Lock the weight when updating cache

README.ipynb

Adagrad.sc

/**
  * @note This [[Adagrad]] hyperparameter is usually used before global [[LearningRate]].
  * e.g. `Adagrad with FixedLearningRate`, not `FixedLearningRate with Adagrad`
  */
trait Adagrad extends com.thoughtworks.deeplearning.plugins.INDArrayWeights {

  import org.nd4j.linalg.api.ndarray.INDArray
  import org.nd4j.linalg.factory.Nd4j
  import org.nd4j.linalg.ops.transforms.Transforms

  /** The hyperparmaeter `eps`, which should be configured in `Factory.newInstance()` */
  def eps: Double

  trait INDArrayWeightApi extends super.INDArrayWeightApi { this: INDArrayWeight =>
    /** The `cache` state injected to `INDArrayWeight` by this `Adagrad` plug-in */
    var cache: Option[INDArray] = None
  }

  override type INDArrayWeight <: INDArrayWeightApi with Weight

  trait INDArrayOptimizerApi extends super.INDArrayOptimizerApi { this: INDArrayOptimizer =>
    private lazy val delta0: INDArray = {
      // The original delta computed by previous plug-ins
      val superDelta = super.delta

      import org.nd4s.Implicits._
      import weight._
      val newCache = weight.synchronized {
        val newCache = weight.cache.getOrElse(Nd4j.zeros(superDelta.shape: _*)) + superDelta * superDelta
        weight.cache = Some(newCache)
        newCache
      }
      superDelta / (Transforms.sqrt(newCache) + eps)
    }

    /** The computation of delta for injected to `INDArrayOptimizer` by this `Adagrad` plug-in */
    override def delta = delta0
  }
  override type INDArrayOptimizer <: INDArrayOptimizerApi with Optimizer
}

LICENSE

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