Added a test showing example of text classification using TensorFlow in ML.Net

build/Dependencies.props

@@ -21,7 +21,7 @@
     <SystemDrawingCommonPackageVersion>4.5.0</SystemDrawingCommonPackageVersion>
     <SystemIOFileSystemAccessControl>4.5.0</SystemIOFileSystemAccessControl>
     <SystemSecurityPrincipalWindows>4.5.0</SystemSecurityPrincipalWindows>
-    <TensorFlowVersion>1.10.0</TensorFlowVersion>
+    <TensorFlowVersion>1.12.0</TensorFlowVersion>
   </PropertyGroup>
 
   <!-- Code Analyzer Dependencies -->

src/Microsoft.ML.Data/Transforms/ConversionsExtensionsCatalog.cs

@@ -140,5 +140,22 @@ public static ValueMappingEstimator<TInputType, TOutputType> ValueMap<TInputType
             IEnumerable<TOutputType> values,
             params (string outputColumnName, string inputColumnName)[] columns)
             => new ValueMappingEstimator<TInputType, TOutputType>(CatalogUtils.GetEnvironment(catalog), keys, values, columns);
+
+        /// <summary>
+        /// Maps the <paramref name="columns.input"/> using the keys in the dictionary to the values of dictionary i.e.
+        /// a value 'x' in the <paramref name="columns.input"/> would be mappped to a value stored in dictionary[x].
+        /// In this case, the <paramref name="lookupMap"/> is used to build up the dictionary where <paramref name="keyColumn"/>
+        /// and <paramref name="valueColumn"/> specify the keys and values of dictionary respectively.
+        /// </summary>
+        /// <param name="catalog">The categorical transform's catalog</param>
+        /// <param name="lookupMap">An instance of <see cref="IDataView"/> that contains the key and value columns.</param>
+        /// <param name="keyColumn">Name of the key column in <paramref name="lookupMap"/>.</param>
+        /// <param name="valueColumn">Name of the value column in <paramref name="lookupMap"/>.</param>
+        /// <param name="columns">The columns to apply this transform on.</param>
+        /// <returns></returns>
+        public static ValueMappingEstimator ValueMap(
+            this TransformsCatalog.ConversionTransforms catalog,
+            IDataView lookupMap, string keyColumn, string valueColumn, params (string outputColumnName, string inputColumnName)[] columns)
+            => new ValueMappingEstimator(CatalogUtils.GetEnvironment(catalog), lookupMap, keyColumn, valueColumn, columns);
     }
 }

src/Redist/Microsoft.ML.TensorFlow.Redist/libtensorflow-cpu-darwin-x86_64-1.12.0.tar.gz.sha

@@ -0,0 +1 @@
+090706417EC29D91EEEABC5C25576374A86426CF25F27556C0EED4FD815D814C4F09FA7389ED8F614E4B34BF6438B9AE0ADA402BEA7CC9441446AB783A6F187D

src/Redist/Microsoft.ML.TensorFlow.Redist/libtensorflow-cpu-linux-x86_64-1.12.0.tar.gz.sha

@@ -0,0 +1 @@
+5359609DDF69D66474F720D6A1ED669942FEB6842096CFC3EAF44B84FA3F2F659829778446BD3C7C83871F7293CA481AC4732DF6DC7921ADA100B459E37198BD

src/Redist/Microsoft.ML.TensorFlow.Redist/libtensorflow-cpu-windows-x86_64-1.12.0.zip.sha

@@ -0,0 +1 @@
+49DB72CDD8D10B78BB1CD17A058DF508E04B38BD287FF53EB9173A48D3994E11741B1EE6C9108303739819845F2F9D777EE3E767D737C24DB3A28B67FF68C951

test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj

@@ -46,7 +46,7 @@
     <NativeAssemblyReference Condition="'$(OS)' != 'Windows_NT'" Include="tensorflow_framework" />
   </ItemGroup>
   <ItemGroup>
-    <PackageReference Include="Microsoft.ML.TensorFlow.TestModels" Version="0.0.6-test" />
+    <PackageReference Include="Microsoft.ML.TensorFlow.TestModels" Version="0.0.7-test" />
     <PackageReference Include="Microsoft.ML.Onnx.TestModels" Version="0.0.2-test" />
   </ItemGroup>
 </Project>

test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/TensorflowTests.cs

@@ -11,8 +11,10 @@
 using Microsoft.ML.ImageAnalytics;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.Transforms;
+using Microsoft.ML.Transforms.Conversions;
 using Microsoft.ML.Transforms.Normalizers;
 using Microsoft.ML.Transforms.TensorFlow;
+using Microsoft.ML.Transforms.Text;
 using Xunit;
 
 namespace Microsoft.ML.Scenarios
@@ -846,5 +848,59 @@ public void TensorFlowTransformCifarInvalidShape()
             }
             Assert.True(thrown);
         }
+
+        /// <summary>
+        /// Class to hold features and predictions.
+        /// </summary>
+        public class TensorFlowSentiment
+        {
+            public string Sentiment_Text;
+            [VectorType(600)]
+            public int[] Features;
+            [VectorType(2)]
+            public float[] Prediction;
+        }
+
+        [ConditionalFact(typeof(Environment), nameof(Environment.Is64BitProcess))]
+        public void TensorFlowSentimentClassificationTest()
+        {
+            var mlContext = new MLContext(seed: 1, conc: 1);
+            var data = new[] { new TensorFlowSentiment() { 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);
+
+            var lookupMap = mlContext.Data.ReadFromTextFile(@"sentiment_model/imdb_word_index.csv",
+                   columns: new[]
+                   {
+                        new TextLoader.Column("Words", DataKind.TX, 0),
+                        new TextLoader.Column("Ids", DataKind.I4, 1),
+                   },
+                   separatorChar: ','
+               );
+
+            // We cannot resize variable length vector to fixed length vector in ML.NET
+            // The trick here is to create two pipelines.
+            // The first pipeline 'dataPipe' tokenzies the string into words and maps each word to an integer which is an index in the dictionary.
+            // Then this integer vector is retrieved from the pipeline and resized to fixed length.
+            // The second pipeline 'tfEnginePipe' takes the resized integer vector and passes it to TensoFlow and gets the classification scores.
+            var estimator = mlContext.Transforms.Text.TokenizeWords("TokenizedWords", "Sentiment_Text")
+                .Append(mlContext.Transforms.Conversion.ValueMap(lookupMap, "Words", "Ids", new[] { ("Features", "TokenizedWords") }));
+            var dataPipe = estimator.Fit(dataView)
+                .CreatePredictionEngine<TensorFlowSentiment, TensorFlowSentiment>(mlContext);
+
+            // For 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
+            string modelLocation = @"sentiment_model";
+            var tfEnginePipe = mlContext.Transforms.ScoreTensorFlowModel(modelLocation, new[] { "Prediction/Softmax" }, new[] { "Features" })
+                .Append(mlContext.Transforms.CopyColumns(("Prediction", "Prediction/Softmax")))
+                .Fit(dataView)
+                .CreatePredictionEngine<TensorFlowSentiment, TensorFlowSentiment>(mlContext);
+
+            var processedData = dataPipe.Predict(data[0]);
+            Array.Resize(ref processedData.Features, 600);
+            var prediction = tfEnginePipe.Predict(processedData);
+
+            Assert.Equal(2, prediction.Prediction.Length);
+            Assert.InRange(prediction.Prediction[1], 0.650032759 - 0.01, 0.650032759 + 0.01);
+        }
     }
 }