Code from BasicOptimizer.scala:75 executed in 3831.96 seconds (303.681 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: 1100682157383899
Reset training subject: 1100756469099200
Adding measurement 4246c643 to history. Total: 0
LBFGS Accumulation History: 1 points
Constructing line search parameters: GD+Trust
th(0)=1.300358325143777;dx=-1.0461823637391221E-7
Armijo: th(2.154434690031884)=1.300358325143777; dx=-1.0373447746428945E-7 evalInputDelta=0.0
Armijo: th(1.077217345015942)=1.300358325143777; dx=-1.0373447978580014E-7 evalInputDelta=0.0
Armijo: th(0.3590724483386473)=1.300358325143777; dx=-1.0373447990341565E-7 evalInputDelta=0.0
Armijo: th(0.08976811208466183)=1.300358325143777; dx=-1.0373447990304534E-7 evalInputDelta=0.0
Armijo: th(0.017953622416932366)=1.300358325143777; dx=-1.0373447990304534E-7 evalInputDelta=0.0
Armijo: th(0.002992270402822061)=1.300358325143777; dx=-1.0373447990304534E-7 evalInputDelta=0.0
WOLFE (weak): th(4.2746720040315154E-4)=1.300358325143777; dx=-1.0373447990341565E-7 evalInputDelta=0.0
Armijo: th(0.0017098688016126062)=1.300358325143777; dx=-1.0373447990341565E-7 evalInputDelta=0.0
Armijo: th(0.001068668001007879)=1.300358325143777; dx=-1.0373447990341565E-7 evalInputDelta=0.0
WOLFE (weak): th(7.480676007055152E-4)=1.300358325143777; dx=-1.0373447990341565E-7 evalInputDelta=0.0
WOLFE (weak): th(9.083678008566971E-4)=1.300358325143777; dx=-1.0373447990341565E-7 evalInputDelta=0.0
WOLFE (weak): th(9.88517900932288E-4)=1.300358325143777; dx=-1.0373447990304534E-7 evalInputDelta=0.0
WOLFE (weak): th(0.0010285929509700835)=1.300358325143777; dx=-1.0373447990341565E-7 evalInputDelta=0.0
WOLFE (weak): th(0.0010486304759889814)=1.300358325143777; dx=-1.0373447990304534E-7 evalInputDelta=0.0
WOLFE (weak): th(0.0010586492384984303)=1.300358325143777; dx=-1.0373447990341565E-7 evalInputDelta=0.0
Armijo: th(0.0010636586197531545)=1.300358325143777; dx=-1.0373447990341565E-7 evalInputDelta=0.0
WOLFE (weak): th(0.0010611539291257924)=1.300358325143777; dx=-1.0373447990304534E-7 evalInputDelta=0.0
Armijo: th(0.0010624062744394734)=1.300358325143777; dx=-1.03734479
...skipping 1704 bytes...
7 evalInputDelta=0.0
Armijo: th(0.005715809622673184)=1.300358325143777; dx=-1.0373447990341565E-7 evalInputDelta=0.0
WOLFE (weak): th(0.005144228660405866)=1.300358325143777; dx=-1.0373447990341565E-7 evalInputDelta=0.0
Armijo: th(0.005430019141539525)=1.300358325143777; dx=-1.0373447990341565E-7 evalInputDelta=0.0
WOLFE (weak): th(0.005287123900972695)=1.300358325143777; dx=-1.0373447990341565E-7 evalInputDelta=0.0
Armijo: th(0.005358571521256111)=1.300358325143777; dx=-1.0373447990341565E-7 evalInputDelta=0.0
Armijo: th(0.005322847711114403)=1.300358325143777; dx=-1.0373447990304534E-7 evalInputDelta=0.0
WOLFE (weak): th(0.0053049858060435495)=1.300358325143777; dx=-1.0373447990341565E-7 evalInputDelta=0.0
Armijo: th(0.005313916758578976)=1.300358325143777; dx=-1.037344799016274E-7 evalInputDelta=0.0
Armijo: th(0.005309451282311263)=1.300358325143777; dx=-1.037344799016274E-7 evalInputDelta=0.0
Armijo: th(0.005307218544177406)=1.300358325143777; dx=-1.0373447990341565E-7 evalInputDelta=0.0
Armijo: th(0.005306102175110478)=1.300358325143777; dx=-1.0373447990341565E-7 evalInputDelta=0.0
WOLFE (weak): th(0.005305543990577014)=1.300358325143777; dx=-1.0373447990341565E-7 evalInputDelta=0.0
WOLFE (weak): th(0.005305823082843746)=1.300358325143777; dx=-1.0373447990341565E-7 evalInputDelta=0.0
WOLFE (weak): th(0.005305962628977113)=1.300358325143777; dx=-1.0373447990341565E-7 evalInputDelta=0.0
WOLFE (weak): th(0.005306032402043795)=1.300358325143777; dx=-1.0373447990341565E-7 evalInputDelta=0.0
WOLFE (weak): th(0.005306067288577137)=1.300358325143777; dx=-1.0373447990304534E-7 evalInputDelta=0.0
mu ~= nu (0.005306067288577137): th(0.0)=1.300358325143777
Fitness changed from 1.300358325143777 to 1.300358325143777
Static Iteration Total: 1732.8566; Orientation: 0.0996; Line Search: 1593.9454
Iteration 2 failed. Error: 1.300358325143777
Previous Error: 0.0 -> 1.300358325143777
Optimization terminated 2
Final threshold in iteration 2: 1.300358325143777 (> -Infinity) after 3831.958s (< 3600.000s)
1.300358325143777