Code from BasicOptimizer.scala:75 executed in 85.56 seconds (0.828 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: 904533266993800
    Reset training subject: 904534883740800
    Adding measurement 350342e0 to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=-0.2237841784954071;dx=-2.1335797845083497E-9
    Armijo: th(2.154434690031884)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.002992270402822061)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    Armijo: th(0.010472946409877214)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    Armijo: th(0.006732608406349637)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.004862439404585849)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.005797523905467743)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.00626506615590869)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.006498837281129164)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    Armijo: th(0.0066157228437394005)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    Armijo: th(0.006557280062434282)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    Armijo: th(0.006528058671781723)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    Armijo: th(0.006513447976455443)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    Armijo: th(0.006506142628792303)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.006502489954960734)=-0.223784178495407

...skipping 1671 bytes...

    rmijo: th(0.035033580136100306)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.03153022212249028)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    Armijo: th(0.03328190112929529)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.03240606162589278)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    Armijo: th(0.032843981377594034)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    Armijo: th(0.03262502150174341)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.0325155415638181)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    Armijo: th(0.03257028153278076)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    Armijo: th(0.03254291154829943)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    Armijo: th(0.03252922655605876)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    Armijo: th(0.03252238405993843)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.032518962811878264)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.03252067343590835)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.03252152874792339)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.03252195640393091)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.03252217023193467)=-0.2237841784954071; dx=-2.1064460305545665E-9 evalInputDelta=0.0
    mu ~= nu (0.03252217023193467): th(0.0)=-0.2237841784954071
    Fitness changed from -0.2237841784954071 to -0.2237841784954071
    Static Iteration Total: 39.2980; Orientation: 0.0151; Line Search: 36.1133
    Iteration 2 failed. Error: -0.2237841784954071
    Previous Error: 0.0 -> -0.2237841784954071
    Optimization terminated 2
    Final threshold in iteration 2: -0.2237841784954071 (> -Infinity) after 85.564s (< 720.000s)
    

Returns:

    -0.2237841784954071