Code from BasicOptimizer.scala:75 executed in 119.48 seconds (2.855 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]
Reset training subject: 639509399898
Reset training subject: 640939607865
Adding measurement 7de9cb09 to history. Total: 0
LBFGS Accumulation History: 1 points
Constructing line search parameters: GD+Trust
th(0)=0.7338356375694275;dx=-5.674806481108622E-10
New Minimum: 0.7338356375694275 > 0.7338355779647827
WOLFE (weak): th(2.154434690031884)=0.7338355779647827; dx=-5.674806447145109E-10 evalInputDelta=5.9604644775390625E-8
Armijo: th(4.308869380063768)=0.7338356375694275; dx=-5.674807722782793E-10 evalInputDelta=0.0
Armijo: th(3.2316520350478255)=0.7338356375694275; dx=-5.674806431919931E-10 evalInputDelta=0.0
Armijo: th(2.6930433625398544)=0.7338356375694275; dx=-5.674806439736804E-10 evalInputDelta=0.0
Armijo: th(2.423739026285869)=0.7338356375694275; dx=-5.67480644323735E-10 evalInputDelta=0.0
Armijo: th(2.2890868581588766)=0.7338356375694275; dx=-5.674806445089911E-10 evalInputDelta=0.0
Armijo: th(2.2217607740953804)=0.7338356375694275; dx=-5.674806446209272E-10 evalInputDelta=0.0
WOLFE (weak): th(2.188097732063632)=0.7338355779647827; dx=-5.674806446765951E-10 evalInputDelta=5.9604644775390625E-8
Armijo: th(2.204929253079506)=0.7338356375694275; dx=-5.674806446426692E-10 evalInputDelta=0.0
Armijo: th(2.196513492571569)=0.7338356375694275; dx=-5.674806446504108E-10 evalInputDelta=0.0
WOLFE (weak): th(2.1923056123176003)=0.7338355779647827; dx=-5.674806446650604E-10 evalInputDelta=5.9604644775390625E-8
Armijo: th(2.1944095524445846)=0.7338356375694275; dx=-5.674806446580407E-10 evalInputDelta=0.0
WOLFE (weak): th(2.1933575823810925)=0.7338355779647827; dx=-5.674806446617449E-10 evalInputDelta=5.9604644775390625E-8
Armijo: th(2.1938835674128385)=0.7338356375694275; dx=-5.674806446588245E-10 evalInputDelta=0.0
WOLFE (weak): th(2.1936205748969657)=0.7338355779647827; dx=-5.674806446617927E-10 evalInputDelta=5.9604644775390625E-8
Armijo: th(2.1937520711549023)=0.7338356375694275; dx=-5.674806446588245E-10 evalInputDelta=0.0
WOLFE (weak): th(2.193686323025934)=0.7338355779647827; dx=-5.674806446589037E-10 evalInp
...skipping 4254 bytes...
WOLFE (weak): th(0.03881752027965983)=0.7338355779647827; dx=-5.674806412665098E-10 evalInputDelta=0.0
Armijo: th(0.04058195301964437)=0.7338356375694275; dx=-5.674806412591536E-10 evalInputDelta=-5.9604644775390625E-8
Armijo: th(0.0396997366496521)=0.7338356375694275; dx=-5.674806412617079E-10 evalInputDelta=-5.9604644775390625E-8
WOLFE (weak): th(0.03925862846465597)=0.7338355779647827; dx=-5.674806412625543E-10 evalInputDelta=0.0
Armijo: th(0.03947918255715403)=0.7338356375694275; dx=-5.67480641262475E-10 evalInputDelta=-5.9604644775390625E-8
Armijo: th(0.039368905510905)=0.7338356375694275; dx=-5.67480641262475E-10 evalInputDelta=-5.9604644775390625E-8
Armijo: th(0.03931376698778048)=0.7338356375694275; dx=-5.67480641262475E-10 evalInputDelta=-5.9604644775390625E-8
WOLFE (weak): th(0.039286197726218226)=0.7338355779647827; dx=-5.674806412625543E-10 evalInputDelta=0.0
WOLFE (weak): th(0.03929998235699936)=0.7338355779647827; dx=-5.674806412625543E-10 evalInputDelta=0.0
WOLFE (weak): th(0.03930687467238992)=0.7338355779647827; dx=-5.674806412625543E-10 evalInputDelta=0.0
WOLFE (weak): th(0.039310320830085205)=0.7338355779647827; dx=-5.674806412625543E-10 evalInputDelta=0.0
Armijo: th(0.039312043908932844)=0.7338356375694275; dx=-5.67480641262475E-10 evalInputDelta=-5.9604644775390625E-8
Armijo: th(0.03931118236950902)=0.7338356375694275; dx=-5.67480641262475E-10 evalInputDelta=-5.9604644775390625E-8
WOLFE (weak): th(0.03931075159979711)=0.7338355779647827; dx=-5.674806412625543E-10 evalInputDelta=0.0
Armijo: th(0.03931096698465307)=0.7338356375694275; dx=-5.67480641262475E-10 evalInputDelta=-5.9604644775390625E-8
mu ~= nu (0.03931075159979711): th(0.0)=0.7338355779647827
Fitness changed from 0.7338355779647827 to 0.7338355779647827
Static Iteration Total: 37.8901; Orientation: 0.0672; Line Search: 34.9750
Iteration 3 failed. Error: 0.7338355779647827
Previous Error: 0.0 -> 0.7338355779647827
Optimization terminated 3
Final threshold in iteration 3: 0.7338355779647827 (> -Infinity) after 119.479s (< 3600.000s)
0.7338355779647827