Code from BasicOptimizer.scala:75 executed in 234.54 seconds (2.241 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: 907540855548100
    Reset training subject: 907545600997600
    Adding measurement 24331ab6 to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=0.28208789229393005;dx=-5.713760602415963E-9
    Armijo: th(2.154434690031884)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.002992270402822061)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    Armijo: th(0.010472946409877214)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    Armijo: th(0.006732608406349637)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    Armijo: th(0.004862439404585849)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.003927354903703955)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.004394897154144902)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.004628668279365375)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.004745553841975612)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.0048039966232807305)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.00483321801393329)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.00484782870925957)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.004855134056922709)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    Armijo: th(0.004858786730754279)=0.28208789229393005

...skipping 1756 bytes...

    a=0.0
    Armijo: th(0.02616377555494349)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.02354739799944914)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    Armijo: th(0.024855586777196313)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.024201492388322726)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    Armijo: th(0.02452853958275952)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    Armijo: th(0.024365015985541125)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.024283254186931925)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    Armijo: th(0.024324135086236525)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    Armijo: th(0.024303694636584224)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    Armijo: th(0.024293474411758074)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    Armijo: th(0.024288364299345)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.024285809243138463)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.02428708677124173)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.024287725535293365)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.02428804491731918)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.02428820460833209)=0.28208789229393005; dx=-5.668659328413669E-9 evalInputDelta=0.0
    mu ~= nu (0.02428820460833209): th(0.0)=0.28208789229393005
    Fitness changed from 0.28208789229393005 to 0.28208789229393005
    Static Iteration Total: 105.1011; Orientation: 0.0616; Line Search: 97.0195
    Iteration 2 failed. Error: 0.28208789229393005
    Previous Error: 0.0 -> 0.28208789229393005
    Optimization terminated 2
    Final threshold in iteration 2: 0.28208789229393005 (> -Infinity) after 234.543s (< 1800.000s)
    

Returns:

    0.28208789229393005