Code from BasicOptimizer.scala:75 executed in 130.23 seconds (1.676 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: 909916543318900
    Reset training subject: 909918806483100
    Adding measurement 77e6f22d to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=0.8952993452548981;dx=-2.8370482393025938E-8
    Armijo: th(2.154434690031884)=0.8952993452548981; dx=-2.8370482199614014E-8 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    Armijo: th(0.002992270402822061)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    WOLFE (weak): th(4.2746720040315154E-4)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0017098688016126062)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    Armijo: th(0.0023510696022173336)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    Armijo: th(0.00203046920191497)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.001870169001763788)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.001950319101839379)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    Armijo: th(0.0019903941518771744)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    Armijo: th(0.0019703566268582766)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    Armijo: th(0.0019603378643488277)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0019553284830941034)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    Armijo: th(0.0019578331737214656)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0019565808284077845)=0.8952993452548981; dx=-2.83

...skipping 1744 bytes...

    =0.0
    Armijo: th(0.01053870925536637)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.009484838329829734)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    Armijo: th(0.010011773792598051)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.009748306061213893)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    Armijo: th(0.009880039926905972)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    Armijo: th(0.009814172994059933)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.009781239527636914)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    Armijo: th(0.009797706260848422)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    Armijo: th(0.009789472894242668)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    Armijo: th(0.009785356210939792)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    Armijo: th(0.009783297869288353)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.009782268698462633)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.009782783283875492)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.009783040576581923)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.009783169222935137)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.009783233546111745)=0.8952993452548981; dx=-2.8370482393025938E-8 evalInputDelta=0.0
    mu ~= nu (0.009783233546111745): th(0.0)=0.8952993452548981
    Fitness changed from 0.8952993452548981 to 0.8952993452548981
    Static Iteration Total: 58.3821; Orientation: 0.0309; Line Search: 53.7860
    Iteration 2 failed. Error: 0.8952993452548981
    Previous Error: 0.0 -> 0.8952993452548981
    Optimization terminated 2
    Final threshold in iteration 2: 0.8952993452548981 (> -Infinity) after 130.227s (< 720.000s)
    

Returns:

    0.8952993452548981