BasicOptimizer.scala:89 executed in 142.22 seconds (1.966 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: 68923868486900
Reset training subject: 68926525440700
Adding measurement 92fe6e5 to history. Total: 0
LBFGS Accumulation History: 1 points
Constructing line search parameters: GD+Trust
th(0)=-28.411047696107403;dx=-5.915441486564932E-7
Armijo: th(2.154434690031884)=-28.411047696107403; dx=-5.391989723905936E-7 evalInputDelta=0.0
Armijo: th(1.077217345015942)=-28.411047696107403; dx=-5.39273713743637E-7 evalInputDelta=0.0
Armijo: th(0.3590724483386473)=-28.411047696107403; dx=-5.392792900046446E-7 evalInputDelta=0.0
Armijo: th(0.08976811208466183)=-28.411047696107403; dx=-5.39277893272698E-7 evalInputDelta=0.0
Armijo: th(0.017953622416932366)=-28.411047696107403; dx=-5.392512120655478E-7 evalInputDelta=0.0
WOLFE (weak): th(0.002992270402822061)=-28.411047696107403; dx=-5.393939258416516E-7 evalInputDelta=0.0
Armijo: th(0.010472946409877214)=-28.411047696107403; dx=-5.39251700992542E-7 evalInputDelta=0.0
Armijo: th(0.006732608406349637)=-28.411047696107403; dx=-5.39384305478515E-7 evalInputDelta=0.0
Armijo: th(0.004862439404585849)=-28.411047696107403; dx=-5.393186228559704E-7 evalInputDelta=0.0
Armijo: th(0.003927354903703955)=-28.411047696107403; dx=-5.393153096327078E-7 evalInputDelta=0.0
Armijo: th(0.0034598126532630075)=-28.411047696107403; dx=-5.39340293547986E-7 evalInputDelta=0.0
Armijo: th(0.003226041528042534)=-28.411047696107403; dx=-5.393813090748951E-7 evalInputDelta=0.0
Armijo: th(0.003109155965432297)=-28.411047696107403; dx=-5.393758264440103E-7 evalInputDelta=0.0
Armijo: th(0.003050713184127179)=-28.411047696107403; dx=-5.392652627226466E-7 evalInputDelta=0.0
Armijo: th(0.0030214917934746196)=-28.411047696107403; dx=-5.393883916791999E-7 evalInputDelta=0.0
Armijo: th(0.0030068810981483405)=-28.411047696107403; dx=-5.391782853715919E-7 evalInputDelta=0.0
WOLFE (weak): th(0.002999575750485201)=-28.411047696107403; dx=-5.393825023862046E-7 evalInputDelta=0.0
Armijo: th(0.0030032284243167707)=-28.411047696107403; dx=-5.392614626255835E-7 evalInputDelta=0.0
WOLFE (weak): th(0.003001402087400986)=-28.411047696107403; dx=-5.39316465239428E-7 evalInputDelta=0.0
WOLFE (weak): th(0.0030023152558588785)=-28.411047696107403; dx=-5.393702827099619E-7 evalInputDelta=0.0
WOLFE (weak): th(0.0030027718400878244)=-28.411047696107403; dx=-5.393376123552098E-7 evalInputDelta=0.0
Armijo: th(0.0030030001322022973)=-28.411047696107403; dx=-5.393023597599116E-7 evalInputDelta=0.0
WOLFE (weak): th(0.003002885986145061)=-28.411047696107403; dx=-5.393863331035806E-7 evalInputDelta=0.0
Armijo: th(0.003002943059173679)=-28.411047696107403; dx=-5.393826615109779E-7 evalInputDelta=0.0
WOLFE (weak): th(0.00300291452265937)=-28.411047696107403; dx=-5.393747381549338E-7 evalInputDelta=0.0
mu ~= nu (0.00300291452265937): th(0.0)=-28.411047696107403
Fitness changed from -28.411047696107403 to -28.411047696107403
Static Iteration Total: 77.8463; Orientation: 0.0643; Line Search: 70.0829
Iteration 1 failed. Error: -28.411047696107403
Previous Error: 0.0 -> -28.411047696107403
Retrying iteration 1
Reset training subject: 69001715034099
Adding measurement 1a2c73b0 to history. Total: 0
LBFGS Accumulation History: 1 points
th(0)=-28.411047696107403;dx=-5.916537383903988E-7
WOLFE (weak): th(0.0064696139588460615)=-28.411047696107403; dx=-5.394380791702251E-7 evalInputDelta=0.0
WOLFE (weak): th(0.012939227917692123)=-28.411047696107403; dx=-5.39413247553388E-7 evalInputDelta=0.0
Armijo: th(0.03881768375307637)=-28.411047696107403; dx=-5.394491929802043E-7 evalInputDelta=0.0
Armijo: th(0.025878455835384246)=-28.411047696107403; dx=-5.398031374610041E-7 evalInputDelta=0.0
Armijo: th(0.019408841876538185)=-28.411047696107403; dx=-5.392765261714709E-7 evalInputDelta=0.0
Armijo: th(0.016174034897115153)=-28.411047696107403; dx=-5.394296496552598E-7 evalInputDelta=0.0
WOLFE (weak): th(0.014556631407403639)=-28.411047696107403; dx=-5.393410467695964E-7 evalInputDelta=0.0
Armijo: th(0.015365333152259396)=-28.411047696107403; dx=-5.393922699711551E-7 evalInputDelta=0.0
WOLFE (weak): th(0.014960982279831517)=-28.411047696107403; dx=-5.395665991649427E-7 evalInputDelta=0.0
Armijo: th(0.015163157716045456)=-28.411047696107403; dx=-5.393442627232633E-7 evalInputDelta=0.0
Armijo: th(0.015062069997938486)=-28.411047696107403; dx=-5.394300612632678E-7 evalInputDelta=0.0
WOLFE (weak): th(0.015011526138885001)=-28.411047696107403; dx=-5.393762491742653E-7 evalInputDelta=0.0
Armijo: th(0.015036798068411744)=-28.411047696107403; dx=-5.39083580574292E-7 evalInputDelta=0.0
Armijo: th(0.015024162103648372)=-28.411047696107403; dx=-5.393866411195588E-7 evalInputDelta=0.0
Armijo: th(0.015017844121266687)=-28.411047696107403; dx=-5.392881371473277E-7 evalInputDelta=0.0
Armijo: th(0.015014685130075844)=-28.411047696107403; dx=-5.394392715945244E-7 evalInputDelta=0.0
Armijo: th(0.015013105634480422)=-28.411047696107403; dx=-5.393811559273878E-7 evalInputDelta=0.0
Armijo: th(0.015012315886682712)=-28.411047696107403; dx=-5.392870060196989E-7 evalInputDelta=0.0
Armijo: th(0.015011921012783857)=-28.411047696107403; dx=-5.392700957361955E-7 evalInputDelta=0.0
WOLFE (weak): th(0.01501172357583443)=-28.411047696107403; dx=-5.394647749834075E-7 evalInputDelta=0.0
Armijo: th(0.015011822294309144)=-28.411047696107403; dx=-5.394574492199785E-7 evalInputDelta=0.0
mu ~= nu (0.01501172357583443): th(0.0)=-28.411047696107403
Fitness changed from -28.411047696107403 to -28.411047696107403
Static Iteration Total: 64.3722; Orientation: 0.0559; Line Search: 59.4082
Iteration 2 failed. Error: -28.411047696107403
Previous Error: 0.0 -> -28.411047696107403
Optimization terminated 2
Final threshold in iteration 2: -28.411047696107403 (> -Infinity) after 142.219s (< 5400.000s)

Returns

    -28.411047696107403