BasicOptimizer.scala:89 executed in 125.84 seconds (0.774 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: 564502844353900
Reset training subject: 564505206687900
Adding measurement 3ccb56bc to history. Total: 0
LBFGS Accumulation History: 1 points
Constructing line search parameters: GD+Trust
th(0)=-25.22057262747057;dx=-2.883645620019497E-7
Armijo: th(2.154434690031884)=-25.22057262747057; dx=-2.8648971055689047E-7 evalInputDelta=0.0
Armijo: th(1.077217345015942)=-25.22057262747057; dx=-2.879149916508155E-7 evalInputDelta=0.0
Armijo: th(0.3590724483386473)=-25.22057262747057; dx=-2.8757364501378643E-7 evalInputDelta=0.0
Armijo: th(0.08976811208466183)=-25.22057262747057; dx=-2.876574323155733E-7 evalInputDelta=0.0
Armijo: th(0.017953622416932366)=-25.22057262747057; dx=-2.876090565675063E-7 evalInputDelta=0.0
WOLFE (weak): th(0.002992270402822061)=-25.22057262747057; dx=-2.877403788980459E-7 evalInputDelta=0.0
Armijo: th(0.010472946409877214)=-25.22057262747057; dx=-2.874832134826974E-7 evalInputDelta=0.0
Armijo: th(0.006732608406349637)=-25.22057262747057; dx=-2.8590114982443074E-7 evalInputDelta=0.0
WOLFE (weak): th(0.004862439404585849)=-25.22057262747057; dx=-2.8786331345441E-7 evalInputDelta=0.0
WOLFE (weak): th(0.005797523905467743)=-25.22057262747057; dx=-2.8786234754646577E-7 evalInputDelta=0.0
Armijo: th(0.00626506615590869)=-25.22057262747057; dx=-2.8791485720201337E-7 evalInputDelta=0.0
WOLFE (weak): th(0.006031295030688217)=-25.22057262747057; dx=-2.8733841625408437E-7 evalInputDelta=0.0
WOLFE (weak): th(0.006148180593298453)=-25.22057262747057; dx=-2.87876302517606E-7 evalInputDelta=0.0
Armijo: th(0.006206623374603572)=-25.22057262747057; dx=-2.875719392471222E-7 evalInputDelta=0.0
Armijo: th(0.006177401983951013)=-25.22057262747057; dx=-2.8699187883521577E-7 evalInputDelta=0.0
Armijo: th(0.0061627912886247325)=-25.22057262747057; dx=-2.876113824222559E-7 evalInputDelta=0.0
WOLFE (weak): th(0.006155485940961593)=-25.22057262747057; dx=-2.8771159917582774E-7 evalInputDelta=0.0
WOLFE (weak): th(0.006159138614793162)=-25.22057262747057; dx=-2.8771747681295606E-7 evalInputDelta=0.0
Armijo: th(0.0061609649517089474)=-25.22057262747057; dx=-2.876554172002212E-7 evalInputDelta=0.0
WOLFE (weak): th(0.006160051783251055)=-25.22057262747057; dx=-2.878763208329727E-7 evalInputDelta=0.0
Armijo: th(0.006160508367480002)=-25.22057262747057; dx=-2.8750148134260765E-7 evalInputDelta=0.0
Armijo: th(0.006160280075365529)=-25.22057262747057; dx=-2.860008804446025E-7 evalInputDelta=0.0
Armijo: th(0.006160165929308292)=-25.22057262747057; dx=-2.8788489098852415E-7 evalInputDelta=0.0
Armijo: th(0.006160108856279673)=-25.22057262747057; dx=-2.8777560088590416E-7 evalInputDelta=0.0
mu ~= nu (0.006160051783251055): th(0.0)=-25.22057262747057
Fitness changed from -25.22057262747057 to -25.22057262747057
Static Iteration Total: 67.9917; Orientation: 0.0357; Line Search: 60.6416
Iteration 1 failed. Error: -25.22057262747057
Previous Error: 0.0 -> -25.22057262747057
Retrying iteration 1
Reset training subject: 564570836386800
Adding measurement 12215244 to history. Total: 0
LBFGS Accumulation History: 1 points
th(0)=-25.22057262747057;dx=-2.8746800293888824E-7
WOLFE (weak): th(0.013271490734285199)=-25.22057262747057; dx=-2.87462910153108E-7 evalInputDelta=0.0
WOLFE (weak): th(0.026542981468570398)=-25.22057262747057; dx=-2.862267852078107E-7 evalInputDelta=0.0
Armijo: th(0.0796289444057112)=-25.22057262747057; dx=-2.871105823630373E-7 evalInputDelta=0.0
Armijo: th(0.053085962937140796)=-25.22057262747057; dx=-2.8741781549124097E-7 evalInputDelta=0.0
Armijo: th(0.0398144722028556)=-25.22057262747057; dx=-2.8695224146465733E-7 evalInputDelta=0.0
Armijo: th(0.033178726835713)=-25.22057262747057; dx=-2.8748976505157476E-7 evalInputDelta=0.0
WOLFE (weak): th(0.029860854152141698)=-25.22057262747057; dx=-2.8757168575319747E-7 evalInputDelta=0.0
Armijo: th(0.03151979049392735)=-25.22057262747057; dx=-2.8740901656655724E-7 evalInputDelta=0.0
WOLFE (weak): th(0.030690322323034522)=-25.22057262747057; dx=-2.8623000980294607E-7 evalInputDelta=0.0
Armijo: th(0.031105056408480936)=-25.22057262747057; dx=-2.874775132423999E-7 evalInputDelta=0.0
Armijo: th(0.03089768936575773)=-25.22057262747057; dx=-2.871399538211383E-7 evalInputDelta=0.0
WOLFE (weak): th(0.030794005844396125)=-25.22057262747057; dx=-2.876166051510323E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03084584760507693)=-25.22057262747057; dx=-2.862203530294027E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03087176848541733)=-25.22057262747057; dx=-2.877016737080145E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03088472892558753)=-25.22057262747057; dx=-2.8752274067001975E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03089120914567263)=-25.22057262747057; dx=-2.8775722559387785E-7 evalInputDelta=0.0
WOLFE (weak): th(0.03089444925571518)=-25.22057262747057; dx=-2.871978440033542E-7 evalInputDelta=0.0
WOLFE (weak): th(0.030896069310736456)=-25.22057262747057; dx=-2.8762911805765025E-7 evalInputDelta=0.0
Armijo: th(0.030896879338247092)=-25.22057262747057; dx=-2.8773643898023163E-7 evalInputDelta=0.0
WOLFE (weak): th(0.030896474324491774)=-25.22057262747057; dx=-2.877553551724458E-7 evalInputDelta=0.0
Armijo: th(0.030896676831369433)=-25.22057262747057; dx=-2.8700333702052154E-7 evalInputDelta=0.0
mu ~= nu (0.030896474324491774): th(0.0)=-25.22057262747057
Fitness changed from -25.22057262747057 to -25.22057262747057
Static Iteration Total: 57.8433; Orientation: 0.0355; Line Search: 53.2530
Iteration 2 failed. Error: -25.22057262747057
Previous Error: 0.0 -> -25.22057262747057
Optimization terminated 2
Final threshold in iteration 2: -25.22057262747057 (> -Infinity) after 125.835s (< 5400.000s)

Returns

    -25.22057262747057