Github user mengxr commented on a diff in the pull request:

    https://github.com/apache/spark/pull/9229#discussion_r56207844
  
    --- Diff: mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala ---
    @@ -32,20 +32,43 @@ import org.apache.spark.util.random.XORShiftRandom
      *
      */
     private[ann] trait Layer extends Serializable {
    +
    +  /**
    +   * Number of weights that is used to allocate memory for the weights 
vector
    +   */
    +  val weightSize: Int
    +
    +  /**
    +   * Returns the output size given the input size (not counting the stack 
size).
    +   * Output size is used to allocate memory for the output.
    +   * @param inputSize input size
    +   * @return output size
    +   */
    +  def outputSize(inputSize: Int): Int
    +
    +  /**
    +   * If true, the memory is not allocated for the output of this layer.
    +   * The memory allocated to the previous layer is used to write the 
output of this layer.
    +   * Developer can set this to true if computing delta of a previous layer
    +   * does not involve its output, so the current layer can write there.
    +   * This also mean that both layers have the same number of outputs.
    +   */
    +  val inPlace: Boolean
    +
       /**
    -   * Returns the instance of the layer based on weights provided
    +   * Returns the instance of the layer based on weights provided.
    +   * Size of weights must be equal to weightSize
        * @param weights vector with layer weights
    -   * @param position position of weights in the vector
        * @return the layer model
        */
    -  def getInstance(weights: Vector, position: Int): LayerModel
    -
    +  def model(weights: BDV[Double]): LayerModel
    --- End diff --
    
    * `createModel` and rename `weights` to `initialWeights`


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