0
Armijo: th(0.14037831817320454)=4.47716043012299; dx=-1.5576214674158494E-8 evalInputDelta=0.0
Armijo: th(0.14031928607977595)=4.47716043012299; dx=-1.5576214675329345E-8 evalInputDelta=0.0
Armijo: th(0.14028977003306164)=4.47716043012299; dx=-1.5310636323250067E-8 evalInputDelta=0.0
WOLFE (weak): th(0.1402750120097045)=4.47716043012299; dx=-1.5576214674767597E-8 evalInputDelta=0.0
WOLFE (weak): th(0.14028239102138307)=4.47716043012299; dx=-1.55762146754636E-8 evalInputDelta=0.0
WOLFE (weak): th(0.14028608052722236)=4.47716043012299; dx=-1.5576214674683675E-8 evalInputDelta=0.0
WOLFE (weak): th(0.140287925280142)=4.47716043012299; dx=-1.5576214672948435E-8 evalInputDelta=0.0
WOLFE (weak): th(0.14028884765660182)=4.47716043012299; dx=-1.5576214673239318E-8 evalInputDelta=0.0
mu ~= nu (0.14028884765660182): th(0.0)=4.47716043012299
Fitness changed from 4.47716043012299 to 4.47716043012299
Static Iteration Total: 396.0842; Orientation: 0.2553; Line Search: 365.9069
Iteration 2 failed. Error: 4.47716043012299
Previous Error: 0.0 -> 4.47716043012299
Optimization terminated 2
Final threshold in iteration 2: 4.47716043012299 (> -Infinity) after 840.836s (< 10800.000s)

Returns

    4.47716043012299