Code from BasicOptimizer.scala:75 executed in 172.94 seconds (2.550 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: 916655114823200
    Reset training subject: 916658242343600
    Adding measurement 4562fe65 to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=1.0656364858150482;dx=-6.902052599835338E-8
    Armijo: th(2.154434690031884)=1.0656365007162094; dx=-6.89666284024962E-8 evalInputDelta=-1.4901161193847656E-8
    Armijo: th(1.077217345015942)=1.0656364858150482; dx=-6.896662931233848E-8 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=1.0656364858150482; dx=-6.89666294605581E-8 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    Armijo: th(0.002992270402822061)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    WOLFE (weak): th(4.2746720040315154E-4)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    Armijo: th(0.0017098688016126062)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.001068668001007879)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0013892684013102426)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0015495686014614244)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    Armijo: th(0.0016297187015370152)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0015896436514992198)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    Armijo: th(0.0016096811765181174)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0015996624140086685)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.001604671795263393)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0016071764858907552)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0016084288312044363)=1.0656364

...skipping 1739 bytes...

    -8 evalInputDelta=0.0
    Armijo: th(0.008663743193684095)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.007797368874315685)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    Armijo: th(0.00823055603399989)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.008013962454157787)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    Armijo: th(0.008122259244078838)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    Armijo: th(0.008068110849118314)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.00804103665163805)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    Armijo: th(0.008054573750378182)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    Armijo: th(0.008047805201008116)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    Armijo: th(0.008044420926323084)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    Armijo: th(0.008042728788980567)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.008041882720309309)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.008042305754644938)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.008042517271812752)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.00804262303039666)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.008042675909688613)=1.0656364858150482; dx=-6.896662950472473E-8 evalInputDelta=0.0
    mu ~= nu (0.008042675909688613): th(0.0)=1.0656364858150482
    Fitness changed from 1.0656364858150482 to 1.0656364858150482
    Static Iteration Total: 78.5504; Orientation: 0.0618; Line Search: 72.3967
    Iteration 2 failed. Error: 1.0656364858150482
    Previous Error: 0.0 -> 1.0656364858150482
    Optimization terminated 2
    Final threshold in iteration 2: 1.0656364858150482 (> -Infinity) after 172.943s (< 1800.000s)
    

Returns:

    1.0656364858150482