[SPARK-11262][ML] Unit test for gradient, loss layers, memory management for multilayer perceptron

1.Implement LossFunction trait and implement squared error and cross entropy
loss with it
2.Implement unit test for gradient and loss
3.Implement InPlace trait and in-place layer evaluation
4.Refactor interface for ActivationFunction
5.Update of Layer and LayerModel interfaces
6.Fix random weights assignment
7.Implement memory allocation by MLP model instead of individual layers

These features decreased the memory usage and increased flexibility of
internal API.

Author: Alexander Ulanov <nashb@yandex.ru>
Author: avulanov <avulanov@gmail.com>

Closes #9229 from avulanov/mlp-refactoring.

1 files changed, 5 insertions, 0 deletions

diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 94621d7fa3..ff11775412 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -592,6 +592,11 @@ object MimaExcludes {
       ) ++ Seq(
         // [SPARK-14205][SQL] remove trait Queryable
         ProblemFilters.exclude[MissingTypesProblem]("org.apache.spark.sql.Dataset")
+      ) ++ Seq(
+        // [SPARK-11262][ML] Unit test for gradient, loss layers, memory management
+        // for multilayer perceptron.
+        // This class is marked as `private`.
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.ml.ann.SoftmaxFunction")
       )
     case v if v.startsWith("1.6") =>
       Seq(