BasicOptimizer.scala:89 executed in 145.29 seconds (1.523 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: 113731272197300
Reset training subject: 113733925195800
Corrupt weights measurement
LBFGS Accumulation History: 0 points
Constructing line search parameters: GD+Trust
th(0)=-28.14743074337851;dx=-1.1742828592991736E-6
Armijo: th(2.154434690031884)=-28.14743074337851; dx=-1.0811166263857522E-6 evalInputDelta=0.0
Armijo: th(1.077217345015942)=-28.14743074337851; dx=-1.0809351139439703E-6 evalInputDelta=0.0
Armijo: th(0.3590724483386473)=-28.14742924120555; dx=-1.081260871777828E-6 evalInputDelta=-1.5021729602437972E-6
Armijo: th(0.08976811208466183)=-28.14743074337851; dx=-1.081244404346481E-6 evalInputDelta=0.0
Armijo: th(0.017953622416932366)=-28.14743074337851; dx=-1.0811168683897441E-6 evalInputDelta=0.0
Armijo: th(0.002992270402822061)=-28.14743074337851; dx=-1.0812626154981322E-6 evalInputDelta=0.0
WOLFE (weak): th(4.2746720040315154E-4)=-28.14743074337851; dx=-1.0808953136465533E-6 evalInputDelta=0.0
Armijo: th(0.0017098688016126062)=-28.14743074337851; dx=-1.081106727077566E-6 evalInputDelta=0.0
WOLFE (weak): th(0.001068668001007879)=-28.14743074337851; dx=-1.081085389810092E-6 evalInputDelta=0.0
WOLFE (weak): th(0.0013892684013102426)=-28.14743074337851; dx=-1.0808876096448648E-6 evalInputDelta=0.0
Armijo: th(0.0015495686014614244)=-28.14743074337851; dx=-1.0809523889078495E-6 evalInputDelta=0.0
WOLFE (weak): th(0.0014694185013858336)=-28.14743074337851; dx=-1.0812473617232735E-6 evalInputDelta=0.0
WOLFE (weak): th(0.001509493551423629)=-28.14743074337851; dx=-1.0810619741994399E-6 evalInputDelta=0.0
Armijo: th(0.0015295310764425266)=-28.14743074337851; dx=-1.0811784205618196E-6 evalInputDelta=0.0
Armijo: th(0.0015195123139330777)=-28.14743074337851; dx=-1.0808656458694978E-6 evalInputDelta=0.0
Armijo: th(0.0015145029326783534)=-28.14743074337851; dx=-1.0809958795696823E-6 evalInputDelta=0.0
WOLFE (weak): th(0.0015119982420509913)=-28.14743074337851; dx=-1.081271341021906E-6 evalInputDelta=0.0
Armijo: th(0.0015132505873646724)=-28.14743074337851; dx=-1.080983220521748E-6 evalInputDelta=0.0
WOLFE (weak): th(0.0015126244147078318)=-28.14743074337851; dx=-1.0812737114020026E-6 evalInputDelta=0.0
Armijo: th(0.0015129375010362522)=-28.14743074337851; dx=-1.0810159979388477E-6 evalInputDelta=0.0
Armijo: th(0.0015127809578720421)=-28.14743074337851; dx=-1.0812724072613674E-6 evalInputDelta=0.0
WOLFE (weak): th(0.001512702686289937)=-28.14743074337851; dx=-1.0810643390858343E-6 evalInputDelta=0.0
Armijo: th(0.0015127418220809896)=-28.14743074337851; dx=-1.0809637416513251E-6 evalInputDelta=0.0
Armijo: th(0.0015127222541854632)=-28.14743074337851; dx=-1.0812704119655926E-6 evalInputDelta=0.0
WOLFE (weak): th(0.0015127124702377002)=-28.14743074337851; dx=-1.0808314583211933E-6 evalInputDelta=0.0
mu ~= nu (0.0015127124702377002): th(0.0)=-28.14743074337851
Fitness changed from -28.22986195410434 to -28.22986195410434
Static Iteration Total: 79.1076; Orientation: 0.0380; Line Search: 71.1498
Iteration 1 failed. Error: -28.22986195410434
Previous Error: 0.0 -> -28.22986195410434
Retrying iteration 1
Reset training subject: 113810379963100
Corrupt weights measurement
LBFGS Accumulation History: 0 points
th(0)=-28.14743074337851;dx=-1.17406357650979E-6
WOLFE (weak): th(0.003259050761362158)=-28.14743074337851; dx=-1.0808871558587195E-6 evalInputDelta=0.0
WOLFE (weak): th(0.006518101522724316)=-28.14743074337851; dx=-1.080999488946135E-6 evalInputDelta=0.0
Armijo: th(0.019554304568172948)=-28.14743074337851; dx=-1.0807087693050386E-6 evalInputDelta=0.0
Armijo: th(0.013036203045448632)=-28.14743074337851; dx=-1.0808113222060557E-6 evalInputDelta=0.0
Armijo: th(0.009777152284086474)=-28.14743074337851; dx=-1.0806367297664636E-6 evalInputDelta=0.0
Armijo: th(0.008147626903405395)=-28.14743074337851; dx=-1.0808332654746173E-6 evalInputDelta=0.0
WOLFE (weak): th(0.007332864213064855)=-28.14743074337851; dx=-1.0810069431484364E-6 evalInputDelta=0.0
Armijo: th(0.007740245558235125)=-28.14743074337851; dx=-1.0809542246792498E-6 evalInputDelta=0.0
WOLFE (weak): th(0.00753655488564999)=-28.14743074337851; dx=-1.0806874923707392E-6 evalInputDelta=0.0
Armijo: th(0.007638400221942558)=-28.14743074337851; dx=-1.080810113488259E-6 evalInputDelta=0.0
Armijo: th(0.007587477553796274)=-28.14743074337851; dx=-1.0808539609930989E-6 evalInputDelta=0.0
WOLFE (weak): th(0.007562016219723132)=-28.14743074337851; dx=-1.0808942948848982E-6 evalInputDelta=0.0
Armijo: th(0.007574746886759702)=-28.14743074337851; dx=-1.0809361019137572E-6 evalInputDelta=0.0
Armijo: th(0.007568381553241417)=-28.14743074337851; dx=-1.0810015338176213E-6 evalInputDelta=0.0
Armijo: th(0.007565198886482274)=-28.14743074337851; dx=-1.0810058091343643E-6 evalInputDelta=0.0
WOLFE (weak): th(0.0075636075531027025)=-28.14743074337851; dx=-1.08094926821512E-6 evalInputDelta=0.0
WOLFE (weak): th(0.007564403219792489)=-28.14743074337851; dx=-1.080731454212426E-6 evalInputDelta=0.0
WOLFE (weak): th(0.007564801053137381)=-28.14743074337851; dx=-1.0807969570807203E-6 evalInputDelta=0.0
Armijo: th(0.007564999969809827)=-28.14743074337851; dx=-1.0808610892924507E-6 evalInputDelta=0.0
WOLFE (weak): th(0.007564900511473604)=-28.14743074337851; dx=-1.080990230977566E-6 evalInputDelta=0.0
WOLFE (weak): th(0.007564950240641716)=-28.14743074337851; dx=-1.0807560237763634E-6 evalInputDelta=0.0
mu ~= nu (0.007564950240641716): th(0.0)=-28.14743074337851
Fitness changed from -28.22986195410434 to -28.22986195410434
Static Iteration Total: 66.1830; Orientation: 0.0333; Line Search: 61.0015
Iteration 2 failed. Error: -28.22986195410434
Previous Error: 0.0 -> -28.22986195410434
Optimization terminated 2
Final threshold in iteration 2: -28.22986195410434 (> -Infinity) after 145.291s (< 5400.000s)

Returns

    -28.22986195410434