BasicOptimizer.scala:89 executed in 124.91 seconds (1.382 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: 635053483815600
Reset training subject: 635055949305500
Adding measurement 508b4121 to history. Total: 0
LBFGS Accumulation History: 1 points
Constructing line search parameters: GD+Trust
th(0)=-23.070732460134458;dx=-2.305555492224928E-7
Armijo: th(2.154434690031884)=-23.070732460134458; dx=-2.298412413230419E-7 evalInputDelta=0.0
Armijo: th(1.077217345015942)=-23.070732460134458; dx=-2.2985278560754643E-7 evalInputDelta=0.0
Armijo: th(0.3590724483386473)=-23.070732460134458; dx=-2.2978361682342976E-7 evalInputDelta=0.0
Armijo: th(0.08976811208466183)=-23.070732460134458; dx=-2.2973659557270516E-7 evalInputDelta=0.0
Armijo: th(0.017953622416932366)=-23.070732460134458; dx=-2.2947923832471252E-7 evalInputDelta=0.0
WOLFE (weak): th(0.002992270402822061)=-23.070732460134458; dx=-2.2916662608740848E-7 evalInputDelta=0.0
Armijo: th(0.010472946409877214)=-23.070732460134458; dx=-2.2981720071293253E-7 evalInputDelta=0.0
WOLFE (weak): th(0.006732608406349637)=-23.070732460134458; dx=-2.2990106636541599E-7 evalInputDelta=0.0
Armijo: th(0.008602777408113426)=-23.070732460134458; dx=-2.2970442477799109E-7 evalInputDelta=0.0
WOLFE (weak): th(0.0076676929072315315)=-23.070732460134458; dx=-2.287158605333991E-7 evalInputDelta=0.0
Armijo: th(0.008135235157672479)=-23.070732460134458; dx=-2.2966899093936627E-7 evalInputDelta=0.0
Armijo: th(0.007901464032452005)=-23.070732460134458; dx=-2.2944155739854595E-7 evalInputDelta=0.0
Armijo: th(0.007784578469841768)=-23.070732460134458; dx=-2.2984297289124598E-7 evalInputDelta=0.0
Armijo: th(0.00772613568853665)=-23.070732460134458; dx=-2.2972089234391695E-7 evalInputDelta=0.0
WOLFE (weak): th(0.007696914297884091)=-23.070732460134458; dx=-2.2986363056440863E-7 evalInputDelta=0.0
Armijo: th(0.007711524993210371)=-23.070732460134458; dx=-2.29484028669132E-7 evalInputDelta=0.0
WOLFE (weak): th(0.00770421964554723)=-23.070732460134458; dx=-2.2967957466128044E-7 evalInputDelta=0.0
Armijo: th(0.0077078723193788005)=-23.070732460134458; dx=-2.2986452082925066E-7 evalInputDelta=0.0
Armijo: th(0.007706045982463015)=-23.070732460134458; dx=-2.2982765800766553E-7 evalInputDelta=0.0
Armijo: th(0.007705132814005123)=-23.070732460134458; dx=-2.2983222006242677E-7 evalInputDelta=0.0
WOLFE (weak): th(0.007704676229776176)=-23.070732460134458; dx=-2.2970752534471608E-7 evalInputDelta=0.0
Armijo: th(0.007704904521890649)=-23.070732460134458; dx=-2.2996369791360052E-7 evalInputDelta=0.0
Armijo: th(0.0077047903758334126)=-23.070732460134458; dx=-2.2928894715275922E-7 evalInputDelta=0.0
Armijo: th(0.007704733302804794)=-23.070732460134458; dx=-2.2965722808651453E-7 evalInputDelta=0.0
mu ~= nu (0.007704676229776176): th(0.0)=-23.070732460134458
Fitness changed from -23.070732460134458 to -23.070732460134458
Static Iteration Total: 68.5939; Orientation: 0.0354; Line Search: 61.3056
Iteration 1 failed. Error: -23.070732460134458
Previous Error: 0.0 -> -23.070732460134458
Retrying iteration 1
Reset training subject: 635122077967900
Adding measurement 772f5fbd to history. Total: 0
LBFGS Accumulation History: 1 points
th(0)=-23.070732460134458;dx=-2.3229581110239523E-7
WOLFE (weak): th(0.01659928322495022)=-23.070732460134458; dx=-2.3097510716149356E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03319856644990044)=-23.070732460134458; dx=-2.3139361003019053E-7 evalInputDelta=0.0
Armijo: th(0.09959569934970133)=-23.070732460134458; dx=-2.3080543862476087E-7 evalInputDelta=0.0
Armijo: th(0.06639713289980088)=-23.070732460134458; dx=-2.311593805545322E-7 evalInputDelta=0.0
Armijo: th(0.04979784967485067)=-23.070732460134458; dx=-2.298006618658216E-7 evalInputDelta=0.0
Armijo: th(0.04149820806237556)=-23.070732460134458; dx=-2.312708588954794E-7 evalInputDelta=0.0
WOLFE (weak): th(0.037348387256137996)=-23.070732460134458; dx=-2.3118602581246172E-7 evalInputDelta=0.0
Armijo: th(0.03942329765925678)=-23.070732460134458; dx=-2.2996624457441912E-7 evalInputDelta=0.0
Armijo: th(0.03838584245769738)=-23.070732460134458; dx=-2.3116669537384636E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03786711485691769)=-23.070732460134458; dx=-2.308142221528073E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03812647865730753)=-23.070732460134458; dx=-2.313268954629319E-7 evalInputDelta=0.0
Armijo: th(0.03825616055750246)=-23.070732460134458; dx=-2.3108919136822139E-7 evalInputDelta=0.0
WOLFE (weak): th(0.038191319607404996)=-23.070732460134458; dx=-2.3109758984036356E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03822374008245373)=-23.070732460134458; dx=-2.3122134058000128E-7 evalInputDelta=0.0
Armijo: th(0.038239950319978094)=-23.070732460134458; dx=-2.3074515140038063E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03823184520121591)=-23.070732460134458; dx=-2.3119310420441105E-7 evalInputDelta=0.0
Armijo: th(0.03823589776059701)=-23.070732460134458; dx=-2.3121661497110626E-7 evalInputDelta=0.0
WOLFE (weak): th(0.038233871480906456)=-23.070732460134458; dx=-2.314463114429052E-7 evalInputDelta=0.0
Armijo: th(0.03823488462075173)=-23.070732460134458; dx=-2.3054702528632683E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03823437805082909)=-23.070732460134458; dx=-2.312121415920095E-7 evalInputDelta=0.0
WOLFE (weak): th(0.038234631335790414)=-23.070732460134458; dx=-2.3116813656334008E-7 evalInputDelta=0.0
mu ~= nu (0.038234631335790414): th(0.0)=-23.070732460134458
Fitness changed from -23.070732460134458 to -23.070732460134458
Static Iteration Total: 56.3133; Orientation: 0.0332; Line Search: 52.0147
Iteration 2 failed. Error: -23.070732460134458
Previous Error: 0.0 -> -23.070732460134458
Optimization terminated 2
Final threshold in iteration 2: -23.070732460134458 (> -Infinity) after 124.908s (< 5400.000s)

Returns

    -23.070732460134458