CumulativeFloatLayers

Given a FloatWeight,

import com.thoughtworks.deeplearning.plugins._
import com.thoughtworks.feature.Factory
val hyperparameters = Factory[FloatTraining with ImplicitsSingleton with Operators with CumulativeFloatLayers with FloatWeights].newInstance()
import hyperparameters.implicits._
val weight1 = hyperparameters.FloatWeight(10)

then the training result should be applied on it

weight1.train.map { result =>
  result should be(10.0f)
  weight1.data should be < 10.0f
}

Given two FloatWeights,

import com.thoughtworks.deeplearning.plugins._
import com.thoughtworks.feature.Factory
val hyperparameters = Factory[FloatTraining with ImplicitsSingleton with Operators with CumulativeFloatLayers with FloatWeights].newInstance()
import hyperparameters.implicits._
val weight1 = hyperparameters.FloatWeight(10)
val weight2 = hyperparameters.FloatWeight(300)

when adding them together,

val weight1PlusWeight2 = weight1 + weight2

then the training result should be applied on both weight

weight1PlusWeight2.train.map { result =>
  result should be(310.0f)
  weight2.data should be < 300.0f
  weight1.data should be < 10.0f
}