Code from BasicOptimizer.scala:75 executed in 324.61 seconds (2.857 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: 903692288665800
    Reset training subject: 903699079515600
    Adding measurement dab1f89 to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=2.27861025929451;dx=-2.5750789638978447E-8
    Armijo: th(2.154434690031884)=2.27861025929451; dx=-2.57507920862967E-8 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=2.27861025929451; dx=-2.5750790032064157E-8 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.002992270402822061)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    Armijo: th(0.010472946409877214)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.006732608406349637)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.008602777408113426)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    Armijo: th(0.00953786190899532)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    Armijo: th(0.009070319658554373)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    Armijo: th(0.0088365485333339)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    Armijo: th(0.008719662970723662)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    Armijo: th(0.008661220189418544)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    Armijo: th(0.008631998798765986)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.008617388103439705)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    Armijo: th(0.008624693451102845)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.008621040777271275)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    Armijo: th(0.0086228

...skipping 1514 bytes...

    929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    Armijo: th(0.046443356395708416)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.041799020756137575)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    Armijo: th(0.044121188575922995)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.042960104666030285)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    Armijo: th(0.04354064662097664)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    Armijo: th(0.043250375643503466)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.043105240154766876)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    Armijo: th(0.04317780789913517)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    Armijo: th(0.043141524026951025)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    Armijo: th(0.04312338209085895)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    Armijo: th(0.043114311122812915)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0431097756387899)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.04311204338080141)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.04311317725180716)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.04311374418731004)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.043114027655061476)=2.27861025929451; dx=-2.5750789638978447E-8 evalInputDelta=0.0
    mu ~= nu (0.043114027655061476): th(0.0)=2.27861025929451
    Fitness changed from 2.27861025929451 to 2.27861025929451
    Static Iteration Total: 149.9411; Orientation: 0.0660; Line Search: 138.5947
    Iteration 2 failed. Error: 2.27861025929451
    Previous Error: 0.0 -> 2.27861025929451
    Optimization terminated 2
    Final threshold in iteration 2: 2.27861025929451 (> -Infinity) after 324.600s (< 3600.000s)
    

Returns:

    2.27861025929451