contrib/boosted_trees: minor spelling tweaks

tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc

@@ -253,7 +253,7 @@ class CreateQuantileAccumulatorOp : public OpKernel {
  private:
   float epsilon_;
   int32 num_quantiles_;
-  // An upperbound on the number of enteries that the summaries might have
+  // An upper bound on the number of entries that the summaries might have
   // for a feature.
   int64 max_elements_;
   bool generate_quantiles_;

tensorflow/contrib/boosted_trees/lib/utils/batch_features.cc

@@ -54,7 +54,7 @@ Status BatchFeatures::Initialize(
     TF_CHECK_AND_RETURN_IF_ERROR(
         dense_float_feature.dim_size(1) == 1,
         errors::InvalidArgument(
-            "Dense float features may not be multi-valent: dim_size(1) = ",
+            "Dense float features may not be multivalent: dim_size(1) = ",
             dense_float_feature.dim_size(1)));
     dense_float_feature_columns_.emplace_back(dense_float_feature);
   }

tensorflow/contrib/boosted_trees/lib/utils/batch_features_test.cc

@@ -59,7 +59,7 @@ TEST_F(BatchFeaturesTest, DenseFloatFeatures_Multivalent) {
   BatchFeatures batch_features(1);
   auto dense_vec = AsTensor<float>({3.0f, 7.0f}, {1, 2});
   auto expected_error = InvalidArgument(
-      "Dense float features may not be multi-valent: dim_size(1) = 2");
+      "Dense float features may not be multivalent: dim_size(1) = 2");
   EXPECT_EQ(expected_error,
             batch_features.Initialize({dense_vec}, {}, {}, {}, {}, {}, {}));
 }

tensorflow/contrib/boosted_trees/lib/utils/dropout_utils.cc

@@ -54,7 +54,7 @@ Status DropoutUtils::DropOutTrees(
   if (probability_of_skipping_dropout < 0 ||
       probability_of_skipping_dropout > 1) {
     return errors::InvalidArgument(
-        "Probability of skiping dropout must be in [0,1] range");
+        "Probability of skipping dropout must be in [0,1] range");
   }
   const auto num_trees = weights.size();
 

tensorflow/contrib/boosted_trees/lib/utils/dropout_utils.h

@@ -66,7 +66,7 @@ class DropoutUtils {
       // Current weights and num_updates will be updated as a result of this
       // func
       std::vector<float>* current_weights,
-      // How many weight assignements have been done for each tree already.
+      // How many weight assignments have been done for each tree already.
       std::vector<int32>* num_updates);
 };
 

tensorflow/contrib/boosted_trees/lib/utils/sparse_column_iterable_test.cc

@@ -34,7 +34,7 @@ TEST_F(SparseColumnIterableTest, Empty) {
 }
 
 TEST_F(SparseColumnIterableTest, Iterate) {
-  // 8 examples having 7 sparse features with the 3rd and 7th multi-valent.
+  // 8 examples having 7 sparse features with the 3rd and 7th multivalent.
   // This can be visualized like the following:
   // Instance | Sparse |
   // 0        |  x     |

tensorflow/contrib/boosted_trees/proto/tree_config.proto

@@ -53,7 +53,7 @@ message DenseFloatBinarySplit {
   // Float feature column and split threshold describing
   // the rule feature <= threshold.
   int32 feature_column = 1;
-  // If feature column is multivalent, this holds the index of the dimensiong
+  // If feature column is multivalent, this holds the index of the dimension
   // for the split. Defaults to 0.
   int32 dimension_id = 5;
   float threshold = 2;

tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py

@@ -120,8 +120,8 @@ def setUp(self):
     """Sets up the prediction tests.
 
     Create a batch of two examples having one dense float, two sparse float
-    single valued, one sparse float multidimensionl and one sparse int features.
-    The data looks like the following:
+    single valued, one sparse float multidimensional and one sparse int
+    features.  The data looks like the following:
     | Instance | Dense0 | SparseF0 | SparseF1 | SparseI0 | SparseM
     | 0        |  7     |    -3    |          |    9,1   | __, 5.0
     | 1        | -2     |          | 4        |          |  3, ___
@@ -810,7 +810,7 @@ def testDropoutCenterBiasWithGrowingMeta(self):
     # building. This tree should never be dropped.
     num_trees = 10
     with self.test_session():
-      # Empty tree ensenble.
+      # Empty tree ensemble.
       tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
       # Add 10 trees with some weights.
       for i in range(0, num_trees):
@@ -951,7 +951,7 @@ def testDropoutSeed(self):
 
   def testDropOutZeroProb(self):
     with self.test_session():
-      # Empty tree ensenble.
+      # Empty tree ensemble.
       tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
       # Add 1000 trees with some weights.
       for i in range(0, 999):
@@ -994,7 +994,7 @@ def testDropOutZeroProb(self):
 
   def testAveragingAllTrees(self):
     with self.test_session():
-      # Empty tree ensenble.
+      # Empty tree ensemble.
       tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
       adjusted_tree_ensemble_config = (
           tree_config_pb2.DecisionTreeEnsembleConfig())

tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py

@@ -482,7 +482,7 @@ def setUp(self):
     """Sets up the quantile op tests.
 
     Create a batch of 4 examples having 2 dense and 4 sparse features.
-    Forth sparse feature is multivalent (3 dimensional)
+    Fourth sparse feature is multivalent (3 dimensional)
     The data looks like this
     | Instance | Dense 0 | Dense 1 | Sparse 0 | Sparse 1 |Sparse 2| SparseM
     | 0        |   -0.1  |  -1     |   -2     |   0.1    |        |_ ,1,_

tensorflow/contrib/boosted_trees/python/ops/quantile_ops.py

@@ -184,7 +184,7 @@ def flush(self, stamp_token, next_stamp_token):
     """Finalizes quantile summary stream and resets it for next iteration.
 
     Args:
-      stamp_token: Exepcted current token.
+      stamp_token: Expected current token.
       next_stamp_token: Next value for the token.
     Returns:
       A list of quantiles or approximate boundaries.