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Merge pull request #740 from dotnet/master
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‎machine-learning/tutorials/TransferLearningTF.sln‎

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

‎machine-learning/tutorials/TransferLearningTF/ImageData.cs‎

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

‎machine-learning/tutorials/TransferLearningTF/ImagePrediction.cs‎

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

‎machine-learning/tutorials/TransferLearningTF/Program.cs‎

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

‎machine-learning/tutorials/TransferLearningTF/TransferLearningTF.csproj‎

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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<OutputType>Exe</OutputType>
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<TargetFramework>netcoreapp2.2</TargetFramework>
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<LangVersion>7.2</LangVersion>
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="Microsoft.ML" Version="0.10.0" />
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<PackageReference Include="Microsoft.ML.ImageAnalytics" Version="0.10.0" />
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<PackageReference Include="Microsoft.ML.TensorFlow" Version="0.10.0" />
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</ItemGroup>
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<None Update="assets\inputs-predict\data\broccoli.png">
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</None>
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<None Update="assets\inputs-train\inception\imagenet.tsv">
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‎machine-learning/tutorials/TransferLearningTF/assets/inputs-predict/data/broccoli.png‎

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‎machine-learning/tutorials/TransferLearningTF/assets/inputs-predict/data/pizza3.jpg‎

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

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

‎machine-learning/tutorials/TransferLearningTF/assets/inputs-predict/data/teddy6.jpg‎

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‎machine-learning/tutorials/TransferLearningTF/assets/inputs-predict/data/toaster3.jpg‎

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