BasicOptimizer.scala:89 executed in 123.97 seconds (0.874 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: 566484915596300
Reset training subject: 566487338406100
Adding measurement 1a34cf91 to history. Total: 0
LBFGS Accumulation History: 1 points
Constructing line search parameters: GD+Trust
th(0)=-26.508857163539677;dx=-2.8272999536060853E-7
Armijo: th(2.154434690031884)=-26.508857163539677; dx=-2.8204557301981226E-7 evalInputDelta=0.0
Armijo: th(1.077217345015942)=-26.508857163539677; dx=-2.815558468053616E-7 evalInputDelta=0.0
Armijo: th(0.3590724483386473)=-26.508857163539677; dx=-2.8123437402944617E-7 evalInputDelta=0.0
Armijo: th(0.08976811208466183)=-26.508857163539677; dx=-2.8183446437987995E-7 evalInputDelta=0.0
Armijo: th(0.017953622416932366)=-26.508857163539677; dx=-2.8177211208125084E-7 evalInputDelta=0.0
WOLFE (weak): th(0.002992270402822061)=-26.508857163539677; dx=-2.8228145993004176E-7 evalInputDelta=0.0
Armijo: th(0.010472946409877214)=-26.508857163539677; dx=-2.8216488414064655E-7 evalInputDelta=0.0
Armijo: th(0.006732608406349637)=-26.508857163539677; dx=-2.811613638199988E-7 evalInputDelta=0.0
WOLFE (weak): th(0.004862439404585849)=-26.508857163539677; dx=-2.8216429164491514E-7 evalInputDelta=0.0
WOLFE (weak): th(0.005797523905467743)=-26.508857163539677; dx=-2.819979147577227E-7 evalInputDelta=0.0
WOLFE (weak): th(0.00626506615590869)=-26.508857163539677; dx=-2.818654890709197E-7 evalInputDelta=0.0
Armijo: th(0.006498837281129164)=-26.508857163539677; dx=-2.818128859561641E-7 evalInputDelta=0.0
Armijo: th(0.006381951718518927)=-26.508857163539677; dx=-2.8231774987492436E-7 evalInputDelta=0.0
Armijo: th(0.0063235089372138086)=-26.508857163539677; dx=-2.822523692617418E-7 evalInputDelta=0.0
Armijo: th(0.006294287546561249)=-26.508857163539677; dx=-2.822086587531587E-7 evalInputDelta=0.0
WOLFE (weak): th(0.006279676851234969)=-26.508857163539677; dx=-2.821700846977991E-7 evalInputDelta=0.0
Armijo: th(0.00628698219889811)=-26.508857163539677; dx=-2.8226619825023685E-7 evalInputDelta=0.0
Armijo: th(0.0062833295250665396)=-26.508857163539677; dx=-2.8219271049684967E-7 evalInputDelta=0.0
WOLFE (weak): th(0.0062815031881507544)=-26.508857163539677; dx=-2.821078653837086E-7 evalInputDelta=0.0
WOLFE (weak): th(0.006282416356608647)=-26.508857163539677; dx=-2.822718673205733E-7 evalInputDelta=0.0
WOLFE (weak): th(0.006282872940837594)=-26.508857163539677; dx=-2.8227819232544024E-7 evalInputDelta=0.0
Armijo: th(0.006283101232952067)=-26.508857163539677; dx=-2.8219353190681924E-7 evalInputDelta=0.0
Armijo: th(0.00628298708689483)=-26.508857163539677; dx=-2.821519253247769E-7 evalInputDelta=0.0
Armijo: th(0.006282930013866212)=-26.508857163539677; dx=-2.8189728244446073E-7 evalInputDelta=0.0
mu ~= nu (0.006282872940837594): th(0.0)=-26.508857163539677
Fitness changed from -26.508857163539677 to -26.508857163539677
Static Iteration Total: 65.9376; Orientation: 0.0349; Line Search: 59.0837
Iteration 1 failed. Error: -26.508857163539677
Previous Error: 0.0 -> -26.508857163539677
Retrying iteration 1
Reset training subject: 566550853369500
Adding measurement a79c219 to history. Total: 0
LBFGS Accumulation History: 1 points
th(0)=-26.508857163539677;dx=-2.826229371319771E-7
WOLFE (weak): th(0.013536100896859513)=-26.508857163539677; dx=-2.8194046030605505E-7 evalInputDelta=0.0
WOLFE (weak): th(0.027072201793719025)=-26.508857163539677; dx=-2.819002214039037E-7 evalInputDelta=0.0
Armijo: th(0.08121660538115708)=-26.508857163539677; dx=-2.8150168885230367E-7 evalInputDelta=0.0
Armijo: th(0.05414440358743805)=-26.508857163539677; dx=-2.818224964434523E-7 evalInputDelta=0.0
Armijo: th(0.04060830269057854)=-26.508857163539677; dx=-2.818160951998847E-7 evalInputDelta=0.0
Armijo: th(0.03384025224214878)=-26.508857163539677; dx=-2.8186097305577E-7 evalInputDelta=0.0
WOLFE (weak): th(0.030456227017933903)=-26.508857163539677; dx=-2.8185063396241754E-7 evalInputDelta=0.0
Armijo: th(0.03214823963004134)=-26.508857163539677; dx=-2.8202829204183855E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03130223332398762)=-26.508857163539677; dx=-2.8096686755495335E-7 evalInputDelta=0.0
Armijo: th(0.03172523647701448)=-26.508857163539677; dx=-2.8183854107277833E-7 evalInputDelta=0.0
Armijo: th(0.03151373490050105)=-26.508857163539677; dx=-2.8159395839245713E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03140798411224434)=-26.508857163539677; dx=-2.813256699453667E-7 evalInputDelta=0.0
Armijo: th(0.031460859506372695)=-26.508857163539677; dx=-2.7959431399210116E-7 evalInputDelta=0.0
Armijo: th(0.03143442180930851)=-26.508857163539677; dx=-2.8174046438896576E-7 evalInputDelta=0.0
WOLFE (weak): th(0.031421202960776426)=-26.508857163539677; dx=-2.8178363567056277E-7 evalInputDelta=0.0
Armijo: th(0.03142781238504247)=-26.508857163539677; dx=-2.8100927003516145E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03142450767290945)=-26.508857163539677; dx=-2.8200515189043487E-7 evalInputDelta=0.0
WOLFE (weak): th(0.031426160028975955)=-26.508857163539677; dx=-2.81847585172751E-7 evalInputDelta=0.0
Armijo: th(0.03142698620700921)=-26.508857163539677; dx=-2.799987617474003E-7 evalInputDelta=0.0
Armijo: th(0.03142657311799259)=-26.508857163539677; dx=-2.81484087347104E-7 evalInputDelta=0.0
Armijo: th(0.03142636657348427)=-26.508857163539677; dx=-2.8138346125452493E-7 evalInputDelta=0.0
mu ~= nu (0.031426160028975955): th(0.0)=-26.508857163539677
Fitness changed from -26.508857163539677 to -26.508857163539677
Static Iteration Total: 58.0348; Orientation: 0.0314; Line Search: 53.3129
Iteration 2 failed. Error: -26.508857163539677
Previous Error: 0.0 -> -26.508857163539677
Optimization terminated 2
Final threshold in iteration 2: -26.508857163539677 (> -Infinity) after 123.973s (< 5400.000s)

Returns

    -26.508857163539677