fixes based on PR comments

Author: michaelgsharp

src/Microsoft.ML.TensorFlow/TensorflowTransform.cs

@@ -110,8 +110,9 @@ internal TensorFlowTransformer(IHostEnvironment env, TensorFlowModel tfModelInfo
         /// <param name="outputColumnNames">The output columns to generate. Names must match model specifications. Data types are inferred from model.</param>
         /// <param name="addBatchDimensionInput">Add a batch dimension to the input e.g. input = [224, 224, 3] => [-1, 224, 224, 3].
         /// This parameter is used to deal with models that have unknown shape but the internal operators in the model require data to have batch dimension as well.</param>
-        internal TensorFlowTransformer(IHostEnvironment env, TensorFlowModel tfModelInfo, string[] outputColumnNames, string[] inputColumnNames, bool addBatchDimensionInput = false)
-            : this(env, tfModelInfo.Session, outputColumnNames, inputColumnNames, IsSavedModel(env, tfModelInfo.ModelPath) ? tfModelInfo.ModelPath : null, false, addBatchDimensionInput)
+        /// <param name="treatOutputAsBatched">If the first dimension of the output is unknown, should it be treated as batched or not.</param>
+        internal TensorFlowTransformer(IHostEnvironment env, TensorFlowModel tfModelInfo, string[] outputColumnNames, string[] inputColumnNames, bool addBatchDimensionInput = false, bool treatOutputAsBatched = true)
+            : this(env, tfModelInfo.Session, outputColumnNames, inputColumnNames, IsSavedModel(env, tfModelInfo.ModelPath) ? tfModelInfo.ModelPath : null, false, addBatchDimensionInput, treatOutputAsBatched: treatOutputAsBatched)
         {
         }
 
@@ -898,9 +899,9 @@ internal sealed class Options : TransformInputBase
             /// If the first dimension of the output is unknown, should it be treated as batched or not. e.g. output = [-1] will be read as a vector of unknown length when this is false.
             /// </summary>
             /// <remarks>
-            /// This parameter is used to deal with models that have unknown output shape and it needs to be interpreted in ML.NET as a vector of unkown length and not as a batch dimension.
+            /// This parameter is used to deal with models that have unknown output shape and it needs to be interpreted in ML.NET as a vector of unknown length and not as a batch dimension.
             /// </remarks>
-            [Argument(ArgumentType.AtMostOnce, HelpText = "If the first dimension of the output is unknown, should it be treated as batched or not. e.g. output = [-1] will be read as a vector of unkown length when this is false.", SortOrder = 17)]
+            [Argument(ArgumentType.AtMostOnce, HelpText = "If the first dimension of the output is unknown, should it be treated as batched or not. e.g. output = [-1] will be read as a vector of unknown length when this is false.", SortOrder = 17)]
             public bool TreatOutputAsBatched = true;
         }
 

src/Microsoft.ML.TensorFlow/TensorflowUtils.cs

@@ -99,9 +99,9 @@ internal static DataViewSchema GetModelSchema(IExceptionContext ectx, Graph grap
                             columnType = new VectorDataViewType(mlType, tensorShape[0] > 0 ? tensorShape : tensorShape.Skip(1).ToArray());
                         }
                         // When treatOutputAsBatched is false, if the first value is less than 0 we want to set it to 0. TensorFlow
-                        // represents an unkown size as -1, but ML.NET represents it as 0 so we need to convert it.
-                        // I.E. if the input dimensions are [-1, 5], ML.NET will read the -1 as a dimension of unkown length, and so the ML.NET
-                        // data type will be a vector of 2 dimensions, where the first dimension is unkown and the second has a length of 5.
+                        // represents an unknown size as -1, but ML.NET represents it as 0 so we need to convert it.
+                        // I.E. if the input dimensions are [-1, 5], ML.NET will read the -1 as a dimension of unknown length, and so the ML.NET
+                        // data type will be a vector of 2 dimensions, where the first dimension is unknown and the second has a length of 5.
                         else
                         {
                             if (tensorShape[0] < 0)

test/BaselineOutput/Common/EntryPoints/core_manifest.json

@@ -23617,7 +23617,7 @@
         {
           "Name": "TreatOutputAsBatched",
           "Type": "Bool",
-          "Desc": "If the first dimension of the output is unknown, should it be treated as batched or not. e.g. output = [-1] will be read as a vector of unkown length when this is false.",
+          "Desc": "If the first dimension of the output is unknown, should it be treated as batched or not. e.g. output = [-1] will be read as a vector of unknown length when this is false.",
           "Required": false,
           "SortOrder": 17.0,
           "IsNullable": false,

test/Microsoft.ML.Tests/TensorFlowEstimatorTests.cs

@@ -183,7 +183,7 @@ public void TestTensorFlow()
                 Assert.Equal(4, numRows);
             }
         }
-        
+
         [TensorFlowFact]
         public void TreatOutputAsBatched()
         {
@@ -211,7 +211,7 @@ public void TreatOutputAsBatched()
             var schema = pipe.Fit(data).Transform(data).Schema;
 
             // The dimensions of the output with treatOutputAsBatched set to false should be * 10
-            // as the first dimension of -1 is treated as an unkown dimension.
+            // as the first dimension of -1 is treated as an unknown dimension.
             Assert.Equal(new VectorDataViewType(NumberDataViewType.Single, 0, 10), schema["Output"].Type);
 
             // Note that CamelCase column names are there to match the TF graph node names.