Update live with current master

machine-learning/tutorials/TransferLearningTF.sln

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+
+Microsoft Visual Studio Solution File, Format Version 12.00
+# Visual Studio 15
+VisualStudioVersion = 15.0.28307.271
+MinimumVisualStudioVersion = 10.0.40219.1
+Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "TransferLearningTF", "TransferLearningTF\TransferLearningTF.csproj", "{50CB6FC0-9A1A-4219-AFBD-9E5ACD6B17AA}"
+EndProject
+Global
+	GlobalSection(SolutionConfigurationPlatforms) = preSolution
+		Debug|Any CPU = Debug|Any CPU
+		Release|Any CPU = Release|Any CPU
+	EndGlobalSection
+	GlobalSection(ProjectConfigurationPlatforms) = postSolution
+		{50CB6FC0-9A1A-4219-AFBD-9E5ACD6B17AA}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{50CB6FC0-9A1A-4219-AFBD-9E5ACD6B17AA}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{50CB6FC0-9A1A-4219-AFBD-9E5ACD6B17AA}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{50CB6FC0-9A1A-4219-AFBD-9E5ACD6B17AA}.Release|Any CPU.Build.0 = Release|Any CPU
+	EndGlobalSection
+	GlobalSection(SolutionProperties) = preSolution
+		HideSolutionNode = FALSE
+	EndGlobalSection
+	GlobalSection(ExtensibilityGlobals) = postSolution
+		SolutionGuid = {1F2992B4-389D-4FEA-B6F0-65684DCF7EBE}
+	EndGlobalSection
+EndGlobal

machine-learning/tutorials/TransferLearningTF/ImageData.cs

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+// <SnippetAddUsings>
+using Microsoft.ML.Data;
+// </SnippetAddUsings>
+namespace TransferLearningTF
+{
+    // <SnippetDeclareTypes>
+    public class ImageData
+    {
+        [LoadColumn(0)]
+        public string ImagePath;
+
+        [LoadColumn(1)]
+        public string Label;
+    }
+    // <SnippetDeclareTypes>
+}

machine-learning/tutorials/TransferLearningTF/ImagePrediction.cs

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+using System;
+
+namespace TransferLearningTF
+{
+    // <SnippetDeclareTypes>
+    public class ImagePrediction
+    {
+        public float[] Score;
+
+        public string PredictedLabelValue;
+    }
+    // </SnippetDeclareTypes>
+}

machine-learning/tutorials/TransferLearningTF/Program.cs

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+// <SnippetAddUsings>
+using System;
+using System.Collections.Generic;
+using System.IO;
+using System.Linq;
+using Microsoft.Data.DataView;
+using Microsoft.ML;
+using Microsoft.ML.Core.Data;
+using Microsoft.ML.Data;
+using Microsoft.ML.ImageAnalytics;
+using Microsoft.ML.Trainers;
+// </SnippetAddUsings>
+
+namespace TransferLearningTF
+{
+    class Program
+    {
+        // <SnippetDeclareGlobalVariables>
+        static readonly string _assetsPath = Path.Combine(Environment.CurrentDirectory, "assets");
+        static readonly string _trainTagsTsv = Path.Combine(_assetsPath, "inputs-train", "data", "tags.tsv");
+        static readonly string _predictTagsTsv = Path.Combine(_assetsPath, "inputs-predict", "data", "tags.tsv");
+        static readonly string _trainImagesFolder = Path.Combine(_assetsPath, "inputs-train", "data");
+        static readonly string _predictImagesFolder = Path.Combine(_assetsPath, "inputs-predict", "data");
+        static readonly string _inceptionPb = Path.Combine(_assetsPath, "inputs-train", "inception", "tensorflow_inception_graph.pb");
+        static readonly string _inputImageClassifierZip = Path.Combine(_assetsPath, "inputs-predict", "imageClassifier.zip");
+        static readonly string _outputImageClassifierZip = Path.Combine(_assetsPath, "outputs", "imageClassifier.zip");
+        private static string LabelTokey = nameof(LabelTokey);
+        private static string ImageReal = nameof(ImageReal);
+        private static string PredictedLabelValue = nameof(PredictedLabelValue);
+        // </SnippetDeclareGlobalVariables>
+
+        static void Main(string[] args)
+        {
+            // Create MLContext to be shared across the model creation workflow objects 
+            // <SnippetCreateMLContext>
+            MLContext mlContext = new MLContext(seed:1);
+            // </SnippetCreateMLContext>
+
+            // <SnippetCallReuseAndTuneInceptionModel>
+            ReuseAndTuneInceptionModel(mlContext, _trainTagsTsv, _trainImagesFolder, _inceptionPb, _outputImageClassifierZip);
+            // </CallSnippetReuseAndTuneInceptionModel>
+
+            // <SnippetCallClassifyImages>
+            ClassifyImages(mlContext, _predictTagsTsv, _predictImagesFolder, _outputImageClassifierZip);
+            // </SnippetCallClassifyImages>
+        }
+
+        // <SnippetInceptionSettings>
+        private struct InceptionSettings
+        {
+            public const int ImageHeight = 224;
+            public const int ImageWidth = 224;
+            public const float Mean = 117;
+            public const float Scale = 1;
+            public const bool ChannelsLast = true;
+        }
+        // </SnippetInceptionSettings>
+
+        // Build and train model
+        public static void ReuseAndTuneInceptionModel(MLContext mlContext, string dataLocation, string imagesFolder, string inputModelLocation, string outputModelLocation)
+        {
+
+            Console.WriteLine("Read model");
+            Console.WriteLine($"Model location: {inputModelLocation}");
+            Console.WriteLine($"Images folder: {_trainImagesFolder}");
+            Console.WriteLine($"Training file: {dataLocation}");
+            Console.WriteLine($"Default parameters: image size=({InceptionSettings.ImageWidth},{InceptionSettings.ImageHeight}), image mean: {InceptionSettings.Mean}");
+
+            // <SnippetLoadData>
+            var data = mlContext.Data.ReadFromTextFile<ImageData>(path: dataLocation, hasHeader: true);
+            // </SnippetLoadData>
+
+            // <SnippetMapValueToKey1>
+            var estimator = mlContext.Transforms.Conversion.MapValueToKey(outputColumnName: LabelTokey, inputColumnName: DefaultColumnNames.Label)
+                            // </SnippetMapValueToKey1>
+                            // The image transforms transform the images into the model's expected format.
+                            // <SnippetImageTransforms>
+                            .Append(mlContext.Transforms.LoadImages(_trainImagesFolder, (ImageReal, nameof(ImageData.ImagePath))))
+                            .Append(mlContext.Transforms.Resize(outputColumnName: ImageReal, imageWidth: InceptionSettings.ImageWidth, imageHeight: InceptionSettings.ImageHeight, inputColumnName: ImageReal))
+                            .Append(mlContext.Transforms.ExtractPixels(new ImagePixelExtractorTransformer.ColumnInfo(name: "input", inputColumnName: ImageReal, interleave: InceptionSettings.ChannelsLast, offset: InceptionSettings.Mean)))
+                            // </SnippetImageTransforms>
+                            // The ScoreTensorFlowModel transform scores the TensorFlow model and allows communication 
+                            // <SnippetScoreTensorFlowModel>
+                            .Append(mlContext.Transforms.ScoreTensorFlowModel(modelLocation: inputModelLocation, outputColumnNames: new[] { "softmax2_pre_activation" }, inputColumnNames: new[] { "input" }))
+                            // </SnippetScoreTensorFlowModel>
+                            // <SnippetAddTrainer> 
+                            .Append(mlContext.MulticlassClassification.Trainers.LogisticRegression(labelColumn: LabelTokey, featureColumn: "softmax2_pre_activation"))
+                            // </SnippetAddTrainer>
+                            // <SnippetMapValueToKey2>
+                            .Append(mlContext.Transforms.Conversion.MapKeyToValue((PredictedLabelValue, DefaultColumnNames.PredictedLabel)));
+                            // </SnippetMapValueToKey2>
+
+            // Train the model
+            Console.WriteLine("=============== Training classification model ===============");
+            // Create and train the model based on the dataset that has been loaded, transformed.
+            // <SnippetTrainModel>
+            ITransformer model = estimator.Fit(data);
+            // </SnippetTrainModel>
+
+            // Process the training data through the model
+            // This is an optional step, but it's useful for debugging issues
+            // <SnippetTransformData>
+            var predictions = model.Transform(data);
+            // </SnippetTransformData>
+
+            // Create enumerables for both the ImageData and ImagePrediction DataViews 
+            // for displaying results
+            // <SnippetEnumerateDataViews>
+            var imageData = mlContext.CreateEnumerable<ImageData>(data, false, true);
+            var imagePredictionData = mlContext.CreateEnumerable<ImagePrediction>(predictions, false, true);
+            // </SnippetEnumerateDataViews>
+
+            // Read the tags.tsv file and add the filepath to the image file name 
+            // before loading into ImageData 
+            // <SnippetCallPairAndDisplayResults1>
+            PairAndDisplayResults(imageData, imagePredictionData);
+            // </SnippetCallPairAndDisplayResults1>
+
+            // Get some performance metrics on the model using training data
+            Console.WriteLine("=============== Classification metrics ===============");
+
+            // <SnippetEvaluate>           
+            var regressionContext = new MulticlassClassificationCatalog(mlContext);
+            var metrics = regressionContext.Evaluate(predictions, label: LabelTokey, predictedLabel: DefaultColumnNames.PredictedLabel);
+            // </SnippetEvaluate>
+
+            //<SnippetDisplayMetrics>
+            Console.WriteLine($"LogLoss is: {metrics.LogLoss}");
+            Console.WriteLine($"PerClassLogLoss is: {String.Join(" , ", metrics.PerClassLogLoss.Select(c => c.ToString()))}");
+            //</SnippetDisplayMetrics>
+
+            // Save the model to assets/outputs
+            Console.WriteLine("=============== Save model to local file ===============");
+
+            // <SnippetSaveModel>
+            using (var fileStream = new FileStream(outputModelLocation, FileMode.Create))
+                mlContext.Model.Save(model, fileStream);
+            // </SnippetSaveModel>
+
+            Console.WriteLine($"Model saved: {outputModelLocation}");
+        }
+
+        public static void ClassifyImages(MLContext mlContext, string dataLocation, string imagesFolder, string outputModelLocation)
+        {
+            Console.WriteLine($"=============== Loading model ===============");
+            Console.WriteLine($"Model loaded: {outputModelLocation}");
+
+            // Load the model
+            // <SnippetLoadModel>
+            ITransformer loadedModel;
+            using (var fileStream = new FileStream(outputModelLocation, FileMode.Open))
+                loadedModel = mlContext.Model.Load(fileStream);
+            // </SnippetLoadModel>
+
+            // Read the tags.tsv file and add the filepath to the image file name 
+            // before loading into ImageData 
+            // <SnippetReadFromTSV> 
+            var imageData = ReadFromTsv(dataLocation, imagesFolder);
+            var imageDataView = mlContext.Data.ReadFromEnumerable<ImageData>(imageData);
+            // </SnippetReadFromTSV>  
+
+            // <SnippetPredict>  
+            var predictions = loadedModel.Transform(imageDataView);
+            var imagePredictionData = mlContext.CreateEnumerable<ImagePrediction>(predictions, false,true);
+            // </SnippetPredict> 
+
+            Console.WriteLine("=============== Making classifications ===============");
+            // <SnippetCallPairAndDisplayResults2>
+            PairAndDisplayResults(imageData, imagePredictionData);
+            // </SnippetCallPairAndDisplayResults2> 
+
+        }
+
+        private static void PairAndDisplayResults(IEnumerable<ImageData> imageNetData, IEnumerable<ImagePrediction> imageNetPredictionData)
+        {
+            // Builds pairs of (image, prediction) to sync up for display
+            // <SnippetBuildImagePredictionPairs>
+            IEnumerable<(ImageData image, ImagePrediction prediction)> imagesAndPredictions = imageNetData.Zip(imageNetPredictionData, (image, prediction) => (image, prediction));
+            // </SnippetBuildImagePredictionPairs>
+
+            // <SnippetDisplayPredictions>
+            foreach ((ImageData image, ImagePrediction prediction) item in imagesAndPredictions)
+            {
+                Console.WriteLine($"Image: {Path.GetFileName(item.image.ImagePath)} predicted as: {item.prediction.PredictedLabelValue} with score: {item.prediction.Score.Max()} ");
+            }
+            // </SnippetDisplayPredictions>
+        }
+
+        public static IEnumerable<ImageData> ReadFromTsv(string file, string folder)
+        {
+            //Need to parse through the tags.tsv file to combine the file path to the 
+            // image name for the ImagePath property so that the image file can be found.
+
+            // <SnippetReadFromTsv>
+            return File.ReadAllLines(file)
+             .Select(line => line.Split('\t'))
+             .Select(line => new ImageData()
+             {
+                 ImagePath = Path.Combine(folder, line[0]),
+                 Label = line[1],
+             });
+            // </SnippetReadFromTsv>
+        }
+    }
+
+}

machine-learning/tutorials/TransferLearningTF/TransferLearningTF.csproj

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+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>netcoreapp2.2</TargetFramework>
+    <LangVersion>7.2</LangVersion>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Microsoft.ML" Version="0.10.0" />
+    <PackageReference Include="Microsoft.ML.ImageAnalytics" Version="0.10.0" />
+    <PackageReference Include="Microsoft.ML.TensorFlow" Version="0.10.0" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <None Update="assets\inputs-predict\data\broccoli.png">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="assets\inputs-predict\data\pizza3.jpg">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="assets\inputs-predict\data\tags.tsv">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="assets\inputs-predict\data\teddy6.jpg">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="assets\inputs-predict\data\toaster3.jpg">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="assets\inputs-predict\imageClassifier.zip">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="assets\inputs-train\data\broccoli.jpg">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="assets\inputs-train\data\pizza.jpg">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="assets\inputs-train\data\pizza2.jpg">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="assets\inputs-train\data\tags.tsv">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="assets\inputs-train\data\teddy2.jpg">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="assets\inputs-train\data\teddy3.jpg">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="assets\inputs-train\data\teddy4.jpg">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="assets\inputs-train\data\toaster.jpg">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="assets\inputs-train\data\toaster2.png">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="assets\inputs-train\data\wikimedia.md">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="assets\inputs-train\inception\imagenet.csv">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="assets\inputs-train\inception\imagenet.tsv">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="assets\inputs-train\inception\imagenet_comp_graph_label_strings.txt">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="assets\inputs-train\inception\tensorflow_inception_graph.pb">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="assets\outputs\placeholder.txt">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="assets\outputs\TextFile1.txt">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+  </ItemGroup>
+
+</Project>

machine-learning/tutorials/TransferLearningTF/assets/inputs-predict/data/tags.tsv

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+broccoli.png	broccoli
+pizza3.jpg	pizza
+teddy6.jpg	teddy
+toaster3.jpg	toaster

machine-learning/tutorials/TransferLearningTF/assets/inputs-predict/data/wikimedia.md

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+MichalPL, [Ok Toaster](https://commons.wikimedia.org/wiki/File:OK._Toaster.jpg) [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/deed.en)  
+Agnieszka Kwiecień, [Spinach pizza](https://commons.wikimedia.org/wiki/File:Spinach_pizza.jpg), [GNU Free Documentation License v1.2 or later](https://en.wikipedia.org/wiki/en:GNU_Free_Documentation_License) and [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/legalcode)  
+David Monniaux, [Broccoli DSC00862](https://commons.wikimedia.org/wiki/File:Broccoli_DSC00862.png), [GNU Free Documentation License v1.2 or later](https://en.wikipedia.org/wiki/en:GNU_Free_Documentation_License) and [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/legalcode)  
+Earlgray.Ler2ra, [My teddy bear](https://commons.wikimedia.org/wiki/File:My_teddy_bear.jpg),  [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/deed.en)  

machine-learning/tutorials/TransferLearningTF/assets/inputs-train/data/tags.tsv

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+broccoli.jpg	broccoli
+pizza.jpg	pizza
+pizza2.jpg	pizza
+teddy2.jpg	teddy
+teddy3.jpg	teddy
+teddy4.jpg	teddy
+toaster.jpg	toaster
+toaster2.png	toaster