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/*
* Copyright 2016 The BigDL Authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.intel.analytics.bigdl.optim
import com.intel.analytics.bigdl.optim.SGD.{Default, LearningRateSchedule}
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.utils.{T, Table}
import scala.collection.mutable.ArrayBuffer
import scala.reflect.ClassTag
/**
* A plain implementation of SGD
* @param learningRate learning rate
* @param learningRateDecay learning rate decay
* @param weightDecay weight decay
* @param momentum momentum
* @param dampening dampening for momentum
* @param nesterov enables Nesterov momentum
* @param learningRates 1D tensor of individual learning rates
* @param weightDecays 1D tensor of individual weight decays
* @tparam T
*/
class SGD[@specialized(Float, Double) T: ClassTag](
var learningRate: Double = 1e-3,
var learningRateDecay: Double = 0.0,
var weightDecay: Double = 0.0,
var momentum: Double = 0.0,
var dampening: Double = Double.MaxValue,
var nesterov: Boolean = false,
var learningRateSchedule: LearningRateSchedule = Default(),
var learningRates: Tensor[T] = null,
var weightDecays: Tensor[T] = null
)(implicit ev: TensorNumeric[T])
extends OptimMethod[T] {
import SGD._
/**
*
* @param feval a function that takes a single input (X), the point of a evaluation,
* and returns f(X) and df/dX
* @param x the initial point
* @return the new x 1D tensor and the function list, evaluated before the update
*/
override def optimize(feval: (Tensor[T]) => (T, Tensor[T]), x: Tensor[T])
: (Tensor[T], Array[T]) = {
this.updateHyperParameter()
if (this.dampening == Double.MaxValue) this.dampening = this.momentum
val wd = this.weightDecay
val mom = this.momentum
val damp = this.dampening
val nesterov = this.nesterov
val lrs = this.learningRates
val wds = this.weightDecays
val clr = ev.fromType(this.learningRateSchedule.currentRate)
require(!nesterov || (mom > 0 && damp == 0),
"Nesterov momentum requires a momentum and zero dampening")
var (fx, dfdx) = feval(x)
if (wd != 0 || wds != null) {
require(!state.get[Boolean]("isLayerwiseScaled").getOrElse(false),
"SGD: Can't set layerwise scale and weight decay at the same time")
}
if (wd != 0) {
dfdx.add(ev.fromType[Double](wd), x)
} else if (wds != null) {
val decayParameters = state.get[Tensor[T]]("decayParameters").getOrElse({
val DP = Tensor[T]().resizeAs(dfdx)
state("decayParameters") = DP
DP
})
decayParameters.copy(wds).cmul(x)
dfdx.add(decayParameters)
}
if (mom != 0) {
val stateDFDX = state.get[Tensor[T]]("dfdx") match {
case None =>
val DFDX = Tensor[T]().resizeAs(dfdx).copy(dfdx)
state("dfdx") = DFDX
DFDX
case s: Some[Tensor[T]] => s.get.mul(ev.fromType[Double](mom)).
add(ev.fromType[Double](1 - damp), dfdx)
}
if (nesterov) {
dfdx.add(ev.fromType[Double](mom), stateDFDX)
} else {
dfdx = stateDFDX
}
}
if (lrs != null) {
val deltaParameters = state.get[Tensor[T]]("deltaParameters").getOrElse({
val deltaP = Tensor[T]().resizeAs(dfdx)
state("deltaParameters") = deltaP
deltaP
})
deltaParameters.copy(lrs).cmul(dfdx)
x.add(clr, deltaParameters)
} else {
x.add(clr, dfdx)
}
(x, Array(fx))
}
override def loadFromTable(config: Table): this.type = {
this.learningRate = config.get[Double]("learningRate").getOrElse(this.learningRate)
this.learningRateDecay = config.get[Double]("learningRateDecay")
.getOrElse(this.learningRateDecay)
this.weightDecay = config.get[Double]("weightDecay").getOrElse(this.weightDecay)
this.momentum = config.get[Double]("momentum").getOrElse(this.momentum)
this.dampening = config.get[Double]("dampening").getOrElse(this.dampening)
this.nesterov = config.get[Boolean]("nesterov").getOrElse(this.nesterov)
this.learningRateSchedule = config.get[LearningRateSchedule]("learningRateSchedule")
.getOrElse(this.learningRateSchedule)
this.learningRates = config.get[Tensor[T]]("learningRates").getOrElse(this.learningRates)
this.weightDecays = config.get[Tensor[T]]("weightDecays").getOrElse(this.weightDecays)
this
}
override def clearHistory(): Unit = {
state.delete("decayParameters")
state.delete("dfdx")
state.delete("deltaParameters")
}
/**
* return an string of current hyperParameter.
*/
override def getHyperParameter(): String = {
val clr = -this.learningRateSchedule.currentRate
val wd = this.weightDecay
val mom = this.momentum
val damp = this.dampening
val nesterov = this.nesterov
val lrs = this.learningRates
val wds = this.weightDecays
s"Current learning rate is $clr. " +
{if (wd != 0) s"Current weight decay is $wd. " else ""} +
{if (mom != 0) s"Current momentum is $mom. " else ""} +
{if (damp != 0) s"Current dampening is $damp. " else ""} +
{if (nesterov) s"Current nesterov is true. " else ""} +
{if (null != lrs) s"Current learningRates is a Tensor. " else ""} +
{if (null != wds) s"Current weightDecays is a Tensor. " else ""}
}
override def updateHyperParameter(): Unit = {
this.learningRateSchedule.updateHyperParameter(this)
}
/**
* return an string of current hyperParameter.
*/
override def getHyperParameter(config: Table): String = {
val clr = -config[Double]("clr")
val wd = config.get[Double]("weightDecay").getOrElse(0.0)
val mom = config.get[Double]("momentum").getOrElse(0.0)
val damp = config.get[Double]("dampening").getOrElse(mom)
val nesterov = config.get[Boolean]("nesterov").getOrElse(false)
val lrs = config.get[Tensor[T]]("learningRates").getOrElse(null)
val wds = config.get[Tensor[T]]("weightDecays").getOrElse(null)
s"Current learning rate is $clr. " +
{if (wd != 0) s"Current weight decay is $wd. " else ""} +
{if (mom != 0) s"Current momentum is $mom. " else ""} +
{if (damp != 0) s"Current dampening is $damp. " else ""} +
{if (nesterov) s"Current nesterov is true. " else ""} +
{if (null != lrs) s"Current learningRates is a Tensor. " else ""} +
{if (null != wds) s"Current weightDecays is a Tensor. " else ""}
}
override def updateHyperParameter(config: Table, state: Table): Unit = {
val lrSchedule = config.get[LearningRateSchedule]("learningRateSchedule").getOrElse(Default())
lrSchedule.updateHyperParameter(config, state)
}
override def getLearningRate(): Double = this.learningRateSchedule.currentRate
}
object SGD {
/**
* Hyper parameter schedule for SGD
*/
trait LearningRateSchedule {
/**
* update learning rate by config table and state table
* @param optimMethod init optiMethod.
*/
def updateHyperParameter[T](optimMethod : SGD[T]) : Unit
@deprecated("Please input SGD instead of Table", "0.2.0")
def updateHyperParameter(config : Table, state : Table) : Unit = {}
var currentRate : Double = 0.0
// iteration numbers needed to be excluded for a new learningRateSchedule
private[SGD] var excludeIterations : Int = 0
// epoch numbers needed to be excluded for a new learningRateSchedule
private[SGD] var excludeEpochs: Int = 0
// accumulated iteration numbers of a new learningRateSchedule
private[SGD] var maxIterations: Int = 0
}
/**
* [[EpochSchedule]] is a learning rate schedule which configure the learning
* rate according to some pre-defined [[Regime]]. If the running epoch is within
* the interval of a regime `r` [r.startEpoch, r.endEpoch], then the learning
* rate will take the "learningRate" in r.config.
*
* @param regimes an array of pre-defined [[Regime]].
*/
case class EpochSchedule(regimes : Array[Regime]) extends LearningRateSchedule {
override def updateHyperParameter(config: Table, state: Table): Unit = {
val epoch = state[Int]("epoch") - excludeEpochs
for (r <- regimes) {
if (epoch >= r.startEpoch && epoch <= r.endEpoch) {
config.add(r.config)
}
}
config("clr") = -config.get[Double]("learningRate").getOrElse(1e-3)
}
override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = {
val epoch = optimMethod.state[Int]("epoch") - excludeEpochs
for (r <- regimes) {
if (epoch >= r.startEpoch && epoch <= r.endEpoch) {
val config = r.config
val keys = config.keySet.toArray.map(_.toString)
var i = 0
while (i < keys.length) {
keys(i) match {
case "learningRate" =>
optimMethod.learningRate = config.get[Double](keys(i)).get
case "learningRateDecay" =>
optimMethod.learningRateDecay = config.get[Double](keys(i)).get
case "weightDecay" =>
optimMethod.weightDecay = config.get[Double](keys(i)).get
case "momentum" =>
optimMethod.momentum = config.get[Double](keys(i)).get
case "dampening" =>
optimMethod.dampening = config.get[Double](keys(i)).get
case "nesterov" =>
optimMethod.nesterov = config.get[Boolean](keys(i)).get
case "leaningRateSchedule" =>
optimMethod.learningRateSchedule = config.get[LearningRateSchedule](keys(i)).get
case "learningRates" =>
optimMethod.learningRates = config.get[Tensor[T]](keys(i)).get
case "weightDecays" =>
optimMethod.weightDecays = config.get[Tensor[T]](keys(i)).get
case _ => throw new IllegalArgumentException(
s"EpochSchedule: ${keys(i)} is not a member of SGD")
}
i += 1
}
}
}
currentRate = -optimMethod.learningRate
}
}
/**
* A learning rate decay policy, where the effective learning rate
* follows a polynomial decay, to be zero by the max_iteration.
* Calculation: base_lr (1 - iter/maxIteration) `^` (power)
*
* @param power coeffient of decay, refer to calculation formula
* @param maxIteration max iteration when lr becomes zero
*/
case class Poly(power : Double, maxIteration : Int) extends LearningRateSchedule {
override def updateHyperParameter(config: Table, state: Table): Unit = {
val lr = config.get[Double]("learningRate").getOrElse(1e-3)
val nevals = state.get[Int]("evalCounter").getOrElse(0)
val polyIter = nevals // fix: should have no exclude iterations.
val clr = if (polyIter > maxIteration) {
0.0
} else {
-lr * math.pow(1.0 - polyIter.toDouble / maxIteration, power)
}
println(s"iteration is : ${nevals}. current learning rate is $clr")
state("evalCounter") = nevals + 1
config("clr") = clr
}
override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = {
val nevals = optimMethod.state.get[Int]("evalCounter").getOrElse(0)
val lr = optimMethod.learningRate
val polyIter = nevals // fix: should have no exclude iterations.
val clr = if (polyIter > maxIteration) {
0.0
} else {
-lr * math.pow(1.0 - polyIter.toDouble / maxIteration, power)
}
println(s"iteration is : ${nevals}. current learning rate is $clr")
optimMethod.state("evalCounter") = nevals + 1
currentRate = clr
}
}
/**
* A learning rate decay policy, where the effective learning rate
* is calculated as base_lr * gamma `^` (floor(iter / stepSize))
*
* @param stepSize the inteval for lr decay
* @param gamma coefficient of decay, refer to calculation formula
*/
case class Step(stepSize : Int, gamma : Double) extends LearningRateSchedule {
override def updateHyperParameter(config: Table, state: Table): Unit = {
var clr = - config.get[Double]("learningRate").getOrElse(1e-3)
val nevals = state.get[Int]("evalCounter").getOrElse(0)
var i = 0
while(i < (nevals - excludeIterations) / stepSize) {
clr *= gamma
i += 1
}
state("evalCounter") = nevals + 1
config("clr") = clr
}
override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = {
var clr = - optimMethod.learningRate
val nevals = optimMethod.state.get[Int]("evalCounter").getOrElse(0)
var i = 0
while(i < (nevals - excludeIterations) / stepSize) {
clr *= gamma
i += 1
}
optimMethod.state("evalCounter") = nevals + 1
currentRate = clr
}
}
/**
* similar to step but it allows non uniform steps defined by stepSizes
* @param stepSizes the series of step sizes used for lr decay
* @param gamma coefficient of decay
*/
case class MultiStep(stepSizes : Array[Int], gamma : Double) extends LearningRateSchedule {
override def updateHyperParameter(config: Table, state: Table): Unit = {
var clr = - config.get[Double]("learningRate").getOrElse(1e-3)
val nevals = state.get[Int]("evalCounter").getOrElse(0)
var currentStep = 0
while (currentStep < stepSizes.length &&
(nevals - excludeIterations) >= stepSizes(currentStep)) {
clr *= gamma
currentStep += 1
}
state("evalCounter") = nevals + 1
config("clr") = clr
}
override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = {
val nevals = optimMethod.state.get[Int]("evalCounter").getOrElse(0)
var clr = - optimMethod.learningRate
var currentStep = 0
while (currentStep < stepSizes.length &&
(nevals - excludeIterations) >= stepSizes(currentStep)) {
clr *= gamma
currentStep += 1
}
optimMethod.state("evalCounter") = nevals + 1
currentRate = clr
}
}
/**
* It is an epoch decay learning rate schedule
* The learning rate decays through a function argument on number of run epochs
*
* l_{n + 1} = l_{n} * 0.1 `^` decayType(epoch)
*
* @param decayType is a function with number of run epochs as the argument
*/
case class EpochDecay(decayType: (Int) => Double) extends LearningRateSchedule {
override def updateHyperParameter(config: Table, state: Table): Unit = {
var clr = - config.get[Double]("learningRate").getOrElse(1e-1)
val epoch = state[Int]("epoch")
val decay = decayType(epoch - excludeEpochs)
clr = clr * math.pow(0.1, decay)
config("clr") = clr
}
override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = {
var clr = - optimMethod.learningRate
val epoch = optimMethod.state[Int]("epoch")
val decay = decayType(epoch - excludeEpochs)
clr = clr * math.pow(0.1, decay)
currentRate = clr
}
}
/**
* [[EpochStep]] is a learning rate schedule, which rescale the learning rate by `gamma`
* for each `stepSize` epochs.
*
* @param stepSize For how many epochs to update the learning rate once
* @param gamma the rescale factor
*/
case class EpochStep(stepSize : Int, gamma : Double) extends LearningRateSchedule {
override def updateHyperParameter(config: Table, state: Table): Unit = {
var clr = - config.get[Double]("learningRate").getOrElse(1e-3)
val epoch = state[Int]("epoch")
var i = 0
while(i < (epoch - excludeEpochs) / stepSize) {
clr *= gamma
i += 1
}
config("clr") = clr
}
override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = {
var clr = - optimMethod.learningRate
val epoch = optimMethod.state[Int]("epoch")
var i = 0
while(i < (epoch - excludeEpochs) / stepSize) {
clr *= gamma
i += 1
}
currentRate = clr
}
}
/**
* [[NaturalExp]] is a learning rate schedule, which rescale the learning rate by
* exp ( -decay_rate * iter / decay_step )
* referring to tensorflow's learning rate decay # natural_exp_decay
*
* @param decay_step how often to apply decay
* @param gamma the decay rate. e.g. 0.96
*/
case class NaturalExp(decay_step : Int, gamma : Double)
extends LearningRateSchedule {
override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = {
val lr = optimMethod.learningRate
val nevals = optimMethod.state.get[Int]("evalCounter").getOrElse(0)
val p = (nevals - excludeIterations) / decay_step
val clr = -lr * math.exp(-gamma * p)
optimMethod.state("evalCounter") = nevals + 1
currentRate = clr
}
}
/**
* [[Exponential]] is a learning rate schedule, which rescale the learning rate by
* lr_{n + 1} = lr * decayRate `^` (iter / decayStep)
* @param decayStep the inteval for lr decay
* @param decayRate decay rate
* @param stairCase if true, iter / decayStep is an integer division
* and the decayed learning rate follows a staircase function.
*/
case class Exponential(decayStep: Int, decayRate: Double,
stairCase: Boolean = false) extends LearningRateSchedule {
override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = {
val lr = optimMethod.learningRate
val nevals = optimMethod.state.get[Int]("evalCounter").getOrElse(0)
var p = (nevals - excludeIterations) / decayStep.toDouble
if (stairCase) {
p = p.floor
}
val clr = -lr * Math.pow(decayRate, p)
optimMethod.state("evalCounter") = nevals + 1
currentRate = clr
}
}
/**
* It is the default learning rate schedule.
* For each iteration, the learning rate would
* update with the following formula:
*
* l_{n + 1} = l / (1 + n * learning_rate_decay)
*
* where `l` is the initial learning rate
*/
case class Default() extends LearningRateSchedule {
override def updateHyperParameter(config: Table, state: Table): Unit = {
val lr = config.get[Double]("learningRate").getOrElse(1e-3)
val lrd = config.get[Double]("learningRateDecay").getOrElse(0.0)
val nevals = state.get[Int]("evalCounter").getOrElse(0)
config("clr") = -lr / (1 + (nevals - excludeIterations) * lrd)
state("evalCounter") = nevals + 1
}
override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = {
val lr = optimMethod.learningRate
val lrd = optimMethod.learningRateDecay
val nevals = optimMethod.state.get[Int]("evalCounter").getOrElse(0)
currentRate = -lr / (1 + (nevals - excludeIterations) * lrd)
optimMethod.state("evalCounter") = nevals + 1
}
}
/**
* A structure to specify hyper parameters by start epoch and end epoch.
* Usually work with [[EpochSchedule]].
* @param startEpoch start epoch
* @param endEpoch end epoch
* @param config config table contains hyper parameters
*/
case class Regime(startEpoch: Int, endEpoch: Int, config: Table)
/**
* Plateau is the learning rate schedule when a metric has stopped improving.
* Models often benefit from reducing the learning rate by a factor of 2-10
* once learning stagnates. It monitors a quantity and if no improvement
* is seen for a 'patience' number of epochs, the learning rate is reduced.
* @param monitor quantity to be monitored, can be Loss or score
* @param factor factor by which the learning rate will be reduced. new_lr = lr * factor
* @param patience number of epochs with no improvement after which learning rate will be reduced.
* @param mode one of {min, max}.
* In min mode, lr will be reduced when the quantity monitored has stopped decreasing;
* in max mode it will be reduced when the quantity monitored has stopped increasing
* @param epsilon threshold for measuring the new optimum, to only focus on significant changes.
* @param cooldown number of epochs to wait before resuming normal operation
* after lr has been reduced.
* @param minLr lower bound on the learning rate.
*/
case class Plateau(monitor: String, factor: Float = 0.1f,
patience: Int = 10, mode: String = "min", epsilon: Float = 1e-4f,
cooldown: Int = 0, minLr: Float = 0) extends LearningRateSchedule {
require(factor < 1, "Plateau does not support a factor >= 1.0")
require(mode == "min" || mode == "max",
s"Learning Rate Plateau Reducing mode ${ mode } is unknown, please use min | max")
var (monitorOp, best) = if (mode == "min") {
((a: Float, b: Float) => a < b - epsilon, Float.PositiveInfinity)
} else {
((a: Float, b: Float) => a > b + epsilon, Float.NegativeInfinity)
}
private var cooldownCounter: Int = 0
private var waitCounter: Int = 0
private val lrEpsilon: Float = minLr * 1e-4f
private var curEpoch = 1
/**
* update learning rate by config table and state table
* @param optimMethod init optiMethod.
*/
override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = {
val epoch = optimMethod.state[Int]("epoch") - excludeEpochs
if (epoch == 1) currentRate = - optimMethod.learningRate
if (epoch == curEpoch) return
curEpoch = epoch
val current = optimMethod.state.get[Float](monitor)
require(current.isDefined, s"Learning Rate Plateau Reducing requires ${monitor} available!")
if (cooldownCounter > 0) {
cooldownCounter -= 1
waitCounter = 0
}
if (monitorOp(current.get, best)) {
best = current.get
waitCounter = 0
} else if (cooldownCounter <= 0) {
if (waitCounter >= patience) {
if (currentRate.abs > minLr + lrEpsilon) {
currentRate = - Math.max(currentRate.abs * factor, minLr)
cooldownCounter = cooldown
waitCounter = 0
}
}
waitCounter += 1
}
}
}
/**
* A learning rate gradual increase policy, where the effective learning rate
* increase delta after each iteration.
* Calculation: base_lr + delta * iteration
*
* @param delta increase amount after each iteration
*/
case class Warmup(delta: Double) extends LearningRateSchedule {
override def updateHyperParameter(config: Table, state: Table): Unit = {
val lr = config.get[Double]("learningRate").getOrElse(1e-3)
val nevals = state.get[Int]("evalCounter").getOrElse(0)
val clr = - lr - delta * nevals
config("clr") = clr
state("evalCounter") = nevals + 1
}
override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = {
val nevals = optimMethod.state.get[Int]("evalCounter").getOrElse(0)
val lr = optimMethod.learningRate
val clr = - lr - delta * (nevals - excludeIterations)
currentRate = clr
println(s"iteration is : ${nevals}. current learning rate is $clr")
optimMethod.state("evalCounter") = nevals + 1
}
}
/**
* Stack several learning rate schedulers.
*
* @param iterationPerEpoch iteration numbers per epoch
*/
case class SequentialSchedule(iterationPerEpoch: Int) extends LearningRateSchedule {
val schedules: ArrayBuffer[LearningRateSchedule] = ArrayBuffer[LearningRateSchedule]()
var cur: Int = 0
/**
* Add a learning rate scheduler to the contained `schedules`
*
* @param schedule learning rate scheduler to be add
* @param maxIteration iteration numbers this scheduler will run
* @return this container
*/
def add(schedule: LearningRateSchedule, maxIteration: Int):
this.type = {
schedule.excludeIterations = if (schedules.isEmpty) 0 else schedules.last.maxIterations
schedule.maxIterations = schedule.excludeIterations + maxIteration
schedule.excludeEpochs = schedule.excludeIterations / iterationPerEpoch
schedules += schedule
this
}
override def updateHyperParameter(config: Table, state: Table): Unit = {
val nevals = state.get[Int]("evalCounter").getOrElse(0)
if (nevals > schedules(cur).maxIterations) {
config("learningRate") = - currentRate
cur += 1
}
schedules(cur).updateHyperParameter(config, state)
}
override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = {
val nevals = optimMethod.state.get[Int]("evalCounter").getOrElse(0)
if (nevals > schedules(cur).maxIterations) {
optimMethod.learningRate = - currentRate
cur += 1
}
schedules(cur).updateHyperParameter(optimMethod)
currentRate = schedules(cur).currentRate
}
}
/**
* Learning rate schedule based on warm up Iterations
* @param warmUpIteration Warm up iteration number
* @param warmUpDelta Warm up dealta value applied to warm up iteration
* @param decayType A function to calculate decay on epochs
*/
case class EpochDecayWithWarmUp(
warmUpIteration: Int,
warmUpDelta: Double,
decayType: (Int) => Double) extends LearningRateSchedule {
override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = {
val lr = optimMethod.learningRate
val nevals = optimMethod.state.get[Int]("evalCounter").getOrElse(0)
val clr = if (nevals < warmUpIteration) {
- lr - warmUpDelta * nevals
} else {
val epoch = optimMethod.state[Int]("epoch")
val decay = decayType(epoch)
val maxLr = lr + warmUpDelta * warmUpIteration
- maxLr * math.pow(0.1, decay)
}
optimMethod.state("evalCounter") = nevals + 1
currentRate = clr
}
}
}
