Added some edits to address Yael's comments

Author: harshithapv

docs/samples/Microsoft.ML.Samples/Dynamic/ImageClassification/ResnetV2101TransferLearningTrainTestSplit.cs

@@ -30,8 +30,6 @@ public static void Example()
             //Download the image set and unzip
             string finalImagesFolderName = DownloadImageSet(
                 imagesDownloadFolderPath);
-            // Use this for testing on big flower datasets:
-            //string finalImagesFolderName = "flower_photos"; 
             string fullImagesetFolderPath = Path.Combine(
                 imagesDownloadFolderPath, finalImagesFolderName);
 

docs/samples/Microsoft.ML.Samples/Program.cs

@@ -24,8 +24,7 @@ internal static void RunAll()
                 }
             }
 
-            Console.WriteLine("Number of samples that ran without any exception: " + samples);          
-            
+            Console.WriteLine("Number of samples that ran without any exception: " + samples);           
 
         }
     }

src/Microsoft.ML.Dnn/ImageClassificationTransform.cs

@@ -189,8 +189,9 @@ private void CheckTrainingParameters(ImageClassificationEstimator.Options option
             return (jpegData, resizedImage);
         }
 
-        private static Tensor Encode(VBuffer<byte> buffer, int length)
+        private static Tensor Encode(VBuffer<byte> buffer)
         {
+            int length = buffer.Length;
             var size = c_api.TF_StringEncodedSize((UIntPtr)length);
             var handle = c_api.TF_AllocateTensor(TF_DataType.TF_STRING, IntPtr.Zero, 0, (UIntPtr)((ulong)size + 8));
             //AllocationType = AllocationType.Tensorflow;
@@ -221,7 +222,7 @@ public ImageProcessor(ImageClassificationTransformer transformer)
 
             public Tensor ProcessImage(VBuffer<byte> imgBuf)
             {
-                var imageTensor = Encode(imgBuf, imgBuf.Length);
+                var imageTensor = Encode(imgBuf);
                 var processedTensor = _imagePreprocessingRunner.AddInput(imageTensor, 0).Run()[0];
                 imageTensor.Dispose();
                 return processedTensor;
@@ -1170,7 +1171,7 @@ internal sealed class Options : TransformInputBase
         private readonly IHost _host;
         private readonly Options _options;
         private readonly DnnModel _dnnModel;
-        private readonly TF_DataType[] _tfInputTypes;
+        private readonly DataViewType[] _inputTypes;
         private readonly DataViewType[] _outputTypes;
         private ImageClassificationTransformer _transformer;
 
@@ -1179,7 +1180,7 @@ internal ImageClassificationEstimator(IHostEnvironment env, Options options, Dnn
             _host = Contracts.CheckRef(env, nameof(env)).Register(nameof(ImageClassificationEstimator));
             _options = options;
             _dnnModel = dnnModel;
-            _tfInputTypes = new[] { TF_DataType.TF_STRING };
+            _inputTypes = new[] { new VectorDataViewType(NumberDataViewType.Byte) };
             _outputTypes = new[] { new VectorDataViewType(NumberDataViewType.Single), NumberDataViewType.UInt32.GetItemType() };
         }
 
@@ -1206,9 +1207,9 @@ public SchemaShape GetOutputSchema(SchemaShape inputSchema)
                 var input = _options.InputColumns[i];
                 if (!inputSchema.TryFindColumn(input, out var col))
                     throw _host.ExceptSchemaMismatch(nameof(inputSchema), "input", input);
-                //var expectedType = DnnUtils.Tf2MlNetType(_tfInputTypes[i]);
-                //if (col.ItemType != expectedType)
-                //    throw _host.ExceptSchemaMismatch(nameof(inputSchema), "input", input, expectedType.ToString(), col.ItemType.ToString());
+                var expectedType = _inputTypes[i];
+                if (!col.ItemType.Equals(expectedType))
+                    throw _host.ExceptSchemaMismatch(nameof(inputSchema), "input", input, expectedType.ToString(), col.ItemType.ToString());
             }
             for (var i = 0; i < _options.OutputColumns.Length; i++)
             {