Moved TensorFlow samples to its own directory in Samples project.

docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlowTransform.cs → docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlow/ImageClassification.cs

@@ -2,14 +2,14 @@
 using System.Linq;
 using Microsoft.ML.Data;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Microsoft.ML.Samples.Dynamic.TensorFlow
 {
-    class TensorFlowTransformExample
+    class ImageClassification
     {
         /// <summary>
-        /// Example use of the TensorFlowEstimator in a ML.NET pipeline.
+        /// Example use of the TensorFlow image model in a ML.NET pipeline.
         /// </summary>
-        public static void TensorFlowScoringSample()
+        public static void ScoringWithImageClassificationModelSample()
         {
             // Download the ResNet 101 model from the location below.
             // https://storage.googleapis.com/download.tensorflow.org/models/tflite_11_05_08/resnet_v2_101.tgz

docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlow/TextClassification.cs

@@ -0,0 +1,136 @@
+using System;
+using System.IO;
+using System.Linq;
+using Microsoft.ML.Data;
+using Microsoft.ML.Transforms.TensorFlow;
+
+namespace Microsoft.ML.Samples.Dynamic.TensorFlow
+{
+    class TextClassification
+    {
+        public const int MaxSentenceLenth = 600;
+        /// <summary>
+        /// Example use of the TensorFlow sentiment classification model.
+        /// </summary>
+        public static void ScoringWithTextClassificationModelSample()
+        {
+            string modelLocation = SamplesUtils.DatasetUtils.DownloadTensorFlowSentimentModel();
+
+            var mlContext = new MLContext();
+            var data = new[] { new IMDBSentiment() {
+                Sentiment_Text = "this film was just brilliant casting location scenery story direction " +
+                "everyone's really suited the part they played and you could just imagine being there robert " +
+                "is an amazing actor and now the same being director  father came from the same scottish " +
+                "island as myself so i loved the fact there was a real connection with this film the witty " +
+                "remarks throughout the film were great it was just brilliant so much that i bought the " +
+                "film as soon as it was released for  and would recommend it to everyone to watch and the " +
+                "fly fishing was amazing really cried at the end it was so sad and you know what they say " +
+                "if you cry at a film it must have been good and this definitely was also  to the two " +
+                "little boy's that played the  of norman and paul they were just brilliant children are " +
+                "often left out of the  list i think because the stars that play them all grown up are " +
+                "such a big profile for the whole film but these children are amazing and should be praised " +
+                "for what they have done don't you think the whole story was so lovely because it was true " +
+                "and was someone's life after all that was shared with us all" } };
+            var dataView = mlContext.Data.ReadFromEnumerable(data);
+
+            // This is the dictionary to convert words into the integer indexes.
+            var lookupMap = mlContext.Data.ReadFromTextFile(Path.Combine(modelLocation, "imdb_word_index.csv"),
+                   columns: new[]
+                   {
+                        new TextLoader.Column("Words", DataKind.TX, 0),
+                        new TextLoader.Column("Ids", DataKind.I4, 1),
+                   },
+                   separatorChar: ','
+               );
+
+            // Load the TensorFlow model once.
+            //      - Use it for quering the schema for input and output in the model
+            //      - Use it for prediction in the pipeline.
+            var modelInfo = TensorFlowUtils.LoadTensorFlowModel(mlContext, modelLocation);
+            var schema = modelInfo.GetModelSchema();
+            var featuresType = (VectorType)schema["Features"].Type;
+            Console.WriteLine("Name: {0}, Type: {1}, Shape: (-1, {2})", "Features", featuresType.ItemType.RawType, featuresType.Dimensions[0]);
+            var predictionType = (VectorType)schema["Prediction/Softmax"].Type;
+            Console.WriteLine("Name: {0}, Type: {1}, Shape: (-1, {2})", "Prediction/Softmax", predictionType.ItemType.RawType, predictionType.Dimensions[0]);
+
+            // The model expects the input feature vector to be a fixed length vector.
+            // In this sample, CustomMappingEstimator is used to resize variable length vector to fixed length vector.
+            // The following ML.NET pipeline
+            //      1. tokenzies the string into words, 
+            //      2. maps each word to an integer which is an index in the dictionary ('lookupMap'),
+            //      3. Resizes the integer vector to a fixed length vector using CustomMappingEstimator ('ResizeFeaturesAction')
+            //      4. Passes the data to TensorFlow for scoring.
+            //      5. Retreives the 'Prediction' from TensorFlow and put it into ML.NET Pipeline 
+
+            Action<IMDBSentiment, IntermediateFeatures> ResizeFeaturesAction = (i, j) =>
+            {
+                j.Sentiment_Text = i.Sentiment_Text;
+                var features = i.VariableLenghtFeatures;
+                Array.Resize(ref features, MaxSentenceLenth);
+                j.Features = features;
+            };
+
+            var engine = mlContext.Transforms.Text.TokenizeWords("TokenizedWords", "Sentiment_Text")
+                .Append(mlContext.Transforms.Conversion.ValueMap(lookupMap, "Words", "Ids", new[] { ("VariableLenghtFeatures", "TokenizedWords") }))
+                .Append(mlContext.Transforms.CustomMapping(ResizeFeaturesAction, "Resize"))
+                .Append(mlContext.Transforms.ScoreTensorFlowModel(modelInfo, new[] { "Prediction/Softmax" }, new[] { "Features" }))
+                .Append(mlContext.Transforms.CopyColumns(("Prediction", "Prediction/Softmax")))
+                .Fit(dataView)
+                .CreatePredictionEngine<IMDBSentiment, OutputScores>(mlContext);
+
+            // Predict with TensorFlow pipeline.
+            var prediction = engine.Predict(data[0]);
+
+            Console.WriteLine("Number of classes: {0}", prediction.Prediction.Length); 
+            Console.WriteLine("Is sentiment/review positive? {0}", prediction.Prediction[1] > 0.5 ? "Yes." : "No."); 
+            Console.WriteLine("Prediction Confidence: {0}", prediction.Prediction[1].ToString("0.00"));
+
+            /////////////////////////////////// Expected output ///////////////////////////////////
+            // 
+            // Name: Features, Type: System.Int32, Shape: (-1, 600)
+            // Name: Prediction/Softmax, Type: System.Single, Shape: (-1, 2)
+            // 
+            // Number of classes: 2
+            // Is sentiment/review positive ? Yes
+            // Prediction Confidence: 0.65
+        }
+
+
+        /// <summary>
+        /// Class to hold original sentiment data.
+        /// </summary>
+        public class IMDBSentiment
+        {
+            public string Sentiment_Text { get; set; }
+
+            /// <summary>
+            /// This is a variable length vector designated by VectorType(0) attribute.
+            /// Variable length vectors are produced by applying operations such as 'TokenizeWords' on strings
+            /// resulting in vectors of tokens of variable lengths.
+            /// </summary>
+            [VectorType(0)]
+            public int[] VariableLenghtFeatures { get; set; }
+        }
+
+        /// <summary>
+        /// Class to hold intermediate data. Mostly used by CustomMapping Estimator
+        /// </summary>
+        public class IntermediateFeatures
+        {
+            public string Sentiment_Text { get; set; }
+
+            [VectorType(MaxSentenceLenth)]
+            public int[] Features { get; set; }
+        }
+
+        /// <summary>
+        /// Class to contain the output values from the transformation.
+        /// </summary>
+        class OutputScores
+        {
+            [VectorType(2)]
+            public float[] Prediction { get; set; }
+        }
+
+    }
+}

docs/samples/Microsoft.ML.Samples/Microsoft.ML.Samples.csproj

@@ -23,7 +23,7 @@
     <NativeAssemblyReference Include="FastTreeNative" />
     <NativeAssemblyReference Include="MatrixFactorizationNative" />
     <NativeAssemblyReference Include="LdaNative" />
-    <PackageReference Include="Microsoft.ML.TensorFlow.Redist" Version="0.7.0" />
+    <PackageReference Include="Microsoft.ML.TensorFlow.Redist" Version="0.10.0" />
 
     <ProjectReference Include="..\..\..\src\Microsoft.ML.Analyzer\Microsoft.ML.Analyzer.csproj">
       <ReferenceOutputAssembly>false</ReferenceOutputAssembly>

src/Microsoft.ML.SamplesUtils/SamplesDatasetUtils.cs

@@ -56,6 +56,35 @@ public static string DownloadImages()
             return $"{path}{Path.DirectorySeparatorChar}images.tsv";
         }
 
+        /// <summary>
+        /// Downloads sentiment_model from the dotnet/machinelearning-testdata repo.
+        /// </summary>
+        /// <remarks>
+        /// The model is downloaded from
+        /// https://github.com/dotnet/machinelearning-testdata/blob/master/Microsoft.ML.TensorFlow.TestModels/sentiment_model
+        /// The model is in 'SavedModel' format. For further explanation on how was the `sentiment_model` created
+        /// c.f. https://github.com/dotnet/machinelearning-testdata/blob/master/Microsoft.ML.TensorFlow.TestModels/sentiment_model/README.md
+        /// </remarks>
+        public static string DownloadTensorFlowSentimentModel()
+        {
+            string remotePath = "https://github.com/dotnet/machinelearning-testdata/raw/master/Microsoft.ML.TensorFlow.TestModels/sentiment_model/";
+
+            string path = "sentiment_model";
+            if(!Directory.Exists(path))
+                Directory.CreateDirectory(path);
+
+            string varPath = Path.Combine(path, "variables");
+            if (!Directory.Exists(varPath))
+                Directory.CreateDirectory(varPath);
+
+            Download(Path.Combine(remotePath, "saved_model.pb"), Path.Combine(path,"saved_model.pb"));
+            Download(Path.Combine(remotePath, "imdb_word_index.csv"), Path.Combine(path, "imdb_word_index.csv"));
+            Download(Path.Combine(remotePath, "variables", "variables.data-00000-of-00001"), Path.Combine(varPath, "variables.data-00000-of-00001"));
+            Download(Path.Combine(remotePath, "variables", "variables.index"), Path.Combine(varPath, "variables.index"));
+
+            return path;
+        }
+
         private static string Download(string baseGitPath, string dataFile)
         {
             using (WebClient client = new WebClient())

src/Microsoft.ML.TensorFlow/TensorFlowModelInfo.cs

@@ -43,7 +43,7 @@ internal TensorFlowModelInfo(IHostEnvironment env, TFSession session, string mod
         /// <summary>
         /// Get <see cref="Schema"/> for complete model. Every node in the TensorFlow model will be included in the <see cref="Schema"/> object.
         /// </summary>
-        internal Schema GetModelSchema()
+        public Schema GetModelSchema()
         {
             return TensorFlowUtils.GetModelSchema(_env, Session.Graph);
         }

src/Microsoft.ML.TensorFlow/TensorflowCatalog.cs

@@ -21,7 +21,8 @@ public static class TensorflowCatalog
         /// <example>
         /// <format type="text/markdown">
         /// <![CDATA[
-        /// [!code-csharp[ScoreTensorFlowModel](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlowTransform.cs)]
+        /// [!code-csharp[ScoreTensorFlowModel](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlow/ImageClassification.cs)]
+        /// [!code-csharp[ScoreTensorFlowModel](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlow/TextClassification.cs)]
         /// ]]>
         /// </format>
         /// </example>