AdagradOptimizer
class AdagradOptimizer(val featureSize: Int, val learningRate: ScalarExpr, val epsilon: Double = 1.0E-10, concurrency: Concurrency = Concurrency.None) : Optimizer(source)
Adagrad: accumulates squared gradients per coordinate; the effective per-coord learning rate is lr / sqrt(sumG2[i] + epsilon). Adapts faster on rare features.
Constructors
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constructor(featureSize: Int, learningRate: ScalarExpr, epsilon: Double = 1.0E-10, concurrency: Concurrency = Concurrency.None)
Properties
Functions
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open override fun computeDelta(coordIndex: Int, gradient: Double, observationWeight: Double): Double
Per-coordinate update. Reads/writes any auxiliary state owned by the optimizer, then returns the delta the caller should add to w[coordIndex].
AdagradOptimizer
constructor(featureSize: Int, learningRate: ScalarExpr, epsilon: Double = 1.0E-10, concurrency: Concurrency = Concurrency.None)(source)
advance
computeDelta
epsilon
featureSize
Number of weight coordinates this optimizer manages.
learningRate
Base learning-rate schedule.