1 files changed, 13 insertions, 23 deletions

diff --git a/tensorflow/contrib/linear_optimizer/ops/sdca_ops.cc b/tensorflow/contrib/linear_optimizer/ops/sdca_ops.cc
index ff2bae8fea..7c0e27d19d 100644
--- a/tensorflow/contrib/linear_optimizer/ops/sdca_ops.cc
+++ b/tensorflow/contrib/linear_optimizer/ops/sdca_ops.cc
@@ -24,7 +24,7 @@ REGISTER_OP("SdcaSolver")
     .Attr("num_dense_features: int >= 0")
     .Attr("l1: float >= 0")
     .Attr("l2: float >= 1")
-    .Attr("num_inner_iterations: int >= 2")
+    .Attr("num_inner_iterations: int >= 1")
     .Attr("container: string")
     .Attr("solver_uuid: string")
     .Input("sparse_features_indices: num_sparse_features * int64")
@@ -69,7 +69,7 @@ example_labels: a vector which contains the label/target associated with each
 example_ids: a vector which contains the unique identifier associated with each
   example.
 sparse_weights: a list of vectors where each value is the weight associated with
-  a feature index.
+  a feature group.
 dense_weights: a list of vectors where the value is the weight associated with
   a dense feature group.
 )doc");
@@ -89,38 +89,28 @@ num_dense_features: Number of dense feature groups to train on.
 l1: Symmetric l1 regularization strength.
 l2: Symmetric l2 regularization strength.
 sparse_weights: a list of vectors where each value is the weight associated with
-  a feature index.
+  a feature group.
 dense_weights: a list of vectors where the value is the weight associated with
   a dense feature group.
 )doc");
 
-// TODO(katsiapis): We should expand this scope of this op to compute other
-// statistics about the data.
-REGISTER_OP("ComputeDualityGap")
-    .Attr("num_sparse_features: int >= 0")
-    .Attr("num_dense_features: int >= 0")
-    .Attr("l1: float >= 0")
-    .Attr("l2: float >= 1")
+REGISTER_OP("SdcaTrainingStats")
     .Attr("container: string")
     .Attr("solver_uuid: string")
-    .Input("sparse_weights: Ref(num_sparse_features * float)")
-    .Input("dense_weights: Ref(num_dense_features * float)")
-    .Output("duality_gap: float")
+    .Output("primal_loss: float64")
+    .Output("dual_loss: float64")
+    .Output("example_weights: float64")
     .Doc(R"doc(
-Computes duality gap over all examples seen by the optimizer.
+Computes statistics over all examples seen by the optimizer.
 
-num_sparse_features: Number of sparse feature groups to train on.
-num_dense_features: Number of dense feature groups to train on.
-l1: Symmetric l1 regularization strength.
-l2: Symmetric l2 regularization strength.
 container: Name of the Container that stores data across invocations of this
   Kernel. Together with SolverUUID form an isolation unit for this solver.
 solver_uuid: Universally Unique Identifier for this solver.
-sparse_weights: a list of vectors where each value is the weight associated with
-  a feature index.
-dense_weights: a list of vectors where the value is the weight associated with
-  a dense feature group.
-duality_gap: duality gap over all examples seen by the optimizer.
+primal_loss: total primal loss of all examples seen by the optimizer.
+dual_loss: total dual loss of all examples seen by the optimizer.
+example_weights: total example weights of all examples seen by the optimizer
+  (guaranteed to be positive; otherwise returns FAILED_PRECONDITION as it
+   probably indicates a bug in the training data).
 )doc");
 
 }  // namespace tensorflow