Code from BasicOptimizer.scala:75 executed in 192.46 seconds (1.733 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: 914650270707500
    Reset training subject: 914653665790300
    Adding measurement 54c15c3d to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=0.7647788524627686;dx=-3.3440666661266897E-8
    Armijo: th(2.154434690031884)=0.7647788524627686; dx=-3.343974261283708E-8 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    Armijo: th(0.002992270402822061)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    WOLFE (weak): th(4.2746720040315154E-4)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    Armijo: th(0.0017098688016126062)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.001068668001007879)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0013892684013102426)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0015495686014614244)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0016297187015370152)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    Armijo: th(0.0016697937515748108)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.001649756226555913)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0016597749890653619)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    Armijo: th(0.0016647843703200863)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    Armijo: th(0.001662279679692724)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    Armijo: th(0.001661027334379043)=0.7647788524627686; dx=

...skipping 1753 bytes...

    a=0.0
    Armijo: th(0.008940825024834332)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0080467425223509)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    Armijo: th(0.008493783773592615)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.008270263147971756)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    Armijo: th(0.008382023460782186)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    Armijo: th(0.008326143304376971)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.008298203226174364)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    Armijo: th(0.008312173265275669)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    Armijo: th(0.008305188245725017)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    Armijo: th(0.008301695735949691)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    Armijo: th(0.008299949481062028)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.008299076353618196)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.008299512917340112)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.00829973119920107)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.008299840340131549)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.008299894910596788)=0.7647788524627686; dx=-3.3439742624711525E-8 evalInputDelta=0.0
    mu ~= nu (0.008299894910596788): th(0.0)=0.7647788524627686
    Fitness changed from 0.7647788524627686 to 0.7647788524627686
    Static Iteration Total: 87.7954; Orientation: 0.0601; Line Search: 80.2156
    Iteration 2 failed. Error: 0.7647788524627686
    Previous Error: 0.0 -> 0.7647788524627686
    Optimization terminated 2
    Final threshold in iteration 2: 0.7647788524627686 (> -Infinity) after 192.454s (< 1800.000s)
    

Returns:

    0.7647788524627686