Code from BasicOptimizer.scala:75 executed in 60.54 seconds (0.610 gc):

    val lineSearchInstance: LineSearchStrategy = lineSearchFactory
    IterativeTrainer.wrap(trainable)
      .setOrientation(orientation())
      .setMonitor(new TrainingMonitor() {
        override def clear(): Unit = trainingMonitor.clear()
  
        override def log(msg: String): Unit = trainingMonitor.log(msg)
  
        override def onStepFail(currentPoint: Step): Boolean = {
          BasicOptimizer.this.onStepFail(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, currentPoint)
          trainingMonitor.onStepComplete(currentPoint)
          super.onStepComplete(currentPoint)
        }
      })
      .setTimeout(trainingMinutes, TimeUnit.MINUTES)
      .setMaxIterations(trainingIterations)
      .setLineSearchFactory((_: CharSequence) => lineSearchInstance)
      .setTerminateThreshold(java.lang.Double.NEGATIVE_INFINITY)
      .runAndFree
      .asInstanceOf[lang.Double]

Logging:

    Reset training subject: 829836098146
    Reset training subject: 830814075070
    Adding measurement 3dcebd1d to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=0.9303533434867859;dx=-9.968065426641856E-9
    Armijo: th(2.154434690031884)=0.9303533434867859; dx=-9.968063230314182E-9 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=0.9303533434867859; dx=-9.968063713670606E-9 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=0.9303533434867859; dx=-9.968064862416243E-9 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=0.9303533434867859; dx=-9.96806539595999E-9 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=0.9303533434867859; dx=-9.96806542137462E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.002992270402822061)=0.9303533434867859; dx=-9.968065425687387E-9 evalInputDelta=0.0
    Armijo: th(0.010472946409877214)=0.9303533434867859; dx=-9.968065423718057E-9 evalInputDelta=0.0
    Armijo: th(0.006732608406349637)=0.9303533434867859; dx=-9.968065424871362E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.004862439404585849)=0.9303533434867859; dx=-9.968065425278035E-9 evalInputDelta=0.0
    Armijo: th(0.005797523905467743)=0.9303533434867859; dx=-9.968065424782897E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.005329981655026796)=0.9303533434867859; dx=-9.968065425080003E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.0055637527802472695)=0.9303533434867859; dx=-9.968065425120654E-9 evalInputDelta=0.0
    Armijo: th(0.005680638342857506)=0.9303533434867859; dx=-9.968065424925885E-9 evalInputDelta=0.0
    Armijo: th(0.005622195561552388)=0.9303533434867859; dx=-9.968065425120654E-9 evalInputDelta=0.0
    Armijo: th(0.005592974170899829)=0.9303533434867859; dx=-9.968065425120654E-9 evalInputDelta=0.0
    Armijo: th(0.005578363475573549)=0.9303533434867859; dx=-9.968065425120654E-9 evalInputDelta=0.0
    Armijo: th(0.005571058127910409)=0.9303533434867859; dx=-9.968065425120654E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.00556740545407884)=0.9303533434867859; dx=-9.968065425120654E-9 evalInputDelta=0.0
    Armijo: th(0.00556923179099

...skipping 1596 bytes...

    859; dx=-9.968065414441847E-9 evalInputDelta=0.0
    Armijo: th(0.02999459786674769)=0.9303533434867859; dx=-9.96806541602048E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.026995138080072922)=0.9303533434867859; dx=-9.968065416808538E-9 evalInputDelta=0.0
    Armijo: th(0.028494867973410304)=0.9303533434867859; dx=-9.968065415841355E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.027745003026741613)=0.9303533434867859; dx=-9.968065416379478E-9 evalInputDelta=0.0
    Armijo: th(0.02811993550007596)=0.9303533434867859; dx=-9.968065416244593E-9 evalInputDelta=0.0
    Armijo: th(0.027932469263408786)=0.9303533434867859; dx=-9.968065416439558E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.027838736145075198)=0.9303533434867859; dx=-9.968065416439558E-9 evalInputDelta=0.0
    Armijo: th(0.027885602704241993)=0.9303533434867859; dx=-9.968065416439558E-9 evalInputDelta=0.0
    Armijo: th(0.027862169424658596)=0.9303533434867859; dx=-9.968065416439558E-9 evalInputDelta=0.0
    Armijo: th(0.0278504527848669)=0.9303533434867859; dx=-9.968065416439558E-9 evalInputDelta=0.0
    Armijo: th(0.027844594464971048)=0.9303533434867859; dx=-9.968065416439558E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.027841665305023123)=0.9303533434867859; dx=-9.968065416439558E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.027843129884997084)=0.9303533434867859; dx=-9.968065416439558E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.027843862174984066)=0.9303533434867859; dx=-9.968065416439558E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.027844228319977557)=0.9303533434867859; dx=-9.968065416439558E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.027844411392474303)=0.9303533434867859; dx=-9.968065416439558E-9 evalInputDelta=0.0
    mu ~= nu (0.027844411392474303): th(0.0)=0.9303533434867859
    Fitness changed from 0.9303533434867859 to 0.9303533434867859
    Static Iteration Total: 27.5491; Orientation: 0.0663; Line Search: 25.5468
    Iteration 2 failed. Error: 0.9303533434867859
    Previous Error: 0.0 -> 0.9303533434867859
    Optimization terminated 2
    Final threshold in iteration 2: 0.9303533434867859 (> -Infinity) after 60.536s (< 3600.000s)
    

Returns:

    0.9303533434867859